clean metrics before pruning
This commit is contained in:
yangdsh
Dana Van Aken
parent
8da203fdc3
commit
e7d5b0338a
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@ -24,3 +24,6 @@ KNOB_IDENT_USE_PRUNED_METRICS = False
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# The background tasks only process workloads containing this minimum amount of results
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MIN_WORKLOAD_RESULTS_COUNT = 5
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# The views used for metrics pruning
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VIEWS_FOR_PRUNING = ['dba_hist_osstat', 'dba_hist_sysstat', 'dba_hist_system_event']
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@ -89,30 +89,6 @@ class ConfigurationRecommendation(BaseTask): # pylint: disable=abstract-method
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task_meta.save()
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def clean_metric_data(metric_matrix, metric_labels, session):
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# Makes sure that all knobs in the dbms are included in the knob_matrix and knob_labels
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metric_objs = MetricCatalog.objects.filter(dbms=session.dbms)
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metric_cat = [session.target_objective]
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for metric_obj in metric_objs:
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metric_cat.append(metric_obj.name)
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missing_columns = sorted(set(metric_cat) - set(metric_labels))
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unused_columns = set(metric_labels) - set(metric_cat)
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LOG.debug("clean_metric_data: added %d metrics and removed %d metric.", len(missing_columns),
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len(unused_columns))
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default_val = 0
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metric_cat_size = len(metric_cat)
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matrix = np.ones((len(metric_matrix), metric_cat_size)) * default_val
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metric_labels_dict = {n: i for i, n in enumerate(metric_labels)}
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# column labels in matrix has the same order as ones in metric catalog
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# missing values are filled with default_val
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for i, metric_name in enumerate(metric_cat):
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if metric_name in metric_labels_dict:
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index = metric_labels_dict[metric_name]
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matrix[:, i] = metric_matrix[:, index]
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LOG.debug("clean_metric_data: final ~ matrix: %s, labels: %s", matrix.shape, len(metric_cat))
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return matrix, metric_cat
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def save_execution_time(start_ts, fn, result):
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end_ts = time.time()
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exec_time = end_ts - start_ts
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@ -513,12 +489,12 @@ def train_ddpg(train_ddpg_input):
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prev_objective = prev_metric_data[target_obj_idx]
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# Clean metric data
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metric_data, metric_labels = clean_metric_data(agg_data['y_matrix'],
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metric_data, _ = DataUtil.clean_metric_data(agg_data['y_matrix'],
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agg_data['y_columnlabels'], session)
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metric_data = metric_data.flatten()
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metric_scalar = MinMaxScaler().fit(metric_data.reshape(1, -1))
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normalized_metric_data = metric_scalar.transform(metric_data.reshape(1, -1))[0]
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prev_metric_data, _ = clean_metric_data(prev_agg_data['y_matrix'],
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prev_metric_data, _ = DataUtil.clean_metric_data(prev_agg_data['y_matrix'],
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prev_agg_data['y_columnlabels'], session)
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prev_metric_data = prev_metric_data.flatten()
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prev_metric_scalar = MinMaxScaler().fit(prev_metric_data.reshape(1, -1))
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@ -642,7 +618,8 @@ def configuration_recommendation_ddpg(recommendation_ddpg_input): # pylint: dis
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params = JSONUtil.loads(session.hyperparameters)
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agg_data = DataUtil.aggregate_data(result_list)
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metric_data, _ = clean_metric_data(agg_data['y_matrix'], agg_data['y_columnlabels'], session)
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metric_data, _ = DataUtil.clean_metric_data(agg_data['y_matrix'],
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agg_data['y_columnlabels'], session)
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metric_data = metric_data.flatten()
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metric_scalar = MinMaxScaler().fit(metric_data.reshape(1, -1))
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normalized_metric_data = metric_scalar.transform(metric_data.reshape(1, -1))[0]
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@ -22,7 +22,7 @@ from analysis.preprocessing import (Bin, get_shuffle_indices,
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consolidate_columnlabels)
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from website.models import PipelineData, PipelineRun, Result, Workload, ExecutionTime
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from website.settings import (ENABLE_DUMMY_ENCODER, KNOB_IDENT_USE_PRUNED_METRICS,
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MIN_WORKLOAD_RESULTS_COUNT, TIME_ZONE)
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MIN_WORKLOAD_RESULTS_COUNT, TIME_ZONE, VIEWS_FOR_PRUNING)
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from website.types import PipelineTaskType, WorkloadStatusType
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from website.utils import DataUtil, JSONUtil
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@ -82,8 +82,6 @@ def run_background_tasks():
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# Check that there are enough results in the workload
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LOG.info("Not enough results in workload %s (# results: %s, # required: %s).",
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workload_name, num_wkld_results, MIN_WORKLOAD_RESULTS_COUNT)
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workload.status = WorkloadStatusType.PROCESSED
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workload.save()
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continue
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LOG.info("Aggregating data for workload %s...", workload_name)
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@ -92,18 +90,11 @@ def run_background_tasks():
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LOG.debug("Aggregated knob data: rowlabels=%s, columnlabels=%s, data=%s.",
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len(knob_data['rowlabels']), len(knob_data['columnlabels']),
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knob_data['data'].shape)
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LOG.debug("Aggregated metric data: rowlabels=%s, columnlabels=%s, data=%s.",
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len(metric_data['rowlabels']), len(metric_data['columnlabels']),
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metric_data['data'].shape)
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LOG.info("Done aggregating data for workload %s.", workload_name)
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num_valid_results = knob_data['data'].shape[0] # pylint: disable=unsubscriptable-object
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if num_valid_results < MIN_WORKLOAD_RESULTS_COUNT:
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# Check that there are enough valid results in the workload
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LOG.info("Not enough valid results in workload %s (# valid results: "
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"%s, # required: %s).", workload_name, num_valid_results,
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MIN_WORKLOAD_RESULTS_COUNT)
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workload.status = WorkloadStatusType.PROCESSED
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workload.save()
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continue
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# Knob_data and metric_data are 2D numpy arrays. Convert them into a
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# JSON-friendly (nested) lists and then save them as new PipelineData
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# objects.
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@ -264,6 +255,14 @@ def run_workload_characterization(metric_data):
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matrix = metric_data['data']
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columnlabels = metric_data['columnlabels']
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LOG.debug("Workload characterization ~ initial data size: %s", matrix.shape)
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useful_labels = []
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for label in columnlabels:
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for view in VIEWS_FOR_PRUNING:
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if view in label:
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useful_labels.append(label)
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break
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matrix, columnlabels = DataUtil.clean_metric_data(matrix, columnlabels, None, useful_labels)
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LOG.debug("Workload characterization ~ cleaned data size: %s", matrix.shape)
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# Bin each column (metric) in the matrix by its decile
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binner = Bin(bin_start=1, axis=0)
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@ -298,6 +297,7 @@ def run_workload_characterization(metric_data):
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# Components: metrics * factors
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components = fa_model.components_.T.copy()
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LOG.info("Workload characterization first part costs %.0f seconds.", time.time() - start_ts)
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# Run Kmeans for # clusters k in range(1, num_nonduplicate_metrics - 1)
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# K should be much smaller than n_cols in detK, For now max_cluster <= 20
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@ -25,7 +25,7 @@ from django.utils.text import capfirst
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from django_db_logger.models import StatusLog
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from djcelery.models import TaskMeta
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from .models import DBMSCatalog, KnobCatalog, Result, Session, SessionKnob
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from .models import DBMSCatalog, MetricCatalog, KnobCatalog, Result, Session, SessionKnob
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from .settings import common
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from .types import LabelStyleType, VarType
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@ -211,6 +211,35 @@ class DataUtil(object):
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rowlabels_unique[i] = tuple(rowlabels[dup_idxs])
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return X_unique, y_unique, rowlabels_unique
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@staticmethod
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def clean_metric_data(metric_matrix, metric_labels, session, useful_labels=None):
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# Make metric_labels identical to useful_labels (if given)
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# If useful_labels is not given, let it to be all metrics in the catalog.
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if useful_labels is None:
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metric_objs = MetricCatalog.objects.filter(dbms=session.dbms)
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metric_cat = [session.target_objective]
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for metric_obj in metric_objs:
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metric_cat.append(metric_obj.name)
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else:
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metric_cat = useful_labels
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missing_columns = sorted(set(metric_cat) - set(metric_labels))
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unused_columns = set(metric_labels) - set(metric_cat)
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LOG.debug("clean_metric_data: added %d metrics and removed %d metric.",
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len(missing_columns), len(unused_columns))
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default_val = 0
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metric_cat_size = len(metric_cat)
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matrix = np.ones((len(metric_matrix), metric_cat_size)) * default_val
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metric_labels_dict = {n: i for i, n in enumerate(metric_labels)}
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# column labels in matrix has the same order as ones in metric catalog
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# missing values are filled with default_val
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for i, metric_name in enumerate(metric_cat):
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if metric_name in metric_labels_dict:
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index = metric_labels_dict[metric_name]
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matrix[:, i] = metric_matrix[:, index]
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LOG.debug("clean_metric_data: final ~ matrix: %s, labels: %s", matrix.shape,
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len(metric_cat))
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return matrix, metric_cat
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@staticmethod
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def clean_knob_data(knob_matrix, knob_labels, sessions):
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# Filter and amend knob_matrix and knob_labels according to the tunable knobs in the session
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