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This patch replaces colon in several filenames with dash to make these filenames Windows compatible. Author: Stevo Slavić <sslavic@gmail.com> Author: Stevo Slavic <sslavic@gmail.com> Closes #739 from sslavic/SPARK-1803 and squashes the following commits: 3ec66eb [Stevo Slavic] Removed extra empty line which was causing test to fail b967cc3 [Stevo Slavić] Aligned tests and names of test resources 2b12776 [Stevo Slavić] Fixed a typo in file name 1c5dfff [Stevo Slavić] Replaced colon in file name with dash 8f5bf7f [Stevo Slavić] Replaced colon in file name with dash c5b5083 [Stevo Slavić] Replaced colon in file name with dash a49801f [Stevo Slavić] Replaced colon in file name with dash 401d99e [Stevo Slavić] Replaced colon in file name with dash 40a9621 [Stevo Slavić] Replaced colon in file name with dash 4774580 [Stevo Slavić] Replaced colon in file name with dash 004f8bb [Stevo Slavić] Replaced colon in file name with dash d6a3e2c [Stevo Slavić] Replaced colon in file name with dash b585126 [Stevo Slavić] Replaced colon in file name with dash 028e48a [Stevo Slavić] Replaced colon in file name with dash ece0507 [Stevo Slavić] Replaced colon in file name with dash 84f5d2f [Stevo Slavić] Replaced colon in file name with dash 2fc7854 [Stevo Slavić] Replaced colon in file name with dash 9e1467d [Stevo Slavić] Replaced colon in file name with dash |
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catalyst | ||
core | ||
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