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https://issues.apache.org/jira/browse/PHOENIX-1071?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Andrew Purtell updated PHOENIX-1071:
------------------------------------
Description:
A core concept of Apache Spark is the resilient distributed dataset (RDD), a
"fault-tolerant collection of elements that can be operated on in parallel".
One can create a RDDs referencing a dataset in any external storage system
offering a Hadoop InputFormat, like PhoenixInputFormat and PhoenixOutputFormat.
There could be opportunities for additional interesting and deep integration.
Add the ability to save RDDs back to Phoenix with a {{saveAsPhoenixTable}}
action, implicitly creating necessary schema on demand.
Add support for {{filter}} transformations that push predicates to the server.
Add a new {{select}} transformation supporting a LINQ-like DSL, for example:
{code}
// Count the number of different coffee varieties offered by each
// supplier from Guatemala
phoenixTable("coffees")
.select(c =>
where(c.origin == "GT"))
.countByKey()
.foreach(r => println(r._1 + "=" + r._2))
{code}
Support conversions between Scala and Java types and Phoenix table data.
was:
A core concept of Apache Spark is the resilient distributed dataset (RDD), a
"fault-tolerant collection of elements that can be operated on in parallel".
One can create a RDDs referencing a dataset in any external storage system
offering a Hadoop InputFormat, like HBase's TableInputFormat and
TableSnapshotInputFormat. Phoenix as JDBC driver supporting a SQL dialect can
provide interesting and deep integration.
Add the ability to save RDDs back to Phoenix with a {{saveAsPhoenixTable}}
action, implicitly creating necessary schema on demand.
Add support for {{filter}} transformations that push predicates to the server.
Add a new {{select}} transformation supporting a LINQ-like DSL, for example:
{code}
// Count the number of different coffee varieties offered by each
// supplier from Guatemala
phoenixTable("coffees")
.select(c =>
where(c.origin == "GT"))
.countByKey()
.foreach(r => println(r._1 + "=" + r._2))
{code}
Support conversions between Scala and Java types and Phoenix table data.
> Provide integration for exposing Phoenix tables as Spark RDDs
> -------------------------------------------------------------
>
> Key: PHOENIX-1071
> URL: https://issues.apache.org/jira/browse/PHOENIX-1071
> Project: Phoenix
> Issue Type: New Feature
> Reporter: Andrew Purtell
>
> A core concept of Apache Spark is the resilient distributed dataset (RDD), a
> "fault-tolerant collection of elements that can be operated on in parallel".
> One can create a RDDs referencing a dataset in any external storage system
> offering a Hadoop InputFormat, like PhoenixInputFormat and
> PhoenixOutputFormat. There could be opportunities for additional interesting
> and deep integration.
> Add the ability to save RDDs back to Phoenix with a {{saveAsPhoenixTable}}
> action, implicitly creating necessary schema on demand.
> Add support for {{filter}} transformations that push predicates to the server.
> Add a new {{select}} transformation supporting a LINQ-like DSL, for example:
> {code}
> // Count the number of different coffee varieties offered by each
> // supplier from Guatemala
> phoenixTable("coffees")
> .select(c =>
> where(c.origin == "GT"))
> .countByKey()
> .foreach(r => println(r._1 + "=" + r._2))
> {code}
> Support conversions between Scala and Java types and Phoenix table data.
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