[jira] [Updated] (SPARK-2365) Add IndexedRDD, an efficient updatable key-value store
[ https://issues.apache.org/jira/browse/SPARK-2365?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen updated SPARK-2365: - Target Version/s: (was: 1.2.0) > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-2365) Add IndexedRDD, an efficient updatable key-value store
[ https://issues.apache.org/jira/browse/SPARK-2365?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Patrick Wendell updated SPARK-2365: --- Target Version/s: 1.2.0 (was: 1.1.0) > 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. -- This message was sent by Atlassian JIRA (v6.2#6252) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-2365) Add IndexedRDD, an efficient updatable key-value store
[ https://issues.apache.org/jira/browse/SPARK-2365?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Ankur Dave updated SPARK-2365: -- Attachment: 2014-07-07-IndexedRDD-design-review.pdf Slides explaining the motivation, design, and performance of IndexedRDD. > 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. -- This message was sent by Atlassian JIRA (v6.2#6252)
