Environment Specific Config in Spark Scala Projects

Environment config files return different values for the test, development, staging, and production environments.

In Spark projects, you will often want a variable to point to a local CSV file in the test environment and a CSV file in S3 in the production environment.

This episode will demonstrate how to add environment config to your projects and how to set environment variables to change the environment.

Basic use case

Let’s create a Config object with one Map[String, String] with test configuration and another Map[String, String] with production config.

package com.github.mrpowers.spark.spec.sql

object Config {

  var test: Map[String, String] = {
    Map(
      "libsvmData" -> new java.io.File("./src/test/resources/sample_libsvm_data.txt").getCanonicalPath,
      "somethingElse" -> "hi"
    )
  }

  var production: Map[String, String] = {
    Map(
      "libsvmData" -> "s3a://my-cool-bucket/fun-data/libsvm.txt",
      "somethingElse" -> "whatever"
    )
  }

  var environment = sys.env.getOrElse("PROJECT_ENV", "production")

  def get(key: String): String = {
    if (environment == "test") {
      test(key)
    } else {
      production(key)
    }
  }

}

The Config.get() method will grab values from the test or production map depending on the PROJECT_ENV value.

Let’s use the sbt console command to demonstrate this.

$ PROJECT_ENV=test sbt console
scala> com.github.mrpowers.spark.spec.sql.Config.get("somethingElse")
res0: String = hi

Let’s restart the SBT console and run the same code in the production environment.

$ PROJECT_ENV=production sbt console
scala> com.github.mrpowers.spark.spec.sql.Config.get("somethingElse")
res0: String = whatever

Here is how the Config object can be used to fetch a file in your GitHub repository in the test environment and also fetch a file from S3 in the production environment.

val training = spark
  .read
  .format("libsvm")
  .load(Config.get("libsvmData"))

This solution is elegant and does not clutter our application code with environment logic.

Environment specific code anitpattern

Here is an example of how you should not add environment paths to your code.

var environment = sys.env.getOrElse("PROJECT_ENV", "production")
val training = if (environment == "test") {
  spark
    .read
    .format("libsvm")
    .load(new java.io.File("./src/test/resources/sample_libsvm_data.txt").getCanonicalPath)
} else {
  spark
    .read
    .format("libsvm")
    .load("s3a://my-cool-bucket/fun-data/libsvm.txt")
}

An anti-pattern is a common response to a recurring problem that is usually ineffective and risks being highly counterproductive. – source

You should never write code with different execution paths in the production and test environments because then your test suite won’t really be testing the actual code that’s run in production.

Overriding config

The Config.test and Config.production maps are defined as variables (with the var keyword), so they can be overridden.

scala> import com.github.mrpowers.spark.spec.sql.Config
scala> Config.get("somethingElse")
res1: String = hi

scala> Config.test = Config.test ++ Map("somethingElse" -> "give me clean air")
scala> Config.get("somethingElse")
res2: String = give me clean air

Giving users the ability to swap out config on the fly makes your codebase more flexible for a variety of use cases.

Setting the PROJECT_ENV variable for test runs

The Config object uses the production environment by default. You’re not going to want to have to remember to set the PROJECT_ENV to test everytime you run your test suite (e.g. you don’t want to type PROJECT_ENV=test sbt test).

You can update your build.sbt file as follows to set PROJECT_ENV to test whenever the test suite is run.

fork in Test := true
envVars in Test := Map("PROJECT_ENV" -> "test")

Big thanks to the StackOverflow community for helping me figure this out.

Other implementations

This StackOverflow thread discusses other solutions.

One answer relies on an external library, one is in Java, and one doesn’t allow for overrides. I will add an answer with the implementation discussed in this blog post now.

Next steps

Feel free to extend this solution to account for other environments. For example, you might want to add a staging environment that uses different paths to test code before it’s run in production.

Just remember to follow best practices and avoid the config anti-pattern that can litter your codebase and reduce the protection offered by your test suite.

Adding Config objects to your functions adds a dependency you might not want. In a future blog post, we’ll discuss how dependency injection can abstract these Config depencencies and how the Config object can be leveraged to access smart defaults – the best of both worlds!

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