12a345adcb
Still, we keep only a single HiveContext within ThriftServer, and we also create a object called `SQLSession` for isolating the different user states. Developers can obtain/release a new user session via `openSession` and `closeSession`, and `SQLContext` and `HiveContext` will also provide a default session if no `openSession` called, for backward-compatibility. Author: Cheng Hao <hao.cheng@intel.com> Closes #4885 from chenghao-intel/multisessions_singlecontext and squashes the following commits: 1c47b2a [Cheng Hao] rename the tss => tlSession 815b27a [Cheng Hao] code style issue 57e3fa0 [Cheng Hao] openSession is not compatible between Hive0.12 & 0.13.1 4665b0d [Cheng Hao] thriftservice with single context |
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catalyst | ||
core | ||
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 build/sbt hive/console
.
From here you can execute queries with HiveQl and manipulate DataFrame by using DSL.
catalyst$ build/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.util._
import org.apache.spark.sql.execution
import org.apache.spark.sql.functions._
import org.apache.spark.sql.hive._
import org.apache.spark.sql.hive.test.TestHive._
import org.apache.spark.sql.types._
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.DataFrame = org.apache.spark.sql.DataFrame@74448eed
Query results are DataFrames
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 DataFrames
using the query DSL.
scala> query.where('key > 30).select(avg('key)).collect()
res3: Array[org.apache.spark.sql.Row] = Array([274.79025423728814])