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Tathagata Das 0d3a63193c [SPARK-20714][SS] Fix match error when watermark is set with timeout = no timeout / processing timeout 
## What changes were proposed in this pull request?

When watermark is set, and timeout conf is NoTimeout or ProcessingTimeTimeout (both do not need the watermark), the query fails at runtime with the following exception.
```
MatchException: Some(org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate1a9b798e) (of class scala.Some)
    org.apache.spark.sql.execution.streaming.FlatMapGroupsWithStateExec$$anonfun$doExecute$1.apply(FlatMapGroupsWithStateExec.scala:120)
    org.apache.spark.sql.execution.streaming.FlatMapGroupsWithStateExec$$anonfun$doExecute$1.apply(FlatMapGroupsWithStateExec.scala:116)
    org.apache.spark.sql.execution.streaming.state.package$StateStoreOps$$anonfun$1.apply(package.scala:70)
    org.apache.spark.sql.execution.streaming.state.package$StateStoreOps$$anonfun$1.apply(package.scala:65)
    org.apache.spark.sql.execution.streaming.state.StateStoreRDD.compute(StateStoreRDD.scala:64)
```

The match did not correctly handle cases where watermark was defined by the timeout was different from EventTimeTimeout.

## How was this patch tested?
New unit tests.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #17954 from tdas/SPARK-20714.
2017-05-12 10:49:50 -07:00
..
catalyst [SPARK-19951][SQL] Add string concatenate operator || to Spark SQL 2017-05-12 09:55:51 -07:00
core [SPARK-20714][SS] Fix match error when watermark is set with timeout = no timeout / processing timeout 2017-05-12 10:49:50 -07:00
hive [SPARK-20718][SQL][FOLLOWUP] Fix canonicalization for HiveTableScanExec 2017-05-12 20:43:22 +08:00
hive-thriftserver [SPARK-20393][WEBU UI] Strengthen Spark to prevent XSS vulnerabilities 2017-05-10 10:59:57 +01:00
README.md [SPARK-16557][SQL] Remove stale doc in sql/README.md 2016-07-14 19:24:42 -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.