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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-6745) Table API / SQL Docs: Overview Page
Date Thu, 01 Jun 2017 11:46:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-6745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16032834#comment-16032834

ASF GitHub Bot commented on FLINK-6745:

Github user alpinegizmo commented on a diff in the pull request:

    --- Diff: docs/dev/tableApi.md ---
    @@ -25,32 +25,16 @@ specific language governing permissions and limitations
     under the License.
    -**Table API and SQL are experimental features**
    +Apache Flink features two relational APIs - the Table API and SQL - for unified stream
and batch processing. The Table API is a language-integrated query API for Scala and Java
that allows the composition of queries from relational operators such as selection, filter,
and join in a very intuitive way. Flink's SQL support is based on [Apache Calcite](https://calcite.apache.org)
which implements the SQL standard. Queries specified in either interface have the same semantics
and specify the same result regardless whether the input is a batch input (DataSet) or a stream
input (DataStream).
    -The Table API is a SQL-like expression language for relational stream and batch processing
that can be easily embedded in Flink's DataSet and DataStream APIs (Java and Scala).
    -The Table API and SQL interface operate on a relational `Table` abstraction, which can
be created from external data sources, or existing DataSets and DataStreams. With the Table
API, you can apply relational operators such as selection, aggregation, and joins on `Table`s.
    +The Table API and the SQL interfaces are tightly integrated with each other as well as
Flink's DataStream and DataSet APIs. You can easily switch between all APIs and libraries
which build upon the APIs. For instance, you can extract patterns from a DataStream using
the [CEP library]({{ site.baseurl }}/dev/libs/cep.html) and later use the Table API to analyze
the patterns, or you scan, filter, and aggregate a batch table using a SQL query before running
a [Gelly graph algorithm]({{ site.baseurl }}/dev/libs/gelly) on the preprocessed data.
    --- End diff --
    ... or you might scan ...

> Table API / SQL Docs: Overview Page
> -----------------------------------
>                 Key: FLINK-6745
>                 URL: https://issues.apache.org/jira/browse/FLINK-6745
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Documentation, Table API & SQL
>    Affects Versions: 1.3.0
>            Reporter: Fabian Hueske
>            Assignee: Fabian Hueske
> Update and refine ./docs/dev/tableApi.md in feature branch https://github.com/apache/flink/tree/tableDocs

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