[jira] [Commented] (KAFKA-2856) add KTable

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/KAFKA-2856?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15042375#comment-15042375 ] 

ASF GitHub Bot commented on KAFKA-2856:
---------------------------------------

Github user asfgit closed the pull request at:

    https://github.com/apache/kafka/pull/604


> add KTable
> ----------
>
>                 Key: KAFKA-2856
>                 URL: https://issues.apache.org/jira/browse/KAFKA-2856
>             Project: Kafka
>          Issue Type: Sub-task
>          Components: kafka streams
>            Reporter: Yasuhiro Matsuda
>             Fix For: 0.9.1.0
>
>
> KTable is a special type of the stream that represents a changelog of a database table (or a key-value store).
> A changelog has to meet the following requirements.
> * Key-value mapping is surjective in the database table (the key must be the primary key).
> * All insert/update/delete events are delivered in order for the same key
> * An update event has the whole data (not just delta).
> * A delete event is represented by the null value.
> KTable does not necessarily materialized as a local store. It may be materialized when necessary. (see below)
> KTable supports look-up by key. KTable is materialized implicitly when look-up is necessary.
> * KTable may be created from a topic. (Base KTable)
> * KTable may be created from another KTable by filter(), filterOut(), mapValues(). (Derived KTable)
> * A call to the user supplied function is skipped when the value is null since such an event represents a deletion. 
> * Instead of dropping, events filtered out by filter() or filterOut() are converted to delete events. (Can we avoid this?)
> * map(), flatMap() and flatMapValues() are not supported since they may violate the changelog requirements
> A derived KTable may be persisted to a topic by to() or through(). through() creates another base KTable. 
> KTable can be converted to KStream by the toStream() method.



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