Draconian Overlord

Oppressing software entropy

Spark Test

At Bizo we’re currently evaluating Spark as a replacement for Hive for reporting.

So far we’re still in the prototype stage, but it’s looking promising.

As an example of some Spark code, I thought I’d copy/paste one of my “let’s try Spark” unit tests:

object SparkTest {
  val sc = new SparkContext("local", "unit test")
}

class SparkTest extends ShouldMatchers {
  import SparkTest._
  import SparkContext._

  @Test
  def test() {
    // make collection (table) a, with 3 rows, each
    // row is a tuple of (key, value), or (Int, String)
    val a = sc.parallelize(List((1, "a"), (1, "b"), (2, "c")))

    // make collection (table) b, also with 3 rows
    val b = sc.parallelize(List((1, 5.00), (1, 6.00), (3, 7.00)))

    // typical join, all that match in both a and b
    a.join(b).collect() should be === Array(
      (1, ("a", 5.00)),
      (1, ("a", 6.00)),
      (1, ("b", 5.00)),
      (1, ("b", 6.00)))

    // typical left join, include all from a
    a.leftOuterJoin(b).collect() should be === Array(
      (1, ("a", Some(5.00))),
      (1, ("a", Some(6.00))),
      (1, ("b", Some(5.00))),
      (1, ("b", Some(6.00))),
      (2, ("c", None)))

    // typical right join, include all from b
    a.rightOuterJoin(b).collect() should be === Array(
      (3, (None, 7.00)),
      (1, (Some("a"), 5.00)),
      (1, (Some("a"), 6.00)),
      (1, (Some("b"), 5.00)),
      (1, (Some("b"), 6.00)))
    // cogroup, which is the primitive, and returns each
    // key with the key's elements from both a and b
    a.cogroup(b).collect() should be === Array(
      (3, (Seq(), Seq(7.00))),
      (1, (Seq("a", "b"), Seq(5.00, 6.00))),
      (2, (Seq("c"), Seq())))
  }
}

A few things to note:

  1. Spark’s primary API is a Scala DSL, with a collection-like RDD type, so our reports are mostly map, filter, and join calls.

  2. Holy shit this is a unit test–completely unlike Hive, Spark can run locally (see the "local" parameter to SparkContext) very trivially, by being embedded in a unit test.

    The above test takes ~2 seconds to spin up Spark in local mode (which is not actually in-process) and execute the tests. Amazing!

    This is a huge reason as to why I am liking Spark much more than Hive, where our reports were basically not under test.

  3. The above test is just kicking the tires of cogroup, join, leftOuterJoin, and rightOuterJoin.

    While the join flavors are recognizable from SQL, it is interesting that cogroup is actually the primitive operation, and the join operations are implemented as secondary reorganizations of the data (via map/flatMap calls) returned from cogroup.

    Refreshingly, you can read the Spark implementation of the various join operations, and they really are just 4-5 lines of very readable Scala code.

This is obviously a small example, where I was just loading some very small, hard-coded data into Spark (via the parallelize calls). Eventually I hope to post more Spark-related techniques/results as the report we’re working on gets more flushed out.