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hyukjinkwon 7525ce98b4 [SPARK-20431][SS][FOLLOWUP] Specify a schema by using a DDL-formatted string in DataStreamReader 
## What changes were proposed in this pull request?

This pr supported a DDL-formatted string in `DataStreamReader.schema`.
This fix could make users easily define a schema without importing the type classes.

For example,

```scala
scala> spark.readStream.schema("col0 INT, col1 DOUBLE").load("/tmp/abc").printSchema()
root
 |-- col0: integer (nullable = true)
 |-- col1: double (nullable = true)
```

## How was this patch tested?

Added tests in `DataStreamReaderWriterSuite`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18373 from HyukjinKwon/SPARK-20431.
2017-06-24 11:39:41 +08:00
..
docs [SPARK-20707][ML] ML deprecated APIs should be removed in major release. 2017-05-16 10:08:23 +08:00
lib [SPARK-17960][PYSPARK][UPGRADE TO PY4J 0.10.4] 2016-10-21 09:48:24 +01:00
pyspark [SPARK-20431][SS][FOLLOWUP] Specify a schema by using a DDL-formatted string in DataStreamReader 2017-06-24 11:39:41 +08:00
test_support [SPARK-19610][SQL] Support parsing multiline CSV files 2017-02-28 13:34:33 -08:00
.gitignore [SPARK-3946] gitignore in /python includes wrong directory 2014-10-14 14:09:39 -07:00
MANIFEST.in [SPARK-18652][PYTHON] Include the example data and third-party licenses in pyspark package. 2016-12-07 06:09:27 +08:00
pylintrc [SPARK-13596][BUILD] Move misc top-level build files into appropriate subdirs 2016-03-07 14:48:02 -08:00
README.md [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
run-tests [SPARK-8583] [SPARK-5482] [BUILD] Refactor python/run-tests to integrate with dev/run-tests module system 2015-06-27 20:24:34 -07:00
run-tests.py [SPARK-19955][PYSPARK] Jenkins Python Conda based test. 2017-03-29 11:41:17 -07:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-21193][PYTHON] Specify Pandas version in setup.py 2017-06-23 21:51:55 +08:00

README.md

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.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page

Python Packaging

This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".

The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to setup your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.

NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.

Python Requirements

At its core PySpark depends on Py4J (currently version 0.10.4), but additional sub-packages have their own requirements (including numpy and pandas).