Re: Spark SQL API changes and stabilization
Reynold, Your clarification is much appreciated. One issue though, that I would strongly encourage you to work on, is to make sure that the Scaladoc CAN be generated manually if needed (a Use at your own risk clause would be perfectly legitimate here). The reason I say this is that currently even hacking SparkBuild.scala to include SparkSQL in the unidoc target doesn't help, as scaladoc itself fails with errors such as these. [error] /Users/alex/git/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala:359: polymorphic expression cannot be instantiated to expected type; [error] found : [T(in method apply)]org.apache.spark.sql.catalyst.dsl.ScalaUdfBuilder[T(in method apply)] [error] required: org.apache.spark.sql.catalyst.dsl.package.ScalaUdfBuilder[T(in method functionToUdfBuilder)] [error] implicit def functionToUdfBuilder[T: TypeTag](func: Function22[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, T]): ScalaUdfBuilder[T] = ScalaUdfBuilder(func) [error] ^ [error] /Users/alex/git/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala:147: value q is not a member of StringContext [error] Note: implicit class Evaluate2 is not applicable here because it comes after the application point and it lacks an explicit result type [error] q [error] ^ [error] /Users/alex/git/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala:181: value q is not a member of StringContext [error] q [error] ^ [error] /Users/alex/git/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala:198: value q is not a member of StringContext While I understand you desire to discourage users from relying on the internal private APIs, there is no reason to prevent people from gaining a better understanding of how things work by allow them--with some effort--to get to the docs. Thanks, Alex On Thu, Jan 15, 2015 at 10:33 AM, Reynold Xin r...@databricks.com wrote: 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
Re: Spark SQL API changes and stabilization
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.
Re: Spark SQL API changes and stabilization
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.
Re: Spark SQL API changes and stabilization
Reynold, One thing I'd like worked into the public portion of the API is the json inferencing logic that creates a Set[(String, StructType)] out of Map[String,Any]. SPARK-5260 addresses this so that I can use Accumulators to infer my schema instead of forcing a map/reduce phase to occur on an RDD in order to get the final schema. Do you (or anyone else) see a path forward in exposing this to users? A utility class perhaps? On Thu, Jan 15, 2015 at 1:33 PM, Reynold Xin r...@databricks.com wrote: 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.
