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Initial commit with BSL

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This commit is contained in:
Andy Pavlo
2019-08-23 11:47:19 -04:00
commit 3e564ce922
286 changed files with 177642 additions and 0 deletions
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5
server/website/website/__init__.py Normal file
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#
# OtterTune - __init__.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#

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server/website/website/admin.py Normal file
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#
# OtterTune - admin.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
from django.contrib import admin
from djcelery.models import TaskMeta
from .models import (BackupData, DBMSCatalog, KnobCatalog,
KnobData, MetricCatalog, MetricData,
PipelineData, PipelineRun, Project,
Result, Session, Workload, Hardware,
SessionKnob)
class BaseAdmin(admin.ModelAdmin):
@staticmethod
def dbms_info(obj):
try:
return obj.dbms.full_name
except AttributeError:
return obj.full_name
class DBMSCatalogAdmin(BaseAdmin):
list_display = ['dbms_info']
class KnobCatalogAdmin(BaseAdmin):
list_display = ['name', 'dbms_info', 'tunable']
ordering = ['name', 'dbms__type', 'dbms__version']
list_filter = ['tunable']
class MetricCatalogAdmin(BaseAdmin):
list_display = ['name', 'dbms_info', 'metric_type']
ordering = ['name', 'dbms__type', 'dbms__version']
list_filter = ['metric_type']
class ProjectAdmin(admin.ModelAdmin):
list_display = ('name', 'user', 'last_update', 'creation_time')
fields = ['name', 'user', 'last_update', 'creation_time']
class SessionAdmin(admin.ModelAdmin):
list_display = ('name', 'user', 'last_update', 'creation_time')
list_display_links = ('name',)
class SessionKnobAdmin(admin.ModelAdmin):
list_display = ('knob', 'session', 'minval', 'maxval', 'tunable')
class HardwareAdmin(admin.ModelAdmin):
list_display = ('cpu', 'memory', 'storage')
class KnobDataAdmin(BaseAdmin):
list_display = ['name', 'dbms_info', 'creation_time']
fields = ['session', 'name', 'creation_time',
'knobs', 'data', 'dbms']
class MetricDataAdmin(BaseAdmin):
list_display = ['name', 'dbms_info', 'creation_time']
fields = ['session', 'name', 'creation_time',
'metrics', 'data', 'dbms']
class TaskMetaAdmin(admin.ModelAdmin):
list_display = ['id', 'status', 'date_done']
class ResultAdmin(BaseAdmin):
list_display = ['result_id', 'dbms_info', 'workload', 'creation_time',
'observation_time']
list_filter = ['dbms__type', 'dbms__version']
ordering = ['id']
@staticmethod
def result_id(obj):
return obj.id
@staticmethod
def workload(obj):
return obj.workload.name
class BackupDataAdmin(admin.ModelAdmin):
list_display = ['id', 'result_id']
@staticmethod
def result_id(obj):
return obj.id
class PipelineDataAdmin(admin.ModelAdmin):
list_display = ['id', 'version', 'task_type', 'workload',
'creation_time']
ordering = ['-creation_time']
@staticmethod
def version(obj):
return obj.pipeline_run.id
class PipelineRunAdmin(admin.ModelAdmin):
list_display = ['id', 'start_time', 'end_time']
class PipelineResultAdmin(BaseAdmin):
list_display = ['task_type', 'dbms_info',
'hardware_info', 'creation_timestamp']
@staticmethod
def hardware_info(obj):
return obj.hardware.name
class WorkloadAdmin(admin.ModelAdmin):
list_display = ['workload_id', 'name']
@staticmethod
def workload_id(obj):
return obj.pk
admin.site.register(DBMSCatalog, DBMSCatalogAdmin)
admin.site.register(KnobCatalog, KnobCatalogAdmin)
admin.site.register(MetricCatalog, MetricCatalogAdmin)
admin.site.register(Session, SessionAdmin)
admin.site.register(Project, ProjectAdmin)
admin.site.register(KnobData, KnobDataAdmin)
admin.site.register(MetricData, MetricDataAdmin)
admin.site.register(TaskMeta, TaskMetaAdmin)
admin.site.register(Result, ResultAdmin)
admin.site.register(BackupData, BackupDataAdmin)
admin.site.register(PipelineData, PipelineDataAdmin)
admin.site.register(PipelineRun, PipelineRunAdmin)
admin.site.register(Workload, WorkloadAdmin)
admin.site.register(SessionKnob, SessionKnobAdmin)
admin.site.register(Hardware, HardwareAdmin)

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server/website/website/fixtures/dbms_catalog.json Normal file
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[
{
"model":"website.DBMSCatalog",
"pk":2,
"fields":{
"type":2,
"version":"9.2"
}
},
{
"model":"website.DBMSCatalog",
"pk":3,
"fields":{
"type":2,
"version":"9.3"
}
},
{
"model":"website.DBMSCatalog",
"pk":4,
"fields":{
"type":2,
"version":"9.4"
}
},
{
"model":"website.DBMSCatalog",
"pk":5,
"fields":{
"type":2,
"version":"9.5"
}
},
{
"model":"website.DBMSCatalog",
"pk":1,
"fields":{
"type":2,
"version":"9.6"
}
},
{
"model":"website.DBMSCatalog",
"pk":9,
"fields":{
"type":9,
"version":"5.6"
}
},
{
"model":"website.DBMSCatalog",
"pk":18,
"fields":{
"type":4,
"version":"19.0.0.0.0"
}
}
]

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server/website/website/fixtures/postgres-92_metrics.json Normal file
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[
{
"fields": {
"dbms": 2,
"name": "pg_stat_bgwriter.buffers_backend",
"summary": "Number of buffers written directly by a backend",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_bgwriter.checkpoints_timed",
"summary": "Number of scheduled checkpoints that have been performed",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_bgwriter.buffers_alloc",
"summary": "Number of buffers allocated",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_bgwriter.buffers_clean",
"summary": "Number of buffers written by the background writer",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_bgwriter.buffers_backend_fsync",
"summary": "Number of times a backend had to execute its own fsync call (normally the background writer handles those even when the backend does its own write)",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_bgwriter.checkpoint_sync_time",
"summary": "\"Total amount of time that has been spent in the portion of checkpoint processing where files are synchronized to disk, in milliseconds\"",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_bgwriter.checkpoints_req",
"summary": "Number of requested checkpoints that have been performed",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_bgwriter.checkpoint_write_time",
"summary": "\"Total amount of time that has been spent in the portion of checkpoint processing where files are written to disk, in milliseconds\"",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_bgwriter.maxwritten_clean",
"summary": "Number of times the background writer stopped a cleaning scan because it had written too many buffers",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_bgwriter.buffers_checkpoint",
"summary": "Number of buffers written during checkpoints",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_bgwriter.stats_reset",
"summary": "Time at which these statistics were last reset",
"vartype": 6,
"metric_type": 2,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.numbackends",
"summary": "Number of backends currently connected to this database. This is the only column in this view that returns a value reflecting current state; all other columns return the accumulated values since the last reset.",
"vartype": 2,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.datname",
"summary": "Name of this database",
"vartype": 1,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.blks_read",
"summary": "Number of disk blocks read in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.deadlocks",
"summary": "Number of deadlocks detected in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.tup_fetched",
"summary": "Number of rows fetched by queries in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.tup_updated",
"summary": "Number of rows updated by queries in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.stats_reset",
"summary": "Time at which these statistics were last reset",
"vartype": 6,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.tup_inserted",
"summary": "Number of rows inserted by queries in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.datid",
"summary": "OID of a database",
"vartype": 2,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.xact_commit",
"summary": "Number of transactions in this database that have been committed",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.tup_deleted",
"summary": "Number of rows deleted by queries in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.blk_read_time",
"summary": "\"Time spent reading data file blocks by backends in this database, in milliseconds\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.xact_rollback",
"summary": "Number of transactions in this database that have been rolled back",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.conflicts",
"summary": "\"Number of queries canceled due to conflicts with recovery in this database. (Conflicts occur only on standby servers; see pg_stat_database_conflicts for details.)\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.blks_hit",
"summary": "\"Number of times disk blocks were found already in the buffer cache, so that a read was not necessary (this only includes hits in the PostgreSQL buffer cache, not the operating system's file system cache)\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.tup_returned",
"summary": "Number of rows returned by queries in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.temp_files",
"summary": "\"Number of temporary files created by queries in this database. All temporary files are counted, regardless of why the temporary file was created (e.g., sorting or hashing), and regardless of the log_temp_files setting.\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.blk_write_time",
"summary": "\"Time spent writing data file blocks by backends in this database, in milliseconds\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database.temp_bytes",
"summary": "\"Total amount of data written to temporary files by queries in this database. All temporary files are counted, regardless of why the temporary file was created, and regardless of the log_temp_files setting.\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database_conflicts.datname",
"summary": "Name of this database",
"vartype": 1,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database_conflicts.confl_deadlock",
"summary": "Number of queries in this database that have been canceled due to deadlocks",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database_conflicts.confl_bufferpin",
"summary": "Number of queries in this database that have been canceled due to pinned buffers",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database_conflicts.datid",
"summary": "OID of a database",
"vartype": 2,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database_conflicts.confl_tablespace",
"summary": "Number of queries in this database that have been canceled due to dropped tablespaces",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database_conflicts.confl_lock",
"summary": "Number of queries in this database that have been canceled due to lock timeouts",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_database_conflicts.confl_snapshot",
"summary": "Number of queries in this database that have been canceled due to old snapshots",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_indexes.indexrelid",
"summary": "OID of this index",
"vartype": 2,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_indexes.relid",
"summary": "OID of the table for this index",
"vartype": 2,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_indexes.indexrelname",
"summary": "Name of this index",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_indexes.relname",
"summary": "Name of the table for this index",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_indexes.idx_tup_fetch",
"summary": "Number of live table rows fetched by simple index scans using this index",
"vartype": 2,
"metric_type": 1,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_indexes.idx_tup_read",
"summary": "Number of index entries returned by scans on this index",
"vartype": 2,
"metric_type": 1,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_indexes.idx_scan",
"summary": "Number of index scans initiated on this index",
"vartype": 2,
"metric_type": 1,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_indexes.schemaname",
"summary": "Name of the schema this index is in",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.last_vacuum",
"summary": "Last time at which this table was manually vacuumed (not counting VACUUM FULL)",
"vartype": 6,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.n_tup_ins",
"summary": "Number of rows inserted",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.n_dead_tup",
"summary": "Estimated number of dead rows",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.last_analyze",
"summary": "Last time at which this table was manually analyzed",
"vartype": 6,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.idx_tup_fetch",
"summary": "Number of live rows fetched by index scans",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.n_tup_upd",
"summary": "Number of rows updated",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.schemaname",
"summary": "Name of the schema that this table is in",
"vartype": 1,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.seq_tup_read",
"summary": "Number of live rows fetched by sequential scans",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.vacuum_count",
"summary": "Number of times this table has been manually vacuumed (not counting VACUUM FULL)",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.n_mod_since_analyze",
"summary": "Estimated number of rows modified since this table was last analyzed",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.n_tup_del",
"summary": "Number of rows deleted",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.last_autovacuum",
"summary": "Last time at which this table was vacuumed by the autovacuum daemon",
"vartype": 6,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.seq_scan",
"summary": "Number of sequential scans initiated on this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.relid",
"summary": "OID of a table",
"vartype": 2,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.n_tup_hot_upd",
"summary": "\"Number of rows HOT updated (i.e., with no separate index update required)\"",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.autoanalyze_count",
"summary": "Number of times this table has been analyzed by the autovacuum daemon",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.n_live_tup",
"summary": "Estimated number of live rows",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.relname",
"summary": "Name of this table",
"vartype": 1,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.last_autoanalyze",
"summary": "Last time at which this table was analyzed by the autovacuum daemon",
"vartype": 6,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.idx_scan",
"summary": "Number of index scans initiated on this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.autovacuum_count",
"summary": "Number of times this table has been vacuumed by the autovacuum daemon",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_stat_user_tables.analyze_count",
"summary": "Number of times this table has been manually analyzed",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_indexes.indexrelid",
"summary": "OID of this index",
"vartype": 2,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_indexes.relid",
"summary": "OID of the table for this index",
"vartype": 2,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_indexes.indexrelname",
"summary": "Name of this index",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_indexes.idx_blks_hit",
"summary": "Number of buffer hits in this index",
"vartype": 2,
"metric_type": 1,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_indexes.relname",
"summary": "Name of the table for this index",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_indexes.idx_blks_read",
"summary": "Number of disk blocks read from this index",
"vartype": 2,
"metric_type": 1,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_indexes.schemaname",
"summary": "Name of the schema this index is in",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_tables.relid",
"summary": "OID of a table",
"vartype": 2,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_tables.heap_blks_hit",
"summary": "Number of buffer hits in this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_tables.tidx_blks_read",
"summary": "Number of disk blocks read from this table's TOAST table index (if any)",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_tables.tidx_blks_hit",
"summary": "Number of buffer hits in this table's TOAST table index (if any)",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_tables.toast_blks_hit",
"summary": "Number of buffer hits in this table's TOAST table (if any)",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_tables.idx_blks_hit",
"summary": "Number of buffer hits in all indexes on this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_tables.relname",
"summary": "Name of this table",
"vartype": 1,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_tables.toast_blks_read",
"summary": "Number of disk blocks read from this table's TOAST table (if any)",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_tables.idx_blks_read",
"summary": "Number of disk blocks read from all indexes on this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_tables.schemaname",
"summary": "Name of the schema that this table is in",
"vartype": 1,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 2,
"name": "pg_statio_user_tables.heap_blks_read",
"summary": "Number of disk blocks read from this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
}
]

4622
server/website/website/fixtures/postgres-93_knobs.json Normal file
View File

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937
server/website/website/fixtures/postgres-93_metrics.json Normal file
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@@ -0,0 +1,937 @@
[
{
"fields": {
"dbms": 3,
"name": "pg_stat_bgwriter.buffers_backend",
"summary": "Number of buffers written directly by a backend",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_bgwriter.checkpoints_timed",
"summary": "Number of scheduled checkpoints that have been performed",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_bgwriter.buffers_alloc",
"summary": "Number of buffers allocated",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_bgwriter.buffers_clean",
"summary": "Number of buffers written by the background writer",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_bgwriter.buffers_backend_fsync",
"summary": "Number of times a backend had to execute its own fsync call (normally the background writer handles those even when the backend does its own write)",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_bgwriter.checkpoint_sync_time",
"summary": "\"Total amount of time that has been spent in the portion of checkpoint processing where files are synchronized to disk, in milliseconds\"",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_bgwriter.checkpoints_req",
"summary": "Number of requested checkpoints that have been performed",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_bgwriter.checkpoint_write_time",
"summary": "\"Total amount of time that has been spent in the portion of checkpoint processing where files are written to disk, in milliseconds\"",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_bgwriter.maxwritten_clean",
"summary": "Number of times the background writer stopped a cleaning scan because it had written too many buffers",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_bgwriter.buffers_checkpoint",
"summary": "Number of buffers written during checkpoints",
"vartype": 2,
"metric_type": 1,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_bgwriter.stats_reset",
"summary": "Time at which these statistics were last reset",
"vartype": 6,
"metric_type": 2,
"scope": "global"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.numbackends",
"summary": "Number of backends currently connected to this database. This is the only column in this view that returns a value reflecting current state; all other columns return the accumulated values since the last reset.",
"vartype": 2,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.datname",
"summary": "Name of this database",
"vartype": 1,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.blks_read",
"summary": "Number of disk blocks read in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.deadlocks",
"summary": "Number of deadlocks detected in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.tup_fetched",
"summary": "Number of rows fetched by queries in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.tup_updated",
"summary": "Number of rows updated by queries in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.stats_reset",
"summary": "Time at which these statistics were last reset",
"vartype": 6,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.tup_inserted",
"summary": "Number of rows inserted by queries in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.datid",
"summary": "OID of a database",
"vartype": 2,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.xact_commit",
"summary": "Number of transactions in this database that have been committed",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.tup_deleted",
"summary": "Number of rows deleted by queries in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.blk_read_time",
"summary": "\"Time spent reading data file blocks by backends in this database, in milliseconds\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.xact_rollback",
"summary": "Number of transactions in this database that have been rolled back",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.conflicts",
"summary": "\"Number of queries canceled due to conflicts with recovery in this database. (Conflicts occur only on standby servers; see pg_stat_database_conflicts for details.)\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.blks_hit",
"summary": "\"Number of times disk blocks were found already in the buffer cache, so that a read was not necessary (this only includes hits in the PostgreSQL buffer cache, not the operating system's file system cache)\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.tup_returned",
"summary": "Number of rows returned by queries in this database",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.temp_files",
"summary": "\"Number of temporary files created by queries in this database. All temporary files are counted, regardless of why the temporary file was created (e.g., sorting or hashing), and regardless of the log_temp_files setting.\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.blk_write_time",
"summary": "\"Time spent writing data file blocks by backends in this database, in milliseconds\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database.temp_bytes",
"summary": "\"Total amount of data written to temporary files by queries in this database. All temporary files are counted, regardless of why the temporary file was created, and regardless of the log_temp_files setting.\"",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database_conflicts.datname",
"summary": "Name of this database",
"vartype": 1,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database_conflicts.confl_deadlock",
"summary": "Number of queries in this database that have been canceled due to deadlocks",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database_conflicts.confl_bufferpin",
"summary": "Number of queries in this database that have been canceled due to pinned buffers",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database_conflicts.datid",
"summary": "OID of a database",
"vartype": 2,
"metric_type": 2,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database_conflicts.confl_tablespace",
"summary": "Number of queries in this database that have been canceled due to dropped tablespaces",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database_conflicts.confl_lock",
"summary": "Number of queries in this database that have been canceled due to lock timeouts",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_database_conflicts.confl_snapshot",
"summary": "Number of queries in this database that have been canceled due to old snapshots",
"vartype": 2,
"metric_type": 1,
"scope": "database"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_indexes.indexrelid",
"summary": "OID of this index",
"vartype": 2,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_indexes.relid",
"summary": "OID of the table for this index",
"vartype": 2,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_indexes.indexrelname",
"summary": "Name of this index",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_indexes.relname",
"summary": "Name of the table for this index",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_indexes.idx_tup_fetch",
"summary": "Number of live table rows fetched by simple index scans using this index",
"vartype": 2,
"metric_type": 1,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_indexes.idx_tup_read",
"summary": "Number of index entries returned by scans on this index",
"vartype": 2,
"metric_type": 1,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_indexes.idx_scan",
"summary": "Number of index scans initiated on this index",
"vartype": 2,
"metric_type": 1,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_indexes.schemaname",
"summary": "Name of the schema this index is in",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.last_vacuum",
"summary": "Last time at which this table was manually vacuumed (not counting VACUUM FULL)",
"vartype": 6,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.n_tup_ins",
"summary": "Number of rows inserted",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.n_dead_tup",
"summary": "Estimated number of dead rows",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.last_analyze",
"summary": "Last time at which this table was manually analyzed",
"vartype": 6,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.idx_tup_fetch",
"summary": "Number of live rows fetched by index scans",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.n_tup_upd",
"summary": "Number of rows updated",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.schemaname",
"summary": "Name of the schema that this table is in",
"vartype": 1,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.seq_tup_read",
"summary": "Number of live rows fetched by sequential scans",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.vacuum_count",
"summary": "Number of times this table has been manually vacuumed (not counting VACUUM FULL)",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.n_mod_since_analyze",
"summary": "Estimated number of rows modified since this table was last analyzed",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.n_tup_del",
"summary": "Number of rows deleted",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.last_autovacuum",
"summary": "Last time at which this table was vacuumed by the autovacuum daemon",
"vartype": 6,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.seq_scan",
"summary": "Number of sequential scans initiated on this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.relid",
"summary": "OID of a table",
"vartype": 2,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.n_tup_hot_upd",
"summary": "\"Number of rows HOT updated (i.e., with no separate index update required)\"",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.autoanalyze_count",
"summary": "Number of times this table has been analyzed by the autovacuum daemon",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.n_live_tup",
"summary": "Estimated number of live rows",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.relname",
"summary": "Name of this table",
"vartype": 1,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.last_autoanalyze",
"summary": "Last time at which this table was analyzed by the autovacuum daemon",
"vartype": 6,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.idx_scan",
"summary": "Number of index scans initiated on this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.autovacuum_count",
"summary": "Number of times this table has been vacuumed by the autovacuum daemon",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_stat_user_tables.analyze_count",
"summary": "Number of times this table has been manually analyzed",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_indexes.indexrelid",
"summary": "OID of this index",
"vartype": 2,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_indexes.relid",
"summary": "OID of the table for this index",
"vartype": 2,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_indexes.indexrelname",
"summary": "Name of this index",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_indexes.idx_blks_hit",
"summary": "Number of buffer hits in this index",
"vartype": 2,
"metric_type": 1,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_indexes.relname",
"summary": "Name of the table for this index",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_indexes.idx_blks_read",
"summary": "Number of disk blocks read from this index",
"vartype": 2,
"metric_type": 1,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_indexes.schemaname",
"summary": "Name of the schema this index is in",
"vartype": 1,
"metric_type": 2,
"scope": "index"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_tables.relid",
"summary": "OID of a table",
"vartype": 2,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_tables.heap_blks_hit",
"summary": "Number of buffer hits in this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_tables.tidx_blks_read",
"summary": "Number of disk blocks read from this table's TOAST table index (if any)",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_tables.tidx_blks_hit",
"summary": "Number of buffer hits in this table's TOAST table index (if any)",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_tables.toast_blks_hit",
"summary": "Number of buffer hits in this table's TOAST table (if any)",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_tables.idx_blks_hit",
"summary": "Number of buffer hits in all indexes on this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_tables.relname",
"summary": "Name of this table",
"vartype": 1,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_tables.toast_blks_read",
"summary": "Number of disk blocks read from this table's TOAST table (if any)",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_tables.idx_blks_read",
"summary": "Number of disk blocks read from all indexes on this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_tables.schemaname",
"summary": "Name of the schema that this table is in",
"vartype": 1,
"metric_type": 2,
"scope": "table"
},
"model": "website.MetricCatalog"
},
{
"fields": {
"dbms": 3,
"name": "pg_statio_user_tables.heap_blks_read",
"summary": "Number of disk blocks read from this table",
"vartype": 2,
"metric_type": 1,
"scope": "table"
},
"model": "website.MetricCatalog"
}
]

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[
{
"fields": {
"hardware": 16,
"dbms": 1,
"task_type": 1,
"creation_timestamp": "2016-12-04 11:00",
"value": "[\n \"throughput_txn_per_sec\", \n \"pg_stat_bgwriter.buffers_alloc\", \n \"pg_stat_bgwriter.buffers_checkpoint\", \n \"pg_stat_bgwriter.checkpoints_req\", \n \"pg_stat_bgwriter.maxwritten_clean\", \n \"pg_stat_database.blks_hit\", \n \"pg_stat_database.tup_deleted\", \n \"pg_stat_database.tup_inserted\", \n \"pg_stat_database.tup_returned\", \n \"pg_stat_database.tup_updated\", \n \"pg_stat_user_tables.autoanalyze_count\"\n]"
},
"model": "website.PipelineResult"
}
]

12
server/website/website/fixtures/postgres-96_m3xlarge_ranked_knobs.json Normal file
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[
{
"fields": {
"hardware": 16,
"dbms": 1,
"task_type": 2,
"creation_timestamp": "2016-12-04 11:00",
"value": "[\n \"global.shared_buffers\", \n \"global.effective_cache_size\", \n \"global.bgwriter_lru_maxpages\", \n \"global.bgwriter_delay\", \n \"global.checkpoint_completion_target\", \n \"global.deadlock_timeout\", \n \"global.default_statistics_target\", \n \"global.effective_io_concurrency\", \n \"global.checkpoint_timeout\", \n \"global.commit_delay\", \n \"global.commit_siblings\", \n \"global.wal_buffers\", \n \"global.temp_buffers\", \n \"global.from_collapse_limit\", \n \"global.join_collapse_limit\", \n \"global.bgwriter_lru_multiplier\", \n \"global.random_page_cost\", \n \"global.work_mem\", \n \"global.maintenance_work_mem\", \n \"global.min_wal_size\", \n \"global.max_parallel_workers_per_gather\", \n \"global.seq_page_cost\", \n \"global.max_worker_processes\", \n \"global.wal_sync_method\", \n \"global.checkpoint_flush_after\", \n \"global.wal_writer_delay\", \n \"global.backend_flush_after\", \n \"global.bgwriter_flush_after\", \n \"global.min_parallel_relation_size\", \n \"global.wal_writer_flush_after\", \n \"global.max_wal_size\"\n]"
},
"model": "website.PipelineResult"
}
]

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[
{
"model": "auth.user",
"pk": 1,
"fields": {
"password": "pbkdf2_sha256$30000$wefcDHxU5ctV$8NUjP4GZouOdr4LU47/WGZgqGU4V4brcS8Xt1Yx7ut0=",
"last_login": null,
"is_superuser": false,
"username": "user",
"first_name": "test",
"last_name": "user",
"email": "user@email.com",
"is_staff": false,
"is_active": true,
"date_joined": "2017-12-04T20:46:28.365Z",
"groups": [],
"user_permissions": []
}
}
]

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server/website/website/fixtures/test_user_sessions.json Normal file
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[
{
"fields": {
"user": 1,
"name": "test_project",
"description": "",
"creation_time": "2017-11-30T02:00:49.611Z",
"last_update": "2017-11-30T02:00:49.611Z"
},
"model": "website.Project",
"pk": 1
},
{
"fields": {
"type": 1,
"name": "New Hardware",
"cpu": 4,
"memory": 16.0,
"storage": "32",
"storage_type": "",
"additional_specs": ""
},
"model": "website.Hardware",
"pk": 1
},
{
"fields": {
"user": 1,
"name": "basic_session",
"description": "(no tuning)",
"dbms": 1,
"hardware": 1,
"project": 1,
"upload_code": "1234567890",
"tuning_session": "no_tuning_session",
"target_objective": null,
"nondefault_settings": null,
"creation_time": "2017-11-30T02:00:49.611Z",
"last_update": "2017-11-30T02:00:49.611Z"
},
"model": "website.Session",
"pk": 1
},
{
"fields": {
"user": 1,
"name": "tuning_session",
"description": "",
"dbms": 1,
"hardware": 1,
"project": 1,
"upload_code": "0987654321",
"tuning_session": "tuning_session",
"target_objective": "throughput_txn_per_sec",
"nondefault_settings": null,
"creation_time": "2017-11-30T02:00:49.611Z",
"last_update": "2017-11-30T02:00:49.611Z"
},
"model": "website.Session",
"pk": 2
}
]

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server/website/website/forms.py Normal file
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#
# OtterTune - forms.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
'''
Created on Jul 25, 2017
@author: dvanaken
'''
from django import forms
from django.db.models import Max
from .models import Session, Project, Hardware, SessionKnob
class NewResultForm(forms.Form):
upload_code = forms.CharField(max_length=30)
metrics_before = forms.FileField()
metrics_after = forms.FileField()
knobs = forms.FileField()
summary = forms.FileField()
class ProjectForm(forms.ModelForm):
class Meta: # pylint: disable=old-style-class,no-init
model = Project
fields = ['name', 'description']
widgets = {
'name': forms.TextInput(attrs={'required': True}),
'description': forms.Textarea(attrs={'maxlength': 500,
'rows': 5}),
}
class SessionForm(forms.ModelForm):
gen_upload_code = forms.BooleanField(widget=forms.CheckboxInput,
initial=False,
required=False,
label='Get new upload code')
cpu = forms.IntegerField(label='Number of Processors')
memory = forms.FloatField(label='RAM (GB)')
storage = forms.IntegerField(label='Storage (GB)')
def __init__(self, *args, **kwargs):
super(SessionForm, self).__init__(*args, **kwargs)
self.fields['description'].required = False
self.fields['target_objective'].required = False
self.fields['tuning_session'].required = True
self.fields['cpu'].initial = 2
self.fields['memory'].initial = 16.0
self.fields['storage'].initial = 32
def save(self, commit=True):
model = super(SessionForm, self).save(commit=False)
cpu2 = self.cleaned_data['cpu']
memory2 = self.cleaned_data['memory']
storage2 = self.cleaned_data['storage']
if hasattr(model, 'hardware'):
model.hardware.cpu = cpu2
model.hardware.memory = memory2
model.hardware.storage = storage2
model.hardware.save()
else:
last_type = Hardware.objects.aggregate(Max('type'))['type__max']
if last_type is None:
last_type = 0
model.hardware = Hardware.objects.create(type=last_type + 1,
name='New Hardware',
cpu=cpu2,
memory=memory2,
storage=storage2,
storage_type='Default',
additional_specs='{}')
if commit:
model.save()
return model
class Meta: # pylint: disable=old-style-class,no-init
model = Session
fields = ('name', 'description', 'tuning_session', 'dbms', 'cpu', 'memory', 'storage',
'target_objective')
widgets = {
'name': forms.TextInput(attrs={'required': True}),
'description': forms.Textarea(attrs={'maxlength': 500,
'rows': 5}),
}
labels = {
'dbms': 'DBMS',
}
class SessionKnobForm(forms.ModelForm):
name = forms.CharField(max_length=128)
def __init__(self, *args, **kwargs):
super(SessionKnobForm, self).__init__(*args, **kwargs)
self.fields['session'].required = False
self.fields['knob'].required = False
self.fields['name'].widget.attrs['readonly'] = True
class Meta: # pylint: disable=old-style-class,no-init
model = SessionKnob
fields = ['session', 'knob', 'minval', 'maxval', 'tunable']

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server/website/website/migrations/0001_initial.py Normal file
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# -*- coding: utf-8 -*-
# Generated by Django 1.10.1 on 2018-03-26 02:21
from django.conf import settings
import django.core.validators
from django.db import migrations, models
import django.db.models.deletion
import re
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='BackupData',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('raw_knobs', models.TextField()),
('raw_initial_metrics', models.TextField()),
('raw_final_metrics', models.TextField()),
('raw_summary', models.TextField()),
('knob_log', models.TextField()),
('metric_log', models.TextField()),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='DBMSCatalog',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('type', models.IntegerField(choices=[(1, b'MySQL'), (2, b'Postgres'), (3, b'Db2'), (4, b'Oracle'), (5, b'SQL Server'), (6, b'SQLite'), (7, b'HStore'), (8, b'Vector'), (9, b'MyRocks')])),
('version', models.CharField(max_length=16)),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='Hardware',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('type', models.IntegerField()),
('name', models.CharField(max_length=32)),
('cpu', models.IntegerField()),
('memory', models.FloatField()),
('storage', models.CharField(max_length=64, validators=[django.core.validators.RegexValidator(re.compile('^\\d+(?:\\,\\d+)*\\Z'), code='invalid', message='Enter only digits separated by commas.')])),
('storage_type', models.CharField(max_length=16)),
('additional_specs', models.TextField(null=True)),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='KnobCatalog',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=128)),
('vartype', models.IntegerField(choices=[(1, b'STRING'), (2, b'INTEGER'), (3, b'REAL'), (4, b'BOOL'), (5, b'ENUM'), (6, b'TIMESTAMP')], verbose_name=b'variable type')),
('unit', models.IntegerField(choices=[(1, b'bytes'), (2, b'milliseconds'), (3, b'other')])),
('category', models.TextField(null=True)),
('summary', models.TextField(null=True, verbose_name=b'description')),
('description', models.TextField(null=True)),
('scope', models.CharField(max_length=16)),
('minval', models.CharField(max_length=32, null=True, verbose_name=b'minimum value')),
('maxval', models.CharField(max_length=32, null=True, verbose_name=b'maximum value')),
('default', models.TextField(verbose_name=b'default value')),
('enumvals', models.TextField(null=True, verbose_name=b'valid values')),
('context', models.CharField(max_length=32)),
('tunable', models.BooleanField(verbose_name=b'tunable')),
('resource', models.IntegerField(choices=[(1, b'Memory'), (2, b'CPU'), (3, b'Storage'), (4, b'Other')], default=4)),
('dbms', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.DBMSCatalog')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='KnobData',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=50)),
('creation_time', models.DateTimeField()),
('data', models.TextField()),
('knobs', models.TextField()),
('dbms', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.DBMSCatalog')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='MetricCatalog',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=128)),
('vartype', models.IntegerField(choices=[(1, b'STRING'), (2, b'INTEGER'), (3, b'REAL'), (4, b'BOOL'), (5, b'ENUM'), (6, b'TIMESTAMP')])),
('summary', models.TextField(null=True, verbose_name=b'description')),
('scope', models.CharField(max_length=16)),
('metric_type', models.IntegerField(choices=[(1, b'COUNTER'), (2, b'INFO'), (3,b'STATISTICS')])),
('dbms', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.DBMSCatalog')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='MetricData',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=50)),
('creation_time', models.DateTimeField()),
('data', models.TextField()),
('metrics', models.TextField()),
('dbms', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.DBMSCatalog')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='PipelineData',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('task_type', models.IntegerField(choices=[(1, b'Pruned Metrics'), (2, b'Ranked Knobs'), (3, b'Knob Data'), (4, b'Metric Data')])),
('data', models.TextField()),
('creation_time', models.DateTimeField()),
],
),
migrations.CreateModel(
name='PipelineRun',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('start_time', models.DateTimeField()),
('end_time', models.DateTimeField(null=True)),
],
options={
'ordering': ['-id'],
},
),
migrations.CreateModel(
name='Project',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=64, verbose_name=b'project name')),
('description', models.TextField(blank=True, null=True)),
('creation_time', models.DateTimeField()),
('last_update', models.DateTimeField()),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='Result',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('creation_time', models.DateTimeField()),
('observation_start_time', models.DateTimeField()),
('observation_end_time', models.DateTimeField()),
('observation_time', models.FloatField()),
('task_ids', models.CharField(max_length=180, null=True)),
('next_configuration', models.TextField(null=True)),
('dbms', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.DBMSCatalog')),
('knob_data', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.KnobData')),
('metric_data', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.MetricData')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='Session',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=64, verbose_name=b'session name')),
('description', models.TextField(blank=True, null=True)),
('creation_time', models.DateTimeField()),
('last_update', models.DateTimeField()),
('upload_code', models.CharField(max_length=30, unique=True)),
('tuning_session', models.CharField(choices=[('tuning_sesion', 'Tuning Session'), ('no_tuning_session', 'No Tuning'), ('randomly_generate', 'Randomly Generate')], max_length=64)),
('target_objective', models.CharField(choices=[(b'throughput_txn_per_sec', b'Throughput'), (b'99th_lat_ms', b'99 Percentile Latency')], max_length=64, null=True)),
('nondefault_settings', models.TextField(null=True)),
('dbms', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.DBMSCatalog')),
('hardware', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.Hardware')),
('project', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.Project')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='Workload',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=128, verbose_name=b'workload name')),
('dbms', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.DBMSCatalog')),
('hardware', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.Hardware')),
],
),
migrations.AddField(
model_name='result',
name='session',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.Session', verbose_name=b'session name'),
),
migrations.AddField(
model_name='result',
name='workload',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.Workload'),
),
migrations.AddField(
model_name='pipelinedata',
name='pipeline_run',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.PipelineRun'),
),
migrations.AddField(
model_name='pipelinedata',
name='workload',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.Workload'),
),
migrations.AddField(
model_name='metricdata',
name='session',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.Session'),
),
migrations.AddField(
model_name='knobdata',
name='session',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.Session'),
),
migrations.AddField(
model_name='backupdata',
name='result',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.Result'),
),
migrations.AlterUniqueTogether(
name='workload',
unique_together=set([('dbms', 'hardware', 'name')]),
),
migrations.AlterUniqueTogether(
name='pipelinedata',
unique_together=set([('pipeline_run', 'task_type', 'workload')]),
),
]

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server/website/website/migrations/0002_enable_compression.py Normal file
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# -*- coding: utf-8 -*-
import logging
from django.conf import settings
#from django.core.exceptions import ProgrammingError
from django.db import connection, migrations, ProgrammingError
LOG = logging.getLogger(__name__)
TABLES_TO_COMPRESS = [
"website_backupdata",
"website_knobdata",
"website_metricdata",
"website_pipelinedata",
]
class Migration(migrations.Migration):
dependencies = [
('website', '0001_initial'),
]
try:
LOG.info('***** VENDOR: %s', connection.vendor)
if connection.vendor == 'mysql':
version = (0, 0, 0)
with connection.cursor() as cursor:
cursor.execute('SELECT VERSION()')
version = cursor.fetchone()[0]
version = version.split('-')[0]
version = version.split('.')
version = tuple(int(v) for v in version)
LOG.info('***** DB VERSION: %s', version)
if version >= (5, 7, 0):
operations = [
migrations.RunSQL(["ALTER TABLE " + table_name + " COMPRESSION='zlib';",
"OPTIMIZE TABLE " + table_name + ";"],
["ALTER TABLE " + table_name + " COMPRESSION='none';",
"OPTIMIZE TABLE " + table_name + ";"])
for table_name in TABLES_TO_COMPRESS
]
LOG.info('***** DONE ENABLING COMPRESSION')
else:
operations = []
LOG.info('***** COMPRESSION NOT SUPPORTED: %s < (5, 7, 0)', version)
else:
LOG.info('***** DB COMPRESSION NOT SUPPORTED: %s', connection.vendor)
except ProgrammingError as err:
LOG.warning("Error applying migration '0002_enable_compression'... Skipping")
operations = []

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# -*- coding: utf-8 -*-
# Generated by Django 1.10.1 on 2018-08-02 07:58
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('website', '0002_enable_compression'),
]
operations = [
migrations.AddField(
model_name='workload',
name='status',
field=models.IntegerField(choices=[(1, 'MODIFIED'), (2, 'PROCESSING'), (3, 'PROCESSED')], default=1, editable=False),
)
]

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server/website/website/migrations/0004_load_initial_data.py Normal file
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# -*- coding: utf-8 -*-
# Generated by Django 1.10.1 on 2017-12-07 19:51
from django.core.management import call_command
from django.db import migrations
def load_initial_data(apps, schema_editor):
initial_data_fixtures = [
"dbms_catalog.json",
"postgres-96_knobs.json",
"postgres-96_metrics.json",
"postgres-92_knobs.json",
"postgres-92_metrics.json",
"postgres-93_knobs.json",
"postgres-93_metrics.json",
"postgres-94_knobs.json",
"postgres-94_metrics.json",
"myrocks-5.6_knobs.json",
"myrocks-5.6_metrics.json",
"oracle_knobs.json",
"oracle_metrics.json"
]
for fixture in initial_data_fixtures:
call_command("loaddata", fixture, app_label="website")
def unload_initial_data(apps, schema_editor):
model_names = [
"DBMSCatalog",
"KnobCatalog",
"MetricCatalog",
"Hardware"
]
for model_name in model_names:
model = apps.get_model("website", model_name)
model.objects.all().delete()
class Migration(migrations.Migration):
dependencies = [
('website', '0003_background_task_optimization'),
]
operations = [
migrations.RunPython(load_initial_data, unload_initial_data)
]

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server/website/website/migrations/0005_adding_session_knob.py Normal file
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# -*- coding: utf-8 -*-
# Generated by Django 1.10.1 on 2019-08-07 18:18
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('website', '0004_load_initial_data'),
]
operations = [
migrations.CreateModel(
name='SessionKnob',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('minval', models.CharField(max_length=32, null=True, verbose_name='minimum value')),
('maxval', models.CharField(max_length=32, null=True, verbose_name='maximum value')),
('tunable', models.BooleanField(verbose_name='tunable')),
],
options={
'abstract': False,
},
),
migrations.AlterField(
model_name='dbmscatalog',
name='type',
field=models.IntegerField(choices=[(1, 'MySQL'), (2, 'Postgres'), (3, 'Db2'), (4, 'Oracle'), (6, 'SQLite'), (7, 'HStore'), (8, 'Vector'), (5, 'SQL Server'), (9, 'MyRocks')]),
),
migrations.AlterField(
model_name='knobcatalog',
name='default',
field=models.TextField(verbose_name='default value'),
),
migrations.AlterField(
model_name='knobcatalog',
name='enumvals',
field=models.TextField(null=True, verbose_name='valid values'),
),
migrations.AlterField(
model_name='knobcatalog',
name='maxval',
field=models.CharField(max_length=32, null=True, verbose_name='maximum value'),
),
migrations.AlterField(
model_name='knobcatalog',
name='minval',
field=models.CharField(max_length=32, null=True, verbose_name='minimum value'),
),
migrations.AlterField(
model_name='knobcatalog',
name='resource',
field=models.IntegerField(choices=[(1, 'Memory'), (2, 'CPU'), (3, 'Storage'), (4, 'Other')], default=4),
),
migrations.AlterField(
model_name='knobcatalog',
name='summary',
field=models.TextField(null=True, verbose_name='description'),
),
migrations.AlterField(
model_name='knobcatalog',
name='tunable',
field=models.BooleanField(verbose_name='tunable'),
),
migrations.AlterField(
model_name='knobcatalog',
name='unit',
field=models.IntegerField(choices=[(1, 'bytes'), (2, 'milliseconds'), (3, 'other')]),
),
migrations.AlterField(
model_name='knobcatalog',
name='vartype',
field=models.IntegerField(choices=[(1, 'STRING'), (2, 'INTEGER'), (3, 'REAL'), (4, 'BOOL'), (5, 'ENUM'), (6, 'TIMESTAMP')], verbose_name='variable type'),
),
migrations.AlterField(
model_name='metriccatalog',
name='metric_type',
field=models.IntegerField(choices=[(1, 'COUNTER'), (2, 'INFO'), (3, 'STATISTICS')]),
),
migrations.AlterField(
model_name='metriccatalog',
name='summary',
field=models.TextField(null=True, verbose_name='description'),
),
migrations.AlterField(
model_name='metriccatalog',
name='vartype',
field=models.IntegerField(choices=[(1, 'STRING'), (2, 'INTEGER'), (3, 'REAL'), (4, 'BOOL'), (5, 'ENUM'), (6, 'TIMESTAMP')]),
),
migrations.AlterField(
model_name='pipelinedata',
name='task_type',
field=models.IntegerField(choices=[(1, 'Pruned Metrics'), (2, 'Ranked Knobs'), (3, 'Knob Data'), (4, 'Metric Data')]),
),
migrations.AlterField(
model_name='project',
name='name',
field=models.CharField(max_length=64, verbose_name='project name'),
),
migrations.AlterField(
model_name='result',
name='session',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.Session', verbose_name='session name'),
),
migrations.AlterField(
model_name='session',
name='name',
field=models.CharField(max_length=64, verbose_name='session name'),
),
migrations.AlterField(
model_name='session',
name='target_objective',
field=models.CharField(choices=[('throughput_txn_per_sec', 'Throughput'), ('99th_lat_ms', '99 Percentile Latency')], max_length=64, null=True),
),
migrations.AlterField(
model_name='session',
name='tuning_session',
field=models.CharField(choices=[('tuning_session', 'Tuning Session'), ('no_tuning_session', 'No Tuning'), ('randomly_generate', 'Randomly Generate')], default='tuning_sesion', max_length=64),
),
migrations.AlterField(
model_name='workload',
name='name',
field=models.CharField(max_length=128, verbose_name='workload name'),
),
migrations.AddField(
model_name='sessionknob',
name='knob',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.KnobCatalog'),
),
migrations.AddField(
model_name='sessionknob',
name='session',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='website.Session'),
),
]

0
server/website/website/migrations/__init__.py Normal file
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#
# OtterTune - models.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
from collections import namedtuple, OrderedDict
from django.contrib.auth.models import User
from django.core.validators import validate_comma_separated_integer_list
from django.db import models, DEFAULT_DB_ALIAS
from django.utils.timezone import now
from .types import (DBMSType, LabelStyleType, MetricType, KnobUnitType,
PipelineTaskType, VarType, KnobResourceType,
WorkloadStatusType)
class BaseModel(models.Model):
def __str__(self):
return self.__unicode__()
def __unicode__(self):
return self.name
@classmethod
def get_labels(cls, style=LabelStyleType.DEFAULT_STYLE):
from .utils import LabelUtil
labels = {}
fields = cls._meta.get_fields()
for field in fields:
try:
verbose_name = field.verbose_name
if field.name == 'id':
verbose_name = cls._model_name() + ' id'
labels[field.name] = verbose_name
except AttributeError:
pass
return LabelUtil.style_labels(labels, style)
@classmethod
def _model_name(cls):
return cls.__name__
class Meta: # pylint: disable=old-style-class,no-init
abstract = True
class DBMSCatalog(BaseModel):
type = models.IntegerField(choices=DBMSType.choices())
version = models.CharField(max_length=16)
@property
def name(self):
return DBMSType.name(self.type)
@property
def key(self):
return '{}_{}'.format(self.name, self.version)
@property
def full_name(self):
return '{} v{}'.format(self.name, self.version)
def __unicode__(self):
return self.full_name
class KnobCatalog(BaseModel):
dbms = models.ForeignKey(DBMSCatalog)
name = models.CharField(max_length=128)
vartype = models.IntegerField(choices=VarType.choices(), verbose_name="variable type")
unit = models.IntegerField(choices=KnobUnitType.choices())
category = models.TextField(null=True)
summary = models.TextField(null=True, verbose_name='description')
description = models.TextField(null=True)
scope = models.CharField(max_length=16)
minval = models.CharField(max_length=32, null=True, verbose_name="minimum value")
maxval = models.CharField(max_length=32, null=True, verbose_name="maximum value")
default = models.TextField(verbose_name="default value")
enumvals = models.TextField(null=True, verbose_name="valid values")
context = models.CharField(max_length=32)
tunable = models.BooleanField(verbose_name="tunable")
resource = models.IntegerField(choices=KnobResourceType.choices(), default=4)
MetricMeta = namedtuple('MetricMeta',
['name', 'pprint', 'unit', 'short_unit', 'scale', 'improvement'])
class MetricManager(models.Manager):
# Direction of performance improvement
LESS_IS_BETTER = '(less is better)'
MORE_IS_BETTER = '(more is better)'
# Possible objective functions
THROUGHPUT = 'throughput_txn_per_sec'
THROUGHPUT_META = (THROUGHPUT, 'Throughput',
'transactions / second',
'txn/sec', 1, MORE_IS_BETTER)
LATENCY_99 = '99th_lat_ms'
LATENCY_99_META = (LATENCY_99, '99 Percentile Latency',
'milliseconds', 'ms', 1, LESS_IS_BETTER)
# Objective function metric metadata
OBJ_META = {THROUGHPUT: THROUGHPUT_META, LATENCY_99: LATENCY_99_META}
@staticmethod
def get_default_metrics(target_objective=None):
# get the target_objective, return the default one if target_objective is None
if target_objective is not None:
default_metrics = [target_objective]
else:
default_metrics = [MetricManager.get_default_objective_function()]
return default_metrics
@staticmethod
def get_default_objective_function():
return MetricManager.THROUGHPUT
@staticmethod
def get_metric_meta(dbms, target_objective=None):
numeric_metric_names = MetricCatalog.objects.filter(
dbms=dbms, metric_type=MetricType.COUNTER).values_list('name', flat=True)
numeric_metrics = {}
for metname in numeric_metric_names:
numeric_metrics[metname] = MetricMeta(
metname, metname, 'events / second', 'events/sec', 1, '')
sorted_metrics = [(mname, mmeta) for mname, mmeta in
sorted(numeric_metrics.items())]
if target_objective is not None:
mname = target_objective
else:
mname = MetricManager.get_default_objective_function()
mmeta = MetricManager.OBJ_META[mname]
sorted_metrics.insert(0, (mname, MetricMeta(*mmeta)))
return OrderedDict(sorted_metrics)
class MetricCatalog(BaseModel):
objects = MetricManager()
dbms = models.ForeignKey(DBMSCatalog)
name = models.CharField(max_length=128)
vartype = models.IntegerField(choices=VarType.choices())
summary = models.TextField(null=True, verbose_name='description')
scope = models.CharField(max_length=16)
metric_type = models.IntegerField(choices=MetricType.choices())
class Project(BaseModel):
user = models.ForeignKey(User)
name = models.CharField(max_length=64, verbose_name="project name")
description = models.TextField(null=True, blank=True)
creation_time = models.DateTimeField()
last_update = models.DateTimeField()
def delete(self, using=DEFAULT_DB_ALIAS, keep_parents=False):
sessions = Session.objects.filter(project=self)
for x in sessions:
x.delete()
super(Project, self).delete(using, keep_parents)
class Hardware(BaseModel):
type = models.IntegerField()
name = models.CharField(max_length=32)
cpu = models.IntegerField()
memory = models.FloatField()
storage = models.CharField(
max_length=64, validators=[validate_comma_separated_integer_list])
storage_type = models.CharField(max_length=16)
additional_specs = models.TextField(null=True)
def __unicode__(self):
return 'CPU:{}, RAM:{}, Storage:{}'.format(self.cpu, self.memory, self.storage)
class Session(BaseModel):
user = models.ForeignKey(User)
name = models.CharField(max_length=64, verbose_name="session name")
description = models.TextField(null=True, blank=True)
dbms = models.ForeignKey(DBMSCatalog)
hardware = models.ForeignKey(Hardware)
project = models.ForeignKey(Project)
creation_time = models.DateTimeField()
last_update = models.DateTimeField()
upload_code = models.CharField(max_length=30, unique=True)
TUNING_OPTIONS = [
("tuning_session", "Tuning Session"),
("no_tuning_session", "No Tuning"),
("randomly_generate", "Randomly Generate")
]
tuning_session = models.CharField(choices=TUNING_OPTIONS,
max_length=64, default='tuning_sesion')
TARGET_OBJECTIVES = [
('throughput_txn_per_sec', 'Throughput'),
('99th_lat_ms', '99 Percentile Latency')
]
target_objective = models.CharField(choices=TARGET_OBJECTIVES, max_length=64, null=True)
nondefault_settings = models.TextField(null=True)
def clean(self):
if self.target_objective is None:
self.target_objective = MetricManager.get_default_objective_function()
def delete(self, using=DEFAULT_DB_ALIAS, keep_parents=False):
targets = KnobData.objects.filter(session=self)
results = Result.objects.filter(session=self)
for t in targets:
t.delete()
for r in results:
r.delete()
super(Session, self).delete(using=DEFAULT_DB_ALIAS, keep_parents=False)
class SessionKnobManager(models.Manager):
@staticmethod
def get_knobs_for_session(session):
# Returns a dict of the knob
knobs = KnobCatalog.objects.filter(dbms=session.dbms)
knob_dicts = list(knobs.values())
for i, _ in enumerate(knob_dicts):
if SessionKnob.objects.filter(session=session, knob=knobs[i]).exists():
new_knob = SessionKnob.objects.filter(session=session, knob=knobs[i])[0]
knob_dicts[i]["minval"] = new_knob.minval
knob_dicts[i]["maxval"] = new_knob.maxval
knob_dicts[i]["tunable"] = new_knob.tunable
knob_dicts = [knob for knob in knob_dicts if knob["tunable"]]
return knob_dicts
def __unicode__(self):
return self.session.name + " " + self.knob.name
class SessionKnob(BaseModel):
objects = SessionKnobManager()
session = models.ForeignKey(Session)
knob = models.ForeignKey(KnobCatalog)
minval = models.CharField(max_length=32, null=True, verbose_name="minimum value")
maxval = models.CharField(max_length=32, null=True, verbose_name="maximum value")
tunable = models.BooleanField(verbose_name="tunable")
class DataModel(BaseModel):
session = models.ForeignKey(Session)
name = models.CharField(max_length=50)
creation_time = models.DateTimeField()
data = models.TextField()
dbms = models.ForeignKey(DBMSCatalog)
class Meta: # pylint: disable=old-style-class,no-init
abstract = True
class DataManager(models.Manager):
@staticmethod
def create_name(data_obj, key):
ts = data_obj.creation_time.strftime("%m-%d-%y")
return (key + '@' + ts + '#' + str(data_obj.pk))
class KnobDataManager(DataManager):
def create_knob_data(self, session, knobs, data, dbms):
try:
return KnobData.objects.get(session=session,
knobs=knobs)
except KnobData.DoesNotExist:
knob_data = self.create(session=session,
knobs=knobs,
data=data,
dbms=dbms,
creation_time=now())
knob_data.name = self.create_name(knob_data, dbms.key)
knob_data.save()
return knob_data
class KnobData(DataModel):
objects = KnobDataManager()
knobs = models.TextField()
class MetricDataManager(DataManager):
def create_metric_data(self, session, metrics, data, dbms):
metric_data = self.create(session=session,
metrics=metrics,
data=data,
dbms=dbms,
creation_time=now())
metric_data.name = self.create_name(metric_data, dbms.key)
metric_data.save()
return metric_data
class MetricData(DataModel):
objects = MetricDataManager()
metrics = models.TextField()
class WorkloadManager(models.Manager):
def create_workload(self, dbms, hardware, name):
# (dbms,hardware,name) should be unique for each workload
try:
return Workload.objects.get(dbms=dbms, hardware=hardware, name=name)
except Workload.DoesNotExist:
return self.create(dbms=dbms,
hardware=hardware,
name=name)
class Workload(BaseModel):
# __DEFAULT_FMT = '{db}_{hw}_UNASSIGNED'.format
objects = WorkloadManager()
dbms = models.ForeignKey(DBMSCatalog)
hardware = models.ForeignKey(Hardware)
name = models.CharField(max_length=128, verbose_name='workload name')
status = models.IntegerField(choices=WorkloadStatusType.choices(),
default=WorkloadStatusType.MODIFIED,
editable=False)
def delete(self, using=DEFAULT_DB_ALIAS, keep_parents=False):
# The results should not have corresponding workloads.
# results = Result.objects.filter(workload=self)
# if results.exists():
# raise Exception("Cannot delete {} workload since results exist. ".format(self.name))
# Delete PipelineData with corresponding workloads
pipelinedatas = PipelineData.objects.filter(workload=self)
for x in pipelinedatas:
x.delete()
super(Workload, self).delete(using, keep_parents)
class Meta: # pylint: disable=old-style-class,no-init
unique_together = ("dbms", "hardware", "name")
# @property
# def isdefault(self):
# return self.cluster_name == self.default
#
# @property
# def default(self):
# return self.__DEFAULT_FMT(db=self.dbms.pk,
# hw=self.hardware.pk)
#
# @staticmethod
# def get_default(dbms_id, hw_id):
# return Workload.__DEFAULT_FMT(db=dbms_id,
# hw=hw_id)
class ResultManager(models.Manager):
def create_result(self, session, dbms, workload,
knob_data, metric_data,
observation_start_time,
observation_end_time,
observation_time,
task_ids=None,
next_config=None):
return self.create(
session=session,
dbms=dbms,
workload=workload,
knob_data=knob_data,
metric_data=metric_data,
observation_start_time=observation_start_time,
observation_end_time=observation_end_time,
observation_time=observation_time,
task_ids=task_ids,
next_configuration=next_config,
creation_time=now())
class Result(BaseModel):
objects = ResultManager()
session = models.ForeignKey(Session, verbose_name='session name')
dbms = models.ForeignKey(DBMSCatalog)
workload = models.ForeignKey(Workload)
knob_data = models.ForeignKey(KnobData)
metric_data = models.ForeignKey(MetricData)
creation_time = models.DateTimeField()
observation_start_time = models.DateTimeField()
observation_end_time = models.DateTimeField()
observation_time = models.FloatField()
task_ids = models.CharField(max_length=180, null=True)
next_configuration = models.TextField(null=True)
def __unicode__(self):
return str(self.pk)
class PipelineRunManager(models.Manager):
def get_latest(self):
return self.all().exclude(end_time=None).first()
class PipelineRun(models.Model):
objects = PipelineRunManager()
start_time = models.DateTimeField()
end_time = models.DateTimeField(null=True)
class Meta: # pylint: disable=old-style-class,no-init
ordering = ["-id"]
class PipelineData(models.Model):
pipeline_run = models.ForeignKey(PipelineRun)
task_type = models.IntegerField(choices=PipelineTaskType.choices())
workload = models.ForeignKey(Workload)
data = models.TextField()
creation_time = models.DateTimeField()
class Meta: # pylint: disable=old-style-class,no-init
unique_together = ("pipeline_run", "task_type", "workload")
class BackupData(BaseModel):
result = models.ForeignKey(Result)
raw_knobs = models.TextField()
raw_initial_metrics = models.TextField()
raw_final_metrics = models.TextField()
raw_summary = models.TextField()
knob_log = models.TextField()
metric_log = models.TextField()

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#
# OtterTune - __init__.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
from .parser import Parser

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#
# OtterTune - base.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
'''
Created on Dec 12, 2017
@author: dvanaken
Parser interface.
'''
from abc import ABCMeta, abstractmethod, abstractproperty
from collections import OrderedDict
from website.models import KnobCatalog, MetricCatalog
from website.types import BooleanType, MetricType, VarType
# pylint: disable=no-self-use
class BaseParser(object, metaclass=ABCMeta):
def __init__(self, dbms_id):
self.dbms_id_ = dbms_id
knobs = KnobCatalog.objects.filter(dbms__pk=self.dbms_id_)
self.knob_catalog_ = {k.name: k for k in knobs}
self.tunable_knob_catalog_ = {k: v for k, v in
list(self.knob_catalog_.items()) if v.tunable is True}
metrics = MetricCatalog.objects.filter(dbms__pk=self.dbms_id_)
self.metric_catalog_ = {m.name: m for m in metrics}
self.numeric_metric_catalog_ = {m: v for m, v in
list(self.metric_catalog_.items()) if
v.metric_type == MetricType.COUNTER or
v.metric_type == MetricType.STATISTICS}
self.valid_true_val = list()
self.valid_false_val = list()
@abstractproperty
def base_configuration_settings(self):
pass
@abstractproperty
def knob_configuration_filename(self):
pass
@abstractproperty
def transactions_counter(self):
pass
@abstractproperty
def latency_timer(self):
pass
def target_metric(self, target_objective=None):
if target_objective == 'throughput_txn_per_sec' or target_objective is None:
# throughput
return self.transactions_counter
elif target_objective == '99th_lat_ms':
# 99 percentile latency
return self.latency_timer
else:
raise Exception("Target Objective {} Not Supported".format(target_objective))
@abstractmethod
def parse_version_string(self, version_string):
pass
def convert_bool(self, bool_value, metadata):
if bool_value in self.valid_true_val:
return BooleanType.TRUE
elif bool_value in self.valid_false_val:
return BooleanType.FALSE
else:
raise Exception("Invalid Boolean {}".format(bool_value))
def convert_enum(self, enum_value, metadata):
enumvals = metadata.enumvals.split(',')
try:
return enumvals.index(enum_value)
except ValueError:
raise Exception('Invalid enum value for variable {} ({})'.format(
metadata.name, enum_value))
def convert_integer(self, int_value, metadata):
try:
return int(int_value)
except ValueError:
return int(float(int_value))
def convert_real(self, real_value, metadata):
return float(real_value)
def convert_string(self, string_value, metadata):
return string_value
def convert_timestamp(self, timestamp_value, metadata):
return timestamp_value
def valid_boolean_val_to_string(self):
str_true = 'valid true values: '
for bval in self.valid_true_val:
str_true += str(bval) + ' '
str_false = 'valid false values: '
for bval in self.valid_false_val:
str_false += str(bval) + ' '
return str_true + '; ' + str_false
def convert_dbms_knobs(self, knobs):
knob_data = {}
for name, metadata in list(self.tunable_knob_catalog_.items()):
if metadata.tunable is False:
continue
if name not in knobs:
continue
value = knobs[name]
conv_value = None
if metadata.vartype == VarType.BOOL:
if not self._check_knob_bool_val(value):
raise Exception('Knob boolean value not valid! '
'Boolean values should be one of: {}, '
'but the actual value is: {}'
.format(self.valid_boolean_val_to_string(),
str(value)))
conv_value = self.convert_bool(value, metadata)
elif metadata.vartype == VarType.ENUM:
conv_value = self.convert_enum(value, metadata)
elif metadata.vartype == VarType.INTEGER:
conv_value = self.convert_integer(value, metadata)
if not self._check_knob_num_in_range(conv_value, metadata):
raise Exception('Knob integer num value not in range! '
'min: {}, max: {}, actual: {}'
.format(metadata.minval,
metadata.maxval, str(conv_value)))
elif metadata.vartype == VarType.REAL:
conv_value = self.convert_real(value, metadata)
if not self._check_knob_num_in_range(conv_value, metadata):
raise Exception('Knob real num value not in range! '
'min: {}, max: {}, actual: {}'
.format(metadata.minval,
metadata.maxval, str(conv_value)))
elif metadata.vartype == VarType.STRING:
conv_value = self.convert_string(value, metadata)
elif metadata.vartype == VarType.TIMESTAMP:
conv_value = self.convert_timestamp(value, metadata)
else:
raise Exception(
'Unknown variable type: {}'.format(metadata.vartype))
if conv_value is None:
raise Exception(
'Param value for {} cannot be null'.format(name))
knob_data[name] = conv_value
return knob_data
def _check_knob_num_in_range(self, value, mdata):
return value >= float(mdata.minval) and value <= float(mdata.maxval)
def _check_knob_bool_val(self, value):
return value in self.valid_true_val or value in self.valid_false_val
def convert_dbms_metrics(self, metrics, observation_time, target_objective=None):
# if len(metrics) != len(self.numeric_metric_catalog_):
# raise Exception('The number of metrics should be equal!')
metric_data = {}
for name, metadata in list(self.numeric_metric_catalog_.items()):
value = metrics[name]
if metadata.metric_type == MetricType.COUNTER:
converted = self.convert_integer(value, metadata)
metric_data[name] = float(converted) / observation_time
elif metadata.metric_type == MetricType.STATISTICS:
converted = self.convert_integer(value, metadata)
metric_data[name] = float(converted)
else:
raise Exception(
'Unknown metric type for {}: {}'.format(name, metadata.metric_type))
if target_objective is not None and self.target_metric(target_objective) not in metric_data:
raise Exception("Cannot find objective function")
if target_objective is not None:
metric_data[target_objective] = metric_data[self.target_metric(target_objective)]
else:
# default
metric_data['throughput_txn_per_sec'] = \
metric_data[self.target_metric(target_objective)]
return metric_data
@staticmethod
def extract_valid_variables(variables, catalog, default_value=None):
valid_variables = {}
diff_log = []
valid_lc_variables = {k.lower(): v for k, v in list(catalog.items())}
# First check that the names of all variables are valid (i.e., listed
# in the official catalog). Invalid variables are logged as 'extras'.
# Variable names that are valid but differ in capitalization are still
# added to valid_variables but with the proper capitalization. They
# are also logged as 'miscapitalized'.
for var_name, var_value in list(variables.items()):
lc_var_name = var_name.lower()
if lc_var_name in valid_lc_variables:
valid_name = valid_lc_variables[lc_var_name].name
if var_name != valid_name:
diff_log.append(('miscapitalized', valid_name, var_name, var_value))
valid_variables[valid_name] = var_value
else:
diff_log.append(('extra', None, var_name, var_value))
# Next find all item names that are listed in the catalog but missing from
# variables. Missing variables are added to valid_variables with the given
# default_value if provided (or the item's actual default value if not) and
# logged as 'missing'.
lc_variables = {k.lower(): v for k, v in list(variables.items())}
for valid_lc_name, metadata in list(valid_lc_variables.items()):
if valid_lc_name not in lc_variables:
diff_log.append(('missing', metadata.name, None, None))
valid_variables[metadata.name] = default_value if \
default_value is not None else metadata.default
assert len(valid_variables) == len(catalog)
return valid_variables, diff_log
def parse_helper(self, scope, valid_variables, view_variables):
for view_name, variables in list(view_variables.items()):
for var_name, var_value in list(variables.items()):
full_name = '{}.{}'.format(view_name, var_name)
if full_name not in valid_variables:
valid_variables[full_name] = []
valid_variables[full_name].append(var_value)
return valid_variables
def parse_dbms_variables(self, variables):
valid_variables = {}
for scope, sub_vars in list(variables.items()):
if sub_vars is None:
continue
if scope == 'global':
valid_variables.update(self.parse_helper(scope, valid_variables, sub_vars))
elif scope == 'local':
for _, viewnames in list(sub_vars.items()):
for viewname, objnames in list(viewnames.items()):
for _, view_vars in list(objnames.items()):
valid_variables.update(self.parse_helper(
scope, valid_variables, {viewname: view_vars}))
else:
raise Exception('Unsupported variable scope: {}'.format(scope))
return valid_variables
def parse_dbms_knobs(self, knobs):
valid_knobs = self.parse_dbms_variables(knobs)
for k in list(valid_knobs.keys()):
assert len(valid_knobs[k]) == 1
valid_knobs[k] = valid_knobs[k][0]
# Extract all valid knobs
return BaseParser.extract_valid_variables(
valid_knobs, self.knob_catalog_)
def parse_dbms_metrics(self, metrics):
# Some DBMSs measure different types of stats (e.g., global, local)
# at different scopes (e.g. indexes, # tables, database) so for now
# we just combine them
valid_metrics = self.parse_dbms_variables(metrics)
# Extract all valid metrics
valid_metrics, diffs = BaseParser.extract_valid_variables(
valid_metrics, self.metric_catalog_, default_value='0')
# Combine values
for name, values in list(valid_metrics.items()):
metric = self.metric_catalog_[name]
if metric.metric_type == MetricType.INFO or len(values) == 1:
valid_metrics[name] = values[0]
elif metric.metric_type == MetricType.COUNTER or \
metric.metric_type == MetricType.STATISTICS:
values = [int(v) for v in values if v is not None]
if len(values) == 0:
valid_metrics[name] = 0
else:
valid_metrics[name] = str(sum(values))
else:
raise Exception(
'Invalid metric type: {}'.format(metric.metric_type))
return valid_metrics, diffs
def calculate_change_in_metrics(self, metrics_start, metrics_end):
adjusted_metrics = {}
for met_name, start_val in list(metrics_start.items()):
end_val = metrics_end[met_name]
met_info = self.metric_catalog_[met_name]
if met_info.vartype == VarType.INTEGER or \
met_info.vartype == VarType.REAL:
conversion_fn = self.convert_integer if \
met_info.vartype == VarType.INTEGER else \
self.convert_real
start_val = conversion_fn(start_val, met_info)
end_val = conversion_fn(end_val, met_info)
if met_info.metric_type == MetricType.COUNTER:
adj_val = end_val - start_val
else: # MetricType.STATISTICS or MetricType.INFO
adj_val = end_val
assert adj_val >= 0, '{} wrong metric type '.format(met_name)
adjusted_metrics[met_name] = adj_val
else:
# This metric is either a bool, enum, string, or timestamp
# so take last recorded value from metrics_end
adjusted_metrics[met_name] = end_val
return adjusted_metrics
def create_knob_configuration(self, tuning_knobs):
configuration = {}
for knob_name, knob_value in sorted(tuning_knobs.items()):
# FIX ME: for now it only shows the global knobs, works for Postgres
if knob_name.startswith('global.'):
knob_name_global = knob_name[knob_name.find('.') + 1:]
configuration[knob_name_global] = knob_value
configuration = OrderedDict(sorted(configuration.items()))
return configuration
def get_nondefault_knob_settings(self, knobs):
nondefault_settings = OrderedDict()
for knob_name, metadata in list(self.knob_catalog_.items()):
if metadata.tunable is True:
continue
if knob_name not in knobs:
continue
knob_value = knobs[knob_name]
if knob_value != metadata.default:
nondefault_settings[knob_name] = knob_value
return nondefault_settings
def format_bool(self, bool_value, metadata):
return 'on' if bool_value == BooleanType.TRUE else 'off'
def format_enum(self, enum_value, metadata):
enumvals = metadata.enumvals.split(',')
return enumvals[enum_value]
def format_integer(self, int_value, metadata):
return int(round(int_value))
def format_real(self, real_value, metadata):
return float(real_value)
def format_string(self, string_value, metadata):
return string_value
def format_timestamp(self, timestamp_value, metadata):
return timestamp_value
def format_dbms_knobs(self, knobs):
formatted_knobs = {}
for knob_name, knob_value in list(knobs.items()):
metadata = self.knob_catalog_.get(knob_name, None)
if (metadata is None):
raise Exception('Unknown knob {}'.format(knob_name))
fvalue = None
if metadata.vartype == VarType.BOOL:
fvalue = self.format_bool(knob_value, metadata)
elif metadata.vartype == VarType.ENUM:
fvalue = self.format_enum(knob_value, metadata)
elif metadata.vartype == VarType.INTEGER:
fvalue = self.format_integer(knob_value, metadata)
elif metadata.vartype == VarType.REAL:
fvalue = self.format_real(knob_value, metadata)
elif metadata.vartype == VarType.STRING:
fvalue = self.format_string(knob_value, metadata)
elif metadata.vartype == VarType.TIMESTAMP:
fvalue = self.format_timestamp(knob_value, metadata)
else:
raise Exception('Unknown variable type for {}: {}'.format(
knob_name, metadata.vartype))
if fvalue is None:
raise Exception('Cannot format value for {}: {}'.format(
knob_name, knob_value))
formatted_knobs[knob_name] = fvalue
return formatted_knobs
def filter_numeric_metrics(self, metrics):
return OrderedDict([(k, v) for k, v in list(metrics.items()) if
k in self.numeric_metric_catalog_])
def filter_tunable_knobs(self, knobs):
return OrderedDict([(k, v) for k, v in list(knobs.items()) if
k in self.tunable_knob_catalog_])
# pylint: enable=no-self-use

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#
# OtterTune - myrocks.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
'''
Created on Jan 16, 2018
@author: bohan
'''
import re
from collections import OrderedDict
from .base import BaseParser
from website.models import DBMSCatalog
from website.types import DBMSType, KnobUnitType, MetricType, VarType
from website.utils import ConversionUtil
class MyRocksParser(BaseParser):
MYROCKS_BYTES_SYSTEM = [
(1024 ** 5, 'PB'),
(1024 ** 4, 'TB'),
(1024 ** 3, 'GB'),
(1024 ** 2, 'MB'),
(1024 ** 1, 'kB'),
(1024 ** 0, 'B'),
]
MYROCKS_TIME_SYSTEM = [
(1000 * 60 * 60 * 24, 'd'),
(1000 * 60 * 60, 'h'),
(1000 * 60, 'min'),
(1, 'ms'),
(1000, 's'),
]
MYROCKS_BASE_KNOBS = {
'session_variables.rocksdb_max_open_files': '-1'
}
@property
def base_configuration_settings(self):
return dict(self.MYROCKS_BASE_KNOBS)
@property
def knob_configuration_filename(self):
return 'myrocks.conf'
@property
def transactions_counter(self):
return 'session_status.questions'
def latency_timer(self):
return 'session_status.questions'
def convert_integer(self, int_value, metadata):
converted = None
try:
converted = super(MyRocksParser, self).convert_integer(
int_value, metadata)
except ValueError:
if metadata.unit == KnobUnitType.BYTES:
converted = ConversionUtil.get_raw_size(
int_value, system=self.MYROCKS_BYTES_SYSTEM)
elif metadata.unit == KnobUnitType.MILLISECONDS:
converted = ConversionUtil.get_raw_size(
int_value, system=self.MYROCKS_TIME_SYSTEM)
else:
raise Exception('Unknown unit type: {}'.format(metadata.unit))
if converted is None:
raise Exception('Invalid integer format for {}: {}'.format(
metadata.name, int_value))
return converted
def format_integer(self, int_value, metadata):
if metadata.unit != KnobUnitType.OTHER and int_value > 0:
if metadata.unit == KnobUnitType.BYTES:
int_value = ConversionUtil.get_human_readable(
int_value, MyRocksParser.MYROCKS_BYTES_SYSTEM)
elif metadata.unit == KnobUnitType.MILLISECONDS:
int_value = ConversionUtil.get_human_readable(
int_value, MyRocksParser.MYROCKS_TIME_SYSTEM)
else:
raise Exception('Invalid unit type for {}: {}'.format(
metadata.name, metadata.unit))
else:
int_value = super(MyRocksParser, self).format_integer(
int_value, metadata)
return int_value
def parse_version_string(self, version_string):
dbms_version = version_string.split(',')[0]
return re.search(r'\d+\.\d+(?=\.\d+)', dbms_version).group(0)
def parse_helper(self, scope, valid_variables, view_variables):
for view_name, variables in list(view_variables.items()):
if scope == 'local':
for obj_name, sub_vars in list(variables.items()):
for var_name, var_value in list(sub_vars.items()): # local
full_name = '{}.{}.{}'.format(view_name, obj_name, var_name)
valid_variables[full_name] = var_value
elif scope == 'global':
for var_name, var_value in list(variables.items()): # global
full_name = '{}.{}'.format(view_name, var_name)
valid_variables[full_name] = var_value
else:
raise Exception('Unsupported variable scope: {}'.format(scope))
return valid_variables
# global variable fullname: viewname.varname
# local variable fullname: viewname.objname.varname
# return format: valid_variables = {var_fullname:var_val}
def parse_dbms_variables(self, variables):
valid_variables = {}
for scope, sub_vars in list(variables.items()):
if sub_vars is None:
continue
if scope == 'global':
valid_variables.update(self.parse_helper('global', valid_variables, sub_vars))
elif scope == 'local':
for _, viewnames in list(sub_vars.items()):
for viewname, objnames in list(viewnames.items()):
for obj_name, view_vars in list(objnames.items()):
valid_variables.update(self.parse_helper(
'local', valid_variables, {viewname: {obj_name: view_vars}}))
else:
raise Exception('Unsupported variable scope: {}'.format(scope))
return valid_variables
# local variable: viewname.objname.varname
# global variable: viewname.varname
# This function is to change local variable fullname to viewname.varname, global
# variable remains same. This is because local varialbe in knob_catalog is in
# parial format (i,e. viewname.varname)
@staticmethod
def partial_name(full_name):
var_name = full_name.split('.')
if len(var_name) == 2: # global variable
return full_name
elif len(var_name) == 3: # local variable
return var_name[0] + '.' + var_name[2]
else:
raise Exception('Invalid variable full name: {}'.format(full_name))
@staticmethod
def extract_valid_variables(variables, catalog, default_value=None):
valid_variables = {}
diff_log = []
valid_lc_variables = {k.lower(): v for k, v in list(catalog.items())}
# First check that the names of all variables are valid (i.e., listed
# in the official catalog). Invalid variables are logged as 'extras'.
# Variable names that are valid but differ in capitalization are still
# added to valid_variables but with the proper capitalization. They
# are also logged as 'miscapitalized'.
for var_name, var_value in list(variables.items()):
lc_var_name = var_name.lower()
prt_name = MyRocksParser.partial_name(lc_var_name)
if prt_name in valid_lc_variables:
valid_name = valid_lc_variables[prt_name].name
if prt_name != valid_name:
diff_log.append(('miscapitalized', valid_name, var_name, var_value))
valid_variables[var_name] = var_value
else:
diff_log.append(('extra', None, var_name, var_value))
# Next find all item names that are listed in the catalog but missing from
# variables. Missing global variables are added to valid_variables with
# the given default_value if provided (or the item's actual default value
# if not) and logged as 'missing'. For now missing local variables are
# not added to valid_variables
lc_variables = {MyRocksParser.partial_name(k.lower()): v
for k, v in list(variables.items())}
for valid_lc_name, metadata in list(valid_lc_variables.items()):
if valid_lc_name not in lc_variables:
diff_log.append(('missing', metadata.name, None, None))
if metadata.scope == 'global':
valid_variables[metadata.name] = default_value if \
default_value is not None else metadata.default
return valid_variables, diff_log
def calculate_change_in_metrics(self, metrics_start, metrics_end):
adjusted_metrics = {}
for met_name, start_val in list(metrics_start.items()):
end_val = metrics_end[met_name]
met_info = self.metric_catalog_[MyRocksParser.partial_name(met_name)]
if met_info.vartype == VarType.INTEGER or \
met_info.vartype == VarType.REAL:
conversion_fn = self.convert_integer if \
met_info.vartype == VarType.INTEGER else \
self.convert_real
start_val = conversion_fn(start_val, met_info)
end_val = conversion_fn(end_val, met_info)
adj_val = end_val - start_val
assert adj_val >= 0
adjusted_metrics[met_name] = adj_val
else:
# This metric is either a bool, enum, string, or timestamp
# so take last recorded value from metrics_end
adjusted_metrics[met_name] = end_val
return adjusted_metrics
def parse_dbms_knobs(self, knobs):
valid_knobs = self.parse_dbms_variables(knobs)
# Extract all valid knobs
return MyRocksParser.extract_valid_variables(
valid_knobs, self.knob_catalog_)
def parse_dbms_metrics(self, metrics):
valid_metrics = self.parse_dbms_variables(metrics)
# Extract all valid metrics
valid_metrics, diffs = MyRocksParser.extract_valid_variables(
valid_metrics, self.metric_catalog_, default_value='0')
return valid_metrics, diffs
def convert_dbms_metrics(self, metrics, observation_time, target_objective=None):
metric_data = {}
for name, value in list(metrics.items()):
prt_name = MyRocksParser.partial_name(name)
if prt_name in self.numeric_metric_catalog_:
metadata = self.numeric_metric_catalog_[prt_name]
if metadata.metric_type == MetricType.COUNTER:
converted = self.convert_integer(value, metadata)
metric_data[name] = float(converted) / observation_time
else:
raise Exception('Unknown metric type for {}: {}'.format(
name, metadata.metric_type))
if target_objective is not None and self.target_metric(target_objective) not in metric_data:
raise Exception("Cannot find objective function")
if target_objective is not None:
metric_data[target_objective] = metric_data[self.target_metric(target_objective)]
else:
# default
metric_data['throughput_txn_per_sec'] = \
metric_data[self.target_metric(target_objective)]
return metric_data
def convert_dbms_knobs(self, knobs):
knob_data = {}
for name, value in list(knobs.items()):
prt_name = MyRocksParser.partial_name(name)
if prt_name in self.tunable_knob_catalog_:
metadata = self.tunable_knob_catalog_[prt_name]
assert(metadata.tunable)
value = knobs[name]
conv_value = None
if metadata.vartype == VarType.BOOL:
conv_value = self.convert_bool(value, metadata)
elif metadata.vartype == VarType.ENUM:
conv_value = self.convert_enum(value, metadata)
elif metadata.vartype == VarType.INTEGER:
conv_value = self.convert_integer(value, metadata)
elif metadata.vartype == VarType.REAL:
conv_value = self.convert_real(value, metadata)
elif metadata.vartype == VarType.STRING:
conv_value = self.convert_string(value, metadata)
elif metadata.vartype == VarType.TIMESTAMP:
conv_value = self.convert_timestamp(value, metadata)
else:
raise Exception(
'Unknown variable type: {}'.format(metadata.vartype))
if conv_value is None:
raise Exception(
'Param value for {} cannot be null'.format(name))
knob_data[name] = conv_value
return knob_data
def filter_numeric_metrics(self, metrics):
return OrderedDict([(k, v) for k, v in list(metrics.items()) if
MyRocksParser.partial_name(k) in self.numeric_metric_catalog_])
def filter_tunable_knobs(self, knobs):
return OrderedDict([(k, v) for k, v in list(knobs.items()) if
MyRocksParser.partial_name(k) in self.tunable_knob_catalog_])
class MyRocks56Parser(MyRocksParser):
def __init__(self):
dbms = DBMSCatalog.objects.get(
type=DBMSType.MYROCKS, version='5.6')
super(MyRocks56Parser, self).__init__(dbms.pk)

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#
# OtterTune - oracle.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
from .base import BaseParser
from website.models import DBMSCatalog
from website.types import DBMSType
class OracleParser(BaseParser):
def __init__(self, dbms_id):
super(OracleParser, self).__init__(dbms_id)
self.valid_true_val = ["TRUE", "true", "yes", 1]
self.valid_false_val = ["FALSE", "false", "no", 0]
ORACLE_BASE_KNOBS = {
}
@property
def base_configuration_settings(self):
return dict(self.ORACLE_BASE_KNOBS)
@property
def knob_configuration_filename(self):
return 'initorcldb.ora'
@property
def transactions_counter(self):
return 'global.user commits'
@property
def latency_timer(self):
return 'global.user commits'
def parse_version_string(self, version_string):
return version_string
class Oracle19Parser(OracleParser):
def __init__(self):
dbms = DBMSCatalog.objects.get(
type=DBMSType.ORACLE, version='19.0.0.0.0')
super(Oracle19Parser, self).__init__(dbms.pk)

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#
# OtterTune - parser.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
'''
Created on Dec 12, 2017
@author: dvanaken
'''
from website.models import DBMSCatalog
from website.types import DBMSType
from .myrocks import MyRocks56Parser
from .postgres import Postgres96Parser, PostgresOldParser
from .oracle import Oracle19Parser
class Parser(object):
__DBMS_UTILS_IMPLS = None
@staticmethod
def __utils(dbms_id=None):
if Parser.__DBMS_UTILS_IMPLS is None:
Parser.__DBMS_UTILS_IMPLS = {
DBMSCatalog.objects.get(
type=DBMSType.POSTGRES, version='9.3').pk: PostgresOldParser('9.3'),
DBMSCatalog.objects.get(
type=DBMSType.POSTGRES, version='9.2').pk: PostgresOldParser('9.2'),
DBMSCatalog.objects.get(
type=DBMSType.POSTGRES, version='9.6').pk: Postgres96Parser('9.6'),
DBMSCatalog.objects.get(
type=DBMSType.POSTGRES, version='9.4').pk: Postgres96Parser('9.4'),
DBMSCatalog.objects.get(
type=DBMSType.POSTGRES, version='9.5').pk: Postgres96Parser('9.5'),
DBMSCatalog.objects.get(
type=DBMSType.MYROCKS, version='5.6').pk: MyRocks56Parser(),
DBMSCatalog.objects.get(
type=DBMSType.ORACLE, version='19.0.0.0.0').pk: Oracle19Parser()
}
try:
if dbms_id is None:
return Parser.__DBMS_UTILS_IMPLS
return Parser.__DBMS_UTILS_IMPLS[dbms_id]
except KeyError:
raise NotImplementedError(
'Implement me! ({})'.format(dbms_id))
@staticmethod
def parse_version_string(dbms_type, version_string):
for k, v in list(Parser.__utils(dbms_type).items()):
dbms = DBMSCatalog.objects.get(pk=k)
if dbms.type == dbms_type:
try:
return v.parse_version_string(version_string)
except AttributeError:
pass
return None
@staticmethod
def convert_dbms_knobs(dbms_id, knobs):
return Parser.__utils(dbms_id).convert_dbms_knobs(knobs)
@staticmethod
def convert_dbms_metrics(dbms_id, numeric_metrics, observation_time, target_objective=None):
return Parser.__utils(dbms_id).convert_dbms_metrics(
numeric_metrics, observation_time, target_objective)
@staticmethod
def parse_dbms_knobs(dbms_id, knobs):
return Parser.__utils(dbms_id).parse_dbms_knobs(knobs)
@staticmethod
def parse_dbms_metrics(dbms_id, metrics):
return Parser.__utils(dbms_id).parse_dbms_metrics(metrics)
@staticmethod
def get_nondefault_knob_settings(dbms_id, knobs):
return Parser.__utils(dbms_id).get_nondefault_knob_settings(knobs)
@staticmethod
def create_knob_configuration(dbms_id, tuning_knobs):
return Parser.__utils(dbms_id).create_knob_configuration(tuning_knobs)
@staticmethod
def format_dbms_knobs(dbms_id, knobs):
return Parser.__utils(dbms_id).format_dbms_knobs(knobs)
@staticmethod
def get_knob_configuration_filename(dbms_id):
return Parser.__utils(dbms_id).knob_configuration_filename
@staticmethod
def filter_numeric_metrics(dbms_id, metrics):
return Parser.__utils(dbms_id).filter_numeric_metrics(metrics)
@staticmethod
def filter_tunable_knobs(dbms_id, knobs):
return Parser.__utils(dbms_id).filter_tunable_knobs(knobs)
@staticmethod
def calculate_change_in_metrics(dbms_id, metrics_start, metrics_end):
return Parser.__utils(dbms_id).calculate_change_in_metrics(
metrics_start, metrics_end)

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#
# OtterTune - postgres.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
'''
Created on Dec 12, 2017
@author: dvanaken
'''
import re
from .base import BaseParser
from website.models import DBMSCatalog
from website.types import DBMSType, KnobUnitType
from website.utils import ConversionUtil
class PostgresParser(BaseParser):
def __init__(self, dbms_id):
super(PostgresParser, self).__init__(dbms_id)
self.valid_true_val = ["on", "true", "yes", 1]
self.valid_false_val = ["off", "false", "no", 0]
POSTGRES_BYTES_SYSTEM = [
(1024 ** 5, 'PB'),
(1024 ** 4, 'TB'),
(1024 ** 3, 'GB'),
(1024 ** 2, 'MB'),
(1024 ** 1, 'kB'),
(1024 ** 0, 'B'),
]
POSTGRES_TIME_SYSTEM = [
(1000 * 60 * 60 * 24, 'd'),
(1000 * 60 * 60, 'h'),
(1000 * 60, 'min'),
(1000, 's'),
(1, 'ms'),
]
POSTGRES_BASE_KNOBS = {
'global.data_directory': None,
'global.hba_file': None,
'global.ident_file': None,
'global.external_pid_file': None,
'global.listen_addresses': None,
'global.port': None,
'global.max_connections': None,
'global.unix_socket_directories': None,
'global.log_line_prefix': '%t [%p-%l] %q%u@%d ',
'global.track_counts': 'on',
'global.track_io_timing': 'on',
'global.autovacuum': 'on',
'global.default_text_search_config': 'pg_catalog.english',
}
@property
def base_configuration_settings(self):
return dict(self.POSTGRES_BASE_KNOBS)
@property
def knob_configuration_filename(self):
return 'postgresql.conf'
@property
def transactions_counter(self):
return 'pg_stat_database.xact_commit'
@property
def latency_timer(self):
return 'pg_stat_database.xact_commit'
def convert_integer(self, int_value, metadata):
converted = None
try:
converted = super(PostgresParser, self).convert_integer(
int_value, metadata)
except ValueError:
if metadata.unit == KnobUnitType.BYTES:
converted = ConversionUtil.get_raw_size(
int_value, system=self.POSTGRES_BYTES_SYSTEM)
elif metadata.unit == KnobUnitType.MILLISECONDS:
converted = ConversionUtil.get_raw_size(
int_value, system=self.POSTGRES_TIME_SYSTEM)
else:
raise Exception(
'Unknown unit type: {}'.format(metadata.unit))
if converted is None:
raise Exception('Invalid integer format for {}: {}'.format(
metadata.name, int_value))
return converted
def format_integer(self, int_value, metadata):
int_value = int(round(int_value))
if metadata.unit != KnobUnitType.OTHER and int_value > 0:
if metadata.unit == KnobUnitType.BYTES:
int_value = ConversionUtil.get_human_readable(
int_value, PostgresParser.POSTGRES_BYTES_SYSTEM)
elif metadata.unit == KnobUnitType.MILLISECONDS:
int_value = ConversionUtil.get_human_readable(
int_value, PostgresParser.POSTGRES_TIME_SYSTEM)
else:
raise Exception(
'Invalid unit type for {}: {}'.format(
metadata.name, metadata.unit))
else:
int_value = super(PostgresParser, self).format_integer(
int_value, metadata)
return int_value
def parse_version_string(self, version_string):
dbms_version = version_string.split(',')[0]
return re.search(r'\d+\.\d+(?=\.\d+)', dbms_version).group(0)
class Postgres96Parser(PostgresParser):
def __init__(self, version):
dbms = DBMSCatalog.objects.get(
type=DBMSType.POSTGRES, version=version)
super(Postgres96Parser, self).__init__(dbms.pk)
class PostgresOldParser(PostgresParser):
def __init__(self, version):
dbms = DBMSCatalog.objects.get(
type=DBMSType.POSTGRES, version=version)
super(PostgresOldParser, self).__init__(dbms.pk)

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#
# OtterTune - set_default_knobs.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
from .models import KnobCatalog, SessionKnob
from .types import DBMSType
def turn_knobs_off(session, knob_names):
for knob_name in knob_names:
knob = KnobCatalog.objects.filter(dbms=session.dbms, name=knob_name).first()
SessionKnob.objects.create(session=session,
knob=knob,
minval=knob.minval,
maxval=knob.maxval,
tunable=False)
def set_knob_tuning_range(session, knob_name, minval, maxval):
knob = KnobCatalog.objects.filter(dbms=session.dbms, name=knob_name).first()
SessionKnob.objects.create(session=session,
knob=knob,
minval=minval,
maxval=maxval,
tunable=True)
def set_default_knobs(session):
if session.dbms.type == DBMSType.POSTGRES and session.dbms.version == '9.6':
turn_knobs_off(session, ["global.backend_flush_after", "global.bgwriter_delay",
"global.bgwriter_flush_after", "global.bgwriter_lru_multiplier",
"global.checkpoint_flush_after", "global.commit_delay",
"global.commit_siblings", "global.deadlock_timeout",
"global.effective_io_concurrency", "global.from_collapse_limit",
"global.join_collapse_limit", "global.maintenance_work_mem",
"global.max_worker_processes",
"global.min_parallel_relation_size", "global.min_wal_size",
"global.seq_page_cost", "global.wal_buffers",
"global.wal_sync_method", "global.wal_writer_delay",
"global.wal_writer_flush_after"])
set_knob_tuning_range(session, "global.checkpoint_completion_target", 0.1, 0.9)
set_knob_tuning_range(session, "global.checkpoint_timeout", 60000, 1800000)
set_knob_tuning_range(session, "global.default_statistics_target", 100, 2048)
set_knob_tuning_range(session, "global.effective_cache_size", 4294967296, 17179869184)
set_knob_tuning_range(session, "global.max_parallel_workers_per_gather", 0, 8)
set_knob_tuning_range(session, "global.max_wal_size", 268435456, 17179869184)
set_knob_tuning_range(session, "global.random_page_cost", 1, 10)
set_knob_tuning_range(session, "global.shared_buffers", 134217728, 12884901888)
set_knob_tuning_range(session, "global.temp_buffers", 8388608, 1073741824)
set_knob_tuning_range(session, "global.work_mem", 4194304, 1073741824)

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credentials.py

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#
# OtterTune - __init__.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
from .common import * # pylint: disable=wildcard-import

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#
# OtterTune - common.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
"""
Common Django settings for the OtterTune project.
"""
import os
from os.path import abspath, dirname, exists, join
import sys
import djcelery
# ==============================================
# PATH CONFIGURATION
# ==============================================
# Absolute path to this Django project directory.
PROJECT_ROOT = dirname(dirname(dirname(abspath(__file__))))
# Directory holding all uploaded and intermediate result files
DATA_ROOT = join(PROJECT_ROOT, 'data')
# Absolute path to directory where all oltpbench data is uploaded
UPLOAD_DIR = join(DATA_ROOT, 'media')
# Path to the base DBMS configuration files
CONFIG_DIR = join(PROJECT_ROOT, 'config')
# Where the log files are stored
LOG_DIR = join(PROJECT_ROOT, 'log')
# File/directory upload permissions
FILE_UPLOAD_DIRECTORY_PERMISSIONS = 0o664
FILE_UPLOAD_PERMISSIONS = 0o664
# Path to OtterTune's website and ML modules
OTTERTUNE_LIBS = dirname(PROJECT_ROOT)
# ==============================================
# Path setup
# ==============================================
# Add OtterTune's ML modules to path
sys.path.insert(0, OTTERTUNE_LIBS)
# Try to create the log directory
try:
if not exists(LOG_DIR):
os.mkdir(LOG_DIR)
except OSError: # Invalid permissions
pass
# ==============================================
# DEBUG CONFIGURATION
# ==============================================
DEBUG = False
TEST_RUNNER = 'tests.runner.BaseRunner'
INTERNAL_IPS = ['127.0.0.1']
# ==============================================
# GENERAL CONFIGURATION
# ==============================================
# Local time zone for this installation. Choices can be found here:
# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name
# although not all choices may be available on all operating systems.
# In a Windows environment this must be set to your system time zone.
TIME_ZONE = 'America/New_York'
# Language code for this installation. All choices can be found here:
# http://www.i18nguy.com/unicode/language-identifiers.html
LANGUAGE_CODE = 'en-us'
SITE_ID = 1
# If you set this to False, Django will make some optimizations so as not
# to load the internationalization machinery.
USE_I18N = True
# If you set this to False, Django will not format dates, numbers and
# calendars according to the current locale.
USE_L10N = True
# If you set this to False, Django will not use timezone-aware datetimes.
USE_TZ = True
# ==============================================
# MEDIA CONFIGURATION
# ==============================================
# Absolute filesystem path to the directory that will hold user-uploaded files.
# Example: "/var/www/example.com/media/"
MEDIA_ROOT = join(DATA_ROOT, 'media')
MEDIA_ROOT_URL = '/media/'
# URL that handles the media served from MEDIA_ROOT. Make sure to use a
# trailing slash.
# Examples: "http://example.com/media/", "http://media.example.com/"
MEDIA_URL = '/media/'
# ==============================================
# STATIC FILE CONFIGURATION
# ==============================================
# Absolute path to the directory static files should be collected to.
# Don't put anything in this directory yourself; store your static files
# in apps' "static/" subdirectories and in STATICFILES_DIRS.
# Example: "/var/www/example.com/static/"
STATIC_ROOT = join(PROJECT_ROOT, 'website', 'static')
# URL prefix for static files.
# Example: "http://example.com/static/", "http://static.example.com/"
STATIC_URL = '/static/'
# Additional locations of static files
STATICFILES_DIRS = (
# Put strings here, like "/home/html/static" or "C:/www/django/static".
# Always use forward slashes, even on Windows.
# Don't forget to use absolute paths, not relative paths.
)
# List of finder classes that know how to find static files in
# various locations.
STATICFILES_FINDERS = (
'django.contrib.staticfiles.finders.FileSystemFinder',
'django.contrib.staticfiles.finders.AppDirectoriesFinder',
)
# ==============================================
# TEMPLATE CONFIGURATION
# ==============================================
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [
# TEMPLATE_DIRS (use absolute paths)
join(PROJECT_ROOT, 'website', 'template')
],
'OPTIONS': {
'context_processors': [
'django.contrib.auth.context_processors.auth',
'django.template.context_processors.debug',
'django.template.context_processors.i18n',
'django.template.context_processors.media',
'django.template.context_processors.static',
'django.template.context_processors.tz',
'django.contrib.messages.context_processors.messages',
'django.template.context_processors.csrf',
],
'loaders': [
'django.template.loaders.filesystem.Loader',
'django.template.loaders.app_directories.Loader',
],
'debug': DEBUG,
},
},
]
# ==============================================
# MIDDLEWARE CONFIGURATION
# ==============================================
MIDDLEWARE_CLASSES = (
'debug_toolbar.middleware.DebugToolbarMiddleware',
'django.middleware.common.CommonMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
'request_logging.middleware.LoggingMiddleware',
)
# ==============================================
# APP CONFIGURATION
# ==============================================
INSTALLED_APPS = (
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.sites',
'django.contrib.messages',
'django.contrib.staticfiles',
'debug_toolbar',
# 'django_extensions',
'django.contrib.admin',
# Uncomment the next line to enable admin documentation:
# 'django.contrib.admindocs',
# 'rest_framework',
# 'south',
'djcelery',
# 'django_celery_beat',
'website',
)
# ==============================================
# RABBITMQ/CELERY CONFIGURATION
# ==============================================
# Broker URL for RabbitMq
BROKER_URL = 'amqp://guest:guest@localhost:5672//'
# Enable finer-grained reporting: will report 'started' when
# task is executed by a worker.
CELERY_TRACK_STARTED = True
# Worker will execute at most this many tasks before it's killed
# and replaced with a new worker. This helps with memory leaks.
CELERYD_MAX_TASKS_PER_CHILD = 50
# Number of concurrent workers.
CELERYD_CONCURRENCY = 8
djcelery.setup_loader()
# ==============================================
# LOGGING CONFIGURATION
# ==============================================
# A website logging configuration. The only tangible logging
# performed by this configuration is to send an email to
# the site admins on every HTTP 500 error when DEBUG=False.
# See http://docs.djangoproject.com/en/dev/topics/logging for
# more details on how to customize your logging configuration.
LOGGING = {
'version': 1,
'disable_existing_loggers': True,
'formatters': {
'standard': {
'format': "[%(asctime)s] %(levelname)s [%(name)s:%(lineno)s] %(message)s",
'datefmt': "%d/%b/%Y %H:%M:%S"
},
},
'handlers': {
'logfile': {
'level': 'DEBUG',
'class': 'logging.handlers.RotatingFileHandler',
'filename': join(LOG_DIR, 'website.log'),
'maxBytes': 50000,
'backupCount': 2,
'formatter': 'standard',
},
'console': {
'level': 'INFO',
'class': 'logging.StreamHandler',
'formatter': 'standard'
},
'mail_admins': {
'level': 'ERROR',
'filters': ['require_debug_false'],
'class': 'django.utils.log.AdminEmailHandler'
},
},
'filters': {
'require_debug_false': {
'()': 'django.utils.log.RequireDebugFalse'
}
},
'loggers': {
'django': {
'handlers': ['console', 'logfile'],
'propagate': True,
'level': 'WARN',
},
'django.db.backends': {
'handlers': ['console', 'logfile'],
'level': 'WARN',
'propagate': False,
},
'website': {
'handlers': ['console', 'logfile'],
'propagate': False,
'level': 'DEBUG',
},
'django.request': {
'handlers': ['console'],
'level': 'DEBUG',
'propagate': False,
},
# Uncomment to email admins after encountering an error (and debug=False)
# 'django.request': {
# 'handlers': ['mail_admins'],
# 'level': 'ERROR',
# 'propagate': True,
# },
}
}
# ==============================================
# URL CONFIGURATION
# ==============================================
ROOT_URLCONF = 'website.urls'
# ==============================================
# WSGI CONFIGURATION
# ==============================================
# Python dotted path to the WSGI application used by Django's runserver.
WSGI_APPLICATION = 'website.wsgi.application'
# ==============================================
# PASSWORD VALIDATORS
# ==============================================
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
'OPTIONS': {
'min_length': 6,
}
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# ==============================================
# MISC
# ==============================================
SESSION_SERIALIZER = 'django.contrib.sessions.serializers.JSONSerializer'
# Import and override defaults with custom configuration options
# pylint: disable=wildcard-import,wrong-import-position,unused-wildcard-import
from .credentials import * # pycodestyle: disable=E402
from .constants import * # pycodestyle: disable=E402
# pylint: enable=wildcard-import,wrong-import-position,unused-wildcard-import

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#
# OtterTune - constants.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
# ---PIPELINE CONSTANTS---
# the number of samples (staring points) in gradient descent
NUM_SAMPLES = 30
# the number of selected tuning knobs
IMPORTANT_KNOB_NUMBER = 10
# top K config with best performance put into prediction
TOP_NUM_CONFIG = 10
# ---CONSTRAINTS CONSTANTS---
# Initial probability to flip categorical feature in apply_constraints
# server/analysis/constraints.py
INIT_FLIP_PROB = 0.3
# The probability that we flip the i_th categorical feature is
# FLIP_PROB_DECAY * (probability we flip (i-1)_th categorical feature)
FLIP_PROB_DECAY = 0.5
# ---GPR CONSTANTS---
DEFAULT_LENGTH_SCALE = 1.0
DEFAULT_MAGNITUDE = 1.0
# Max training size in GPR model
MAX_TRAIN_SIZE = 7000
# Batch size in GPR model
BATCH_SIZE = 3000
# Threads for TensorFlow config
NUM_THREADS = 4
# ---GRADIENT DESCENT CONSTANTS---
# the maximum iterations of gradient descent
MAX_ITER = 500
# a small bias when using training data points as starting points.
GPR_EPS = 0.001
DEFAULT_RIDGE = 0.01
DEFAULT_LEARNING_RATE = 0.01
DEFAULT_EPSILON = 1e-6
DEFAULT_SIGMA_MULTIPLIER = 3.0
DEFAULT_MU_MULTIPLIER = 1.0

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#
# OtterTune - credentials_TEMPLATE.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
"""
Private/custom Django settings for the OtterTune project.
"""
# pylint: disable=invalid-name
# ==============================================
# SECRET KEY CONFIGURATION
# ==============================================
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'ADD ME!!'
# ==============================================
# DATABASE CONFIGURATION
# ==============================================
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.mysql',
'NAME': 'ottertune',
'USER': 'ADD ME!!',
'PASSWORD': 'ADD ME!!',
'HOST': '',
'PORT': '',
'OPTIONS': {
'init_command': "SET sql_mode='STRICT_TRANS_TABLES',innodb_strict_mode=1",
},
}
}
# ==============================================
# DEBUG CONFIGURATION
# ==============================================
# Can override the DEBUG setting here
DEBUG = False
# ==============================================
# MANAGER CONFIGURATION
# ==============================================
# Admin and managers for this project. These people receive private
# site alerts.
ADMINS = (
# ('Your Name', 'your_email@example.com'),
)
MANAGERS = ADMINS
# ==============================================
# GENERAL CONFIGURATION
# ==============================================
# Hosts/domain names that are valid for this site; required if DEBUG is False
# See https://docs.djangoproject.com/en/1.5/ref/settings/#allowed-hosts
ALLOWED_HOSTS = []

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@import url("bootstrap.min.css");
@import url("bootstrap-select.min.css");
.table-nonfluid {
width: auto;
}
.boxbody ul {
list-style: none;
margin: 0;
padding: 0 0 0.2em;
# margin-bottom: 0.5em;
}
.boxbody ul li { padding-left: 0.3em; }
.plotcontainer {
margin-bottom: 2em;
}
.FixedHeader_Cloned th { background-color: white; }
/* SIDEBAR */
#sidebar {
font-size: x-small;
}
/* .container { */
/* padding-left: 10px; */
/* } */
.gray-border {
min-height: 20px;
margin: 50px 140px;
background-color: #f5f5f5;
border: 2px solid #dcdcdc;
border-radius: 4px;
-webkit-box-shadow: inset 0 2px 2px rgba(0, 0, 0, 0.1);
box-shadow: inset 0 2px 2px rgba(0, 0, 0, 0.1);
}
a {color: #3498db;}
a:hover {color: #217dbb;}
/* .navbar-default .navbar-brand { */
/* /* color: #18bc9c; */ */
/* color: #ffffff; */
/* font-weight: bold; */
/* font-size: 22px; */
/* } */
/* BREADCRUMBS */
div.breadcrumbs {
background: #79aec8;
padding: 10px 40px;
border: none;
font-size: 14px;
color: #c4dce8;
text-align: left;
}
div.breadcrumbs a {
color: #fff;
}
div.breadcrumbs a:focus, div.breadcrumbs a:hover {
color: #c4dce8;
}
div.miniplot:hover { background-color: #F1F1F1; }
div.miniplot {
cursor: pointer;
margin: 0.5em;
text-align: left;
float: left;
height: 200px;
-moz-border-radius: 10px;
-webkit-border-radius: 10px;
border-radius: 10px;
}
/* #success_message{ display: none;} */

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themes/bootstrap-flatly.min.css

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/*
* Table
*/
table.dataTable {
margin: 0 auto;
clear: both;
width: 100%;
}
table.dataTable thead th {
padding: 3px 18px 3px 10px;
border-bottom: 1px solid black;
font-weight: bold;
cursor: pointer;
*cursor: hand;
}
table.dataTable tfoot th {
padding: 3px 18px 3px 10px;
border-top: 1px solid black;
font-weight: bold;
}
table.dataTable td {
padding: 3px 10px;
}
table.dataTable td.center,
table.dataTable td.dataTables_empty {
text-align: center;
}
table.dataTable tr.odd { background-color: #E2E4FF; }
table.dataTable tr.even { background-color: white; }
table.dataTable tr.odd td.sorting_1 { background-color: #D3D6FF; }
table.dataTable tr.odd td.sorting_2 { background-color: #DADCFF; }
table.dataTable tr.odd td.sorting_3 { background-color: #E0E2FF; }
table.dataTable tr.even td.sorting_1 { background-color: #EAEBFF; }
table.dataTable tr.even td.sorting_2 { background-color: #F2F3FF; }
table.dataTable tr.even td.sorting_3 { background-color: #F9F9FF; }
/*
* Table wrapper
*/
.dataTables_wrapper {
position: relative;
clear: both;
*zoom: 1;
}
/*
* Page length menu
*/
.dataTables_length {
float: left;
}
/*
* Filter
*/
.dataTables_filter {
float: right;
text-align: right;
}
/*
* Table information
*/
.dataTables_info {
clear: both;
float: left;
}
/*
* Pagination
*/
.dataTables_paginate {
float: right;
text-align: right;
}
/* Two button pagination - previous / next */
.paginate_disabled_previous,
.paginate_enabled_previous,
.paginate_disabled_next,
.paginate_enabled_next {
height: 19px;
float: left;
cursor: pointer;
*cursor: hand;
color: #111 !important;
}
.paginate_disabled_previous:hover,
.paginate_enabled_previous:hover,
.paginate_disabled_next:hover,
.paginate_enabled_next:hover {
text-decoration: none !important;
}
.paginate_disabled_previous:active,
.paginate_enabled_previous:active,
.paginate_disabled_next:active,
.paginate_enabled_next:active {
outline: none;
}
.paginate_disabled_previous,
.paginate_disabled_next {
color: #666 !important;
}
.paginate_disabled_previous,
.paginate_enabled_previous {
padding-left: 23px;
}
.paginate_disabled_next,
.paginate_enabled_next {
padding-right: 23px;
margin-left: 10px;
}
.paginate_enabled_previous { background: url('../img/back_enabled.png') no-repeat top left; }
.paginate_enabled_previous:hover { background: url('../img/back_enabled_hover.png') no-repeat top left; }
.paginate_disabled_previous { background: url('../img/back_disabled.png') no-repeat top left; }
.paginate_enabled_next { background: url('../img/forward_enabled.png') no-repeat top right; }
.paginate_enabled_next:hover { background: url('../img/forward_enabled_hover.png') no-repeat top right; }
.paginate_disabled_next { background: url('../img/forward_disabled.png') no-repeat top right; }
/* Full number pagination */
.paging_full_numbers {
height: 22px;
line-height: 22px;
}
.paging_full_numbers a:active {
outline: none
}
.paging_full_numbers a:hover {
text-decoration: none;
}
.paging_full_numbers a.paginate_button,
.paging_full_numbers a.paginate_active {
border: 1px solid #aaa;
-webkit-border-radius: 5px;
-moz-border-radius: 5px;
border-radius: 5px;
padding: 2px 5px;
margin: 0 3px;
cursor: pointer;
*cursor: hand;
color: #333 !important;
}
.paging_full_numbers a.paginate_button {
background-color: #ddd;
}
.paging_full_numbers a.paginate_button:hover {
background-color: #ccc;
text-decoration: none !important;
}
.paging_full_numbers a.paginate_active {
background-color: #99B3FF;
}
/*
* Processing indicator
*/
.dataTables_processing {
position: absolute;
top: 50%;
left: 50%;
width: 250px;
height: 30px;
margin-left: -125px;
margin-top: -15px;
padding: 14px 0 2px 0;
border: 1px solid #ddd;
text-align: center;
color: #999;
font-size: 14px;
background-color: white;
}
/*
* Sorting
*/
.sorting { background: url('../img/sort_both.png') no-repeat center right; }
.sorting_asc { background: url('../img/sort_asc.png') no-repeat center right; }
.sorting_desc { background: url('../img/sort_desc.png') no-repeat center right; }
.sorting_asc_disabled { background: url('../img/sort_asc_disabled.png') no-repeat center right; }
.sorting_desc_disabled { background: url('../img/sort_desc_disabled.png') no-repeat center right; }
table.dataTable thead th:active,
table.dataTable thead td:active {
outline: none;
}
/*
* Scrolling
*/
.dataTables_scroll {
clear: both;
}
.dataTables_scrollBody {
*margin-top: -1px;
-webkit-overflow-scrolling: touch;
}

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/*
Theme Name:
Author: yaminncco
Colors:
Body : #868F9B
Headers : #10161A
Primary : #6195FF
Dark : #FCFCFF
Grey : #F4F4F4 #FAFAFA #EEE
Fonts: Montserrat Varela Round
*/
.bg-img {
position: absolute;
left: 0;
top: 0;
right: 0;
bottom: 0;
z-index: -1;
background-position: center;
background-size: cover;
background-attachment: fixed;
}
.bg-img .overlay {
position: absolute;
left: 0;
top: 0;
right: 0;
bottom: 0;
opacity: .8;
background: #1C1D21;
}
/* -- Buttons -- */
.main-btn, .white-btn, .outline-btn {
display: inline-block;
padding: 10px 35px;
margin: 3px;
border: 2px solid transparent;
border-radius: 3px;
-webkit-transition: 0.2s opacity;
transition: 0.2s opacity;
}
.main-btn {
background: #6195FF;
color: #FFF;
}
.white-btn {
background: #FFF;
color: #10161A !important;
}
.outline-btn {
background: transparent;
color: #6195FF !important;
border-color: #6195FF;
}
.main-btn:hover, .white-btn:hover, .outline-btn:hover {
opacity: 0.8;
}
.white-text {
color: #FFF;
list-style: none;
font-size: 20pt;
}
/* Home Content */
#home {
height: 100vh;
}
#home .home-wrapper {
position: absolute;
left: 0px;
right: 0px;
top: 50%;
-webkit-transform: translateY(-50%);
-ms-transform: translateY(-50%);
transform: translateY(-50%);
text-align: center;
}
.home-content h1 {
text-transform: uppercase;
font-size: 8em;
}
.home-content button {
margin-top: 20px;
}
.header-wrapper h2 {
display: inline-block;
margin-bottom: 0px;
}
.header-wrapper .breadcrumb {
float: right;
background: transparent;
margin-bottom: 0px;
}
.header-wrapper .breadcrumb .breadcrumb-item.active {
color: #868F9B;
}
.breadcrumb>li+li:before {
color: #868F9B;
}
/* Footer Copyright */
.footer-copyright p {
text-align: center;
font-size: 14px;
text-transform: uppercase;
margin: 0;
}

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<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
<svg xmlns="http://www.w3.org/2000/svg">
<metadata></metadata>
<defs>
<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
<font-face units-per-em="1200" ascent="960" descent="-240" />
<missing-glyph horiz-adv-x="500" />
<glyph horiz-adv-x="0" />
<glyph horiz-adv-x="400" />
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/*
* File: FixedHeader.min.js
* Version: 2.0.6
* Author: Allan Jardine (www.sprymedia.co.uk)
*
* Copyright 2009-2011 Allan Jardine, all rights reserved.
*
* This source file is free software, under either the GPL v2 license or a
* BSD (3 point) style license, as supplied with this software.
*
* This source file is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
* or FITNESS FOR A PARTICULAR PURPOSE. See the license files for details.
*/
var FixedHeader=function(a,c){if("function"!=typeof this.fnInit)alert("FixedHeader warning: FixedHeader must be initialised with the 'new' keyword.");else{var b={aoCache:[],oSides:{top:!0,bottom:!1,left:!1,right:!1},oZIndexes:{top:104,bottom:103,left:102,right:101},oMes:{iTableWidth:0,iTableHeight:0,iTableLeft:0,iTableRight:0,iTableTop:0,iTableBottom:0},oOffset:{top:0},nTable:null,bUseAbsPos:!1,bFooter:!1};this.fnGetSettings=function(){return b};this.fnUpdate=function(){this._fnUpdateClones();this._fnUpdatePositions()};
this.fnPosition=function(){this._fnUpdatePositions()};this.fnInit(a,c);"function"==typeof a.fnSettings&&(a._oPluginFixedHeader=this)}};
FixedHeader.prototype={fnInit:function(a,c){var b=this.fnGetSettings(),d=this;this.fnInitSettings(b,c);if("function"==typeof a.fnSettings){if("functon"==typeof a.fnVersionCheck&&!0!==a.fnVersionCheck("1.6.0")){alert("FixedHeader 2 required DataTables 1.6.0 or later. Please upgrade your DataTables installation");return}var e=a.fnSettings();if(""!=e.oScroll.sX||""!=e.oScroll.sY){alert("FixedHeader 2 is not supported with DataTables' scrolling mode at this time");return}b.nTable=e.nTable;e.aoDrawCallback.push({fn:function(){FixedHeader.fnMeasure();
d._fnUpdateClones.call(d);d._fnUpdatePositions.call(d)},sName:"FixedHeader"})}else b.nTable=a;b.bFooter=0<$(">tfoot",b.nTable).length?!0:!1;b.bUseAbsPos=jQuery.browser.msie&&("6.0"==jQuery.browser.version||"7.0"==jQuery.browser.version);b.oSides.top&&b.aoCache.push(d._fnCloneTable("fixedHeader","FixedHeader_Header",d._fnCloneThead));b.oSides.bottom&&b.aoCache.push(d._fnCloneTable("fixedFooter","FixedHeader_Footer",d._fnCloneTfoot));b.oSides.left&&b.aoCache.push(d._fnCloneTable("fixedLeft","FixedHeader_Left",
d._fnCloneTLeft));b.oSides.right&&b.aoCache.push(d._fnCloneTable("fixedRight","FixedHeader_Right",d._fnCloneTRight));FixedHeader.afnScroll.push(function(){d._fnUpdatePositions.call(d)});jQuery(window).resize(function(){FixedHeader.fnMeasure();d._fnUpdateClones.call(d);d._fnUpdatePositions.call(d)});FixedHeader.fnMeasure();d._fnUpdateClones();d._fnUpdatePositions()},fnInitSettings:function(a,c){if("undefined"!=typeof c&&("undefined"!=typeof c.top&&(a.oSides.top=c.top),"undefined"!=typeof c.bottom&&
(a.oSides.bottom=c.bottom),"undefined"!=typeof c.left&&(a.oSides.left=c.left),"undefined"!=typeof c.right&&(a.oSides.right=c.right),"undefined"!=typeof c.zTop&&(a.oZIndexes.top=c.zTop),"undefined"!=typeof c.zBottom&&(a.oZIndexes.bottom=c.zBottom),"undefined"!=typeof c.zLeft&&(a.oZIndexes.left=c.zLeft),"undefined"!=typeof c.zRight&&(a.oZIndexes.right=c.zRight),"undefined"!=typeof c.offsetTop))a.oOffset.top=c.offsetTop;a.bUseAbsPos=jQuery.browser.msie&&("6.0"==jQuery.browser.version||"7.0"==jQuery.browser.version)},
_fnCloneTable:function(a,c,b){var d=this.fnGetSettings(),e;"absolute"!=jQuery(d.nTable.parentNode).css("position")&&(d.nTable.parentNode.style.position="relative");e=d.nTable.cloneNode(!1);e.removeAttribute("id");var f=document.createElement("div");f.style.position="absolute";f.style.top="0px";f.style.left="0px";f.className+=" FixedHeader_Cloned "+a+" "+c;"fixedHeader"==a&&(f.style.zIndex=d.oZIndexes.top);"fixedFooter"==a&&(f.style.zIndex=d.oZIndexes.bottom);"fixedLeft"==a?f.style.zIndex=d.oZIndexes.left:
"fixedRight"==a&&(f.style.zIndex=d.oZIndexes.right);e.style.margin="0";f.appendChild(e);document.body.appendChild(f);return{nNode:e,nWrapper:f,sType:a,sPosition:"",sTop:"",sLeft:"",fnClone:b}},_fnMeasure:function(){var a=this.fnGetSettings(),c=a.oMes,b=jQuery(a.nTable),d=b.offset(),e=this._fnSumScroll(a.nTable.parentNode,"scrollTop");this._fnSumScroll(a.nTable.parentNode,"scrollLeft");c.iTableWidth=b.outerWidth();c.iTableHeight=b.outerHeight();c.iTableLeft=d.left+a.nTable.parentNode.scrollLeft;c.iTableTop=
d.top+e;c.iTableRight=c.iTableLeft+c.iTableWidth;c.iTableRight=FixedHeader.oDoc.iWidth-c.iTableLeft-c.iTableWidth;c.iTableBottom=FixedHeader.oDoc.iHeight-c.iTableTop-c.iTableHeight},_fnSumScroll:function(a,c){for(var b=a[c];(a=a.parentNode)&&!("HTML"==a.nodeName||"BODY"==a.nodeName);)b=a[c];return b},_fnUpdatePositions:function(){var a=this.fnGetSettings();this._fnMeasure();for(var c=0,b=a.aoCache.length;c<b;c++)"fixedHeader"==a.aoCache[c].sType?this._fnScrollFixedHeader(a.aoCache[c]):"fixedFooter"==
a.aoCache[c].sType?this._fnScrollFixedFooter(a.aoCache[c]):"fixedLeft"==a.aoCache[c].sType?this._fnScrollHorizontalLeft(a.aoCache[c]):this._fnScrollHorizontalRight(a.aoCache[c])},_fnUpdateClones:function(){for(var a=this.fnGetSettings(),c=0,b=a.aoCache.length;c<b;c++)a.aoCache[c].fnClone.call(this,a.aoCache[c])},_fnScrollHorizontalRight:function(a){var c=this.fnGetSettings(),b=c.oMes,d=FixedHeader.oWin,e=FixedHeader.oDoc,f=a.nWrapper,g=jQuery(f).outerWidth();d.iScrollRight<b.iTableRight?(this._fnUpdateCache(a,
"sPosition","absolute","position",f.style),this._fnUpdateCache(a,"sTop",b.iTableTop+"px","top",f.style),this._fnUpdateCache(a,"sLeft",b.iTableLeft+b.iTableWidth-g+"px","left",f.style)):b.iTableLeft<e.iWidth-d.iScrollRight-g?c.bUseAbsPos?(this._fnUpdateCache(a,"sPosition","absolute","position",f.style),this._fnUpdateCache(a,"sTop",b.iTableTop+"px","top",f.style),this._fnUpdateCache(a,"sLeft",e.iWidth-d.iScrollRight-g+"px","left",f.style)):(this._fnUpdateCache(a,"sPosition","fixed","position",f.style),
this._fnUpdateCache(a,"sTop",b.iTableTop-d.iScrollTop+"px","top",f.style),this._fnUpdateCache(a,"sLeft",d.iWidth-g+"px","left",f.style)):(this._fnUpdateCache(a,"sPosition","absolute","position",f.style),this._fnUpdateCache(a,"sTop",b.iTableTop+"px","top",f.style),this._fnUpdateCache(a,"sLeft",b.iTableLeft+"px","left",f.style))},_fnScrollHorizontalLeft:function(a){var c=this.fnGetSettings(),b=c.oMes,d=FixedHeader.oWin,e=a.nWrapper,f=jQuery(e).outerWidth();d.iScrollLeft<b.iTableLeft?(this._fnUpdateCache(a,
"sPosition","absolute","position",e.style),this._fnUpdateCache(a,"sTop",b.iTableTop+"px","top",e.style),this._fnUpdateCache(a,"sLeft",b.iTableLeft+"px","left",e.style)):d.iScrollLeft<b.iTableLeft+b.iTableWidth-f?c.bUseAbsPos?(this._fnUpdateCache(a,"sPosition","absolute","position",e.style),this._fnUpdateCache(a,"sTop",b.iTableTop+"px","top",e.style),this._fnUpdateCache(a,"sLeft",d.iScrollLeft+"px","left",e.style)):(this._fnUpdateCache(a,"sPosition","fixed","position",e.style),this._fnUpdateCache(a,
"sTop",b.iTableTop-d.iScrollTop+"px","top",e.style),this._fnUpdateCache(a,"sLeft","0px","left",e.style)):(this._fnUpdateCache(a,"sPosition","absolute","position",e.style),this._fnUpdateCache(a,"sTop",b.iTableTop+"px","top",e.style),this._fnUpdateCache(a,"sLeft",b.iTableLeft+b.iTableWidth-f+"px","left",e.style))},_fnScrollFixedFooter:function(a){var c=this.fnGetSettings(),b=c.oMes,d=FixedHeader.oWin,e=FixedHeader.oDoc,f=a.nWrapper,g=jQuery("thead",c.nTable).outerHeight(),h=jQuery(f).outerHeight();
d.iScrollBottom<b.iTableBottom?(this._fnUpdateCache(a,"sPosition","absolute","position",f.style),this._fnUpdateCache(a,"sTop",b.iTableTop+b.iTableHeight-h+"px","top",f.style),this._fnUpdateCache(a,"sLeft",b.iTableLeft+"px","left",f.style)):d.iScrollBottom<b.iTableBottom+b.iTableHeight-h-g?c.bUseAbsPos?(this._fnUpdateCache(a,"sPosition","absolute","position",f.style),this._fnUpdateCache(a,"sTop",e.iHeight-d.iScrollBottom-h+"px","top",f.style),this._fnUpdateCache(a,"sLeft",b.iTableLeft+"px","left",
f.style)):(this._fnUpdateCache(a,"sPosition","fixed","position",f.style),this._fnUpdateCache(a,"sTop",d.iHeight-h+"px","top",f.style),this._fnUpdateCache(a,"sLeft",b.iTableLeft-d.iScrollLeft+"px","left",f.style)):(this._fnUpdateCache(a,"sPosition","absolute","position",f.style),this._fnUpdateCache(a,"sTop",b.iTableTop+h+"px","top",f.style),this._fnUpdateCache(a,"sLeft",b.iTableLeft+"px","left",f.style))},_fnScrollFixedHeader:function(a){for(var c=this.fnGetSettings(),b=c.oMes,d=FixedHeader.oWin,e=
a.nWrapper,f=0,g=c.nTable.getElementsByTagName("tbody"),h=0;h<g.length;++h)f+=g[h].offsetHeight;b.iTableTop>d.iScrollTop+c.oOffset.top?(this._fnUpdateCache(a,"sPosition","absolute","position",e.style),this._fnUpdateCache(a,"sTop",b.iTableTop+"px","top",e.style),this._fnUpdateCache(a,"sLeft",b.iTableLeft+"px","left",e.style)):d.iScrollTop+c.oOffset.top>b.iTableTop+f?(this._fnUpdateCache(a,"sPosition","absolute","position",e.style),this._fnUpdateCache(a,"sTop",b.iTableTop+f+"px","top",e.style),this._fnUpdateCache(a,
"sLeft",b.iTableLeft+"px","left",e.style)):c.bUseAbsPos?(this._fnUpdateCache(a,"sPosition","absolute","position",e.style),this._fnUpdateCache(a,"sTop",d.iScrollTop+"px","top",e.style),this._fnUpdateCache(a,"sLeft",b.iTableLeft+"px","left",e.style)):(this._fnUpdateCache(a,"sPosition","fixed","position",e.style),this._fnUpdateCache(a,"sTop",c.oOffset.top+"px","top",e.style),this._fnUpdateCache(a,"sLeft",b.iTableLeft-d.iScrollLeft+"px","left",e.style))},_fnUpdateCache:function(a,c,b,d,e){a[c]!=b&&(e[d]=
b,a[c]=b)},_fnCloneThead:function(a){var c=this.fnGetSettings(),b=a.nNode;for(a.nWrapper.style.width=jQuery(c.nTable).outerWidth()+"px";0<b.childNodes.length;)jQuery("thead th",b).unbind("click"),b.removeChild(b.childNodes[0]);a=jQuery("thead",c.nTable).clone(!0)[0];b.appendChild(a);jQuery("thead>tr th",c.nTable).each(function(a){jQuery("thead>tr th:eq("+a+")",b).width(jQuery(this).width())});jQuery("thead>tr td",c.nTable).each(function(a){jQuery("thead>tr td:eq("+a+")",b).width(jQuery(this).width())})},
_fnCloneTfoot:function(a){var c=this.fnGetSettings(),b=a.nNode;for(a.nWrapper.style.width=jQuery(c.nTable).outerWidth()+"px";0<b.childNodes.length;)b.removeChild(b.childNodes[0]);a=jQuery("tfoot",c.nTable).clone(!0)[0];b.appendChild(a);jQuery("tfoot:eq(0)>tr th",c.nTable).each(function(a){jQuery("tfoot:eq(0)>tr th:eq("+a+")",b).width(jQuery(this).width())});jQuery("tfoot:eq(0)>tr td",c.nTable).each(function(a){jQuery("tfoot:eq(0)>tr th:eq("+a+")",b)[0].style.width(jQuery(this).width())})},_fnCloneTLeft:function(a){var c=
this.fnGetSettings(),b=a.nNode,d=$("tbody",c.nTable)[0];for($("tbody tr:eq(0) td",c.nTable);0<b.childNodes.length;)b.removeChild(b.childNodes[0]);b.appendChild(jQuery("thead",c.nTable).clone(!0)[0]);b.appendChild(jQuery("tbody",c.nTable).clone(!0)[0]);c.bFooter&&b.appendChild(jQuery("tfoot",c.nTable).clone(!0)[0]);$("thead tr",b).each(function(){$("th:gt(0)",this).remove()});$("tfoot tr",b).each(function(){$("th:gt(0)",this).remove()});$("tbody tr",b).each(function(){$("td:gt(0)",this).remove()});
this.fnEqualiseHeights("tbody",d.parentNode,b);c=jQuery("thead tr th:eq(0)",c.nTable).outerWidth();b.style.width=c+"px";a.nWrapper.style.width=c+"px"},_fnCloneTRight:function(a){for(var c=this.fnGetSettings(),b=$("tbody",c.nTable)[0],d=a.nNode,e=jQuery("tbody tr:eq(0) td",c.nTable).length;0<d.childNodes.length;)d.removeChild(d.childNodes[0]);d.appendChild(jQuery("thead",c.nTable).clone(!0)[0]);d.appendChild(jQuery("tbody",c.nTable).clone(!0)[0]);c.bFooter&&d.appendChild(jQuery("tfoot",c.nTable).clone(!0)[0]);
jQuery("thead tr th:not(:nth-child("+e+"n))",d).remove();jQuery("tfoot tr th:not(:nth-child("+e+"n))",d).remove();$("tbody tr",d).each(function(){$("td:lt("+(e-1)+")",this).remove()});this.fnEqualiseHeights("tbody",b.parentNode,d);c=jQuery("thead tr th:eq("+(e-1)+")",c.nTable).outerWidth();d.style.width=c+"px";a.nWrapper.style.width=c+"px"},fnEqualiseHeights:function(a,c,b){var d=$(a+" tr:eq(0)",c).children(":eq(0)"),e=d.outerHeight()-d.height(),f=$.browser.msie&&("6.0"==$.browser.version||"7.0"==
$.browser.version);$(a+" tr",b).each(function(b){$.browser.mozilla||$.browser.opera?$(this).children().height($(a+" tr:eq("+b+")",c).outerHeight()):$(this).children().height($(a+" tr:eq("+b+")",c).outerHeight()-e);f||$(a+" tr:eq("+b+")",c).height($(a+" tr:eq("+b+")",c).outerHeight())})}};FixedHeader.oWin={iScrollTop:0,iScrollRight:0,iScrollBottom:0,iScrollLeft:0,iHeight:0,iWidth:0};FixedHeader.oDoc={iHeight:0,iWidth:0};FixedHeader.afnScroll=[];
FixedHeader.fnMeasure=function(){var a=jQuery(window),c=jQuery(document),b=FixedHeader.oWin,d=FixedHeader.oDoc;d.iHeight=c.height();d.iWidth=c.width();b.iHeight=a.height();b.iWidth=a.width();b.iScrollTop=a.scrollTop();b.iScrollLeft=a.scrollLeft();b.iScrollRight=d.iWidth-b.iScrollLeft-b.iWidth;b.iScrollBottom=d.iHeight-b.iScrollTop-b.iHeight};FixedHeader.VERSION="2.0.6";FixedHeader.prototype.VERSION=FixedHeader.VERSION;
jQuery(window).scroll(function(){FixedHeader.fnMeasure();for(var a=0,c=FixedHeader.afnScroll.length;a<c;a++)FixedHeader.afnScroll[a]()});

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var BenchmarkBar = (function(window){
function renderPlot(data, div_id) {
var plotoptions = {
title: {text: data.metric + " " + data.lessisbetter, fontSize: '1.1em'},
seriesDefaults:{
renderer:$.jqplot.BarRenderer,
rendererOptions: {
varyBarColor: true,
},
pointLabels: { show: true }
},
axes:{
yaxis:{
label: data.unit,
labelRenderer: $.jqplot.CanvasAxisLabelRenderer,
min: 0, autoscale:true,
},
xaxis:{
renderer: $.jqplot.CategoryAxisRenderer,
label: 'DBMS Knob Configurations',
labelRenderer: $.jqplot.CanvasAxisLabelRenderer,
tickRenderer: $.jqplot.CanvasAxisTickRenderer,
tickOptions:{angle:-20},
ticks: data.tick,
pad: 1.01,
autoscale:true,
}
},
cursor: {show: true, zoom:true, showTooltip:false, clickReset:true},
};
$("#" + div_id).html('<div id="' + div_id + '_plot"></div><div id="plotdescription"></div>');
var plot = $.jqplot(div_id + '_plot', data.data, plotoptions);
$('#' + div_id + '_plot').bind('jqplotDataHighlight',
function (ev, seriesIndex, pointIndex, pointed_data ) {
$('#chartpseudotooltip').html('<table class="jqplot-highlighter-tooltip"><tr><td>Result ID:</td><td>' + pointed_data[2] + '</td></tr> <tr><td>' + data.metric + ':</td><td>' + pointed_data[1].toFixed(2) + '</td></tr></table>');
x = plot.axes.xaxis.u2p(pointed_data[0]), // convert x axis unita to pixels
y = plot.axes.yaxis.u2p(pointed_data[1]); // convert y axis units to pixels
var mouseX = $('#' + div_id + '_plot').position().left + x;
var mouseY = $('#' + div_id + '_plot').position().top + y - $('#chartpseudotooltip').height();
var cssObj = {
'position' : 'absolute',
'left' : mouseX + 'px',
'top' : mouseY + 'px'
};
$('#chartpseudotooltip').css(cssObj);
}
);
$('#' + div_id + '_plot').bind('jqplotDataUnhighlight',
function (ev) {
$('#chartpseudotooltip').html('');
}
);
}
function render(data) {
$("#plotgrid").html("");
if(data.error !== "None") {
var h = $("#content").height();
$("#plotgrid").html(getLoadText(data.error, h, false));
} else if (data.results[0].data.length === 0) {
var h = $("#content").height();
$("#plotgrid").html(getLoadText("No data available", h, false));
} else {
var met_idx = 0;
for (var metric in data.results) {
var plotid = "plot_" + met_idx;
$("#plotgrid").append('<div id="' + plotid + '" class="plotcontainer"></div>');
renderPlot(data.results[metric], plotid);
met_idx += 1;
}
}
}
function OnMarkerClickHandler(ev, gridpos, datapos, neighbor, plot) {
if (neighbor) {
result_id = neighbor.data[2];
window.location = '/projects/' + defaults.proj + '/sessions/' + defaults.session + '/results/' + result_id;
}
}
function getConfiguration() {
var config = {
id: defaults.workload,
session_id: defaults.session_id,
conf: readCheckbox("input[name^='db_']:checked"),
met: readCheckbox("input[name='metric']:checked"),
};
return config;
}
function refreshContent() {
var h = $("#content").height();//get height for loading text
$("#plotgrid").fadeOut("fast", function() {
$("#plotgrid").html(getLoadText("Loading...", h, true)).show();
$.getJSON("/get_workload_data/", getConfiguration(), render);
});
}
function updateUrl() {
var cfg = getConfiguration();
$.address.autoUpdate(false);
for (var param in cfg) {
$.address.parameter(param, cfg[param]);
}
$.address.update();
}
function updateSub(event) {
var db_name = event.target.value;
$("input[name='db_" + db_name + "']").each( function() {
$(this).prop('checked', event.target.checked);
});
}
function initializeSite(event) {
setValuesOfInputFields(event);
$("input[name^='db_']" ).on('click', updateUrl);
$("input[name='metric']" ).on('click', updateUrl);
}
function refreshSite(event) {
setValuesOfInputFields(event);
refreshContent();
}
function setValuesOfInputFields(event) {
// Either set the default value, or the one parsed from the url
// Set default selected metrics
$("input:checkbox[name='metric']").prop('checked', false);
var metrics = event.parameters.met ? event.parameters.met.split(',') : defaults.metrics;
$("input:checkbox[name='metric']").each(function() {
if ($.inArray($(this).val(), metrics) >= 0) {
$(this).prop('checked', true);
}
});
// Set default selected knob configurations
$("input:checkbox[name^='db_']").removeAttr('checked');
var dbs = event.parameters.db ? event.parameters.db.split(',') : defaults.default_knob_confs;
var sel = $("input[name^='db_']");
$.each(dbs, function(i, db) {
sel.filter("[value='" + db + "']").prop('checked', true);
});
}
function init(def) {
defaults = def;
$.ajaxSetup ({
cache: false
});
// Event listener for clicks on plot markers
$.jqplot.eventListenerHooks.push(['jqplotClick', OnMarkerClickHandler]);
// Init and change handlers are set to the refreshContent handler
$.address.init(initializeSite).change(refreshSite);
}
return {
init: init
};
})(window);

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function readCheckbox(el) {
/* Builds a string that holds all checked values in an input form */
var config = "";
$(el).each(function() {
config += $(this).val() + ",";
});
// Remove last comma
config = config.slice(0, -1);
if (config == "") {
return "none";
}
return config;
}
function getLoadText(text, h, showloader) {
var loadtext = '<div style="text-align:center;">';
var pstyle = "";
if (h > 0) {
h = h - 32;
if(h < 80) { h = 180; }
else if (h > 400) { h = 400; }
pstyle = ' style="line-height:' + h + 'px;"';
}
loadtext += '<p' + pstyle + '>'+ text;
if (showloader) {
loadtext += ' <img src="' + window.STATIC_URL +'img/ajax-loader.gif" align="bottom">';
}
loadtext += '</p></div>';
return loadtext;
}
function valueOrDefault(obj, defaultObj) {
return (obj) ? obj : defaultObj;
}
function allChecked(el) {
var ret = true;
$(el).each(function() {
if ($(this).prop('checked') != true) {
ret = false;
}
});
return ret;
}

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/* jqPlot 1.0.8r1250 | (c) 2009-2013 Chris Leonello | jplot.com
jsDate | (c) 2010-2013 Chris Leonello
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/* jqPlot 1.0.8r1250 | (c) 2009-2013 Chris Leonello | jplot.com
jsDate | (c) 2010-2013 Chris Leonello
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jsDate | (c) 2010-2013 Chris Leonello
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/*
* jQuery Address Plugin v1.5
* http://www.asual.com/jquery/address/
*
* Copyright (c) 2009-2010 Rostislav Hristov
* Dual licensed under the MIT or GPL Version 2 licenses.
* http://jquery.org/license
*
* Date: 2012-11-18 23:51:44 +0200 (Sun, 18 Nov 2012)
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155
server/website/website/static/js/jquery.dataTables.min.js vendored Normal file
View File

@@ -0,0 +1,155 @@
/*
* File: jquery.dataTables.min.js
* Version: 1.9.4
* Author: Allan Jardine (www.sprymedia.co.uk)
* Info: www.datatables.net
*
* Copyright 2008-2012 Allan Jardine, all rights reserved.
*
* This source file is free software, under either the GPL v2 license or a
* BSD style license, available at:
* http://datatables.net/license_gpl2
* http://datatables.net/license_bsd
*
* This source file is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
* or FITNESS FOR A PARTICULAR PURPOSE. See the license files for details.
*/
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fnRowCallback:null,fnServerData:function(e,j,m,k){k.jqXHR=h.ajax({url:e,data:j,success:function(e){e.sError&&k.oApi._fnLog(k,0,e.sError);h(k.oInstance).trigger("xhr",[k,e]);m(e)},dataType:"json",cache:!1,type:k.sServerMethod,error:function(e,h){"parsererror"==h&&k.oApi._fnLog(k,0,"DataTables warning: JSON data from server could not be parsed. This is caused by a JSON formatting error.")}})},fnServerParams:null,fnStateLoad:function(e){var e=this.oApi._fnReadCookie(e.sCookiePrefix+e.sInstance),j;try{j=
"function"===typeof h.parseJSON?h.parseJSON(e):eval("("+e+")")}catch(m){j=null}return j},fnStateLoadParams:null,fnStateLoaded:null,fnStateSave:function(e,h){this.oApi._fnCreateCookie(e.sCookiePrefix+e.sInstance,this.oApi._fnJsonString(h),e.iCookieDuration,e.sCookiePrefix,e.fnCookieCallback)},fnStateSaveParams:null,iCookieDuration:7200,iDeferLoading:null,iDisplayLength:10,iDisplayStart:0,iScrollLoadGap:100,iTabIndex:0,oLanguage:{oAria:{sSortAscending:": activate to sort column ascending",sSortDescending:": activate to sort column descending"},
oPaginate:{sFirst:"First",sLast:"Last",sNext:"Next",sPrevious:"Previous"},sEmptyTable:"No data available in table",sInfo:"Showing _START_ to _END_ of _TOTAL_ entries",sInfoEmpty:"Showing 0 to 0 of 0 entries",sInfoFiltered:"(filtered from _MAX_ total entries)",sInfoPostFix:"",sInfoThousands:",",sLengthMenu:"Show _MENU_ entries",sLoadingRecords:"Loading...",sProcessing:"Processing...",sSearch:"Search:",sUrl:"",sZeroRecords:"No matching records found"},oSearch:h.extend({},j.models.oSearch),sAjaxDataProp:"aaData",
sAjaxSource:null,sCookiePrefix:"SpryMedia_DataTables_",sDom:"lfrtip",sPaginationType:"two_button",sScrollX:"",sScrollXInner:"",sScrollY:"",sServerMethod:"GET"};j.defaults.columns={aDataSort:null,asSorting:["asc","desc"],bSearchable:!0,bSortable:!0,bUseRendered:!0,bVisible:!0,fnCreatedCell:null,fnRender:null,iDataSort:-1,mData:null,mRender:null,sCellType:"td",sClass:"",sContentPadding:"",sDefaultContent:null,sName:"",sSortDataType:"std",sTitle:null,sType:null,sWidth:null};j.models.oSettings={oFeatures:{bAutoWidth:null,
bDeferRender:null,bFilter:null,bInfo:null,bLengthChange:null,bPaginate:null,bProcessing:null,bServerSide:null,bSort:null,bSortClasses:null,bStateSave:null},oScroll:{bAutoCss:null,bCollapse:null,bInfinite:null,iBarWidth:0,iLoadGap:null,sX:null,sXInner:null,sY:null},oLanguage:{fnInfoCallback:null},oBrowser:{bScrollOversize:!1},aanFeatures:[],aoData:[],aiDisplay:[],aiDisplayMaster:[],aoColumns:[],aoHeader:[],aoFooter:[],asDataSearch:[],oPreviousSearch:{},aoPreSearchCols:[],aaSorting:null,aaSortingFixed:null,
asStripeClasses:null,asDestroyStripes:[],sDestroyWidth:0,aoRowCallback:[],aoHeaderCallback:[],aoFooterCallback:[],aoDrawCallback:[],aoRowCreatedCallback:[],aoPreDrawCallback:[],aoInitComplete:[],aoStateSaveParams:[],aoStateLoadParams:[],aoStateLoaded:[],sTableId:"",nTable:null,nTHead:null,nTFoot:null,nTBody:null,nTableWrapper:null,bDeferLoading:!1,bInitialised:!1,aoOpenRows:[],sDom:null,sPaginationType:"two_button",iCookieDuration:0,sCookiePrefix:"",fnCookieCallback:null,aoStateSave:[],aoStateLoad:[],
oLoadedState:null,sAjaxSource:null,sAjaxDataProp:null,bAjaxDataGet:!0,jqXHR:null,fnServerData:null,aoServerParams:[],sServerMethod:null,fnFormatNumber:null,aLengthMenu:null,iDraw:0,bDrawing:!1,iDrawError:-1,_iDisplayLength:10,_iDisplayStart:0,_iDisplayEnd:10,_iRecordsTotal:0,_iRecordsDisplay:0,bJUI:null,oClasses:{},bFiltered:!1,bSorted:!1,bSortCellsTop:null,oInit:null,aoDestroyCallback:[],fnRecordsTotal:function(){return this.oFeatures.bServerSide?parseInt(this._iRecordsTotal,10):this.aiDisplayMaster.length},
fnRecordsDisplay:function(){return this.oFeatures.bServerSide?parseInt(this._iRecordsDisplay,10):this.aiDisplay.length},fnDisplayEnd:function(){return this.oFeatures.bServerSide?!1===this.oFeatures.bPaginate||-1==this._iDisplayLength?this._iDisplayStart+this.aiDisplay.length:Math.min(this._iDisplayStart+this._iDisplayLength,this._iRecordsDisplay):this._iDisplayEnd},oInstance:null,sInstance:null,iTabIndex:0,nScrollHead:null,nScrollFoot:null};j.ext=h.extend(!0,{},j.models.ext);h.extend(j.ext.oStdClasses,
{sTable:"dataTable",sPagePrevEnabled:"paginate_enabled_previous",sPagePrevDisabled:"paginate_disabled_previous",sPageNextEnabled:"paginate_enabled_next",sPageNextDisabled:"paginate_disabled_next",sPageJUINext:"",sPageJUIPrev:"",sPageButton:"paginate_button",sPageButtonActive:"paginate_active",sPageButtonStaticDisabled:"paginate_button paginate_button_disabled",sPageFirst:"first",sPagePrevious:"previous",sPageNext:"next",sPageLast:"last",sStripeOdd:"odd",sStripeEven:"even",sRowEmpty:"dataTables_empty",
sWrapper:"dataTables_wrapper",sFilter:"dataTables_filter",sInfo:"dataTables_info",sPaging:"dataTables_paginate paging_",sLength:"dataTables_length",sProcessing:"dataTables_processing",sSortAsc:"sorting_asc",sSortDesc:"sorting_desc",sSortable:"sorting",sSortableAsc:"sorting_asc_disabled",sSortableDesc:"sorting_desc_disabled",sSortableNone:"sorting_disabled",sSortColumn:"sorting_",sSortJUIAsc:"",sSortJUIDesc:"",sSortJUI:"",sSortJUIAscAllowed:"",sSortJUIDescAllowed:"",sSortJUIWrapper:"",sSortIcon:"",
sScrollWrapper:"dataTables_scroll",sScrollHead:"dataTables_scrollHead",sScrollHeadInner:"dataTables_scrollHeadInner",sScrollBody:"dataTables_scrollBody",sScrollFoot:"dataTables_scrollFoot",sScrollFootInner:"dataTables_scrollFootInner",sFooterTH:"",sJUIHeader:"",sJUIFooter:""});h.extend(j.ext.oJUIClasses,j.ext.oStdClasses,{sPagePrevEnabled:"fg-button ui-button ui-state-default ui-corner-left",sPagePrevDisabled:"fg-button ui-button ui-state-default ui-corner-left ui-state-disabled",sPageNextEnabled:"fg-button ui-button ui-state-default ui-corner-right",
sPageNextDisabled:"fg-button ui-button ui-state-default ui-corner-right ui-state-disabled",sPageJUINext:"ui-icon ui-icon-circle-arrow-e",sPageJUIPrev:"ui-icon ui-icon-circle-arrow-w",sPageButton:"fg-button ui-button ui-state-default",sPageButtonActive:"fg-button ui-button ui-state-default ui-state-disabled",sPageButtonStaticDisabled:"fg-button ui-button ui-state-default ui-state-disabled",sPageFirst:"first ui-corner-tl ui-corner-bl",sPageLast:"last ui-corner-tr ui-corner-br",sPaging:"dataTables_paginate fg-buttonset ui-buttonset fg-buttonset-multi ui-buttonset-multi paging_",
sSortAsc:"ui-state-default",sSortDesc:"ui-state-default",sSortable:"ui-state-default",sSortableAsc:"ui-state-default",sSortableDesc:"ui-state-default",sSortableNone:"ui-state-default",sSortJUIAsc:"css_right ui-icon ui-icon-triangle-1-n",sSortJUIDesc:"css_right ui-icon ui-icon-triangle-1-s",sSortJUI:"css_right ui-icon ui-icon-carat-2-n-s",sSortJUIAscAllowed:"css_right ui-icon ui-icon-carat-1-n",sSortJUIDescAllowed:"css_right ui-icon ui-icon-carat-1-s",sSortJUIWrapper:"DataTables_sort_wrapper",sSortIcon:"DataTables_sort_icon",
sScrollHead:"dataTables_scrollHead ui-state-default",sScrollFoot:"dataTables_scrollFoot ui-state-default",sFooterTH:"ui-state-default",sJUIHeader:"fg-toolbar ui-toolbar ui-widget-header ui-corner-tl ui-corner-tr ui-helper-clearfix",sJUIFooter:"fg-toolbar ui-toolbar ui-widget-header ui-corner-bl ui-corner-br ui-helper-clearfix"});h.extend(j.ext.oPagination,{two_button:{fnInit:function(e,j,m){var k=e.oLanguage.oPaginate,n=function(h){e.oApi._fnPageChange(e,h.data.action)&&m(e)},k=!e.bJUI?'<a class="'+
e.oClasses.sPagePrevDisabled+'" tabindex="'+e.iTabIndex+'" role="button">'+k.sPrevious+'</a><a class="'+e.oClasses.sPageNextDisabled+'" tabindex="'+e.iTabIndex+'" role="button">'+k.sNext+"</a>":'<a class="'+e.oClasses.sPagePrevDisabled+'" tabindex="'+e.iTabIndex+'" role="button"><span class="'+e.oClasses.sPageJUIPrev+'"></span></a><a class="'+e.oClasses.sPageNextDisabled+'" tabindex="'+e.iTabIndex+'" role="button"><span class="'+e.oClasses.sPageJUINext+'"></span></a>';h(j).append(k);var l=h("a",j),
k=l[0],l=l[1];e.oApi._fnBindAction(k,{action:"previous"},n);e.oApi._fnBindAction(l,{action:"next"},n);e.aanFeatures.p||(j.id=e.sTableId+"_paginate",k.id=e.sTableId+"_previous",l.id=e.sTableId+"_next",k.setAttribute("aria-controls",e.sTableId),l.setAttribute("aria-controls",e.sTableId))},fnUpdate:function(e){if(e.aanFeatures.p)for(var h=e.oClasses,j=e.aanFeatures.p,k,l=0,n=j.length;l<n;l++)if(k=j[l].firstChild)k.className=0===e._iDisplayStart?h.sPagePrevDisabled:h.sPagePrevEnabled,k=k.nextSibling,
k.className=e.fnDisplayEnd()==e.fnRecordsDisplay()?h.sPageNextDisabled:h.sPageNextEnabled}},iFullNumbersShowPages:5,full_numbers:{fnInit:function(e,j,m){var k=e.oLanguage.oPaginate,l=e.oClasses,n=function(h){e.oApi._fnPageChange(e,h.data.action)&&m(e)};h(j).append('<a tabindex="'+e.iTabIndex+'" class="'+l.sPageButton+" "+l.sPageFirst+'">'+k.sFirst+'</a><a tabindex="'+e.iTabIndex+'" class="'+l.sPageButton+" "+l.sPagePrevious+'">'+k.sPrevious+'</a><span></span><a tabindex="'+e.iTabIndex+'" class="'+
l.sPageButton+" "+l.sPageNext+'">'+k.sNext+'</a><a tabindex="'+e.iTabIndex+'" class="'+l.sPageButton+" "+l.sPageLast+'">'+k.sLast+"</a>");var t=h("a",j),k=t[0],l=t[1],r=t[2],t=t[3];e.oApi._fnBindAction(k,{action:"first"},n);e.oApi._fnBindAction(l,{action:"previous"},n);e.oApi._fnBindAction(r,{action:"next"},n);e.oApi._fnBindAction(t,{action:"last"},n);e.aanFeatures.p||(j.id=e.sTableId+"_paginate",k.id=e.sTableId+"_first",l.id=e.sTableId+"_previous",r.id=e.sTableId+"_next",t.id=e.sTableId+"_last")},
fnUpdate:function(e,o){if(e.aanFeatures.p){var m=j.ext.oPagination.iFullNumbersShowPages,k=Math.floor(m/2),l=Math.ceil(e.fnRecordsDisplay()/e._iDisplayLength),n=Math.ceil(e._iDisplayStart/e._iDisplayLength)+1,t="",r,B=e.oClasses,u,M=e.aanFeatures.p,L=function(h){e.oApi._fnBindAction(this,{page:h+r-1},function(h){e.oApi._fnPageChange(e,h.data.page);o(e);h.preventDefault()})};-1===e._iDisplayLength?n=k=r=1:l<m?(r=1,k=l):n<=k?(r=1,k=m):n>=l-k?(r=l-m+1,k=l):(r=n-Math.ceil(m/2)+1,k=r+m-1);for(m=r;m<=k;m++)t+=
n!==m?'<a tabindex="'+e.iTabIndex+'" class="'+B.sPageButton+'">'+e.fnFormatNumber(m)+"</a>":'<a tabindex="'+e.iTabIndex+'" class="'+B.sPageButtonActive+'">'+e.fnFormatNumber(m)+"</a>";m=0;for(k=M.length;m<k;m++)u=M[m],u.hasChildNodes()&&(h("span:eq(0)",u).html(t).children("a").each(L),u=u.getElementsByTagName("a"),u=[u[0],u[1],u[u.length-2],u[u.length-1]],h(u).removeClass(B.sPageButton+" "+B.sPageButtonActive+" "+B.sPageButtonStaticDisabled),h([u[0],u[1]]).addClass(1==n?B.sPageButtonStaticDisabled:
B.sPageButton),h([u[2],u[3]]).addClass(0===l||n===l||-1===e._iDisplayLength?B.sPageButtonStaticDisabled:B.sPageButton))}}}});h.extend(j.ext.oSort,{"string-pre":function(e){"string"!=typeof e&&(e=null!==e&&e.toString?e.toString():"");return e.toLowerCase()},"string-asc":function(e,h){return e<h?-1:e>h?1:0},"string-desc":function(e,h){return e<h?1:e>h?-1:0},"html-pre":function(e){return e.replace(/<.*?>/g,"").toLowerCase()},"html-asc":function(e,h){return e<h?-1:e>h?1:0},"html-desc":function(e,h){return e<
h?1:e>h?-1:0},"date-pre":function(e){e=Date.parse(e);if(isNaN(e)||""===e)e=Date.parse("01/01/1970 00:00:00");return e},"date-asc":function(e,h){return e-h},"date-desc":function(e,h){return h-e},"numeric-pre":function(e){return"-"==e||""===e?0:1*e},"numeric-asc":function(e,h){return e-h},"numeric-desc":function(e,h){return h-e}});h.extend(j.ext.aTypes,[function(e){if("number"===typeof e)return"numeric";if("string"!==typeof e)return null;var h,j=!1;h=e.charAt(0);if(-1=="0123456789-".indexOf(h))return null;
for(var k=1;k<e.length;k++){h=e.charAt(k);if(-1=="0123456789.".indexOf(h))return null;if("."==h){if(j)return null;j=!0}}return"numeric"},function(e){var h=Date.parse(e);return null!==h&&!isNaN(h)||"string"===typeof e&&0===e.length?"date":null},function(e){return"string"===typeof e&&-1!=e.indexOf("<")&&-1!=e.indexOf(">")?"html":null}]);h.fn.DataTable=j;h.fn.dataTable=j;h.fn.dataTableSettings=j.settings;h.fn.dataTableExt=j.ext};"function"===typeof define&&define.amd?define(["jquery"],L):jQuery&&!jQuery.fn.dataTable&&
L(jQuery)})(window,document);

22
server/website/website/static/js/jquery.jqpagination.min.js vendored Executable file
View File

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/*!
* jqPagination, a jQuery pagination plugin (obviously)
* Version: 1.4 (26th July 2013)
*
* Copyright (C) 2013 Ben Everard
*
* http://beneverard.github.com/jqPagination
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*
*/(function(e){"use strict";e.jqPagination=function(t,n){var r=this;r.$el=e(t);r.el=t;r.$input=r.$el.find("input");r.$el.data("jqPagination",r);r.init=function(){r.options=e.extend({},e.jqPagination.defaultOptions,n);r.options.max_page===null&&(r.$input.data("max-page")!==undefined?r.options.max_page=r.$input.data("max-page"):r.options.max_page=1);r.$input.data("current-page")!==undefined&&r.isNumber(r.$input.data("current-page"))&&(r.options.current_page=r.$input.data("current-page"));r.$input.removeAttr("readonly");r.updateInput(!0);r.$input.on("focus.jqPagination mouseup.jqPagination",function(t){if(t.type==="focus"){var n=parseInt(r.options.current_page,10);e(this).val(n).select()}if(t.type==="mouseup")return!1});r.$input.on("blur.jqPagination keydown.jqPagination",function(t){var n=e(this),i=parseInt(r.options.current_page,10);if(t.keyCode===27){n.val(i);n.blur()}t.keyCode===13&&n.blur();t.type==="blur"&&r.setPage(n.val())});r.$el.on("click.jqPagination","a",function(t){var n=e(this);if(n.hasClass("disabled"))return!1;if(!t.metaKey&&!t.ctrlKey){t.preventDefault();r.setPage(n.data("action"))}})};r.setPage=function(e,t){if(e===undefined)return r.options.current_page;var n=parseInt(r.options.current_page,10),i=parseInt(r.options.max_page,10);if(isNaN(parseInt(e,10)))switch(e){case"first":e=1;break;case"prev":case"previous":e=n-1;break;case"next":e=n+1;break;case"last":e=i}e=parseInt(e,10);if(isNaN(e)||e<1||e>i){r.setInputValue(n);return!1}r.options.current_page=e;r.$input.data("current-page",e);r.updateInput(t)};r.setMaxPage=function(e,t){if(e===undefined)return r.options.max_page;if(!r.isNumber(e)){console.error("jqPagination: max_page is not a number");return!1}if(e<r.options.current_page){console.error("jqPagination: max_page lower than current_page");return!1}r.options.max_page=e;r.$input.data("max-page",e);r.updateInput(t)};r.updateInput=function(e){var t=parseInt(r.options.current_page,10);r.setInputValue(t);r.setLinks(t);e!==!0&&r.options.paged(t)};r.setInputValue=function(e){var t=r.options.page_string,n=r.options.max_page;t=t.replace("{current_page}",e).replace("{max_page}",n);r.$input.val(t)};r.isNumber=function(e){return!isNaN(parseFloat(e))&&isFinite(e)};r.setLinks=function(e){var t=r.options.link_string,n=parseInt(r.options.current_page,10),i=parseInt(r.options.max_page,10);if(t!==""){var s=n-1;s<1&&(s=1);var o=n+1;o>i&&(o=i);r.$el.find("a.first").attr("href",t.replace("{page_number}","1"));r.$el.find("a.prev, a.previous").attr("href",t.replace("{page_number}",s));r.$el.find("a.next").attr("href",t.replace("{page_number}",o));r.$el.find("a.last").attr("href",t.replace("{page_number}",i))}r.$el.find("a").removeClass("disabled");n===i&&r.$el.find(".next, .last").addClass("disabled");n===1&&r.$el.find(".previous, .first").addClass("disabled")};r.callMethod=function(t,n,i){switch(t.toLowerCase()){case"option":if(i===undefined&&typeof n!="object")return r.options[n];var s={trigger:!0},o=!1;e.isPlainObject(n)&&!i?e.extend(s,n):s[n]=i;var u=s.trigger===!1;s.current_page!==undefined&&(o=r.setPage(s.current_page,u));s.max_page!==undefined&&(o=r.setMaxPage(s.max_page,u));o===!1&&console.error("jqPagination: cannot get / set option "+n);return o;case"destroy":r.$el.off(".jqPagination").find("*").off(".jqPagination");break;default:console.error('jqPagination: method "'+t+'" does not exist');return!1}};r.init()};e.jqPagination.defaultOptions={current_page:1,link_string:"",max_page:null,page_string:"Page {current_page} of {max_page}",paged:function(){}};e.fn.jqPagination=function(){var t=this,n=e(t),r=Array.prototype.slice.call(arguments),i=!1;if(typeof r[0]=="string"){r[2]===undefined?i=n.first().data("jqPagination").callMethod(r[0],r[1]):n.each(function(){i=e(this).data("jqPagination").callMethod(r[0],r[1],r[2])});return i}t.each(function(){new e.jqPagination(this,r[0])})}})(jQuery);if(!console){var console={},func=function(){return!1};console.log=func;console.info=func;console.warn=func;console.error=func};

155
server/website/website/static/js/result10.js Normal file
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var ResultTimeline = (function(window){
// Localize globals
var readCheckbox = window.readCheckbox, getLoadText = window.getLoadText, valueOrDefault = window.valueOrDefault;
function renderPlot(data, div_id) {
var plotdata = [], series = [];
plotdata.push(data.data[defaults.result]);
series.push({"label": "Result #" + defaults.result});
$("input[name^='same_run']:checked").each(function() {
var pk = $(this).val();
series.push({"label": "Result #" + pk});
plotdata.push(data.data[pk]);
});
var plotoptions = {
title: {text: data.metric + " " + data.lessisbetter, fontSize: '1.1em'},
series: series,
axes:{
yaxis:{
label: data.units,
labelRenderer: $.jqplot.CanvasAxisLabelRenderer,
autoscale: true,
min: 0,
tickOptions: {formatString:'%.1f'},
},
xaxis:{
label: 'Time (seconds)',
autoscale: true,
min: 0,
}
},
legend: {show: true},
highlighter: {
show: true,
tooltipLocation: 'nw',
yvalues: 2,
formatString:'<table class="jqplot-highlighter"><tr><td>time:</td><td>%s</td></tr> <tr><td>' + data.metric + ':</td><td>%s</td></tr></table>'
},
cursor: {show: true, zoom:true, showTooltip:false, clickReset:true}
};
$("#" + div_id).html('<div id="' + div_id + '_plot"></div><div id="plotdescription"></div>');
$.jqplot(div_id + '_plot', plotdata, plotoptions);
}
function render() {
$("#plotgrid").html("");
// render single plot when one benchmark is selected
$("input[name^='metric']:checked").each(function() {
var metric = $(this).val();
var plotid = "plot_" + metric;
$("#plotgrid").append('<div id="' + plotid + '" class="plotcontainer"></div>');
renderPlot(defaults.data[metric], plotid);
});
}
function getConfiguration() {
var config = {
id: defaults.result,
met: readCheckbox("input[name='metric']:checked"),
same: readCheckbox("input[name='same_run']:checked"),
};
return config;
}
function updateUrl() {
var cfg = getConfiguration();
$.address.autoUpdate(false);
for (var param in cfg) {
$.address.parameter(param, cfg[param]);
}
$.address.update();
new_id = $(this).attr("value")
$.ajaxSettings.async = false;
$.getJSON('/ajax_new/', {new_id: new_id} , function(ret){
$.each(defaults.all_metrics, function(){
m = this;
defaults.data[this]['data'][new_id] = []
$.each(ret[m],function(i,value){
defaults.data[m]['data'][new_id].push(value)})
})
}
)
refreshContent();
}
function updateUrl_metric() {
var cfg = getConfiguration();
$.address.autoUpdate(false);
for (var param in cfg) {
$.address.parameter(param, cfg[param]);
}
$.address.update();
refreshContent();
}
function refreshContent() {
render();
}
function initializeSite(event) {
setValuesOfInputFields(event);
$("input[name='metric']" ).on('click', updateUrl_metric);
$("input[name='same_run']").on('click', updateUrl);
refreshContent();
}
function refreshSite(event) {
setValuesOfInputFields(event);
refreshContent();
}
function setValuesOfInputFields(event) {
// Either set the default value, or the one parsed from the url
// Set default selected metrics
$("input:checkbox[name='metric']").prop('checked', false);
var metrics = event.parameters.met ? event.parameters.met.split(',') : defaults.metrics;
$("input:checkbox[name='metric']").each(function() {
if ($.inArray($(this).val(), metrics) >= 0) {
$(this).prop('checked', true);
}
});
$("input:checkbox[name='same_run']").prop('checked', false);
var others = event.parameters.same ? event.parameters.same.split(',') : ["none"];
$("input:checkbox[name='same_run']").each(function() {
if ($.inArray($(this).val(), others) >= 0) {
$(this).prop('checked', true);
}
});
}
function init(def) {
defaults = def;
$.address.init(initializeSite)
}
return {
init: init
};
})(window);

379
server/website/website/static/js/timeline.js Normal file
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var Timeline = (function(window){
// Localize globals
var readCheckbox = window.readCheckbox, getLoadText = window.getLoadText, valueOrDefault = window.valueOrDefault;
var baselineColor = "#d8b83f",
seriesColors = ["#4bb2c5", "#EAA228", "#579575", "#953579", "#839557", "#ff5800", "#958c12", "#4b5de4", "#0085cc"],
defaults;
function shouldPlotEquidistant() {
return $("#equidistant").is(':checked');
}
function OnMarkerClickHandler(ev, gridpos, datapos, neighbor, plot) {
if($("input[name='workload']:checked").val() === "grid") { return false; }
if (neighbor) {
result_id = neighbor.data[3];
window.location = "/projects/" + defaults.project + "/sessions/" + defaults.session + "/results/" + result_id;
}
}
function renderPlot(data, div_id) {
var plotdata = [], series = [];
for (dbms in data.data) {
series.push({"label": dbms});
plotdata.push(data.data[dbms]);
}
$("#" + div_id).html('<div id="' + div_id + '_plot"></div><div id="plotdescription"></div>');
var plotoptions = {
title: {text: data.print_metric + " " + data.lessisbetter, fontSize: '1.1em'},
series: series,
axes:{
yaxis:{
label: data.units,
labelRenderer: $.jqplot.CanvasAxisLabelRenderer,
min: 0,
autoscale: true,
},
xaxis:{
renderer: (shouldPlotEquidistant()) ? $.jqplot.CategoryAxisRenderer : $.jqplot.DateAxisRenderer,
label: 'Date',
labelRenderer: $.jqplot.CanvasAxisLabelRenderer,
tickRenderer: $.jqplot.CanvasAxisTickRenderer,
tickOptions:{formatString:'%#m/%#d %H:%M', angle:-40},
autoscale: true,
rendererOptions:{sortMergedLabels:true}
}
},
legend: {show: true, location: 'nw'},
highlighter: {
show: true,
tooltipLocation: 'nw',
yvalues: 2,
formatString:'<table class="jqplot-highlighter"><tr><td>date:</td><td>%s</td></tr> <tr><td>result:</td><td>%s</td></tr></table>'
},
cursor: {show: true, zoom:true, showTooltip:false, clickReset:true}
};
//Render plot
$.jqplot(div_id + '_plot', plotdata, plotoptions);
}
function renderKnobPlot(data, div_id) {
plotdata=[data.data];
$("#" + div_id).html('<div id="' + div_id + '_plot"></div><div id="plotdescription"></div>');
var plotoptions = {
title: {text: data.knob, fontSize: '1.1em'},
//series: [{"label":"Hello"}],
axes:{
yaxis:{
label: data.units,
labelRenderer: $.jqplot.CanvasAxisLabelRenderer,
min: 0,
autoscale: true,
},
xaxis:{
renderer: (shouldPlotEquidistant()) ? $.jqplot.CategoryAxisRenderer : $.jqplot.DateAxisRenderer,
label: 'Date',
labelRenderer: $.jqplot.CanvasAxisLabelRenderer,
tickRenderer: $.jqplot.CanvasAxisTickRenderer,
tickOptions:{formatString:'%#m/%#d %H:%M', angle:-40},
autoscale: true,
rendererOptions:{sortMergedLabels:true}
}
},
legend: {show: true, location: 'nw'},
highlighter: {
show: true,
tooltipLocation: 'nw',
yvalues: 2,
formatString:'<table class="jqplot-highlighter"><tr><td>date:</td><td>%s</td></tr> <tr><td>result:</td><td>%s</td></tr></table>'
},
cursor: {show: true, zoom:true, showTooltip:false, clickReset:true}
};
//Render plot
$.jqplot(div_id + '_plot', plotdata, plotoptions);
}
function renderMiniplot(plotid, data) {
var plotdata = [], series = [];
for (dbms in data.data) {
series.push("");
plotdata.push(data.data[dbms]);
}
var plotoptions = {
title: {text: data.workload + ": " + data.metric, fontSize: '1.1em'},
seriesDefaults: {lineWidth: 2, markerOptions:{style:'circle', size: 6}},
series: series,
axes: {
yaxis: {
min: 0, autoscale:true, showTicks: false
},
xaxis: {
renderer:$.jqplot.DateAxisRenderer,
pad: 1.01,
autoscale:true,
showTicks: false
}
},
highlighter: {show:false},
cursor:{showTooltip: false, style: 'pointer'}
};
$.jqplot(plotid, plotdata, plotoptions);
}
var fixed_header = null;
function gen_url(result_id) {
return "{% url 'result' project_id session_id result_id %}".replace("project_id", defaults.project_id).replace("session_id", defaults.session_id).replace("result_id", result_id)
}
function render(data) {
disable_options(false);
$("#plotgrid").html("");
if(data.error !== "None") {
var h = $("#content").height();//get height for error message
$("#plotgrid").html(getLoadText(data.error, h, false));
return 1;
} else if ($("input[name='workload']:checked").val() === "show_none") {
var h = $("#content").height();//get height for error message
$("#plotgrid").html(getLoadText("Please select a workload on the left", h, false));
} else if (data.timelines.length === 0) {
var h = $("#content").height();//get height for error message
$("#plotgrid").html(getLoadText("No data available", h, false));
} else if ($("input[name='workload']:checked").val() === "grid"){
//Render Grid of plots
disable_options(true);
for (var wkld in data.timelines) {
var plotid = "plot_" + data.timelines[wkld].workload;
$("#plotgrid").append('<div id="' + plotid + '" class="miniplot"></div>');
$("#" + plotid).click(function() {
var wkld = $(this).attr("id").slice(5);
$("#workload_" + wkld).trigger("click");//.prop("checked", true);
updateUrl();
});
renderMiniplot(plotid, data.timelines[wkld]);
}
} else {
// render single plot when one workload is selected
var i = 0;
for (var metric in data.timelines) {
var plotid = "plot_" + i;
$("#plotgrid").append('<div id="' + plotid + '" class="plotcontainer"></div>');
renderPlot(data.timelines[metric], plotid);
i = i + 1;
}
for (var knob in data.knobtimelines) {
var plotid = "plot_" + i;
$("#plotgrid").append('<div id="' + plotid + '" class="plotcontainer"></div>');
renderKnobPlot(data.knobtimelines[knob], plotid);
i = i + 1;
}
}
var dt = $("#dataTable").dataTable( {
"aaData": data.results,
"aoColumns": [
{ "sTitle": data.columnnames[0], "sClass": "center", "sType": "num-html", "mRender": function (data, type, full) {
return '<a href="/projects/' + defaults.project + '/sessions/' + defaults.session + '/results/' + data + '">' + data + '</a>';
}},
{ "sTitle": data.columnnames[1], "sClass": "center"},
{ "sTitle": data.columnnames[2], "sClass": "center", "mRender": function (data, type, full) {
return '<a href="/projects/' + defaults.project + '/sessions/' + defaults.session + '/knobs/' + full[7] + '">' + data + '</a>';
}},
{ "sTitle": data.columnnames[3], "sClass": "center", "mRender": function (data, type, full) {
return '<a href="/projects/' + defaults.project + '/sessions/' + defaults.session + '/metrics/' + full[8] + '">' + data + '</a>';
}},
{ "sTitle": data.columnnames[4], "sClass": "center", "mRender": function (data, type, full) {
return '<a href="/projects/' + defaults.project + '/sessions/' + defaults.session + '/workloads/' + full[9] + '">' + data + '</a>';
}},
{ "sTitle": data.columnnames[5], "sClass": "center", "mRender": function (data, type, full) {
return data.toFixed(2);
}},
],
"bFilter": false,
"bAutoWidth": true,
"sPaginationType": "full_numbers",
"bDestroy": true
});
if (fixed_header != null) {
fixed_header.fnUpdate();
} else {
fixed_header = new FixedHeader(dt);
}
}
function refreshContent() {
var h = $("#content").height();//get height for loading text
$("#plotgrid").fadeOut("fast", function() {
$("#plotgrid").html(getLoadText("Loading...", h, true)).show();
$.getJSON("/get_data/", getConfiguration(), render);
});
}
function updateUrl() {
var cfg = getConfiguration();
$.address.autoUpdate(false);
for (var param in cfg) {
$.address.parameter(param, cfg[param]);
}
$.address.update();
}
function disable_options(value) {
$("#results_per_page").attr("disabled", value);
$('#results_per_page').selectpicker('refresh');
$("#equidistant").attr("disabled", value);
$("input:checkbox[name='metric']").each(function() {
$(this).attr('disabled', value);
});
$.each(defaults.additional, function(i, add) {
$("select[name^='additional_" + add + "']").attr('disabled', value);
$("select[name^='additional_" + add + "']").selectpicker('refresh');
});
}
function getConfiguration() {
var config = {
session:defaults.session,
dbms: readCheckbox("input[name='dbms']:checked"),
wkld: $("input[name='workload']:checked").val(),
spe: readCheckbox("input[name^='specific']:checked"),
met: readCheckbox("input[name='metric']:checked"),
knb: readCheckbox("input[name='knob']:checked"),
nres: $("#results_per_page option:selected").val(),
eq: $("#equidistant").is(':checked') ? "on" : "off"
};
config["add"] = [];
$.each(defaults.additional, function(i, add) {
config["add"].push(add + ":" + $("select[name^='additional_" + add + "']").val());
});
return config;
}
function updateSub(event) {
$("[id^=div_specific]").hide();
$("input[name^='specific']").removeAttr('checked');
var workload = $("input[name='workload']:checked").val();
if (workload != "grid" && workload != "show_none") {
$("div[id='div_specific_" + workload + "']").show();
$("input[id^='specific_" + workload + "_']").prop('checked', true);
}
}
function initializeSite(event) {
setValuesOfInputFields(event);
var mt = $("#metrictable").dataTable({
"aaSorting": [],
"bFilter": false,
"bAutoWidth": true,
"bDestroy": true,
"iDisplayLength": 35,
"aLengthMenu": [[35, 50, 100, -1], [35, 50, 100, "All"]]
});
$("#results_per_page" ).bind('change', updateUrl);
$("input[name='dbms']" ).bind('click', updateUrl);
$("input[name='workload']" ).on('change', updateSub);
$("input[name='workload']" ).on('click', updateUrl);
$("input[name^='specific']" ).on('change', updateUrl);
$("select[name^='additional']").bind('change', updateUrl);
$("input[name='metric']" ).on('click', updateUrl);
$("input[name='knob']" ).on('click', updateUrl);
$("#equidistant" ).bind('change', updateUrl);
}
function refreshSite(event) {
setValuesOfInputFields(event);
refreshContent();
}
function setValuesOfInputFields(event) {
// Either set the default value, or the one parsed from the url
// Set default selected recent results
$("#results_per_page").val(valueOrDefault(event.parameters.nres, defaults.results_per_page));
$('#results_per_page').selectpicker('refresh');
// Set default selected metrics
$("input:checkbox[name='metric']").prop('checked', false);
var metrics = event.parameters.met ? event.parameters.met.split(',') : defaults.metrics;
$("input:checkbox[name='metric']").each(function() {
if ($.inArray($(this).val(), metrics) >= 0) {
$(this).prop('checked', true);
}
});
// Set default selected dbms
$("input:checkbox[name='dbms']").removeAttr('checked');
var dbmss = defaults.dbms.split(',');
var sel = $("input[name='dbms']");
$.each(dbmss, function(i, db) {
sel.filter("[value='" + db + "']").prop('checked', true);
});
// Set default selected workload
var workload = valueOrDefault(event.parameters.wkld, defaults.workload);
$("input:radio[name='workload']").filter("[value='" + workload + "']").attr('checked', true);
$("[id^=div_specific]").hide();
$("input[name^='specific']").removeAttr('checked');
if ($("input[name='workload']:checked").val() != "grid" && $("input[name='workload']:checked").val() != "show_none") {
$("[id=div_specific_" + $("input[name='workload']:checked").val() + "]").show();
sel = $("[id^=specific_" + $("input[name='workload']:checked").val() + "_]");
var specs = event.parameters.spe && event.parameters.spe != "none" ? event.parameters.spe.split(','): defaults.workloads.split(',');
$.each(specs, function(i, spec) {
sel.filter("[value='" + spec + "']").prop('checked', true);
});
}
// Set default selected additional filter
if (event.parameters.add) {
var filters = event.parameters.add.split(',');
$.each(filters, function(i, filter) {
var kv = filter.split(':');
var name = kv[0];
var value = kv[1];
$("select[name^='additional_" + name + "']").val(value);
$("select[name^='additional_" + name + "']").selectpicker('refresh');
});
} else {
$.each(defaults.additional, function(i, add) {
$("select[name^='additional_" + add + "']").val("select_all");
$("select[name^='additional_" + add + "']").selectpicker('refresh');
});
}
// Set equidistant status
$("#equidistant").prop('checked', valueOrDefault(event.parameters.eq, defaults.equidistant) === "on");
}
function init(def) {
defaults = def;
$.ajaxSetup ({
cache: false
});
// Event listener for clicks on plot markers
$.jqplot.eventListenerHooks.push(['jqplotClick', OnMarkerClickHandler]);
// Init and change handlers are set to the refreshContent handler
$.address.init(initializeSite).change(refreshSite);
}
return {
init: init
};
})(window);

11
server/website/website/tasks/__init__.py Normal file
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#
# OtterTune - __init__.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
from .async_tasks import (aggregate_target_results,
configuration_recommendation,
map_workload)
from .periodic_tasks import (run_background_tasks)

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server/website/website/tasks/async_tasks.py Normal file
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#
# OtterTune - async_tasks.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
import random
import queue
import numpy as np
from celery.task import task, Task
from celery.utils.log import get_task_logger
from djcelery.models import TaskMeta
from sklearn.preprocessing import StandardScaler
from analysis.gp import GPRNP
from analysis.gp_tf import GPRGD
from analysis.preprocessing import Bin, DummyEncoder
from analysis.constraints import ParamConstraintHelper
from website.models import (PipelineData, PipelineRun, Result, Workload, KnobCatalog,
MetricCatalog, SessionKnob)
from website.parser import Parser
from website.types import PipelineTaskType
from website.utils import DataUtil, JSONUtil
from website.settings import IMPORTANT_KNOB_NUMBER, NUM_SAMPLES, TOP_NUM_CONFIG # pylint: disable=no-name-in-module
from website.settings import (DEFAULT_LENGTH_SCALE, DEFAULT_MAGNITUDE,
MAX_TRAIN_SIZE, BATCH_SIZE, NUM_THREADS,
DEFAULT_RIDGE, DEFAULT_LEARNING_RATE,
DEFAULT_EPSILON, MAX_ITER, GPR_EPS,
DEFAULT_SIGMA_MULTIPLIER, DEFAULT_MU_MULTIPLIER)
from website.settings import INIT_FLIP_PROB, FLIP_PROB_DECAY
from website.types import VarType
LOG = get_task_logger(__name__)
class UpdateTask(Task): # pylint: disable=abstract-method
def __init__(self):
self.rate_limit = '50/m'
self.max_retries = 3
self.default_retry_delay = 60
class AggregateTargetResults(UpdateTask): # pylint: disable=abstract-method
def on_success(self, retval, task_id, args, kwargs):
super(AggregateTargetResults, self).on_success(retval, task_id, args, kwargs)
# Completely delete this result because it's huge and not
# interesting
task_meta = TaskMeta.objects.get(task_id=task_id)
task_meta.result = None
task_meta.save()
class MapWorkload(UpdateTask): # pylint: disable=abstract-method
def on_success(self, retval, task_id, args, kwargs):
super(MapWorkload, self).on_success(retval, task_id, args, kwargs)
# Replace result with formatted result
if not args[0]['bad']:
new_res = {
'scores': sorted(args[0]['scores'].items()),
'mapped_workload_id': args[0]['mapped_workload'],
}
task_meta = TaskMeta.objects.get(task_id=task_id)
task_meta.result = new_res # Only store scores
task_meta.save()
else:
task_meta = TaskMeta.objects.get(task_id=task_id)
task_meta.result = None
task_meta.save()
class ConfigurationRecommendation(UpdateTask): # pylint: disable=abstract-method
def on_success(self, retval, task_id, args, kwargs):
super(ConfigurationRecommendation, self).on_success(retval, task_id, args, kwargs)
result_id = args[0]['newest_result_id']
result = Result.objects.get(pk=result_id)
# Replace result with formatted result
formatted_params = Parser.format_dbms_knobs(result.dbms.pk, retval['recommendation'])
task_meta = TaskMeta.objects.get(task_id=task_id)
retval['recommendation'] = formatted_params
task_meta.result = retval
task_meta.save()
# Create next configuration to try
config = Parser.create_knob_configuration(result.dbms.pk, retval['recommendation'])
retval['recommendation'] = config
result.next_configuration = JSONUtil.dumps(retval)
result.save()
def clean_knob_data(knob_matrix, knob_labels, session):
# Makes sure that all knobs in the dbms are included in the knob_matrix and knob_labels
knob_cat = SessionKnob.objects.get_knobs_for_session(session)
knob_cat = [knob["name"] for knob in knob_cat if knob["tunable"]]
matrix = np.array(knob_matrix)
missing_columns = set(knob_cat) - set(knob_labels)
unused_columns = set(knob_labels) - set(knob_cat)
LOG.debug("clean_knob_data added %d knobs and removed %d knobs.", len(missing_columns),
len(unused_columns))
# If columns are missing from the matrix
if missing_columns:
for knob in missing_columns:
knob_object = KnobCatalog.objects.get(dbms=session.dbms, name=knob, tunable=True)
index = knob_cat.index(knob)
matrix = np.insert(matrix, index, knob_object.default, axis=1)
knob_labels.insert(index, knob)
# If they are useless columns in the matrix
if unused_columns:
indexes = [i for i, n in enumerate(knob_labels) if n in unused_columns]
# Delete unused columns
matrix = np.delete(matrix, indexes, 1)
for i in sorted(indexes, reverse=True):
del knob_labels[i]
return matrix, knob_labels
@task(base=AggregateTargetResults, name='aggregate_target_results')
def aggregate_target_results(result_id):
# Check that we've completed the background tasks at least once. We need
# this data in order to make a configuration recommendation (until we
# implement a sampling technique to generate new training data).
newest_result = Result.objects.get(pk=result_id)
has_pipeline_data = PipelineData.objects.filter(workload=newest_result.workload).exists()
if not has_pipeline_data:
LOG.debug("Background tasks haven't ran for this workload yet, picking random data.")
if not has_pipeline_data or newest_result.session.tuning_session == 'randomly_generate':
result = Result.objects.filter(pk=result_id)
knobs = SessionKnob.objects.get_knobs_for_session(newest_result.session)
# generate a config randomly
random_knob_result = gen_random_data(knobs)
agg_data = DataUtil.aggregate_data(result)
agg_data['newest_result_id'] = result_id
agg_data['bad'] = True
agg_data['config_recommend'] = random_knob_result
return agg_data
# Aggregate all knob config results tried by the target so far in this
# tuning session and this tuning workload.
target_results = Result.objects.filter(session=newest_result.session,
dbms=newest_result.dbms,
workload=newest_result.workload)
if len(target_results) == 0:
raise Exception('Cannot find any results for session_id={}, dbms_id={}'
.format(newest_result.session, newest_result.dbms))
agg_data = DataUtil.aggregate_data(target_results)
agg_data['newest_result_id'] = result_id
agg_data['bad'] = False
# Clean knob data
cleaned_agg_data = clean_knob_data(agg_data['X_matrix'], agg_data['X_columnlabels'],
newest_result.session)
agg_data['X_matrix'] = np.array(cleaned_agg_data[0])
agg_data['X_columnlabels'] = np.array(cleaned_agg_data[1])
return agg_data
def gen_random_data(knobs):
random_knob_result = {}
for knob in knobs:
name = knob["name"]
if knob["vartype"] == VarType.BOOL:
flag = random.randint(0, 1)
if flag == 0:
random_knob_result[name] = False
else:
random_knob_result[name] = True
elif knob["vartype"] == VarType.ENUM:
enumvals = knob["enumvals"].split(',')
enumvals_len = len(enumvals)
rand_idx = random.randint(0, enumvals_len - 1)
random_knob_result[name] = rand_idx
elif knob["vartype"] == VarType.INTEGER:
random_knob_result[name] = random.randint(int(knob["minval"]), int(knob["maxval"]))
elif knob["vartype"] == VarType.REAL:
random_knob_result[name] = random.uniform(
float(knob["minval"]), float(knob["maxval"]))
elif knob["vartype"] == VarType.STRING:
random_knob_result[name] = "None"
elif knob["vartype"] == VarType.TIMESTAMP:
random_knob_result[name] = "None"
else:
raise Exception(
'Unknown variable type: {}'.format(knob["vartype"]))
return random_knob_result
@task(base=ConfigurationRecommendation, name='configuration_recommendation')
def configuration_recommendation(target_data):
LOG.info('configuration_recommendation called')
latest_pipeline_run = PipelineRun.objects.get_latest()
if target_data['bad'] is True:
target_data_res = {}
target_data_res['status'] = 'bad'
target_data_res['info'] = 'WARNING: no training data, the config is generated randomly'
target_data_res['recommendation'] = target_data['config_recommend']
return target_data_res
# Load mapped workload data
mapped_workload_id = target_data['mapped_workload'][0]
mapped_workload = Workload.objects.get(pk=mapped_workload_id)
workload_knob_data = PipelineData.objects.get(
pipeline_run=latest_pipeline_run,
workload=mapped_workload,
task_type=PipelineTaskType.KNOB_DATA)
workload_knob_data = JSONUtil.loads(workload_knob_data.data)
workload_metric_data = PipelineData.objects.get(
pipeline_run=latest_pipeline_run,
workload=mapped_workload,
task_type=PipelineTaskType.METRIC_DATA)
workload_metric_data = JSONUtil.loads(workload_metric_data.data)
newest_result = Result.objects.get(pk=target_data['newest_result_id'])
cleaned_workload_knob_data = clean_knob_data(workload_knob_data["data"],
workload_knob_data["columnlabels"],
newest_result.session)
X_workload = np.array(cleaned_workload_knob_data[0])
X_columnlabels = np.array(cleaned_workload_knob_data[1])
y_workload = np.array(workload_metric_data['data'])
y_columnlabels = np.array(workload_metric_data['columnlabels'])
rowlabels_workload = np.array(workload_metric_data['rowlabels'])
# Target workload data
newest_result = Result.objects.get(pk=target_data['newest_result_id'])
X_target = target_data['X_matrix']
y_target = target_data['y_matrix']
rowlabels_target = np.array(target_data['rowlabels'])
if not np.array_equal(X_columnlabels, target_data['X_columnlabels']):
raise Exception(('The workload and target data should have '
'identical X columnlabels (sorted knob names)'))
if not np.array_equal(y_columnlabels, target_data['y_columnlabels']):
raise Exception(('The workload and target data should have '
'identical y columnlabels (sorted metric names)'))
# Filter Xs by top 10 ranked knobs
ranked_knobs = PipelineData.objects.get(
pipeline_run=latest_pipeline_run,
workload=mapped_workload,
task_type=PipelineTaskType.RANKED_KNOBS)
ranked_knobs = JSONUtil.loads(ranked_knobs.data)[:IMPORTANT_KNOB_NUMBER]
ranked_knob_idxs = [i for i, cl in enumerate(X_columnlabels) if cl in ranked_knobs]
X_workload = X_workload[:, ranked_knob_idxs]
X_target = X_target[:, ranked_knob_idxs]
X_columnlabels = X_columnlabels[ranked_knob_idxs]
# Filter ys by current target objective metric
target_objective = newest_result.session.target_objective
target_obj_idx = [i for i, cl in enumerate(y_columnlabels) if cl == target_objective]
if len(target_obj_idx) == 0:
raise Exception(('Could not find target objective in metrics '
'(target_obj={})').format(target_objective))
elif len(target_obj_idx) > 1:
raise Exception(('Found {} instances of target objective in '
'metrics (target_obj={})').format(len(target_obj_idx),
target_objective))
metric_meta = MetricCatalog.objects.get_metric_meta(newest_result.session.dbms,
newest_result.session.target_objective)
if metric_meta[target_objective].improvement == '(less is better)':
lessisbetter = True
else:
lessisbetter = False
y_workload = y_workload[:, target_obj_idx]
y_target = y_target[:, target_obj_idx]
y_columnlabels = y_columnlabels[target_obj_idx]
# Combine duplicate rows in the target/workload data (separately)
X_workload, y_workload, rowlabels_workload = DataUtil.combine_duplicate_rows(
X_workload, y_workload, rowlabels_workload)
X_target, y_target, rowlabels_target = DataUtil.combine_duplicate_rows(
X_target, y_target, rowlabels_target)
# Delete any rows that appear in both the workload data and the target
# data from the workload data
dups_filter = np.ones(X_workload.shape[0], dtype=bool)
target_row_tups = [tuple(row) for row in X_target]
for i, row in enumerate(X_workload):
if tuple(row) in target_row_tups:
dups_filter[i] = False
X_workload = X_workload[dups_filter, :]
y_workload = y_workload[dups_filter, :]
rowlabels_workload = rowlabels_workload[dups_filter]
# Combine target & workload Xs for preprocessing
X_matrix = np.vstack([X_target, X_workload])
# Dummy encode categorial variables
categorical_info = DataUtil.dummy_encoder_helper(X_columnlabels,
mapped_workload.dbms)
dummy_encoder = DummyEncoder(categorical_info['n_values'],
categorical_info['categorical_features'],
categorical_info['cat_columnlabels'],
categorical_info['noncat_columnlabels'])
X_matrix = dummy_encoder.fit_transform(X_matrix)
# below two variables are needed for correctly determing max/min on dummies
binary_index_set = set(categorical_info['binary_vars'])
total_dummies = dummy_encoder.total_dummies()
# Scale to N(0, 1)
X_scaler = StandardScaler()
X_scaled = X_scaler.fit_transform(X_matrix)
if y_target.shape[0] < 5: # FIXME
# FIXME (dva): if there are fewer than 5 target results so far
# then scale the y values (metrics) using the workload's
# y_scaler. I'm not sure if 5 is the right cutoff.
y_target_scaler = None
y_workload_scaler = StandardScaler()
y_matrix = np.vstack([y_target, y_workload])
y_scaled = y_workload_scaler.fit_transform(y_matrix)
else:
# FIXME (dva): otherwise try to compute a separate y_scaler for
# the target and scale them separately.
try:
y_target_scaler = StandardScaler()
y_workload_scaler = StandardScaler()
y_target_scaled = y_target_scaler.fit_transform(y_target)
y_workload_scaled = y_workload_scaler.fit_transform(y_workload)
y_scaled = np.vstack([y_target_scaled, y_workload_scaled])
except ValueError:
y_target_scaler = None
y_workload_scaler = StandardScaler()
y_scaled = y_workload_scaler.fit_transform(y_target)
# Set up constraint helper
constraint_helper = ParamConstraintHelper(scaler=X_scaler,
encoder=dummy_encoder,
binary_vars=categorical_info['binary_vars'],
init_flip_prob=INIT_FLIP_PROB,
flip_prob_decay=FLIP_PROB_DECAY)
# FIXME (dva): check if these are good values for the ridge
# ridge = np.empty(X_scaled.shape[0])
# ridge[:X_target.shape[0]] = 0.01
# ridge[X_target.shape[0]:] = 0.1
# FIXME: we should generate more samples and use a smarter sampling
# technique
num_samples = NUM_SAMPLES
X_samples = np.empty((num_samples, X_scaled.shape[1]))
X_min = np.empty(X_scaled.shape[1])
X_max = np.empty(X_scaled.shape[1])
X_scaler_matrix = np.zeros([1, X_scaled.shape[1]])
session_knobs = SessionKnob.objects.get_knobs_for_session(newest_result.session)
# Set min/max for knob values
for i in range(X_scaled.shape[1]):
if i < total_dummies or i in binary_index_set:
col_min = 0
col_max = 1
else:
col_min = X_scaled[:, i].min()
col_max = X_scaled[:, i].max()
for knob in session_knobs:
if X_columnlabels[i] == knob["name"]:
X_scaler_matrix[0][i] = knob["minval"]
col_min = X_scaler.transform(X_scaler_matrix)[0][i]
X_scaler_matrix[0][i] = knob["maxval"]
col_max = X_scaler.transform(X_scaler_matrix)[0][i]
X_min[i] = col_min
X_max[i] = col_max
X_samples[:, i] = np.random.rand(num_samples) * (col_max - col_min) + col_min
# Maximize the throughput, moreisbetter
# Use gradient descent to minimize -throughput
if not lessisbetter:
y_scaled = -y_scaled
q = queue.PriorityQueue()
for x in range(0, y_scaled.shape[0]):
q.put((y_scaled[x][0], x))
i = 0
while i < TOP_NUM_CONFIG:
try:
item = q.get_nowait()
# Tensorflow get broken if we use the training data points as
# starting points for GPRGD. We add a small bias for the
# starting points. GPR_EPS default value is 0.001
# if the starting point is X_max, we minus a small bias to
# make sure it is within the range.
dist = sum(np.square(X_max - X_scaled[item[1]]))
if dist < 0.001:
X_samples = np.vstack((X_samples, X_scaled[item[1]] - abs(GPR_EPS)))
else:
X_samples = np.vstack((X_samples, X_scaled[item[1]] + abs(GPR_EPS)))
i = i + 1
except queue.Empty:
break
model = GPRGD(length_scale=DEFAULT_LENGTH_SCALE,
magnitude=DEFAULT_MAGNITUDE,
max_train_size=MAX_TRAIN_SIZE,
batch_size=BATCH_SIZE,
num_threads=NUM_THREADS,
learning_rate=DEFAULT_LEARNING_RATE,
epsilon=DEFAULT_EPSILON,
max_iter=MAX_ITER,
sigma_multiplier=DEFAULT_SIGMA_MULTIPLIER,
mu_multiplier=DEFAULT_MU_MULTIPLIER)
model.fit(X_scaled, y_scaled, X_min, X_max, ridge=DEFAULT_RIDGE)
res = model.predict(X_samples, constraint_helper=constraint_helper)
best_config_idx = np.argmin(res.minl.ravel())
best_config = res.minl_conf[best_config_idx, :]
best_config = X_scaler.inverse_transform(best_config)
# Decode one-hot encoding into categorical knobs
best_config = dummy_encoder.inverse_transform(best_config)
# Although we have max/min limits in the GPRGD training session, it may
# lose some precisions. e.g. 0.99..99 >= 1.0 may be True on the scaled data,
# when we inversely transform the scaled data, the different becomes much larger
# and cannot be ignored. Here we check the range on the original data
# directly, and make sure the recommended config lies within the range
X_min_inv = X_scaler.inverse_transform(X_min)
X_max_inv = X_scaler.inverse_transform(X_max)
best_config = np.minimum(best_config, X_max_inv)
best_config = np.maximum(best_config, X_min_inv)
conf_map = {k: best_config[i] for i, k in enumerate(X_columnlabels)}
conf_map_res = {}
conf_map_res['status'] = 'good'
conf_map_res['recommendation'] = conf_map
conf_map_res['info'] = 'INFO: training data size is {}'.format(X_scaled.shape[0])
return conf_map_res
def load_data_helper(filtered_pipeline_data, workload, task_type):
pipeline_data = filtered_pipeline_data.get(workload=workload,
task_type=task_type)
LOG.debug("PIPELINE DATA: %s", str(pipeline_data.data))
return JSONUtil.loads(pipeline_data.data)
@task(base=MapWorkload, name='map_workload')
def map_workload(target_data):
# Get the latest version of pipeline data that's been computed so far.
latest_pipeline_run = PipelineRun.objects.get_latest()
if target_data['bad']:
assert target_data is not None
return target_data
assert latest_pipeline_run is not None
newest_result = Result.objects.get(pk=target_data['newest_result_id'])
target_workload = newest_result.workload
X_columnlabels = np.array(target_data['X_columnlabels'])
y_columnlabels = np.array(target_data['y_columnlabels'])
# Find all pipeline data belonging to the latest version with the same
# DBMS and hardware as the target
pipeline_data = PipelineData.objects.filter(
pipeline_run=latest_pipeline_run,
workload__dbms=target_workload.dbms,
workload__hardware=target_workload.hardware)
# FIXME (dva): we should also compute the global (i.e., overall) ranked_knobs
# and pruned metrics but we just use those from the first workload for now
initialized = False
global_ranked_knobs = None
global_pruned_metrics = None
ranked_knob_idxs = None
pruned_metric_idxs = None
# Compute workload mapping data for each unique workload
unique_workloads = pipeline_data.values_list('workload', flat=True).distinct()
assert len(unique_workloads) > 0
workload_data = {}
for unique_workload in unique_workloads:
workload_obj = Workload.objects.get(pk=unique_workload)
wkld_results = Result.objects.filter(workload=workload_obj)
if wkld_results.exists() is False:
# delete the workload
workload_obj.delete()
continue
# Load knob & metric data for this workload
knob_data = load_data_helper(pipeline_data, unique_workload, PipelineTaskType.KNOB_DATA)
knob_data["data"], knob_data["columnlabels"] = clean_knob_data(knob_data["data"],
knob_data["columnlabels"],
newest_result.session)
metric_data = load_data_helper(pipeline_data, unique_workload, PipelineTaskType.METRIC_DATA)
X_matrix = np.array(knob_data["data"])
y_matrix = np.array(metric_data["data"])
rowlabels = np.array(knob_data["rowlabels"])
assert np.array_equal(rowlabels, metric_data["rowlabels"])
if not initialized:
# For now set ranked knobs & pruned metrics to be those computed
# for the first workload
global_ranked_knobs = load_data_helper(
pipeline_data, unique_workload,
PipelineTaskType.RANKED_KNOBS)[:IMPORTANT_KNOB_NUMBER]
global_pruned_metrics = load_data_helper(
pipeline_data, unique_workload, PipelineTaskType.PRUNED_METRICS)
ranked_knob_idxs = [i for i in range(X_matrix.shape[1]) if X_columnlabels[
i] in global_ranked_knobs]
pruned_metric_idxs = [i for i in range(y_matrix.shape[1]) if y_columnlabels[
i] in global_pruned_metrics]
# Filter X & y columnlabels by top ranked_knobs & pruned_metrics
X_columnlabels = X_columnlabels[ranked_knob_idxs]
y_columnlabels = y_columnlabels[pruned_metric_idxs]
initialized = True
# Filter X & y matrices by top ranked_knobs & pruned_metrics
X_matrix = X_matrix[:, ranked_knob_idxs]
y_matrix = y_matrix[:, pruned_metric_idxs]
# Combine duplicate rows (rows with same knob settings)
X_matrix, y_matrix, rowlabels = DataUtil.combine_duplicate_rows(
X_matrix, y_matrix, rowlabels)
workload_data[unique_workload] = {
'X_matrix': X_matrix,
'y_matrix': y_matrix,
'rowlabels': rowlabels,
}
assert len(workload_data) > 0
# Stack all X & y matrices for preprocessing
Xs = np.vstack([entry['X_matrix'] for entry in list(workload_data.values())])
ys = np.vstack([entry['y_matrix'] for entry in list(workload_data.values())])
# Scale the X & y values, then compute the deciles for each column in y
X_scaler = StandardScaler(copy=False)
X_scaler.fit(Xs)
y_scaler = StandardScaler(copy=False)
y_scaler.fit_transform(ys)
y_binner = Bin(bin_start=1, axis=0)
y_binner.fit(ys)
del Xs
del ys
# Filter the target's X & y data by the ranked knobs & pruned metrics.
X_target = target_data['X_matrix'][:, ranked_knob_idxs]
y_target = target_data['y_matrix'][:, pruned_metric_idxs]
# Now standardize the target's data and bin it by the deciles we just
# calculated
X_target = X_scaler.transform(X_target)
y_target = y_scaler.transform(y_target)
y_target = y_binner.transform(y_target)
scores = {}
for workload_id, workload_entry in list(workload_data.items()):
predictions = np.empty_like(y_target)
X_workload = workload_entry['X_matrix']
X_scaled = X_scaler.transform(X_workload)
y_workload = workload_entry['y_matrix']
y_scaled = y_scaler.transform(y_workload)
for j, y_col in enumerate(y_scaled.T):
# Using this workload's data, train a Gaussian process model
# and then predict the performance of each metric for each of
# the knob configurations attempted so far by the target.
y_col = y_col.reshape(-1, 1)
model = GPRNP(length_scale=DEFAULT_LENGTH_SCALE,
magnitude=DEFAULT_MAGNITUDE,
max_train_size=MAX_TRAIN_SIZE,
batch_size=BATCH_SIZE)
model.fit(X_scaled, y_col, ridge=DEFAULT_RIDGE)
predictions[:, j] = model.predict(X_target).ypreds.ravel()
# Bin each of the predicted metric columns by deciles and then
# compute the score (i.e., distance) between the target workload
# and each of the known workloads
predictions = y_binner.transform(predictions)
dists = np.sqrt(np.sum(np.square(
np.subtract(predictions, y_target)), axis=1))
scores[workload_id] = np.mean(dists)
# Find the best (minimum) score
best_score = np.inf
best_workload_id = None
# scores_info = {workload_id: (workload_name, score)}
scores_info = {}
for workload_id, similarity_score in list(scores.items()):
workload_name = Workload.objects.get(pk=workload_id).name
if similarity_score < best_score:
best_score = similarity_score
best_workload_id = workload_id
best_workload_name = workload_name
scores_info[workload_id] = (workload_name, similarity_score)
target_data['mapped_workload'] = (best_workload_id, best_workload_name, best_score)
target_data['scores'] = scores_info
return target_data
#

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#
# OtterTune - periodic_tasks.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
import copy
import numpy as np
from celery.task import periodic_task
from celery.utils.log import get_task_logger
from django.utils.timezone import now
from sklearn.preprocessing import StandardScaler
from analysis.cluster import KMeansClusters, create_kselection_model
from analysis.factor_analysis import FactorAnalysis
from analysis.lasso import LassoPath
from analysis.preprocessing import (Bin, get_shuffle_indices,
DummyEncoder,
consolidate_columnlabels)
from website.models import PipelineData, PipelineRun, Result, Workload
from website.types import PipelineTaskType, WorkloadStatusType
from website.utils import DataUtil, JSONUtil
# Log debug messages
LOG = get_task_logger(__name__)
# Only process workload containing this minimum amount of results
MIN_WORKLOAD_RESULTS_COUNT = 5
# Run the background tasks every 5 minutes
@periodic_task(run_every=300, name="run_background_tasks")
def run_background_tasks():
LOG.debug("Starting background tasks")
# Find modified and not modified workloads, we only have to calculate for the
# modified workloads.
modified_workloads = Workload.objects.filter(status=WorkloadStatusType.MODIFIED)
non_modified_workloads = Workload.objects.filter(status=WorkloadStatusType.PROCESSED)
non_modified_workloads = list(non_modified_workloads.values_list('pk', flat=True))
last_pipeline_run = PipelineRun.objects.get_latest()
if len(modified_workloads) == 0:
# No previous workload data yet. Try again later.
LOG.debug("No workload data yet. Ending background tasks")
return
# Create new entry in PipelineRun table to store the output of each of
# the background tasks
pipeline_run_obj = PipelineRun(start_time=now(), end_time=None)
pipeline_run_obj.save()
for workload in modified_workloads:
wkld_results = Result.objects.filter(workload=workload)
if wkld_results.exists() is False:
# delete the workload
LOG.debug("Deleting workload %d because it has no results.", workload.id)
workload.delete()
continue
# Check that there are enough results in the workload
if wkld_results.count() < MIN_WORKLOAD_RESULTS_COUNT:
LOG.debug("Not enough results in workload %d (only %d results).", workload.id,
wkld_results.count())
continue
workload.status = WorkloadStatusType.PROCESSING
workload.save()
LOG.debug("Aggregating data for workload %d", workload.id)
# Aggregate the knob & metric data for this workload
knob_data, metric_data = aggregate_data(wkld_results)
LOG.debug("knob_data: %s", str(knob_data))
LOG.debug("metric_data: %s", str(metric_data))
# Knob_data and metric_data are 2D numpy arrays. Convert them into a
# JSON-friendly (nested) lists and then save them as new PipelineData
# objects.
knob_data_copy = copy.deepcopy(knob_data)
knob_data_copy['data'] = knob_data_copy['data'].tolist()
knob_data_copy = JSONUtil.dumps(knob_data_copy)
knob_entry = PipelineData(pipeline_run=pipeline_run_obj,
task_type=PipelineTaskType.KNOB_DATA,
workload=workload,
data=knob_data_copy,
creation_time=now())
knob_entry.save()
metric_data_copy = copy.deepcopy(metric_data)
metric_data_copy['data'] = metric_data_copy['data'].tolist()
metric_data_copy = JSONUtil.dumps(metric_data_copy)
metric_entry = PipelineData(pipeline_run=pipeline_run_obj,
task_type=PipelineTaskType.METRIC_DATA,
workload=workload,
data=metric_data_copy,
creation_time=now())
metric_entry.save()
# Execute the Workload Characterization task to compute the list of
# pruned metrics for this workload and save them in a new PipelineData
# object.
LOG.debug("Pruning metrics for workload %d.", workload.id)
pruned_metrics = run_workload_characterization(metric_data=metric_data)
LOG.debug("pruned_metrics: %s", str(pruned_metrics))
pruned_metrics_entry = PipelineData(pipeline_run=pipeline_run_obj,
task_type=PipelineTaskType.PRUNED_METRICS,
workload=workload,
data=JSONUtil.dumps(pruned_metrics),
creation_time=now())
pruned_metrics_entry.save()
# Use the pruned metrics to filter the metric_data
pruned_metric_idxs = [i for i, metric_name in enumerate(metric_data['columnlabels'])
if metric_name in pruned_metrics]
pruned_metric_data = {
'data': metric_data['data'][:, pruned_metric_idxs],
'rowlabels': copy.deepcopy(metric_data['rowlabels']),
'columnlabels': [metric_data['columnlabels'][i] for i in pruned_metric_idxs]
}
# Execute the Knob Identification task to compute an ordered list of knobs
# ranked by their impact on the DBMS's performance. Save them in a new
# PipelineData object.
LOG.debug("Ranking knobs for workload %d.", workload.id)
ranked_knobs = run_knob_identification(knob_data=knob_data,
metric_data=pruned_metric_data,
dbms=workload.dbms)
LOG.debug("ranked_knobs: %s", str(ranked_knobs))
ranked_knobs_entry = PipelineData(pipeline_run=pipeline_run_obj,
task_type=PipelineTaskType.RANKED_KNOBS,
workload=workload,
data=JSONUtil.dumps(ranked_knobs),
creation_time=now())
ranked_knobs_entry.save()
workload.status = WorkloadStatusType.PROCESSED
workload.save()
LOG.debug("Finished processing modified workloads")
non_modified_workloads = Workload.objects.filter(pk__in=non_modified_workloads)
# Update the latest pipeline data for the non modified workloads to have this pipeline run
PipelineData.objects.filter(workload__in=non_modified_workloads,
pipeline_run=last_pipeline_run)\
.update(pipeline_run=pipeline_run_obj)
# Set the end_timestamp to the current time to indicate that we are done running
# the background tasks
pipeline_run_obj.end_time = now()
pipeline_run_obj.save()
LOG.debug("Finished background tasks")
def aggregate_data(wkld_results):
# Aggregates both the knob & metric data for the given workload.
#
# Parameters:
# wkld_results: result data belonging to this specific workload
#
# Returns: two dictionaries containing the knob & metric data as
# a tuple
# Now call the aggregate_data helper function to combine all knob &
# metric data into matrices and also create row/column labels
# (see the DataUtil class in website/utils.py)
#
# The aggregate_data helper function returns a dictionary of the form:
# - 'X_matrix': the knob data as a 2D numpy matrix (results x knobs)
# - 'y_matrix': the metric data as a 2D numpy matrix (results x metrics)
# - 'rowlabels': list of result ids that correspond to the rows in
# both X_matrix & y_matrix
# - 'X_columnlabels': a list of the knob names corresponding to the
# columns in the knob_data matrix
# - 'y_columnlabels': a list of the metric names corresponding to the
# columns in the metric_data matrix
aggregated_data = DataUtil.aggregate_data(wkld_results)
# Separate knob & workload data into two "standard" dictionaries of the
# same form
knob_data = {
'data': aggregated_data['X_matrix'],
'rowlabels': aggregated_data['rowlabels'],
'columnlabels': aggregated_data['X_columnlabels']
}
metric_data = {
'data': aggregated_data['y_matrix'],
'rowlabels': copy.deepcopy(aggregated_data['rowlabels']),
'columnlabels': aggregated_data['y_columnlabels']
}
# Return the knob & metric data
return knob_data, metric_data
def run_workload_characterization(metric_data):
# Performs workload characterization on the metric_data and returns
# a set of pruned metrics.
#
# Parameters:
# metric_data is a dictionary of the form:
# - 'data': 2D numpy matrix of metric data (results x metrics)
# - 'rowlabels': a list of identifiers for the rows in the matrix
# - 'columnlabels': a list of the metric names corresponding to
# the columns in the data matrix
matrix = metric_data['data']
columnlabels = metric_data['columnlabels']
# Remove any constant columns
nonconst_matrix = []
nonconst_columnlabels = []
for col, cl in zip(matrix.T, columnlabels):
if np.any(col != col[0]):
nonconst_matrix.append(col.reshape(-1, 1))
nonconst_columnlabels.append(cl)
assert len(nonconst_matrix) > 0, "Need more data to train the model"
nonconst_matrix = np.hstack(nonconst_matrix)
n_rows, n_cols = nonconst_matrix.shape
# Bin each column (metric) in the matrix by its decile
binner = Bin(bin_start=1, axis=0)
binned_matrix = binner.fit_transform(nonconst_matrix)
# Shuffle the matrix rows
shuffle_indices = get_shuffle_indices(n_rows)
shuffled_matrix = binned_matrix[shuffle_indices, :]
# Fit factor analysis model
fa_model = FactorAnalysis()
# For now we use 5 latent variables
fa_model.fit(shuffled_matrix, nonconst_columnlabels, n_components=5)
# Components: metrics * factors
components = fa_model.components_.T.copy()
# Run Kmeans for # clusters k in range(1, num_nonduplicate_metrics - 1)
# K should be much smaller than n_cols in detK, For now max_cluster <= 20
kmeans_models = KMeansClusters()
kmeans_models.fit(components, min_cluster=1,
max_cluster=min(n_cols - 1, 20),
sample_labels=nonconst_columnlabels,
estimator_params={'n_init': 50})
# Compute optimal # clusters, k, using gap statistics
gapk = create_kselection_model("gap-statistic")
gapk.fit(components, kmeans_models.cluster_map_)
LOG.debug("Found optimal number of clusters: %d", gapk.optimal_num_clusters_)
# Get pruned metrics, cloest samples of each cluster center
pruned_metrics = kmeans_models.cluster_map_[gapk.optimal_num_clusters_].get_closest_samples()
# Return pruned metrics
return pruned_metrics
def run_knob_identification(knob_data, metric_data, dbms):
# Performs knob identification on the knob & metric data and returns
# a set of ranked knobs.
#
# Parameters:
# knob_data & metric_data are dictionaries of the form:
# - 'data': 2D numpy matrix of knob/metric data
# - 'rowlabels': a list of identifiers for the rows in the matrix
# - 'columnlabels': a list of the knob/metric names corresponding
# to the columns in the data matrix
# dbms is the foreign key pointing to target dbms in DBMSCatalog
#
# When running the lasso algorithm, the knob_data matrix is set of
# independent variables (X) and the metric_data is the set of
# dependent variables (y).
knob_matrix = knob_data['data']
knob_columnlabels = knob_data['columnlabels']
metric_matrix = metric_data['data']
metric_columnlabels = metric_data['columnlabels']
# remove constant columns from knob_matrix and metric_matrix
nonconst_knob_matrix = []
nonconst_knob_columnlabels = []
for col, cl in zip(knob_matrix.T, knob_columnlabels):
if np.any(col != col[0]):
nonconst_knob_matrix.append(col.reshape(-1, 1))
nonconst_knob_columnlabels.append(cl)
assert len(nonconst_knob_matrix) > 0, "Need more data to train the model"
nonconst_knob_matrix = np.hstack(nonconst_knob_matrix)
nonconst_metric_matrix = []
nonconst_metric_columnlabels = []
for col, cl in zip(metric_matrix.T, metric_columnlabels):
if np.any(col != col[0]):
nonconst_metric_matrix.append(col.reshape(-1, 1))
nonconst_metric_columnlabels.append(cl)
nonconst_metric_matrix = np.hstack(nonconst_metric_matrix)
# determine which knobs need encoding (enums with >2 possible values)
categorical_info = DataUtil.dummy_encoder_helper(nonconst_knob_columnlabels,
dbms)
# encode categorical variable first (at least, before standardize)
dummy_encoder = DummyEncoder(categorical_info['n_values'],
categorical_info['categorical_features'],
categorical_info['cat_columnlabels'],
categorical_info['noncat_columnlabels'])
encoded_knob_matrix = dummy_encoder.fit_transform(
nonconst_knob_matrix)
encoded_knob_columnlabels = dummy_encoder.new_labels
# standardize values in each column to N(0, 1)
standardizer = StandardScaler()
standardized_knob_matrix = standardizer.fit_transform(encoded_knob_matrix)
standardized_metric_matrix = standardizer.fit_transform(nonconst_metric_matrix)
# shuffle rows (note: same shuffle applied to both knob and metric matrices)
shuffle_indices = get_shuffle_indices(standardized_knob_matrix.shape[0], seed=17)
shuffled_knob_matrix = standardized_knob_matrix[shuffle_indices, :]
shuffled_metric_matrix = standardized_metric_matrix[shuffle_indices, :]
# run lasso algorithm
lasso_model = LassoPath()
lasso_model.fit(shuffled_knob_matrix, shuffled_metric_matrix, encoded_knob_columnlabels)
# consolidate categorical feature columns, and reset to original names
encoded_knobs = lasso_model.get_ranked_features()
consolidated_knobs = consolidate_columnlabels(encoded_knobs)
return consolidated_knobs

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{% extends "base.html" %}
{% block title %}Page not found{% endblock %}
{% block body %}
<div style="text-align: center;">
<h1>Page not found</h1>
<p>Sorry, but the requested page could not be found.</p>
</div>
{% endblock %}

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<!DOCTYPE html>
<html>
<!-- Javascript imports -->
<script type="text/javascript" src="{{ STATIC_URL }}js/jquery-1.10.2.min.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}js/jquery-migrate-1.2.1.min.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}js/bootstrap.min.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}js/bootstrap-select.min.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}js/common.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}js/jquery.address-1.5.min.js"></script>
<script language="javascript" type="text/javascript" src="{{ STATIC_URL }}js/jqplot/jquery.jqplot.min.js"></script>
<script type='text/javascript'>var STATIC_URL = '{{ STATIC_URL }}';</script>
<head>
<meta http-equiv="content-type" content="text/html; charset=utf-8" />
<title>{% block title %}OtterTune{% endblock %}</title>
<!-- CSS imports -->
<link rel="stylesheet" href="{{ STATIC_URL }}css/bootstrap-select.min.css">
<link rel="stylesheet" href="{{ STATIC_URL }}css/bootstrap.min.css">
<link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}js/jqplot/jquery.jqplot.min.css" />
<link rel="stylesheet" href="{{ STATIC_URL }}css/base.css">
<style>
.btn-sm2,
.btn-group-sm > .btn {
padding: 6px 9px;
font-size: 13px;
line-height: 1.5;
border-radius: 3px;
}
.navbar-btn2 {
padding: 4px 6px;
margin-top:12.5px;
margin-bottom:12.5px;
}
.navbar-form2 {
padding: 4px 6px;
margin-top:12.5px;
margin-bottom:12.5px;
}
.form-control2 {
margin: 0 0 0 0;
height: 32px;
width: 150px;
}
</style>
{% block extra_head %}
<link rel="stylesheet" href="{{ STATIC_URL }}css/style.css">
{% endblock %}
</head>
<body>
<!-- Navigation -->
<nav class="navbar navbar-default fixed-top topnav" role="navigation">
<div class="container">
<div class="navbar-header">
<a class="navbar-brand .text-warning" href="{% url 'home_projects' %}">OtterTune</a>
</div>
<div class="navbar-collapse collapse">
<div class="navbar-right">
{% if user.is_authenticated %}
<span class="navbar-text">Hello, {{ user.username }}</span>
<a class="btn btn-default navbar-btn2" href="{% url 'change_password' %}">Change Password</a>
<a class="btn btn-default navbar-btn2" href="{% url 'logout' %}">Sign Out</a>
{% else %}
<form class="form-inline navbar-form2" role="form" action="{% url 'login' %}" method="POST" style="margin:0 0 0 0">
{% csrf_token %}
<input type="text" placeholder="username" class="form-control form-control2" name="username">
<input type="password" placeholder="password" class="form-control form-control2" name="password">
<button type="submit" class="btn btn-default navbar-btn2"">Sign in</button>
</form>
</div>
{% endif %}
</div>
</div>
</div>
</nav>
{% if form.errors %}
{% for field in form %}
{% for error in field.errors %}
<div class="alert alert-danger">
<strong>{{ error|escape }}</strong>
</div>
{% endfor %}
{% endfor %}
{% for error in form.non_field_errors %}
<div class="alert alert-danger">
<strong>{{ error|escape }}</strong>
</div>
{% endfor %}
{% endif %}
{% block body %} {% endblock body %}
</body>
</html>

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{% extends "base.html" %}
{% block body %}
<div class="container">
<form class="gray-border form-horizontal " action="{% url 'change_password' %}" method="post" id="contact_form">{% csrf_token %}
<fieldset>
<!-- Form Name -->
<h2 class="text-center"><b>Change Your Password</b></h2>
<!-- Text input-->
<div class="form-group">
<label class="col-xs-3 control-label">Old Password</label>
<div class="col-xs-6 input-group">
<input name="old_password" placeholder="iheartcatnip" class="form-control" type="password">
</div>
</div>
<!-- Text input-->
<div class="form-group">
<label class="col-xs-3 control-label">New Password</label>
<div class="col-xs-6 input-group">
<input name="new_password1" placeholder="iheartcatnip" class="form-control" type="password">
</div>
</div>
<!-- Text input-->
<div class="form-group">
<label class="col-xs-3 control-label">Confirm New Password</label>
<div class="col-xs-6 input-group">
<input name="new_password2" placeholder="iheartcatnip" class="form-control" type="password">
</div>
</div>
<!-- Button -->
<div class="form-group text-center">
<button type="submit" class="btn btn-success" name="submit" value="submit-value">Change Password <span class="glyphicon glyphicon-send"></span></button>
</div>
</fieldset>
</form>
</div>
{% endblock body %}

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{% extends "base.html" %}
{% load util_functions %}
{% block body %}
<div class="container">
<div class="container" style="overflow: auto;">
<table class="table table-striped table-bordered table-condensed table-hover">
<caption><h4>{{ labels.title }}</h4></caption>
<tbody>
<tr>
<td><div class="text-right">{{ labels.name }}</div></td>
<td>{{ dbms_data.name }}</td>
</tr>
<tr>
<td><div class="text-right">{{ labels.dbms }}</div></td>
<td>{{ dbms_data.dbms.full_name }}</td>
</tr>
<tr>
<td><div class="text-right">{{ labels.creation_time }}</div></td>
<td>{{ dbms_data.creation_time }}</td>
</tr>
</tbody>
</table>
</div>
<hr>
<div class="container">Compare with
<select class="selectpicker" id="compare" onchange="compare_dbms_data()">
<option value="none">None</option>
{% for peer in peer_data %}
<option value="{{ peer.pk }}">{{ peer.name }}</option>
{% endfor %}
</select>
</div>
<div id="featured">
<h4>{{ labels.featured_data }} | <a onclick="switch_content()">Show {{ labels.all_data }}</a></h4>
<table class="table table-striped table-bordered table-condensed table-hover" style="table-layout: fixed;">
<caption></caption>
<tr>
<td><h5><strong>Name</strong><h5></td>
<td><h5><strong>Value</strong></h5></td>
{% if compare != "none" %}
<td><h5><strong>Comparing Value</strong></h5></td>
{% endif %}
</tr>
{% for pair in featured_data %}
{% if pair.2 and pair.2 != pair.1 %}
<tr class="danger">
{% else %}
<tr>
{% endif %}
{% if data_type == "knobs" %}
<td style="word-wrap:break-word;"><a href="{% url 'dbms_knobs_ref' dbms_data.dbms.name dbms_data.dbms.version pair.0 %}">{{ pair.0 }}</a></td>
{% else %}
<td style="word-wrap:break-word;"><a href="{% url 'dbms_metrics_ref' dbms_data.dbms.name dbms_data.dbms.version pair.0 %}">{{ pair.0 }}</a></td>
{% endif %}
<td style="word-wrap:break-word;">{{ pair.1|safe_floatformat }}</td>
{% if compare != "none" %}
<td style="word-wrap:break-word;">{{ pair.2|safe_floatformat }}</td>
{% endif %}
</tr>
{% endfor %}
</table>
</div>
<div id="para_table">
<h4>{{ labels.all_data }} | <a onclick="switch_content()">Show {{ labels.featured_data }}</a></h4>
<table class="table table-striped table-bordered table-condensed table-hover" style="table-layout: fixed;">
<caption></caption>
<tr>
<td><h5><strong>Name</strong><h5></td>
<td><h5><strong>Value</strong></h5></td>
{% if compare != "none" %}
<td><h5><strong>Comparing Value</strong></h5></td>
{% endif %}
</tr>
{% for pair in all_data %}
{% if pair.2 and pair.2 != pair.1 %}
<tr class="danger">
{% else %}
<tr>
{% endif %}
{% if data_type == "knobs" %}
<td style="word-wrap:break-word;"><a href="{% url 'dbms_knobs_ref' dbms_data.dbms.name dbms_data.dbms.version pair.0 %}">{{ pair.0 }}</a></td>
{% else %}
<td style="word-wrap:break-word;"><a href="{% url 'dbms_metrics_ref' dbms_data.dbms.name dbms_data.dbms.version pair.0 %}">{{ pair.0 }}</a></td>
{% endif %}
<td style="word-wrap:break-word;">{{ pair.1|safe_floatformat }}</td>
{% if compare != "none" %}
<td style="word-wrap:break-word;">{{ pair.2|safe_floatformat }}</td>
{% endif %}
</tr>
{% endfor %}
</table>
</div>
</div>
<script language="javascript">
$(function() {
$("#para_table").hide();
$('.selectpicker').selectpicker();
$("select[id='compare']").val('{{ compare }}');
$('.selectpicker').selectpicker('refresh');
});
function switch_content() {
$("#featured").toggle();
$("#para_table").toggle();
}
function compare_dbms_data() {
url = "/projects/{{ dbms_data.session.project.pk }}/sessions/{{ dbms_data.session.pk }}/{{ data_type }}/{{dbms_data.pk}}/?compare=" + $("select[id='compare']").val();
window.location = url;
}
</script>
{% endblock body %}

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{% extends "base.html" %}
{% block extra_head %}
<style>
dl.inner {
border-bottom:1px solid #E8E8E8;
border-spacing:7px;
padding:0 0 0 0;
margin: 0 0 0 0;
}
</style>
{% endblock %}
{% block body %}
<div id="dbms_reference" class="container">
<h3 class="text-right" style="margin-right:15px; margin-top:0px"><span class="label label-primary">{{ dbms }}</span></h3>
<dl class="row inner" style="margin-bottom:0">
{% if is_used %}
<dd class="h2">{{ title }}<small style="color:green;padding-left:30px"><span class="glyphicon glyphicon-ok"></span> {{used_label}}</small></dd>
{% else %}
<dd class="h2">{{ title }}<small style="color:red;padding-left:30px"><span class="glyphicon glyphicon-remove"></span> {{used_label}}</small></dd>
{% endif %}
</dl>
{% for label, value in list_items.items %}
<dl class="row inner">
<dt class="col-sm-3">{{ label }}</dt>
<dd class="col-sm-9">{{ value }}</dd>
</dl>
{% endfor %}
</div>
</div>
{% endblock body %}

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{% extends "base.html" %}
{% load util_functions %}
{% block body %}
<div class="container">
<caption><h4>Edit Knobs</h4></caption>
<table class="table table-striped table-bordered table-hover table-nonfluid"
style="display:block; overflow-y:scroll; height:500px">
<tr>
<td>Name</td>
<td>Min</td>
<td>Max</td>
<td>Tunable</td>
<td></td>
</tr>
{% for form in forms %}
<form action="{% url 'edit_knobs' project.pk session.pk %}" method="POST">
{% csrf_token %}
<tr>
<td>{{ form.name }}</td>
<td>{{ form.minval }}</td>
<td>{{ form.maxval }}</td>
<td>{{ form.tunable }}</td>
<td><button type="submit">Save</button></td>
</tr>
</form>
{% endfor %}
</table>
<a class="btn btn-default" href="{% url 'session' project.pk session.pk %}">Done</a>
</div>
<script language="javascript">
function show_content() {
console.log("In show_content()")
//$("#target_obj_row").toggle()
}
</script>
{% endblock body %}

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{% extends "base.html" %}
{% block body %}
<div class="container">
<form method="POST"> {% csrf_token %}
<table class="table table-striped table-bordered table-hover table-nonfluid">
{{ form.as_table }}
</table>
{% if project %}
<button class="btn btn-default" type="submit" name="id" onclick="location.href='{% url 'edit_project' project.pk %}'">Submit</button>
<a class="btn btn-default" href="{% url 'project_sessions' project.pk %}">Cancel</a>
{% else %}
<button class="btn btn-default" type="submit" name="id" onclick="location.href='{% url 'new_project' %}'">Submit</button>
<a class="btn btn-default" href="{% url 'home_projects' %}">Cancel</a>
{% endif %}
</form>
</div>
{% endblock body %}

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{% extends "base.html" %}
{% block body %}
<div class="container">
{% if session %}
<form action="{% url 'edit_session' project.pk session.pk %}" method="POST">
{% else %}
<form action="{% url 'new_session' project.pk %}" method="POST">
{% endif %}
{% csrf_token %}
<table class="table table-striped table-bordered table-hover table-nonfluid">
<tr>
<td>{{ form.name.label_tag }}</td>
<td>{{ form.name }}</td>
</tr>
<tr>
<td>{{ form.description.label_tag }}</td>
<td>{{ form.description }}</td>
</tr>
<tr id="dbms_row">
<td>{{ form.dbms.label_tag }}</td>
<td>{{ form.dbms }}</td>
</tr>
<tr id="cpu_row">
<td>{{ form.cpu.label_tag }}</td>
<td>{{ form.cpu }}</td>
</tr>
<tr id="memory_row">
<td>{{ form.memory.label_tag }}</td>
<td>{{ form.memory }}</td>
</tr>
<tr id="storage_row">
<td>{{ form.storage.label_tag }}</td>
<td>{{ form.storage }}</td>
</tr>
<tr>
<td>{{ form.tuning_session.label_tag }}</td>
<td>{{ form.tuning_session }}</td>
</tr>
<tr id="target_obj_row">
<td>{{ form.target_objective.label_tag }}</td>
<td>{{ form.target_objective }}</td>
</tr>
<tr id="upload_code_row">
<td>{{ form.gen_upload_code.label_tag }}</td>
<td>{{ form.gen_upload_code }}</td>
</tr>
</table>
<button class="btn btn-default" type="submit" name="id">Submit</button>
{% if session %}
<a class="btn btn-default" href="{% url 'session' project.pk session.pk %}">Cancel</a>
{% else %}
<a class="btn btn-default" href="{% url 'project_sessions' project.pk %}">Cancel</a>
{% endif %}
</form>
</div>
<script language="javascript">
function show_content() {
console.log("In show_content()")
//$("#target_obj_row").toggle()
}
$(function() {
if ("{{ session }}" != "None") {
$("#dbms_row").hide();
$("#cpu_row").hide();
$("#memory_row").hide();
$("#storage_row").hide();
} else {
$("#upload_code_row").hide();
}
/*if (document.getElementById('id_tuning_session').checked) {
$("#target_obj_row").show()
} else {
$("#target_obj_row").hide()
}*/
});
</script>
{% endblock body %}

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