c9f840046f
This PR overrides the `GetInfo` Hive Thrift API to provide correct version information. Another property `spark.sql.hive.version` is added to reveal the underlying Hive version. These are generally useful for Spark SQL ODBC driver providers. The Spark version information is extracted from the jar manifest. Also took the chance to remove the `SET -v` hack, which was a workaround for Simba ODBC driver connectivity. TODO - [x] Find a general way to figure out Hive (or even any dependency) version. This [blog post](http://blog.soebes.de/blog/2014/01/02/version-information-into-your-appas-with-maven/) suggests several methods to inspect application version. In the case of Spark, this can be tricky because the chosen method: 1. must applies to both Maven build and SBT build For Maven builds, we can retrieve the version information from the META-INF/maven directory within the assembly jar. But this doesn't work for SBT builds. 2. must not rely on the original jars of dependencies to extract specific dependency version, because Spark uses assembly jar. This implies we can't read Hive version from Hive jar files since standard Spark distribution doesn't include them. 3. should play well with `SPARK_PREPEND_CLASSES` to ease local testing during development. `SPARK_PREPEND_CLASSES` prevents classes to be loaded from the assembly jar, thus we can't locate the jar file and read its manifest. Given these, maybe the only reliable method is to generate a source file containing version information at build time. pwendell Do you have any suggestions from the perspective of the build process? **Update** Hive version is now retrieved from the newly introduced `HiveShim` object. Author: Cheng Lian <lian.cs.zju@gmail.com> Author: Cheng Lian <lian@databricks.com> Closes #2843 from liancheng/get-info and squashes the following commits: a873d0f [Cheng Lian] Updates test case 53f43cd [Cheng Lian] Retrieves underlying Hive verson via HiveShim 1d282b8 [Cheng Lian] Removes the Simba ODBC "SET -v" hack f857fce [Cheng Lian] Overrides Hive GetInfo Thrift API and adds Hive version property |
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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 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