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Wenchen Fan ce6a71e013 [SPARK-22076][SQL] Expand.projections should not be a Stream 
## What changes were proposed in this pull request?

Spark with Scala 2.10 fails with a group by cube:
```
spark.range(1).select($"id" as "a", $"id" as "b").write.partitionBy("a").mode("overwrite").saveAsTable("rollup_bug")
spark.sql("select 1 from rollup_bug group by rollup ()").show
```

It can be traced back to https://github.com/apache/spark/pull/15484 , which made `Expand.projections` a lazy `Stream` for group by cube.

In scala 2.10 `Stream` captures a lot of stuff, and in this case it captures the entire query plan which has some un-serializable parts.

This change is also good for master branch, to reduce the serialized size of `Expand.projections`.

## How was this patch tested?

manually verified with Spark with Scala 2.10.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #19289 from cloud-fan/bug.
2017-09-20 09:00:43 -07:00
..
catalyst [SPARK-22076][SQL] Expand.projections should not be a Stream 2017-09-20 09:00:43 -07:00
core [SPARK-22049][DOCS] Confusing behavior of from_utc_timestamp and to_utc_timestamp 2017-09-20 20:47:17 +09:00
hive [SPARK-21428][SQL][FOLLOWUP] CliSessionState should point to the actual metastore not a dummy one 2017-09-19 19:35:36 +08:00
hive-thriftserver [SPARK-21428][SQL][FOLLOWUP] CliSessionState should point to the actual metastore not a dummy one 2017-09-19 19:35:36 +08:00
create-docs.sh [MINOR][DOCS] Minor doc fixes related with doc build and uses script dir in SQL doc gen script 2017-08-26 13:56:24 +09:00
gen-sql-markdown.py [SPARK-21485][FOLLOWUP][SQL][DOCS] Describes examples and arguments separately, and note/since in SQL built-in function documentation 2017-08-05 10:10:56 -07:00
mkdocs.yml [SPARK-21485][SQL][DOCS] Spark SQL documentation generation for built-in functions 2017-07-26 09:38:51 -07:00
README.md [SPARK-21485][SQL][DOCS] Spark SQL documentation generation for built-in functions 2017-07-26 09:38:51 -07:00

README.md

Spark SQL

This module provides support for executing relational queries expressed in either SQL or the DataFrame/Dataset API.

Spark SQL is broken up into four subprojects:

  • Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
  • Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
  • Hive Support (sql/hive) - Includes an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allows users to run queries that include Hive UDFs, UDAFs, and UDTFs.
  • HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.

Running sql/create-docs.sh generates SQL documentation for built-in functions under sql/site.