b62c35245a
Sorry for that PR #4330 has some mistakes. I correct it.... so it works correctly now. Author: OopsOutOfMemory <victorshengli@126.com> Closes #4389 from OopsOutOfMemory/doc and squashes the following commits: 843eed9 [OopsOutOfMemory] correct hiveconsole imports
63 lines
2.8 KiB
Markdown
63 lines
2.8 KiB
Markdown
Spark SQL
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=========
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This module provides support for executing relational queries expressed in either SQL or a LINQ-like Scala DSL.
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Spark SQL is broken up into four subprojects:
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- Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
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- 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.
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- 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.
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- HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.
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Other dependencies for developers
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---------------------------------
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In order to create new hive test cases , you will need to set several environmental variables.
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```
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export HIVE_HOME="<path to>/hive/build/dist"
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export HIVE_DEV_HOME="<path to>/hive/"
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export HADOOP_HOME="<path to>/hadoop-1.0.4"
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```
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Using the console
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=================
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An interactive scala console can be invoked by running `build/sbt hive/console`.
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From here you can execute queries with HiveQl and manipulate DataFrame by using DSL.
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```scala
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catalyst$ build/sbt hive/console
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[info] Starting scala interpreter...
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import org.apache.spark.sql.catalyst.analysis._
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import org.apache.spark.sql.catalyst.dsl._
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import org.apache.spark.sql.catalyst.errors._
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import org.apache.spark.sql.catalyst.expressions._
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import org.apache.spark.sql.catalyst.plans.logical._
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import org.apache.spark.sql.catalyst.rules._
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import org.apache.spark.sql.catalyst.util._
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import org.apache.spark.sql.Dsl._
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import org.apache.spark.sql.execution
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import org.apache.spark.sql.hive._
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import org.apache.spark.sql.hive.test.TestHive._
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import org.apache.spark.sql.types._
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import org.apache.spark.sql.parquet.ParquetTestData
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Type in expressions to have them evaluated.
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Type :help for more information.
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scala> val query = sql("SELECT * FROM (SELECT * FROM src) a")
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query: org.apache.spark.sql.DataFrame = org.apache.spark.sql.DataFrame@74448eed
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```
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Query results are `DataFrames` and can be operated as such.
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```
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scala> query.collect()
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res2: Array[org.apache.spark.sql.Row] = Array([238,val_238], [86,val_86], [311,val_311], [27,val_27]...
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```
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You can also build further queries on top of these `DataFrames` using the query DSL.
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```
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scala> query.where('key > 30).select(avg('key)).collect()
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res3: Array[org.apache.spark.sql.Row] = Array([274.79025423728814])
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```
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