Uploaded image for project: 'Spark'
  1. Spark
  2. SPARK-2365

Add IndexedRDD, an efficient updatable key-value store



    • Type: New Feature
    • Status: Closed
    • Priority: Major
    • Resolution: Later
    • Affects Version/s: None
    • Fix Version/s: None
    • Component/s: GraphX, Spark Core
    • Labels:


      RDDs currently provide a bulk-updatable, iterator-based interface. This imposes minimal requirements on the storage layer, which only needs to support sequential access, enabling on-disk and serialized storage.

      However, many applications would benefit from a richer interface. Efficient support for point lookups would enable serving data out of RDDs, but it currently requires iterating over an entire partition to find the desired element. Point updates similarly require copying an entire iterator. Joins are also expensive, requiring a shuffle and local hash joins.

      To address these problems, we propose IndexedRDD, an efficient key-value store built on RDDs. IndexedRDD would extend RDD[(Long, V)] by enforcing key uniqueness and pre-indexing the entries for efficient joins and point lookups, updates, and deletions.

      It would be implemented by (1) hash-partitioning the entries by key, (2) maintaining a hash index within each partition, and (3) using purely functional (immutable and efficiently updatable) data structures to enable efficient modifications and deletions.

      GraphX would be the first user of IndexedRDD, since it currently implements a limited form of this functionality in VertexRDD. We envision a variety of other uses for IndexedRDD, including streaming updates to RDDs, direct serving from RDDs, and as an execution strategy for Spark SQL.


          Issue Links



              • Assignee:
                ankurd Ankur Dave
                ankurd Ankur Dave
              • Votes:
                32 Vote for this issue
                81 Start watching this issue


                • Created: