ebd6480587
This PR adds User-Defined Types (UDTs) to SQL. It is a precursor to using SchemaRDD as a Dataset for the new MLlib API. Currently, the UDT API is private since there is incomplete support (e.g., no Java or Python support yet). Author: Joseph K. Bradley <joseph@databricks.com> Author: Michael Armbrust <michael@databricks.com> Author: Xiangrui Meng <meng@databricks.com> Closes #3063 from marmbrus/udts and squashes the following commits: 7ccfc0d [Michael Armbrust] remove println 46a3aee [Michael Armbrust] Slightly easier to read test output. 6cc434d [Michael Armbrust] Recursively convert rows. e369b91 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into udts 15c10a6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into sql-udt2 f3c72fe [Joseph K. Bradley] Fixing merge e13cd8a [Joseph K. Bradley] Removed Vector UDTs 5817b2b [Joseph K. Bradley] style edits 30ce5b2 [Joseph K. Bradley] updates based on code review d063380 [Joseph K. Bradley] Cleaned up Java UDT Suite, and added warning about element ordering when creating schema from Java Bean a571bb6 [Joseph K. Bradley] Removed old UDT code (registry and Java UDTs). Cleaned up other code. Extended JavaUserDefinedTypeSuite 6fddc1c [Joseph K. Bradley] Made MyLabeledPoint into a Java Bean 20630bc [Joseph K. Bradley] fixed scalastyle fa86b20 [Joseph K. Bradley] Removed Java UserDefinedType, and made UDTs private[spark] for now 8de957c [Joseph K. Bradley] Modified UserDefinedType to store Java class of user type so that registerUDT takes only the udt argument. 8b242ea [Joseph K. Bradley] Fixed merge error after last merge. Note: Last merge commit also removed SQL UDT examples from mllib. 7f29656 [Joseph K. Bradley] Moved udt case to top of all matches. Small cleanups b028675 [Xiangrui Meng] allow any type in UDT 4500d8a [Xiangrui Meng] update example code 87264a5 [Xiangrui Meng] remove debug code 3143ac3 [Xiangrui Meng] remove unnecessary changes cfbc321 [Xiangrui Meng] support UDT in parquet db16139 [Joseph K. Bradley] Added more doc for UserDefinedType. Removed unused code in Suite 759af7a [Joseph K. Bradley] Added more doc to UserDefineType 63626a4 [Joseph K. Bradley] Updated ScalaReflectionsSuite per @marmbrus suggestions 51e5282 [Joseph K. Bradley] fixed 1 test f025035 [Joseph K. Bradley] Cleanups before PR. Added new tests 85872f6 [Michael Armbrust] Allow schema calculation to be lazy, but ensure its available on executors. dff99d6 [Joseph K. Bradley] Added UDTs for Vectors in MLlib, plus DatasetExample using the UDTs cd60cb4 [Joseph K. Bradley] Trying to get other SQL tests to run 34a5831 [Joseph K. Bradley] Added MLlib dependency on SQL. e1f7b9c [Joseph K. Bradley] blah 2f40c02 [Joseph K. Bradley] renamed UDT types 3579035 [Joseph K. Bradley] udt annotation now working b226b9e [Joseph K. Bradley] Changing UDT to annotation fea04af [Joseph K. Bradley] more cleanups 964b32e [Joseph K. Bradley] some cleanups 893ee4c [Joseph K. Bradley] udt finallly working 50f9726 [Joseph K. Bradley] udts 04303c9 [Joseph K. Bradley] udts 39f8707 [Joseph K. Bradley] removed old udt suite 273ac96 [Joseph K. Bradley] basic UDT is working, but deserialization has yet to be done 8bebf24 [Joseph K. Bradley] commented out convertRowToScala for debugging 53de70f [Joseph K. Bradley] more udts... 982c035 [Joseph K. Bradley] still working on UDTs 19b2f60 [Joseph K. Bradley] still working on UDTs 0eaeb81 [Joseph K. Bradley] Still working on UDTs 105c5a3 [Joseph K. Bradley] Adding UserDefinedType to SQL, not done yet. |
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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 for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark with Maven".
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:
./dev/run-tests
Please see the guidance on how to run all automated tests.
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.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.