23f966f475
- Adds optional precision and scale to Spark SQL's decimal type, which behave similarly to those in Hive 13 (https://cwiki.apache.org/confluence/download/attachments/27362075/Hive_Decimal_Precision_Scale_Support.pdf) - Replaces our internal representation of decimals with a Decimal class that can store small values in a mutable Long, saving memory in this situation and letting some operations happen directly on Longs This is still marked WIP because there are a few TODOs, but I'll remove that tag when done. Author: Matei Zaharia <matei@databricks.com> Closes #2983 from mateiz/decimal-1 and squashes the following commits: 35e6b02 [Matei Zaharia] Fix issues after merge 227f24a [Matei Zaharia] Review comments 31f915e [Matei Zaharia] Implement Davies's suggestions in Python eb84820 [Matei Zaharia] Support reading/writing decimals as fixed-length binary in Parquet 4dc6bae [Matei Zaharia] Fix decimal support in PySpark d1d9d68 [Matei Zaharia] Fix compile error and test issues after rebase b28933d [Matei Zaharia] Support decimal precision/scale in Hive metastore 2118c0d [Matei Zaharia] Some test and bug fixes 81db9cb [Matei Zaharia] Added mutable Decimal that will be more efficient for small precisions 7af0c3b [Matei Zaharia] Add optional precision and scale to DecimalType, but use Unlimited for now ec0a947 [Matei Zaharia] Make the result of AVG on Decimals be Decimal, not Double |
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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