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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
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# Copyright (c) 2012 Cloudera, Inc. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# The WorkloadExecutor class encapsulates the execution of a workload. A workload is
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# defined as a set of queries for a given data set, scale factor and a specific test
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# vector. It treats a workload an the unit of parallelism.
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import logging
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import os
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from collections import defaultdict
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from random import shuffle
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from threading import Lock, Thread
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logging.basicConfig(level=logging.INFO, format='%(name)s %(threadName)s: %(message)s')
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LOG = logging.getLogger('workload_executor')
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LOG.setLevel(level=logging.DEBUG)
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class WorkloadExecutor(object):
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"""Execute a workload in parallel.
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A workload execution expects the following arguments:
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query_pipelines: Consists of a list of query pipelines. A query pipeline is defined
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as a mapping of a Query to its QueryExecutor. Each query pipeline
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contains the same Query objects as keys.
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shuffle: Change the order of execution of queries in a workload. By default, the queries
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are executed sorted by name.
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"""
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# TODO: Revisit the class name. The current naming scheme is ambigious.
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def __init__(self, **kwargs):
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self.query_pipelines = kwargs.get('query_pipelines')
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self.shuffle = kwargs.get('shuffle', False)
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self.iterations = kwargs.get('iterations', 1)
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# The result dict maps a query object to a list of QueryExecResult objects.
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self.__results = defaultdict(list)
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self.__result_dict_lock = Lock()
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self.__thread_name = "[%s] " % self.query_pipelines[0].keys()[0].db + "Thread %d"
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self.__workload_threads = []
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self.__create_workload_threads()
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def __create_workload_threads(self):
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"""Create a workload thread per query pipeline"""
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for thread_num, query_pipeline in enumerate(self.query_pipelines):
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self.__workload_threads.append(Thread(target=self.__run_queries,
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args=[query_pipeline, thread_num], name=self.__thread_name % thread_num))
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def __update_results(self, results):
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"""Update the results dictionary maintaining thread safety"""
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self.__result_dict_lock.acquire()
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try:
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for query, result in results.iteritems():
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self.__results[query].extend(result)
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self.__results[query] = list(set(self.__results[query]))
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finally:
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self.__result_dict_lock.release()
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def __run_queries(self, query_pipeline, thread_num):
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"""Runs a query pipeline and updates results."""
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queries = sorted(query_pipeline.keys())
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# Randomize the order of execution if specified.
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if shuffle: shuffle(queries)
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for i in xrange(self.iterations):
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results = dict()
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for query in queries:
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query_executor = query_pipeline[query]
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if i == 0:
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query_executor.thread_name = "%s %s" % (self.__thread_name % thread_num, \
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query_executor.thread_name)
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query_executor.run()
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results[query] = query_executor.get_results()
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# Store the results. This has to be thread safe. Multiple threads can write
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# to the same key (query object).
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self.__update_results(results)
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def run(self):
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"""Run the query pipelines concurrently"""
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for thread_num, t in enumerate(self.__workload_threads):
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LOG.info("Starting %s" % self.__thread_name % thread_num)
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t.start()
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for thread_num,t in enumerate(self.__workload_threads):
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t.join()
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LOG.info("Finished %s" % self.__thread_name % thread_num)
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def get_results(self):
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"""Return execution results."""
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return self.__results
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