Alex, I didn't communicate properly. By "private", I simply meant the expectation that it is not a public API. The plan is to still omit it from the scaladoc/javadoc generation, but no language visibility modifier will be applied on them.
After 1.3, you will likely no longer need to use things in sql.catalyst package directly. Programmatically construct SchemaRDDs is going to be a first class public API. Data types have already been moved out of the sql.catalyst package and now lives in sql.types. They are becoming stable public APIs. When the "data frame" patch is submitted, you will see a public expression library also. There will be few reason for end users or library developers to hook into things in sql.catalyst. For the bravest and the most advanced, they can still use them, with the expectation that it is subject to change. On Thu, Jan 15, 2015 at 7:53 AM, Alessandro Baretta <alexbare...@gmail.com> wrote: > Reynold, > > Thanks for the heads up. In general, I strongly oppose the use of > "private" to restrict access to certain parts of the API, the reason being > that I might find the need to use some of the internals of a library from > my own project. I find that a @DeveloperAPI annotation serves the same > purpose as "private" without imposing unnecessary restrictions: it > discourages people from using the annotated API and reserves the right for > the core developers to change it suddenly in backwards incompatible ways. > > In particular, I would like to express the desire that the APIs to > programmatically construct SchemaRDDs from an RDD[Row] and a StructType > remain public. All the SparkSQL data type objects should be exposed by the > API, and the jekyll build should not hide the docs as it does now. > > Thanks. > > Alex > > On Wed, Jan 14, 2015 at 9:45 PM, Reynold Xin <r...@databricks.com> wrote: > >> 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. >> > >
