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Posted to issues@flink.apache.org by "Liu (JIRA)" <ji...@apache.org> on 2018/12/03 05:31:00 UTC
[jira] [Updated] (FLINK-11050) When IntervalJoin, get left or right
buffer's entries more quickly by assigning lowerBound
[ https://issues.apache.org/jira/browse/FLINK-11050?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Liu updated FLINK-11050:
------------------------
Description:
When IntervalJoin, it is very slow to get left or right buffer's entries. Because we have to scan all buffer's values, including the deleted values which are out of time range. These deleted values's processing consumes too much time in RocksDB's level 0. Since lowerBound is known, it can be optimized by seek from the timestamp of lowerBound.
Our usage is like below:
{code:java}
labelStream.keyBy(uuid).intervalJoin(adLogStream.keyBy(uuid))
.between(Time.milliseconds(0), Time.milliseconds(600000))
.process(new processFunction())
.sink(kafkaProducer)
{code}
Our data is huge. The job always runs for an hour and is stuck by RocksDB's seek when get buffer's entries. We use rocksDB's data to simulate the problem RocksDB.
was:When IntervalJoin, it is very slow to get left or right buffer's entries. Because we have to scan all buffer's values, including the deleted values which are out of time range. These deleted values's processing consumes too much time in RocksDB's level 0. Since lowerBound is known, it can be optimized by seek from the timestamp of lowerBound.
> When IntervalJoin, get left or right buffer's entries more quickly by assigning lowerBound
> ------------------------------------------------------------------------------------------
>
> Key: FLINK-11050
> URL: https://issues.apache.org/jira/browse/FLINK-11050
> Project: Flink
> Issue Type: Improvement
> Components: State Backends, Checkpointing
> Affects Versions: 1.6.2, 1.7.0
> Reporter: Liu
> Priority: Major
> Labels: performance
> Fix For: 1.7.1
>
>
> When IntervalJoin, it is very slow to get left or right buffer's entries. Because we have to scan all buffer's values, including the deleted values which are out of time range. These deleted values's processing consumes too much time in RocksDB's level 0. Since lowerBound is known, it can be optimized by seek from the timestamp of lowerBound.
> Our usage is like below:
> {code:java}
> labelStream.keyBy(uuid).intervalJoin(adLogStream.keyBy(uuid))
> .between(Time.milliseconds(0), Time.milliseconds(600000))
> .process(new processFunction())
> .sink(kafkaProducer)
> {code}
> Our data is huge. The job always runs for an hour and is stuck by RocksDB's seek when get buffer's entries. We use rocksDB's data to simulate the problem RocksDB.
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