Jirapong/spark-sql-perf

Spark SQL Performance Tests

This is a performance testing framework for Spark SQL in Apache Spark 2.2+.

Note: This README is still under development. Please also check our source code for more information.

Quick Start

$ 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

MLlib tests

To run MLlib tests, run /bin/run-ml yamlfile, where yamlfile is the path to a YAML configuration file describing tests to run and their parameters.

TPC-DS

How to use it

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.

Setup a benchmark

Before running any query, a dataset needs to be setup by creating a Benchmark object. Generating the TPCDS data requires dsdgen built and available on the machines. We have a fork of dsdgen that you will need. It can be found here.

// If not done already, you have to set the path for the results
spark.config("spark.sql.perf.results", "/tmp/results")

import com.databricks.spark.sql.perf.tpcds.Tables
// Tables in TPC-DS benchmark used by experiments.
// dsdgenDir is the location of dsdgen tool installed in your machines.
// scaleFactor defines the size of the dataset to generate (in GB)
val tables = new Tables(sqlContext, dsdgenDir, scaleFactor)

// Generate data.
// location is the place there the generated data will be written
// format is a valid spark format like "parquet"
tables.genData(location, format, overwrite, partitionTables, useDoubleForDecimal, clusterByPartitionColumns, filterOutNullPartitionValues)
// Create metastore tables in a specified database for your data.
// Once tables are created, the current database will be switched to the specified database.
tables.createExternalTables(location, format, databaseName, overwrite)
// Or, if you want to create temporary tables
tables.createTemporaryTables(location, format)
// Setup TPC-DS experiment
import com.databricks.spark.sql.perf.tpcds.TPCDS
val tpcds = new TPCDS (sqlContext = sqlContext)

Run benchmarking queries

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

val experiment = tpcds.runExperiment(tpcds.interactiveQueries)
experiment.waitForFinish(60*60*10) // optional: wait for results (with timeout)

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 spark.sql.perf.results (for example /tmp/results/timestamp=1429213883272). The performance results are stored in the JSON format.

Retrieve results

While the experiment is running you can use experiment.html to list the status. Once the experiment is complete, you can load the results from disk.

// Get all experiments results.
val resultTable = spark.read.json(spark.conf.get("spark.sql.perf.results"))
resultTable.createOrReplaceTempView("sqlPerformance")
sqlContext.table("sqlPerformance")
// Get the result of a particular run by specifying the timestamp of that run.
sqlContext.table("sqlPerformance").filter("timestamp = 1429132621024")
// or
val specificResultTable = spark.read.json(experiment.resultPath)