Spark SQL Performance Tests

This is a performance testing framework for Spark SQL in Apache Spark 1.4+.

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

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.

import org.apache.spark.sql.parquet.Tables
// Tables in TPC-DS benchmark used by experiments.
val tables = Tables(sqlContext)
// Setup TPC-DS experiment
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(queriesToRun = tpcds.interactiveQueries)

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.

Retrieve results

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.

// Get experiments results.
import com.databricks.spark.sql.perf.Results
val results = Results(resultsLocation = <the root location of performance results>, sqlContext = sqlContext)
// Get the DataFrame representing all results stored in the dir specified by resultsLocation.
val allResults = results.allResults
// Use DataFrame API to get results of a single run.
allResults.filter("timestamp = 1429132621024")