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https://issues.apache.org/jira/browse/SPARK-2365?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15489250#comment-15489250
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Ganesh Krishnan commented on SPARK-2365:
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Is this only for RDD or can we use it with DataFrames? With Spark 2.0
especially the emphasis is on DataFrames and we would like to use only
DataFrames in our project
> Add IndexedRDD, an efficient updatable key-value store
> ------------------------------------------------------
>
> Key: SPARK-2365
> URL: https://issues.apache.org/jira/browse/SPARK-2365
> Project: Spark
> Issue Type: New Feature
> Components: GraphX, Spark Core
> Reporter: Ankur Dave
> Assignee: Ankur Dave
> Attachments: 2014-07-07-IndexedRDD-design-review.pdf
>
>
> 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.
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