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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-23110) ML 2.3 QA: API: Java compatibility, docs
Date Wed, 17 Jan 2018 00:55:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-23110?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Joseph K. Bradley updated SPARK-23110:
--------------------------------------
    Target Version/s:   (was: 2.2.0)

> ML 2.3 QA: API: Java compatibility, docs
> ----------------------------------------
>
>                 Key: SPARK-23110
>                 URL: https://issues.apache.org/jira/browse/SPARK-23110
>             Project: Spark
>          Issue Type: Sub-task
>          Components: Documentation, Java API, ML, MLlib
>            Reporter: Joseph K. Bradley
>            Priority: Blocker
>
> Check Java compatibility for this release:
> * APIs in {{spark.ml}}
> * New APIs in {{spark.mllib}} (There should be few, if any.)
> Checking compatibility means:
> * Checking for differences in how Scala and Java handle types. Some items to look out
for are:
> ** Check for generic "Object" types where Java cannot understand complex Scala types.
> *** *Note*: The Java docs do not always match the bytecode. If you find a problem, please
verify it using {{javap}}.
> ** Check Scala objects (especially with nesting!) carefully.  These may not be understood
in Java, or they may be accessible only via the weirdly named Java types (with "$" or "#")
which are generated by the Scala compiler.
> ** Check for uses of Scala and Java enumerations, which can show up oddly in the other
language's doc.  (In {{spark.ml}}, we have largely tried to avoid using enumerations, and
have instead favored plain strings.)
> * Check for differences in generated Scala vs Java docs.  E.g., one past issue was that
Javadocs did not respect Scala's package private modifier.
> If you find issues, please comment here, or for larger items, create separate JIRAs and
link here as "requires".
> * Remember that we should not break APIs from previous releases.  If you find a problem,
check if it was introduced in this Spark release (in which case we can fix it) or in a previous
one (in which case we can create a java-friendly version of the API).
> * If needed for complex issues, create small Java unit tests which execute each method.
 (Algorithmic correctness can be checked in Scala.)
> Recommendations for how to complete this task:
> * There are not great tools.  In the past, this task has been done by:
> ** Generating API docs
> ** Building JAR and outputting the Java class signatures for MLlib
> ** Manually inspecting and searching the docs and class signatures for issues
> * If you do have ideas for better tooling, please say so we can make this task easier
in the future!



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