An efficient updatable key-value store for Apache Spark.
IndexedRDD extends RDD[(K, V)] by enforcing key uniqueness and pre-indexing the entries for efficient joins and point lookups, updates, and deletions. It is implemented by (1) hash-partitioning the entries by key, (2) maintaining a radix tree (PART) index within each partition, and (3) using this immutable and efficiently updatable data structure to enable efficient modifications and deletions.
Add the dependency to your SBT project by adding the following to build.sbt (see the Spark Packages listing for spark-submit and Maven instructions):
resolvers += "Spark Packages Repo" at "http://dl.bintray.com/spark-packages/maven"
libraryDependencies += "amplab" % "spark-indexedrdd" % "0.3"Then use IndexedRDD as follows:
import edu.berkeley.cs.amplab.spark.indexedrdd.IndexedRDD
import edu.berkeley.cs.amplab.spark.indexedrdd.IndexedRDD._
// Create an RDD of key-value pairs with Long keys.
val rdd = sc.parallelize((1 to 1000000).map(x => (x.toLong, 0)))
// Construct an IndexedRDD from the pairs, hash-partitioning and indexing
// the entries.
val indexed = IndexedRDD(rdd).cache()
// Perform a point update.
val indexed2 = indexed.put(1234L, 10873).cache()
// Perform a point lookup. Note that the original IndexedRDD remains
// unmodified.
indexed2.get(1234L) // => Some(10873)
indexed.get(1234L) // => Some(0)
// Efficiently join derived IndexedRDD with original.
val indexed3 = indexed.innerJoin(indexed2) { (id, a, b) => b }.filter(_._2 != 0)
indexed3.collect // => Array((1234L, 10873))
// Perform insertions and deletions.
val indexed4 = indexed2.put(-100L, 111).delete(Array(998L, 999L)).cache()
indexed2.get(-100L) // => None
indexed4.get(-100L) // => Some(111)
indexed2.get(999L) // => Some(0)
indexed4.get(999L) // => None