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## What changes were proposed in this pull request? When saving datasets on storage, `partitionBy` provides an easy way to construct the directory structure. However, if a user choose all columns as partition columns, some exceptions occurs. - **ORC with all column partitioning**: `AnalysisException` on **future read** due to schema inference failure. ```scala scala> spark.range(10).write.format("orc").mode("overwrite").partitionBy("id").save("/tmp/data") scala> spark.read.format("orc").load("/tmp/data").collect() org.apache.spark.sql.AnalysisException: Unable to infer schema for ORC at /tmp/data. It must be specified manually; ``` - **Parquet with all-column partitioning**: `InvalidSchemaException` on **write execution** due to Parquet limitation. ```scala scala> spark.range(100).write.format("parquet").mode("overwrite").partitionBy("id").save("/tmp/data") [Stage 0:> (0 + 8) / 8]16/06/02 16:51:17 ERROR Utils: Aborting task org.apache.parquet.schema.InvalidSchemaException: A group type can not be empty. Parquet does not support empty group without leaves. Empty group: spark_schema ... (lots of error messages) ``` Although some formats like JSON support all-column partitioning without any problem, it seems not a good idea to make lots of empty directories. This PR prevents saving with all-column partitioning by consistently raising `AnalysisException` before executing save operation. ## How was this patch tested? Newly added `PartitioningUtilsSuite`. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13486 from dongjoon-hyun/SPARK-15743. |
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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, Python, and R, 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 DataFrames, 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 and project wiki. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
build/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". For developing Spark using an IDE, see Eclipse and IntelliJ.
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" 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 tests for a module, or individual 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.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.