7003c163db
The current PR contains the following changes: * Expose `DataType`s in the sql package (internal details are private to sql). * Users can create Rows. * Introduce `applySchema` to create a `SchemaRDD` by applying a `schema: StructType` to an `RDD[Row]`. * Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`. * `ScalaReflection.typeOfObject` provides a way to infer the Catalyst data type based on an object. Also, we can compose `typeOfObject` with some custom logics to form a new function to infer the data type (for different use cases). * `JsonRDD` has been refactored to use changes introduced by this PR. * Add a field `containsNull` to `ArrayType`. So, we can explicitly mark if an `ArrayType` can contain null values. The default value of `containsNull` is `false`. New APIs are introduced in the sql package object and SQLContext. You can find the scaladoc at [sql package object](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.package) and [SQLContext](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.SQLContext). An example of using `applySchema` is shown below. ```scala import org.apache.spark.sql._ val sqlContext = new org.apache.spark.sql.SQLContext(sc) val schema = StructType( StructField("name", StringType, false) :: StructField("age", IntegerType, true) :: Nil) val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Row(p(0), p(1).trim.toInt)) val peopleSchemaRDD = sqlContext. applySchema(people, schema) peopleSchemaRDD.printSchema // root // |-- name: string (nullable = false) // |-- age: integer (nullable = true) peopleSchemaRDD.registerAsTable("people") sqlContext.sql("select name from people").collect.foreach(println) ``` I will add new contents to the SQL programming guide later. JIRA: https://issues.apache.org/jira/browse/SPARK-2179 Author: Yin Huai <huai@cse.ohio-state.edu> Closes #1346 from yhuai/dataTypeAndSchema and squashes the following commits: 1d45977 [Yin Huai] Clean up. a6e08b4 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema c712fbf [Yin Huai] Converts types of values based on defined schema. 4ceeb66 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema e5f8df5 [Yin Huai] Scaladoc. 122d1e7 [Yin Huai] Address comments. 03bfd95 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 2476ed0 [Yin Huai] Minor updates. ab71f21 [Yin Huai] Format. fc2bed1 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema bd40a33 [Yin Huai] Address comments. 991f860 [Yin Huai] Move "asJavaDataType" and "asScalaDataType" to DataTypeConversions.scala. 1cb35fe [Yin Huai] Add "valueContainsNull" to MapType. 3edb3ae [Yin Huai] Python doc. 692c0b9 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 1d93395 [Yin Huai] Python APIs. 246da96 [Yin Huai] Add java data type APIs to javadoc index. 1db9531 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema d48fc7b [Yin Huai] Minor updates. 33c4fec [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema b9f3071 [Yin Huai] Java API for applySchema. 1c9f33c [Yin Huai] Java APIs for DataTypes and Row. 624765c [Yin Huai] Tests for applySchema. aa92e84 [Yin Huai] Update data type tests. 8da1a17 [Yin Huai] Add Row.fromSeq. 9c99bc0 [Yin Huai] Several minor updates. 1d9c13a [Yin Huai] Update applySchema API. 85e9b51 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema e495e4e [Yin Huai] More comments. 42d47a3 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema c3f4a02 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 2e58dbd [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema b8b7db4 [Yin Huai] 1. Move sql package object and package-info to sql-core. 2. Minor updates on APIs. 3. Update scala doc. 68525a2 [Yin Huai] Update JSON unit test. 3209108 [Yin Huai] Add unit tests. dcaf22f [Yin Huai] Add a field containsNull to ArrayType to indicate if an array can contain null values or not. If an ArrayType is constructed by "ArrayType(elementType)" (the existing constructor), the value of containsNull is false. 9168b83 [Yin Huai] Update comments. fc649d7 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema eca7d04 [Yin Huai] Add two apply methods which will be used to extract StructField(s) from a StructType. 949d6bb [Yin Huai] When creating a SchemaRDD for a JSON dataset, users can apply an existing schema. 7a6a7e5 [Yin Huai] Fix bug introduced by the change made on SQLContext.inferSchema. 43a45e1 [Yin Huai] Remove sql.util.package introduced in a previous commit. 0266761 [Yin Huai] Format 03eec4c [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 90460ac [Yin Huai] Infer the Catalyst data type from an object and cast a data value to the expected type. 3fa0df5 [Yin Huai] Provide easier ways to construct a StructType. 16be3e5 [Yin Huai] This commit contains three changes: * Expose `DataType`s in the sql package (internal details are private to sql). * Introduce `createSchemaRDD` to create a `SchemaRDD` from an `RDD` with a provided schema (represented by a `StructType`) and a provided function to construct `Row`, * Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`. |
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tox.ini |
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLLib for machine learning, GraphX for graph processing, and Spark Streaming.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project webpage at http://spark.apache.org/documentation.html. This README file only contains basic setup instructions.
Building Spark
Spark is built on Scala 2.10. To build Spark and its example programs, run:
./sbt/sbt assembly
(You do not need to do this if you downloaded a pre-built package.)
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples
directory.
To run one of them, use ./bin/run-example <class> [params]
. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./sbt/sbt test
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported
storage systems. Because the protocols have changed in different versions of
Hadoop, you must build Spark against the same version that your cluster runs.
You can change the version by setting -Dhadoop.version
when building Spark.
For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions without YARN, use:
# Apache Hadoop 1.2.1
$ sbt/sbt -Dhadoop.version=1.2.1 assembly
# Cloudera CDH 4.2.0 with MapReduce v1
$ sbt/sbt -Dhadoop.version=2.0.0-mr1-cdh4.2.0 assembly
For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
with YARN, also set -Pyarn
:
# Apache Hadoop 2.0.5-alpha
$ sbt/sbt -Dhadoop.version=2.0.5-alpha -Pyarn assembly
# Cloudera CDH 4.2.0 with MapReduce v2
$ sbt/sbt -Dhadoop.version=2.0.0-cdh4.2.0 -Pyarn assembly
# Apache Hadoop 2.2.X and newer
$ sbt/sbt -Dhadoop.version=2.2.0 -Pyarn assembly
When developing a Spark application, specify the Hadoop version by adding the
"hadoop-client" artifact to your project's dependencies. For example, if you're
using Hadoop 1.2.1 and build your application using SBT, add this entry to
libraryDependencies
:
"org.apache.hadoop" % "hadoop-client" % "1.2.1"
If your project is built with Maven, add this to your POM file's <dependencies>
section:
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>1.2.1</version>
</dependency>
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.
Contributing to Spark
Contributions via GitHub pull requests are gladly accepted from their original author. Along with any pull requests, please state that the contribution is your original work and that you license the work to the project under the project's open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project's open source license and warrant that you have the legal authority to do so.