e4f42631a6
This PR simplify serializer, always use batched serializer (AutoBatchedSerializer as default), even batch size is 1. Author: Davies Liu <davies@databricks.com> This patch had conflicts when merged, resolved by Committer: Josh Rosen <joshrosen@databricks.com> Closes #2920 from davies/fix_autobatch and squashes the following commits: e544ef9 [Davies Liu] revert unrelated change 6880b14 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 1d557fc [Davies Liu] fix tests 8180907 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 76abdce [Davies Liu] clean up 53fa60b [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch d7ac751 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 2cc2497 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch b4292ce [Davies Liu] fix bug in master d79744c [Davies Liu] recover hive tests be37ece [Davies Liu] refactor eb3938d [Davies Liu] refactor serializer in scala 8d77ef2 [Davies Liu] simplify serializer, use AutoBatchedSerializer by default. |
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catalyst | ||
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hive | ||
hive-thriftserver | ||
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 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.
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.SchemaRDD =
== Query Plan ==
== Physical Plan ==
HiveTableScan [key#10,value#11], (MetastoreRelation default, src, None), None
Query results are RDDs and can be operated as such.
scala> query.collect()
res2: Array[org.apache.spark.sql.Row] = Array([238,val_238], [86,val_86], [311,val_311], [27,val_27]...
You can also build further queries on top of these RDDs using the query DSL.
scala> query.where('key === 100).collect()
res3: Array[org.apache.spark.sql.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.queryExecution.analyzed
res4: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [key#10,value#11]
Project [key#10,value#11]
MetastoreRelation default, src, None
scala> query.queryExecution.analyzed transform {
| case Project(projectList, child) if projectList == child.output => child
| }
res5: res17: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [key#10,value#11]
MetastoreRelation default, src, None