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04edc8f534
This change updates the run-benchmark script to enable it to target one or more workloads. Now benchmarks can be run like: ./run-benchmark --workloads=hive-benchmark,tpch We lookup the workload in the workloads directory, then read the associated query .test files and start executing them. To ensure the queries are not duplicated between benchmark and query tests, I moved all existing queries (under fe/src/test/resources/* to the workloads directory. You do NOT need to look through all the .test files, I've just moved them. The one new file is the 'hive-benchmark.test' which contains the hive benchmark queries. Also added support for generating schema for different scale factors as well as executing against these scale factors. For example, let's say we have a dataset with a scale factor called "SF1". We would first generate the schema using: ./generate_schema_statements --workload=<workload> --scale_factor="SF3" This will create tables with a unique names from the other scale factors. Run the generated .sql file to load the data. Alternatively, the data can loaded by running a new python script: ./bin/load-data.py -w <workload1>,<workload2> -e <exploration strategy> -s [scale factor] For example: load-data.sh -w tpch -e core -s SF3 Then run against this: ./run-benchmark --workloads=<workload> --scale_factor=SF3 This changeset also includes a few other minor tweaks to some of the test scripts. Change-Id: Ife8a8d91567d75c9612be37bec96c1e7780f50d6
40 lines
1.4 KiB
Bash
Executable File
40 lines
1.4 KiB
Bash
Executable File
#!/bin/bash
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# Copyright (c) 2012 Cloudera, Inc. All rights reserved.
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if [ x${JAVA_HOME} == x ]; then
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echo JAVA_HOME not set
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exit 1
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fi
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set -u
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set -e
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# Load the data set
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pushd ${IMPALA_HOME}/bin
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./load-data.py --workloads functional-query --exploration_strategy exhaustive
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./load-data.py --workloads functional-planner --exploration_strategy exhaustive
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./load-data.py --workloads tpch --exploration_strategy core
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popd
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# TODO: The multi-format table will move these files. So we need to copy them to a
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# temporary location for that table to use. Should find a better way to handle this.
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echo COPYING DATA FOR DEPENDENT TABLES
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hadoop fs -rm -r -f /test-warehouse/alltypesmixedformat
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hadoop fs -rm -r -f /tmp/alltypes_rc
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hadoop fs -rm -r -f /tmp/alltypes_seq
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hadoop fs -mkdir -p /tmp/alltypes_seq/year=2009
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hadoop fs -mkdir -p /tmp/alltypes_rc/year=2009
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hadoop fs -cp /test-warehouse/alltypes_seq/year=2009/month=2/ /tmp/alltypes_seq/year=2009
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hadoop fs -cp /test-warehouse/alltypes_rc/year=2009/month=3/ /tmp/alltypes_rc/year=2009
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hadoop fs -cp /test-warehouse/alltypes_trevni/year=2009/month=4/ \
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/tmp/alltypes_rc/year=2009
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# For tables that rely on loading data from local fs test-warehouse
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# TODO: Find a good way to integrate this with the normal data loading scripts
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${HIVE_HOME}/bin/hive -hiveconf hive.root.logger=WARN,console -v \
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-f ${IMPALA_HOME}/testdata/bin/load-dependent-tables.sql
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if [ $? != 0 ]; then
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echo DEPENDENT LOAD FAILED
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exit 1
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fi
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