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https://issues.apache.org/jira/browse/SPARK-9999?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14959347#comment-14959347
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Michael Armbrust commented on SPARK-9999:
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Yeah, that Scala code should work. Regarding the Java version, the only
difference is the API I have in mind would be {{Encoder.for(MyClass2.class)}}.
Passing in an encoder instead of a raw {{Class[_]}} gives us some extra
indirection in case we want to support custom encoders some day.
I'll add that we can also play reflection tricks in cases where things are not
erased for Java, and this is the part of the proposal that is the least thought
out at the moment. Any help making this part as powerful/robust as possible
would be greatly appreciated.
I think that is possible that in the long term we will do as you propose and
remake the RDD API as a compatibility layer with the option to infer the
encoder based on the class tag. The problem with this being the primary
implementation is erasure.
{code}
scala> import scala.reflect._
scala> classTag[(Int, Int)].erasure.getTypeParameters
res0: Array[java.lang.reflect.TypeVariable[Class[_$1]]] forSome { type _$1 } =
Array(T1, T2)
{code}
We've lost the type of {{_1}} and {{_2}} and so we are going to have to fall
back on runtime reflection again, per tuple. Where as the encoders that are
checked into master could extract primitive int without any additional boxing
and encode them directly into tungsten buffers.
> 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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