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https://issues.apache.org/jira/browse/SPARK-9999?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14955840#comment-14955840
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Sandy Ryza commented on SPARK-9999:
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If I understand correctly, it seems like there are ways to work around each of
these issues that, necessarily, make the API dirtier, but avoid the need for a
whole new public API.
* groupBy: deprecate the old groupBy and add a groupWith or groupby method that
returns a GroupedRDD.
* partitions: have -1 be a special value that means "determined by the planner"
* encoders: what are the main obstacles to addressing this with an EncodedRDD
that extends RDD?
Regarding the issues Michael brought up:
I'd love to get rid of class tags from the public API as well as take out
JavaRDD, but these seem more like "nice to have" than core to the proposal. Am
I misunderstanding?
All of these of course add ugliness, but I think it's really easy to
underestimate the cost of introducing a new API. Applications everywhere
become legacy and need to be rewritten to take advantage of new features. Code
examples and training materials everywhere become invalidated. Can we point to
systems that have successfully made a transition like this at this point in
their maturity?
> RDD-like API on top of Catalyst/DataFrame
> -----------------------------------------
>
> Key: SPARK-9999
> URL: https://issues.apache.org/jira/browse/SPARK-9999
> Project: Spark
> Issue Type: Story
> Components: SQL
> Reporter: Reynold Xin
> Assignee: Michael Armbrust
>
> The RDD API is very flexible, and as a result harder to optimize its
> execution in some cases. The DataFrame API, on the other hand, is much easier
> to optimize, but lacks some of the nice perks of the RDD API (e.g. harder to
> use UDFs, lack of strong types in Scala/Java).
> The goal of Spark Datasets is to provide an API that allows users to easily
> express transformations on domain objects, while also providing the
> performance and robustness advantages of the Spark SQL execution engine.
> h2. Requirements
> - *Fast* - In most cases, the performance of Datasets should be equal to or
> better than working with RDDs. Encoders should be as fast or faster than
> Kryo and Java serialization, and unnecessary conversion should be avoided.
> - *Typesafe* - Similar to RDDs, objects and functions that operate on those
> objects should provide compile-time safety where possible. When converting
> from data where the schema is not known at compile-time (for example data
> read from an external source such as JSON), the conversion function should
> fail-fast if there is a schema mismatch.
> - *Support for a variety of object models* - Default encoders should be
> provided for a variety of object models: primitive types, case classes,
> tuples, POJOs, JavaBeans, etc. Ideally, objects that follow standard
> conventions, such as Avro SpecificRecords, should also work out of the box.
> - *Java Compatible* - Datasets should provide a single API that works in
> both Scala and Java. Where possible, shared types like Array will be used in
> the API. Where not possible, overloaded functions should be provided for
> both languages. Scala concepts, such as ClassTags should not be required in
> the user-facing API.
> - *Interoperates with DataFrames* - Users should be able to seamlessly
> transition between Datasets and DataFrames, without specifying conversion
> boiler-plate. When names used in the input schema line-up with fields in the
> given class, no extra mapping should be necessary. Libraries like MLlib
> should not need to provide different interfaces for accepting DataFrames and
> Datasets as input.
> For a detailed outline of the complete proposed API:
> [marmbrus/dataset-api|https://github.com/marmbrus/spark/pull/18/files]
> For an initial discussion of the design considerations in this API: [design
> doc|https://docs.google.com/document/d/1ZVaDqOcLm2-NcS0TElmslHLsEIEwqzt0vBvzpLrV6Ik/edit#]
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