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Posted to dev@avro.apache.org by rob-smallshire <gi...@git.apache.org> on 2017/11/08 17:22:05 UTC

[GitHub] avro pull request #258: AVRO-2104: Schema normalization and fingerprinting f...

GitHub user rob-smallshire opened a pull request:

    https://github.com/apache/avro/pull/258

    AVRO-2104: Schema normalization and fingerprinting for Python 3.

    An implementation of schema normalization and fingerprinting for Python
    3. The code is a fairly literal translation of the Java code and is
    entirely additional to the existing code, except for one small change
    in the way schema.Name is implemented to improve compatibility with the
    Java version.
    
    The new implementation is covered by 138 unit tests which have been
    automatically generated by a script (not provided) which executed the
    Java Avro normalization and fingerprinting code to provide expected
    values for the Python implementation to reproduce.

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

    $ git pull https://github.com/sixty-north/avro schema-fingerprint

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

    https://github.com/apache/avro/pull/258.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 #258
    
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commit e3730b9ae7194b7292c5879f253f50541aa194a1
Author: Robert Smallshire <ro...@sixty-north.com>
Date:   2017-09-12T13:21:16Z

    AVRO-2104: Schema normalization and fingerprinting for Python 3.
    
    An implementation of schema normalization and fingerprinting for Python
    3. The code is a fairly literal translation of the Java code and is
    entirely additional to the existing code, except for one small change
    in the way schema.Name is implemented to improve compatibility with the
    Java version.
    
    The new implementation is covered by 138 unit tests which have been
    automatically generated by a script (not provided) which executed the
    Java Avro normalization and fingerprinting code to provide expected
    values for the Python implementation to reproduce.

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