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Dongjoon Hyun 32818d9b37 [SPARK-20345][SQL] Fix STS error handling logic on HiveSQLException 
## What changes were proposed in this pull request?

[SPARK-5100](343d3bfafd) added Spark Thrift Server(STS) UI and the following logic to handle exceptions on case `Throwable`.

```scala
HiveThriftServer2.listener.onStatementError(
  statementId, e.getMessage, SparkUtils.exceptionString(e))
```

However, there occurred a missed case after implementing [SPARK-6964](eb19d3f75c)'s `Support Cancellation in the Thrift Server` by adding case `HiveSQLException` before case `Throwable`.

```scala
case e: HiveSQLException =>
  if (getStatus().getState() == OperationState.CANCELED) {
    return
  } else {
    setState(OperationState.ERROR)
    throw e
  }
  // Actually do need to catch Throwable as some failures don't inherit from Exception and
  // HiveServer will silently swallow them.
case e: Throwable =>
  val currentState = getStatus().getState()
  logError(s"Error executing query, currentState $currentState, ", e)
  setState(OperationState.ERROR)
  HiveThriftServer2.listener.onStatementError(
    statementId, e.getMessage, SparkUtils.exceptionString(e))
  throw new HiveSQLException(e.toString)
```

Logically, we had better add `HiveThriftServer2.listener.onStatementError` on case `HiveSQLException`, too.

## How was this patch tested?

N/A

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #17643 from dongjoon-hyun/SPARK-20345.
2017-06-12 14:05:03 -07:00
..
catalyst [SPARK-17914][SQL] Fix parsing of timestamp strings with nanoseconds 2017-06-12 13:06:14 -07:00
core [SPARK-21046][SQL] simplify the array offset and length in ColumnVector 2017-06-13 00:12:34 +08:00
hive [SPARK-21031][SQL] Add alterTableStats to store spark's stats and let alterTable keep existing stats 2017-06-12 08:23:04 +08:00
hive-thriftserver [SPARK-20345][SQL] Fix STS error handling logic on HiveSQLException 2017-06-12 14:05:03 -07:00
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.