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From Reynold Xin <r...@databricks.com>
Subject Spark SQL API changes and stabilization
Date Thu, 15 Jan 2015 05:45:24 GMT
Hi Spark devs,

Given the growing number of developers that are building on Spark SQL, we
would like to stabilize the API in 1.3 so users and developers can be
confident to build on it. This also gives us a chance to improve the API.

In particular, we are proposing the following major changes. This should
have no impact for most users (i.e. those running SQL through the JDBC
client or SQLContext.sql method).

1. Everything in sql.catalyst package is private to the project.

2. Redesign SchemaRDD DSL (SPARK-5097): We initially added the DSL for
SchemaRDD and logical plans in order to construct test cases. We have
received feedback from a lot of users that the DSL can be incredibly
powerful. In 1.3, we’d like to refactor the DSL to make it suitable for not
only constructing test cases, but also in everyday data pipelines. The new
SchemaRDD API is inspired by the data frame concept in Pandas and R.

3. Reconcile Java and Scala APIs (SPARK-5193): We would like to expose one
set of APIs that will work for both Java and Scala. The current Java API
(sql.api.java) does not share any common ancestor with the Scala API. This
led to high maintenance burden for us as Spark developers and for library
developers. We propose to eliminate the Java specific API, and simply work
on the existing Scala API to make it also usable for Java. This will make
Java a first class citizen as Scala. This effectively means that all public
classes should be usable for both Scala and Java, including SQLContext,
HiveContext, SchemaRDD, data types, and the aforementioned DSL.


Again, this should have no impact on most users since the existing DSL is
rarely used by end users. However, library developers might need to change
the import statements because we are moving certain classes around. We will
keep you posted as patches are merged.

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