a74fbbbca8
Author: witgo <witgo@qq.com> Closes #325 from witgo/SPARK-1413 and squashes the following commits: e57cd8e [witgo] use scala reflection to access and call the SLF4JBridgeHandler methods 45c8f40 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413 5e35d87 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413 0d5f819 [witgo] review commit 45e5b70 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413 fa69dcf [witgo] Merge branch 'master' into SPARK-1413 3c98dc4 [witgo] Merge branch 'master' into SPARK-1413 38160cb [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413 ba09bcd [witgo] remove set the parquet log level a63d574 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413 5231ecd [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413 3feb635 [witgo] parquet logger use parent handler fa00d5d [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413 8bb6ffd [witgo] enableLogForwarding note fix edd9630 [witgo] move to f447f50 [witgo] merging master 5ad52bd [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413 76670c1 [witgo] review commit 70f3c64 [witgo] Fix SPARK-1413 |
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catalyst | ||
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hive | ||
README.md |
Spark SQL
This module provides support for executing relational queries expressed in either SQL or a LINQ-like Scala DSL.
Spark SQL is broken up into three 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.
Other dependencies for developers
In order to create new hive test cases , you will need to set several environmental variables.
export HIVE_HOME="<path to>/hive/build/dist"
export HIVE_DEV_HOME="<path to>/hive/"
export HADOOP_HOME="<path to>/hadoop-1.0.4"
Using the console
An interactive scala console can be invoked by running sbt/sbt hive/console
. From here you can execute queries and inspect the various stages of query optimization.
catalyst$ sbt/sbt hive/console
[info] Starting scala interpreter...
import org.apache.spark.sql.catalyst.analysis._
import org.apache.spark.sql.catalyst.dsl._
import org.apache.spark.sql.catalyst.errors._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.rules._
import org.apache.spark.sql.catalyst.types._
import org.apache.spark.sql.catalyst.util._
import org.apache.spark.sql.execution
import org.apache.spark.sql.hive._
import org.apache.spark.sql.hive.TestHive._
Welcome to Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_45).
Type in expressions to have them evaluated.
Type :help for more information.
scala> val query = sql("SELECT * FROM (SELECT * FROM src) a")
query: org.apache.spark.sql.ExecutedQuery =
SELECT * FROM (SELECT * FROM src) a
=== Query Plan ===
Project [key#6:0.0,value#7:0.1]
HiveTableScan [key#6,value#7], (MetastoreRelation default, src, None), None
Query results are RDDs and can be operated as such.
scala> query.collect()
res8: Array[org.apache.spark.sql.execution.Row] = Array([238,val_238], [86,val_86], [311,val_311]...
You can also build further queries on top of these RDDs using the query DSL.
scala> query.where('key === 100).toRdd.collect()
res11: Array[org.apache.spark.sql.execution.Row] = Array([100,val_100], [100,val_100])
From the console you can even write rules that transform query plans. For example, the above query has redundant project operators that aren't doing anything. This redundancy can be eliminated using the transform
function that is available on all TreeNode
objects.
scala> query.logicalPlan
res1: catalyst.plans.logical.LogicalPlan =
Project {key#0,value#1}
Project {key#0,value#1}
MetastoreRelation default, src, None
scala> query.logicalPlan transform {
| case Project(projectList, child) if projectList == child.output => child
| }
res2: catalyst.plans.logical.LogicalPlan =
Project {key#0,value#1}
MetastoreRelation default, src, None