a83936e109
SQLQuerySuite test failure: [info] - simple select (22 milliseconds) [info] - sorting (722 milliseconds) [info] - external sorting (728 milliseconds) [info] - limit (95 milliseconds) [info] - date row *** FAILED *** (35 milliseconds) [info] Results do not match for query: [info] 'Limit 1 [info] 'Project [CAST(2015-01-28, DateType) AS c0#3630] [info] 'UnresolvedRelation [testData], None [info] [info] == Analyzed Plan == [info] Limit 1 [info] Project [CAST(2015-01-28, DateType) AS c0#3630] [info] LogicalRDD [key#0,value#1], MapPartitionsRDD[1] at mapPartitions at ExistingRDD.scala:35 [info] [info] == Physical Plan == [info] Limit 1 [info] Project [16463 AS c0#3630] [info] PhysicalRDD [key#0,value#1], MapPartitionsRDD[1] at mapPartitions at ExistingRDD.scala:35 [info] [info] == Results == [info] !== Correct Answer - 1 == == Spark Answer - 1 == [info] ![2015-01-28] [2015-01-27] (QueryTest.scala:77) [info] org.scalatest.exceptions.TestFailedException: [info] at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:495) [info] at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555) [info] at org.scalatest.Assertions$class.fail(Assertions.scala:1328) [info] at org.scalatest.FunSuite.fail(FunSuite.scala:1555) [info] at org.apache.spark.sql.QueryTest.checkAnswer(QueryTest.scala:77) [info] at org.apache.spark.sql.QueryTest.checkAnswer(QueryTest.scala:95) [info] at org.apache.spark.sql.SQLQuerySuite$$anonfun$23.apply$mcV$sp(SQLQuerySuite.scala:300) [info] at org.apache.spark.sql.SQLQuerySuite$$anonfun$23.apply(SQLQuerySuite.scala:300) [info] at org.apache.spark.sql.SQLQuerySuite$$anonfun$23.apply(SQLQuerySuite.scala:300) [info] at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22) [info] at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85) [info] at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104) [info] at org.scalatest.Transformer.apply(Transformer.scala:22) [info] at org.scalatest.Transformer.apply(Transformer.scala:20) [info] at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166) [info] at org.scalatest.Suite$class.withFixture(Suite.scala:1122) [info] at org.scalatest.FunSuite.withFixture(FunSuite.scala:1555) [info] at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163) [info] at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) [info] at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) [info] at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306) [info] at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175) [info] at org.scalatest.FunSuite.runTest(FunSuite.scala:1555) [info] at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208) [info] at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208) [info] at org.scalatest.SuperEngine$$anonfun$traverseSubNode Author: wangfei <wangfei1@huawei.com> Closes #4395 from scwf/SQLQuerySuite and squashes the following commits: 1431a2d [wangfei] fix conflicts c35fe5e [wangfei] minor fix 01dab3a [wangfei] fix test failure of SQLQuerySuite |
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| core | ||
| hive | ||
| hive-thriftserver | ||
| README.md | ||
Spark SQL
This module provides support for executing relational queries expressed in either SQL or a LINQ-like Scala DSL.
Spark SQL is broken up into four subprojects:
- Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
- Execution (sql/core) - A query planner / execution engine for translating Catalyst’s logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
- Hive Support (sql/hive) - Includes an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allows users to run queries that include Hive UDFs, UDAFs, and UDTFs.
- HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.
Other dependencies for developers
In order to create new hive test cases , you will need to set several environmental variables.
export HIVE_HOME="<path to>/hive/build/dist"
export HIVE_DEV_HOME="<path to>/hive/"
export HADOOP_HOME="<path to>/hadoop-1.0.4"
Using the console
An interactive scala console can be invoked by running build/sbt hive/console.
From here you can execute queries with HiveQl and manipulate DataFrame by using DSL.
catalyst$ build/sbt hive/console
[info] Starting scala interpreter...
import org.apache.spark.sql.Dsl._
import org.apache.spark.sql.hive._
import org.apache.spark.sql.hive.test.TestHive._
import org.apache.spark.sql.types._
import org.apache.spark.sql.parquet.ParquetTestData
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.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])