mirror of
https://github.com/apache/impala.git
synced 2026-01-02 03:00:32 -05:00
565d15579c
At the moment, a query is the default unit of execution and parallelism in the Impala
performance suite. With this change, we now have the ability to treat a workload as the
unit of execution. A workload is defined as a unique combination of the dataset, scale
factor, a subset (or all) of the queries in the dataset, and a table format (file format,
compression codec and compression scheme).
It introduces two new command line options in bin/run-workload.py:
* --execution_scope
The default scope is 'query', and it maintains previous semantics. The
new scope is 'workload', which toggles the unit of execution to a workload.
* --shuffle_query_exec_order.
Shuffles the order in which queries are executed (only applicable when the
execution_scope if workload), defaults to False.
Change-Id: I790d75f0896210cda8eb999015b0be04246e4c45
Reviewed-on: http://gerrit.ent.cloudera.com:8080/503
Reviewed-by: Ishaan Joshi <ishaan@cloudera.com>
Tested-by: Ishaan Joshi <ishaan@cloudera.com>
100 lines
4.0 KiB
Python
100 lines
4.0 KiB
Python
#!/usr/bin/env python
|
|
# Copyright (c) 2012 Cloudera, Inc. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
# The WorkloadExecutor class encapsulates the execution of a workload. A workload is
|
|
# defined as a set of queries for a given data set, scale factor and a specific test
|
|
# vector. It treats a workload an the unit of parallelism.
|
|
|
|
import logging
|
|
import os
|
|
|
|
from collections import defaultdict
|
|
from random import shuffle
|
|
from threading import Lock, Thread
|
|
|
|
logging.basicConfig(level=logging.INFO, format='%(name)s %(threadName)s: %(message)s')
|
|
LOG = logging.getLogger('workload_executor')
|
|
LOG.setLevel(level=logging.DEBUG)
|
|
|
|
|
|
class WorkloadExecutor(object):
|
|
"""Execute a workload in parallel.
|
|
|
|
A workload execution expects the following arguments:
|
|
query_pipelines: Consists of a list of query pipelines. A query pipeline is defined
|
|
as a mapping of a Query to its QueryExecutor. Each query pipeline
|
|
contains the same Query objects as keys.
|
|
shuffle: Change the order of execution of queries in a workload. By default, the queries
|
|
are executed sorted by name.
|
|
"""
|
|
# TODO: Revisit the class name. The current naming scheme is ambigious.
|
|
def __init__(self, **kwargs):
|
|
self.query_pipelines = kwargs.get('query_pipelines')
|
|
self.shuffle = kwargs.get('shuffle', False)
|
|
self.iterations = kwargs.get('iterations', 1)
|
|
# The result dict maps a query object to a list of QueryExecResult objects.
|
|
self.__results = defaultdict(list)
|
|
self.__result_dict_lock = Lock()
|
|
self.__thread_name = "[%s] " % self.query_pipelines[0].keys()[0].db + "Thread %d"
|
|
self.__workload_threads = []
|
|
self.__create_workload_threads()
|
|
|
|
def __create_workload_threads(self):
|
|
"""Create a workload thread per query pipeline"""
|
|
for thread_num, query_pipeline in enumerate(self.query_pipelines):
|
|
self.__workload_threads.append(Thread(target=self.__run_queries,
|
|
args=[query_pipeline, thread_num], name=self.__thread_name % thread_num))
|
|
|
|
def __update_results(self, results):
|
|
"""Update the results dictionary maintaining thread safety"""
|
|
self.__result_dict_lock.acquire()
|
|
try:
|
|
for query, result in results.iteritems():
|
|
self.__results[query].extend(result)
|
|
self.__results[query] = list(set(self.__results[query]))
|
|
finally:
|
|
self.__result_dict_lock.release()
|
|
|
|
def __run_queries(self, query_pipeline, thread_num):
|
|
"""Runs a query pipeline and updates results."""
|
|
queries = sorted(query_pipeline.keys())
|
|
# Randomize the order of execution if specified.
|
|
if shuffle: shuffle(queries)
|
|
for i in xrange(self.iterations):
|
|
results = dict()
|
|
for query in queries:
|
|
query_executor = query_pipeline[query]
|
|
if i == 0:
|
|
query_executor.thread_name = "%s %s" % (self.__thread_name % thread_num, \
|
|
query_executor.thread_name)
|
|
query_executor.run()
|
|
results[query] = query_executor.get_results()
|
|
# Store the results. This has to be thread safe. Multiple threads can write
|
|
# to the same key (query object).
|
|
self.__update_results(results)
|
|
|
|
def run(self):
|
|
"""Run the query pipelines concurrently"""
|
|
for thread_num, t in enumerate(self.__workload_threads):
|
|
LOG.info("Starting %s" % self.__thread_name % thread_num)
|
|
t.start()
|
|
for thread_num,t in enumerate(self.__workload_threads):
|
|
t.join()
|
|
LOG.info("Finished %s" % self.__thread_name % thread_num)
|
|
|
|
def get_results(self):
|
|
"""Return execution results."""
|
|
return self.__results
|