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From chinmaykolhatkar <...@git.apache.org>
Subject [GitHub] apex-malhar pull request #432: [Review Only] APEXMALHAR-1818 SQL Support for...
Date Mon, 17 Oct 2016 16:30:26 GMT
GitHub user chinmaykolhatkar reopened a pull request:

    https://github.com/apache/apex-malhar/pull/432

    [Review Only] APEXMALHAR-1818 SQL Support for converting given SQL statement to APEX DAG

    Features implemented are:
    1. SELECT STATEMENT
    2. INSERT STATEMENT
    3. INNER JOIN with non-empty equi join condition
    4. WHERE clause
    5. SCALAR functions implemented in calcite are ready to use
    6. Custom scalar functions can be registered.
    7. Endpoint can be File OR Kafka OR Streaming Port for both input and output
    8. CSV Data Format implemented for both input and output side.
    9. Static loading of calcite JDBC driver.
    10. Testing on local as well as cluster mode.

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/chinmaykolhatkar/apex-malhar calcite

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/apex-malhar/pull/432.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #432
    
----
commit 2958c1159780d1ae7f00c67ab61c32f78b903b1e
Author: Chandni Singh <csingh@apache.org>
Date:   2016-10-13T12:03:18Z

    APEXMALHAR-1818 Adding BeanClassGenerator for dynamically creating class

commit 6e82fbd6ee2b15baa53fdc8a8af338326a322f62
Author: Chinmay Kolhatkar <chinmay@datatorrent.com>
Date:   2016-10-13T12:05:16Z

    APEXMALHAR-1818 SQL Support for converting given SQL statement to APEX DAG.
    
    Features implemented are:
    1. SELECT STATEMENT
    2. INSERT STATEMENT
    3. INNER JOIN with non-empty equi join condition
    4. WHERE clause
    5. SCALAR functions implemented in calcite are ready to use
    6. Custom scalar functions can be registered.
    7. Endpoint can be File OR Kafka OR Streaming Port for both input and output
    8. CSV Data Format implemented for both input and output side.
    9. Testing on local as well as cluster mode.

----


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