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address dana's comment

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yangdsh 2020-04-22 07:01:08 +00:00 committed by Dana Van Aken
parent e7d5b0338a
commit 77eb5cdb42
2 changed files with 32 additions and 11 deletions
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7
server/website/website/settings/constants.py
View File

@ -4,6 +4,8 @@
# Copyright (c) 2017-18, Carnegie Mellon University Database Group # Copyright (c) 2017-18, Carnegie Mellon University Database Group
# #
from website.types import DBMSType
# These parameters are not specified for any session, so they can only be set here # These parameters are not specified for any session, so they can only be set here
# If this flag is set, we check if celery is running, and restart celery if it is not. # If this flag is set, we check if celery is running, and restart celery if it is not.
@ -26,4 +28,7 @@ KNOB_IDENT_USE_PRUNED_METRICS = False
MIN_WORKLOAD_RESULTS_COUNT = 5 MIN_WORKLOAD_RESULTS_COUNT = 5
# The views used for metrics pruning # The views used for metrics pruning
VIEWS_FOR_PRUNING = ['dba_hist_osstat', 'dba_hist_sysstat', 'dba_hist_system_event'] VIEWS_FOR_PRUNING = {
DBMSType.ORACLE: ['dba_hist_osstat', 'dba_hist_sysstat', 'dba_hist_system_event',
'dba_workload_replays', 'dba_hist_sys_time_model'],
}

24
server/website/website/tasks/periodic_tasks.py
View File

@ -82,6 +82,8 @@ def run_background_tasks():
# Check that there are enough results in the workload # Check that there are enough results in the workload
LOG.info("Not enough results in workload %s (# results: %s, # required: %s).", LOG.info("Not enough results in workload %s (# results: %s, # required: %s).",
workload_name, num_wkld_results, MIN_WORKLOAD_RESULTS_COUNT) workload_name, num_wkld_results, MIN_WORKLOAD_RESULTS_COUNT)
workload.status = WorkloadStatusType.PROCESSED
workload.save()
continue continue
LOG.info("Aggregating data for workload %s...", workload_name) LOG.info("Aggregating data for workload %s...", workload_name)
@ -95,6 +97,16 @@ def run_background_tasks():
metric_data['data'].shape) metric_data['data'].shape)
LOG.info("Done aggregating data for workload %s.", workload_name) LOG.info("Done aggregating data for workload %s.", workload_name)
num_valid_results = knob_data['data'].shape[0] # pylint: disable=unsubscriptable-object
if num_valid_results < MIN_WORKLOAD_RESULTS_COUNT:
# Check that there are enough valid results in the workload
LOG.info("Not enough valid results in workload %s (# valid results: "
"%s, # required: %s).", workload_name, num_valid_results,
MIN_WORKLOAD_RESULTS_COUNT)
workload.status = WorkloadStatusType.PROCESSED
workload.save()
continue
# Knob_data and metric_data are 2D numpy arrays. Convert them into a # Knob_data and metric_data are 2D numpy arrays. Convert them into a
# JSON-friendly (nested) lists and then save them as new PipelineData # JSON-friendly (nested) lists and then save them as new PipelineData
# objects. # objects.
@ -122,7 +134,7 @@ def run_background_tasks():
# pruned metrics for this workload and save them in a new PipelineData # pruned metrics for this workload and save them in a new PipelineData
# object. # object.
LOG.info("Pruning metrics for workload %s...", workload_name) LOG.info("Pruning metrics for workload %s...", workload_name)
pruned_metrics = run_workload_characterization(metric_data=metric_data) pruned_metrics = run_workload_characterization(metric_data=metric_data, dbms=workload.dbms)
LOG.info("Done pruning metrics for workload %s (# pruned metrics: %s).\n\n" LOG.info("Done pruning metrics for workload %s (# pruned metrics: %s).\n\n"
"Pruned metrics: %s\n", workload_name, len(pruned_metrics), "Pruned metrics: %s\n", workload_name, len(pruned_metrics),
pruned_metrics) pruned_metrics)
@ -240,7 +252,7 @@ def aggregate_data(wkld_results):
return knob_data, metric_data return knob_data, metric_data
def run_workload_characterization(metric_data): def run_workload_characterization(metric_data, dbms):
# Performs workload characterization on the metric_data and returns # Performs workload characterization on the metric_data and returns
# a set of pruned metrics. # a set of pruned metrics.
# #
@ -255,13 +267,17 @@ def run_workload_characterization(metric_data):
matrix = metric_data['data'] matrix = metric_data['data']
columnlabels = metric_data['columnlabels'] columnlabels = metric_data['columnlabels']
LOG.debug("Workload characterization ~ initial data size: %s", matrix.shape) LOG.debug("Workload characterization ~ initial data size: %s", matrix.shape)
views = VIEWS_FOR_PRUNING.get(dbms.type, None)
if views is not None:
useful_labels = [] useful_labels = []
for label in columnlabels: for label in columnlabels:
for view in VIEWS_FOR_PRUNING: for view in views:
if view in label: if view in label:
useful_labels.append(label) useful_labels.append(label)
break break
matrix, columnlabels = DataUtil.clean_metric_data(matrix, columnlabels, None, useful_labels) matrix, columnlabels = DataUtil.clean_metric_data(matrix, columnlabels, None,
useful_labels)
LOG.debug("Workload characterization ~ cleaned data size: %s", matrix.shape) LOG.debug("Workload characterization ~ cleaned data size: %s", matrix.shape)
# Bin each column (metric) in the matrix by its decile # Bin each column (metric) in the matrix by its decile