[SPARK-9999] Dataset API on top of Catalyst/DataFrame

• Type: Story
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Fix Version/s: 2.0.0
• Component/s: SQL
• Target Version/s: 2.0.0
• Assignee: Michael Armbrust
• Reporter: Reynold Xin
• Created: 14/Aug/15 20:20
• Updated: 30/Jun/16 17:46
• Resolved: 15/Mar/16 22:59

Description

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.

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
For an initial discussion of the design considerations in this API: design doc

The initial version of the Dataset API has been merged in Spark 1.6. However, it will take a few more future releases to flush everything out.

Issue Links

• is related to: SPARK-1021 sortByKey() launches a cluster job when it shouldn't
• is related to: SPARK-2991 RDD transforms for scan and scanLeft
• is related to: SPARK-2315 drop, dropRight and dropWhile which take RDD input and return RDD
• is related to: SPARK-2992 The transforms formerly known as non-lazy
• is related to: SPARK-8360 Structured Streaming (aka Streaming DataFrames)
• relates to: SPARK-12171 Support DataSet API in SparkR