663ca7560e
This PR adds the ability to run performance test locally as a stand alone program that reports the results to the console:
```
$ bin/run --help
spark-sql-perf 0.2.0
Usage: spark-sql-perf [options]
-b <value> | --benchmark <value>
the name of the benchmark to run
-f <value> | --filter <value>
a filter on the name of the queries to run
-i <value> | --iterations <value>
the number of iterations to run
--help
prints this usage text
$ bin/run --benchmark DatasetPerformance
```
Author: Michael Armbrust <michael@databricks.com>
Closes #47 from marmbrus/MainClass.
75 lines
3.2 KiB
Markdown
75 lines
3.2 KiB
Markdown
# Spark SQL Performance Tests
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[](https://travis-ci.org/databricks/spark-sql-perf)
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This is a performance testing framework for [Spark SQL](https://spark.apache.org/sql/) in [Apache Spark](https://spark.apache.org/) 1.6+.
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**Note: This README is still under development. Please also check our source code for more information.**
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# Quick Start
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```
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$ bin/run --help
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spark-sql-perf 0.2.0
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Usage: spark-sql-perf [options]
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-b <value> | --benchmark <value>
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the name of the benchmark to run
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-f <value> | --filter <value>
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a filter on the name of the queries to run
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-i <value> | --iterations <value>
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the number of iterations to run
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--help
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prints this usage text
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$ bin/run --benchmark DatasetPerformance
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```
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# TPC-DS
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## How to use it
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The rest of document will use TPC-DS benchmark as an example. We will add contents to explain how to use other benchmarks add the support of a new benchmark dataset in future.
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### Setup a benchmark
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Before running any query, a dataset needs to be setup by creating a `Benchmark` object. Generating
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the TPCDS data requires dsdgen built and available on the machines. We have a fork of dsdgen that
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you will need. It can be found [here](https://github.com/davies/tpcds-kit).
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```
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import com.databricks.spark.sql.perf.tpcds.Tables
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// Tables in TPC-DS benchmark used by experiments.
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// dsdgenDir is the location of dsdgen tool installed in your machines.
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val tables = new Tables(sqlContext, dsdgenDir, scaleFactor)
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// Generate data.
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tables.genData(location, format, overwrite, partitionTables, useDoubleForDecimal, clusterByPartitionColumns, filterOutNullPartitionValues)
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// Create metastore tables in a specified database for your data.
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// Once tables are created, the current database will be switched to the specified database.
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tables.createExternalTables(location, format, databaseName, overwrite)
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// Or, if you want to create temporary tables
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tables.createTemporaryTables(location, format)
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// Setup TPC-DS experiment
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import com.databricks.spark.sql.perf.tpcds.TPCDS
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val tpcds = new TPCDS (sqlContext = sqlContext)
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```
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### Run benchmarking queries
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After setup, users can use `runExperiment` function to run benchmarking queries and record query execution time. Taking TPC-DS as an example, you can start an experiment by using
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```
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val experiment = tpcds.runExperiment(tpcds.interactiveQueries)
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```
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For every experiment run (i.e. every call of `runExperiment`), Spark SQL Perf will use the timestamp of the start time to identify this experiment. Performance results will be stored in the sub-dir named by the timestamp in the given `resultsLocation` (for example `results/1429213883272`). The performance results are stored in the JSON format.
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### Retrieve results
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While the experiment is running you can use `experiment.html` to list the status. Once the experiment is complete, the results will be saved to the table sqlPerformance in json.
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```
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// Get all experiments results.
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tpcds.createResultsTable()
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sqlContext.table("sqlPerformance")
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// Get the result of a particular run by specifying the timestamp of that run.
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sqlContext.table("sqlPerformance").filter("timestamp = 1429132621024")
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```
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