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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-5654) Add processing time OVER RANGE BETWEEN x PRECEDING aggregation to SQL
Date Sun, 26 Mar 2017 19:04:42 GMT

    [ https://issues.apache.org/jira/browse/FLINK-5654?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15942407#comment-15942407
] 

ASF GitHub Bot commented on FLINK-5654:
---------------------------------------

Github user fhueske commented on the issue:

    https://github.com/apache/flink/pull/3590
  
    Hi @rtudoran,
    
    thanks for doing the benchmark and posting the numbers! The recommended state backend
for production settings is the RocksDB backend (see [production-readiness docs](https://ci.apache.org/projects/flink/flink-docs-release-1.2/ops/production_ready.html#choice-of-state-backend)).
The in-memory backends store state as objects on the heap and can easily kill the JVM with
an OutOfMemoryError. Also the in-memory backends do not de/serialize data, so there is not
an actual advantage is using the MapState which was mainly motivated by the reduced serialization
effort. There are plans to implement a state backend using managed memory (similar to the
batch algorithms). This backend would also serialize and deserialize data to/from pre-allocated
byte arrays. So optimizing for de/serialization makes sense, IMO.
    
    The `KeyedOneInputStreamOperatorTestHarness` is part of the `flink-streaming-java` test-jar
artifact. This is added by adding the following dependency to the `flink-table` `pom.xml`.
    
    ```<dependency>
    	<groupId>org.apache.flink</groupId>
    	<artifactId>flink-streaming-java_2.10</artifactId>
    	<version>${project.version}</version>
    	<type>test-jar</type>
    	<scope>test</scope>
    </dependency>
    ```
    
    I'll have a detailed look at your PR tomorrow.
    Best, Fabian


> Add processing time OVER RANGE BETWEEN x PRECEDING aggregation to SQL
> ---------------------------------------------------------------------
>
>                 Key: FLINK-5654
>                 URL: https://issues.apache.org/jira/browse/FLINK-5654
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Table API & SQL
>            Reporter: Fabian Hueske
>            Assignee: radu
>
> The goal of this issue is to add support for OVER RANGE aggregations on processing time
streams to the SQL interface.
> Queries similar to the following should be supported:
> {code}
> SELECT 
>   a, 
>   SUM(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN INTERVAL '1' HOUR PRECEDING
AND CURRENT ROW) AS sumB,
>   MIN(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN INTERVAL '1' HOUR PRECEDING
AND CURRENT ROW) AS minB
> FROM myStream
> {code}
> The following restrictions should initially apply:
> - All OVER clauses in the same SELECT clause must be exactly the same.
> - The PARTITION BY clause is optional (no partitioning results in single threaded execution).
> - The ORDER BY clause may only have procTime() as parameter. procTime() is a parameterless
scalar function that just indicates processing time mode.
> - UNBOUNDED PRECEDING is not supported (see FLINK-5657)
> - FOLLOWING is not supported.
> The restrictions will be resolved in follow up issues. If we find that some of the restrictions
are trivial to address, we can add the functionality in this issue as well.
> This issue includes:
> - Design of the DataStream operator to compute OVER ROW aggregates
> - Translation from Calcite's RelNode representation (LogicalProject with RexOver expression).



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