87bd1f9ef7
This patch marks some existing classes as private[spark] and adds two types of API annotations: - `EXPERIMENTAL API` = experimental user-facing module - `DEVELOPER API - UNSTABLE` = developer-facing API that might change There is some discussion of the different mechanisms for doing this here: https://issues.apache.org/jira/browse/SPARK-1081 I was pretty aggressive with marking things private. Keep in mind that if we want to open something up in the future we can, but we can never reduce visibility. A few notes here: - In the past we've been inconsistent with the visiblity of the X-RDD classes. This patch marks them private whenever there is an existing function in RDD that can directly creat them (e.g. CoalescedRDD and rdd.coalesce()). One trade-off here is users can't subclass them. - Noted that compression and serialization formats don't have to be wire compatible across versions. - Compression codecs and serialization formats are semi-private as users typically don't instantiate them directly. - Metrics sources are made private - user only interacts with them through Spark's reflection Author: Patrick Wendell <pwendell@gmail.com> Author: Andrew Or <andrewor14@gmail.com> Closes #274 from pwendell/private-apis and squashes the following commits: 44179e4 [Patrick Wendell] Merge remote-tracking branch 'apache-github/master' into private-apis 042c803 [Patrick Wendell] spark.annotations -> spark.annotation bfe7b52 [Patrick Wendell] Adding experimental for approximate counts 8d0c873 [Patrick Wendell] Warning in SparkEnv 99b223a [Patrick Wendell] Cleaning up annotations e849f64 [Patrick Wendell] Merge pull request #2 from andrewor14/annotations 982a473 [Andrew Or] Generalize jQuery matching for non Spark-core API docs a01c076 [Patrick Wendell] Merge pull request #1 from andrewor14/annotations c1bcb41 [Andrew Or] DeveloperAPI -> DeveloperApi 0d48908 [Andrew Or] Comments and new lines (minor) f3954e0 [Andrew Or] Add identifier tags in comments to work around scaladocs bug 99192ef [Andrew Or] Dynamically add badges based on annotations 824011b [Andrew Or] Add support for injecting arbitrary JavaScript to API docs 037755c [Patrick Wendell] Some changes after working with andrew or f7d124f [Patrick Wendell] Small fixes c318b24 [Patrick Wendell] Use CSS styles e4c76b9 [Patrick Wendell] Logging f390b13 [Patrick Wendell] Better visibility for workaround constructors d6b0afd [Patrick Wendell] Small chang to existing constructor 403ba52 [Patrick Wendell] Style fix 870a7ba [Patrick Wendell] Work around for SI-8479 7fb13b2 [Patrick Wendell] Changes to UnionRDD and EmptyRDD 4a9e90c [Patrick Wendell] EXPERIMENTAL API --> EXPERIMENTAL c581dce [Patrick Wendell] Changes after building against Shark. 8452309 [Patrick Wendell] Style fixes 1ed27d2 [Patrick Wendell] Formatting and coloring of badges cd7a465 [Patrick Wendell] Code review feedback 2f706f1 [Patrick Wendell] Don't use floats 542a736 [Patrick Wendell] Small fixes cf23ec6 [Patrick Wendell] Marking GraphX as alpha d86818e [Patrick Wendell] Another naming change 5a76ed6 [Patrick Wendell] More visiblity clean-up 42c1f09 [Patrick Wendell] Using better labels 9d48cbf [Patrick Wendell] Initial pass |
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catalyst | ||
core | ||
hive | ||
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 three 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.
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.ExecutedQuery =
SELECT * FROM (SELECT * FROM src) a
=== Query Plan ===
Project [key#6:0.0,value#7:0.1]
HiveTableScan [key#6,value#7], (MetastoreRelation default, src, None), None
Query results are RDDs and can be operated as such.
scala> query.collect()
res8: Array[org.apache.spark.sql.execution.Row] = Array([238,val_238], [86,val_86], [311,val_311]...
You can also build further queries on top of these RDDs using the query DSL.
scala> query.where('key === 100).toRdd.collect()
res11: Array[org.apache.spark.sql.execution.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.logicalPlan
res1: catalyst.plans.logical.LogicalPlan =
Project {key#0,value#1}
Project {key#0,value#1}
MetastoreRelation default, src, None
scala> query.logicalPlan transform {
| case Project(projectList, child) if projectList == child.output => child
| }
res2: catalyst.plans.logical.LogicalPlan =
Project {key#0,value#1}
MetastoreRelation default, src, None