Example of testing DataFrame API and SQL queries

Highlights

• Helper method creates test data
• Separated utilities to calculate contents of DataFrame
• Separated tests for each utility

Requirement

• SBT or Activator are installed. I use activator command in this example.

Libraries included

• Spark 1.6.2
• Scala 2.11.6
• ScalaTest 2.2.4
• ScalaCheck 1.12.2

Getting started

Run application

$ activator run

Test application

$ activator test

Generate Uber jar

This example includes configuration of sbt-assembly, so you can run 'assembly'

$ activator assembly

Static Analyzer

The following static analyzers are included in build.sbt

linter (https://github.com/HairyFotr/linter)

Usage: automatically runs during Compilation and evaluation in console

sbt-scapegoat (https://github.com/sksamuel/sbt-scapegoat)

Usage: automatically runs during Compilation

Open target/scala-2.11/scapegoat.xml or target/scala-2.11/scapegoat.html

Coding Style Checker

ScalaStyle

Usage: sbt scalastyle

Open target/scalastyle-result.xml

Check level are all "warn", change to "error" if you want to reject code changes when integrated with CI tools.

Issues

fork limitation in sbt console

This issue is originally mentioned in Sample Project for Spark 1.3.0 with Scala 2.11.6, a sample Spark application template.

Currently Test and run will fork a JVM. The reason it's necessary is that SBT's classloader doesn't work well with Spark and Spark shell.

However sbt console does not recognize fork key right now. It might throw ScalaReflectionException, etc.

Author

• dobachi ([email protected])

Application template

• Jianshi Huang ([email protected]; [email protected])