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