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# Copyright (C) 2022-present MongoDB, Inc.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the Server Side Public License, version 1,
# as published by MongoDB, Inc.
#
# 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
# Server Side Public License for more details.
#
# You should have received a copy of the Server Side Public License
# along with this program. If not, see
# <http://www.mongodb.com/licensing/server-side-public-license>.
#
# As a special exception, the copyright holders give permission to link the
# code of portions of this program with the OpenSSL library under certain
# conditions as described in each individual source file and distribute
# linked combinations including the program with the OpenSSL library. You
# must comply with the Server Side Public License in all respects for
# all of the code used other than as permitted herein. If you modify file(s)
# with this exception, you may extend this exception to your version of the
# file(s), but you are not obligated to do so. If you do not wish to do so,
# delete this exception statement from your version. If you delete this
# exception statement from all source files in the program, then also delete
# it in the license file.
#
"""Experimenation utility functions.
How to use the utility functions.
First of all we need to run Jupiter Notebook:
sh> python3 -m notebook
Example notebook.
#Imports
import math
import seaborn as sns
import statsmodels.api as sm
import sys
sys.path.append('/home/ubuntu/mongo/buildscripts/cost_model')
import experiment as exp
from config import DatabaseConfig
from database_instance import DatabaseInstance
# Load data
database_config = DatabaseConfig(connection_string='mongodb://localhost',
database_name='abt_calibration', dump_path='',
restore_from_dump=False, dump_on_exit=False)
database = DatabaseInstance(database_config)
df = await exp.load_calibration_data(database, 'calibrationData')
# Descriptive functions
df.describe()
df.head()
# Clean up loaded data
noout_df = exp.remove_outliers(df, 0.0, 0.90)
noout_df.describe()
# Extract ABT nodes
abt_df = exp.extract_abt_nodes(noout_df)
abt_df.head()
# Get IndexScan nodes only.
ixscan_df = abt_df[abt_df['abt_type'] == 'IndexScan']
ixscan_df.describe()
# Add a new column if required.
ixscan_df = ixscan_df[ixscan_df['n_processed'] > 0].copy()
ixscan_df['log_n_processed'] = ixscan_df['n_processed'].apply(math.log)
ixscan_df.describe()
# Check the correlation.
ixscan_df.corr()
# Print a scatter plot to see a dependency between e.g. execution_time and n_processed.
sns.scatterplot(x=ixscan_df['n_processed'], y=ixscan_df['execution_time'])
# Calibrate (train) the cost model for IndexScan
y = ixscan_df['execution_time']
X = ixscan_df[['n_processed', 'keys_length_in_bytes', 'average_document_size_in_bytes', 'log_n_processed']]
X = sm.add_constant(X)
ixscan_lm = sm.OLS(y, X).fit()
ixscan_lm.summary()
# Draw the predictions.
y_pred = ixscan_lm.predict(X)
sns.scatterplot(x=ixscan_df['n_processed'], y=ixscan_df['execution_time'])
sns.lineplot(x=ixscan_df['n_processed'],y=y_pred, color='red')
"""
import json
import dataclasses
import pandas as pd
from database_instance import DatabaseInstance
import execution_tree as sbe
import physical_tree as abt
from parameters_extractor import extract_execution_stats
async def load_calibration_data(database: DatabaseInstance, collection_name: str) -> pd.DataFrame:
"""Load workflow data containing explain output from database and parse it. Retuned calibration DataFrame with parsed SBE and ABT."""
data = await database.get_all_documents(collection_name)
df = pd.DataFrame(data)
df['sbe'] = df.explain.apply(lambda e: sbe.build_execution_tree(
json.loads(e)['executionStats']))
df['abt'] = df.explain.apply(lambda e: abt.build(
json.loads(e)['queryPlanner']['winningPlan']['optimizerPlan']))
df['total_execution_time'] = df.sbe.apply(lambda t: t.total_execution_time)
return df
def remove_outliers(df: pd.DataFrame, lower_percentile: float = 0.1,
upper_percentile: float = 0.9) -> pd.DataFrame:
"""Remove the outliers from the parsed calibration DataFrame."""
def is_not_outlier(df_seq):
low = df_seq.quantile(lower_percentile)
high = df_seq.quantile(upper_percentile)
return (df_seq >= low) & (df_seq <= high)
return df[df.groupby('run_id').total_execution_time.transform(is_not_outlier).eq(1)]
def extract_sbe_stages(df: pd.DataFrame) -> pd.DataFrame:
"""Extract SBE stages from calibration DataFrame."""
def flatten_sbe_stages(explain):
def traverse(node, stages):
execution_time = node['executionTimeMicros']
children_fields = ['innerStage', 'outerStage', 'inputStage']
for field in children_fields:
if field in node and node[field]:
child = node[field]
execution_time -= child['executionTimeMicros']
traverse(child, stages)
del node[field]
node['executionTime'] = execution_time
stages.append(node)
sbe_tree = json.loads(explain)['executionStats']['executionStages']
result = []
traverse(sbe_tree, result)
return result
return pd.DataFrame(list(df.explain.apply(flatten_sbe_stages).explode()))
def get_sbe_stage(stages_df: pd.DataFrame, stage_name: str) -> pd.DataFrame:
"""Filter the SBE stages DataFrame by the given SBE stage name."""
return stages_df[stages_df.stage == stage_name].copy()
def extract_abt_nodes(df: pd.DataFrame) -> pd.DataFrame:
"""Extract ABT Nodes and execution statistics from calibration DataFrame."""
def extract(df_seq):
es_dict = extract_execution_stats(df_seq['sbe'], df_seq['abt'], [])
rows = []
for abt_type, es in es_dict.items():
row = {
'abt_type': abt_type, **dataclasses.asdict(es),
**json.loads(df_seq['query_parameters']), 'run_id': df_seq['run_id']
}
rows.append(row)
return rows
return pd.DataFrame(list(df.apply(extract, axis=1).explode()))
if __name__ == '__main__':
import asyncio
from config import DatabaseConfig
async def test():
"""Smoke tests."""
database_config = DatabaseConfig(connection_string='mongodb://localhost',
database_name='abt_calibration', dump_path='',
restore_from_dump=False, dump_on_exit=False)
database = DatabaseInstance(database_config)
raw_df = await load_calibration_data(database, 'calibrationData')
print(raw_df.head())
cleaned_df = remove_outliers(raw_df, 0.0, 0.9)
print(cleaned_df.head())
sbe_stages_df = extract_sbe_stages(cleaned_df)
print(sbe_stages_df.head())
seek_df = get_sbe_stage(sbe_stages_df, 'seek')
print(seek_df.head())
abt_nodes_df = extract_abt_nodes(cleaned_df)
print(abt_nodes_df.head())
loop = asyncio.get_event_loop()
loop.run_until_complete(test())
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