spark-instrumented-optimizer/docs/README.md
Ahir Reddy c99bcb7fea SPARK-1374: PySpark API for SparkSQL
An initial API that exposes SparkSQL functionality in PySpark. A PythonRDD composed of dictionaries, with string keys and primitive values (boolean, float, int, long, string) can be converted into a SchemaRDD that supports sql queries.

```
from pyspark.context import SQLContext
sqlCtx = SQLContext(sc)
rdd = sc.parallelize([{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}])
srdd = sqlCtx.applySchema(rdd)
sqlCtx.registerRDDAsTable(srdd, "table1")
srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1")
srdd2.collect()
```
The last line yields ```[{"f1" : 1, "f2" : "row1"}, {"f1" : 2, "f2": "row2"}, {"f1" : 3, "f2": "row3"}]```

Author: Ahir Reddy <ahirreddy@gmail.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #363 from ahirreddy/pysql and squashes the following commits:

0294497 [Ahir Reddy] Updated log4j properties to supress Hive Warns
307d6e0 [Ahir Reddy] Style fix
6f7b8f6 [Ahir Reddy] Temporary fix MIMA checker. Since we now assemble Spark jar with Hive, we don't want to check the interfaces of all of our hive dependencies
3ef074a [Ahir Reddy] Updated documentation because classes moved to sql.py
29245bf [Ahir Reddy] Cache underlying SchemaRDD instead of generating and caching PythonRDD
f2312c7 [Ahir Reddy] Moved everything into sql.py
a19afe4 [Ahir Reddy] Doc fixes
6d658ba [Ahir Reddy] Remove the metastore directory created by the HiveContext tests in SparkSQL
521ff6d [Ahir Reddy] Trying to get spark to build with hive
ab95eba [Ahir Reddy] Set SPARK_HIVE=true on jenkins
ded03e7 [Ahir Reddy] Added doc test for HiveContext
22de1d4 [Ahir Reddy] Fixed maven pyrolite dependency
e4da06c [Ahir Reddy] Display message if hive is not built into spark
227a0be [Michael Armbrust] Update API links. Fix Hive example.
58e2aa9 [Michael Armbrust] Build Docs for pyspark SQL Api.  Minor fixes.
4285340 [Michael Armbrust] Fix building of Hive API Docs.
38a92b0 [Michael Armbrust] Add note to future non-python developers about python docs.
337b201 [Ahir Reddy] Changed com.clearspring.analytics stream version from 2.4.0 to 2.5.1 to match SBT build, and added pyrolite to maven build
40491c9 [Ahir Reddy] PR Changes + Method Visibility
1836944 [Michael Armbrust] Fix comments.
e00980f [Michael Armbrust] First draft of python sql programming guide.
b0192d3 [Ahir Reddy] Added Long, Double and Boolean as usable types + unit test
f98a422 [Ahir Reddy] HiveContexts
79621cf [Ahir Reddy] cleaning up cruft
b406ba0 [Ahir Reddy] doctest formatting
20936a5 [Ahir Reddy] Added tests and documentation
e4d21b4 [Ahir Reddy] Added pyrolite dependency
79f739d [Ahir Reddy] added more tests
7515ba0 [Ahir Reddy] added more tests :)
d26ec5e [Ahir Reddy] added test
e9f5b8d [Ahir Reddy] adding tests
906d180 [Ahir Reddy] added todo explaining cost of creating Row object in python
251f99d [Ahir Reddy] for now only allow dictionaries as input
09b9980 [Ahir Reddy] made jrdd explicitly lazy
c608947 [Ahir Reddy] SchemaRDD now has all RDD operations
725c91e [Ahir Reddy] awesome row objects
55d1c76 [Ahir Reddy] return row objects
4fe1319 [Ahir Reddy] output dictionaries correctly
be079de [Ahir Reddy] returning dictionaries works
cd5f79f [Ahir Reddy] Switched to using Scala SQLContext
e948bd9 [Ahir Reddy] yippie
4886052 [Ahir Reddy] even better
c0fb1c6 [Ahir Reddy] more working
043ca85 [Ahir Reddy] working
5496f9f [Ahir Reddy] doesn't crash
b8b904b [Ahir Reddy] Added schema rdd class
67ba875 [Ahir Reddy] java to python, and python to java
bcc0f23 [Ahir Reddy] Java to python
ab6025d [Ahir Reddy] compiling
2014-04-15 00:07:55 -07:00

3.3 KiB

Welcome to the Spark documentation!

This readme will walk you through navigating and building the Spark documentation, which is included here with the Spark source code. You can also find documentation specific to release versions of Spark at http://spark.apache.org/documentation.html.

Read on to learn more about viewing documentation in plain text (i.e., markdown) or building the documentation yourself. Why build it yourself? So that you have the docs that corresponds to whichever version of Spark you currently have checked out of revision control.

Generating the Documentation HTML

We include the Spark documentation as part of the source (as opposed to using a hosted wiki, such as the github wiki, as the definitive documentation) to enable the documentation to evolve along with the source code and be captured by revision control (currently git). This way the code automatically includes the version of the documentation that is relevant regardless of which version or release you have checked out or downloaded.

In this directory you will find textfiles formatted using Markdown, with an ".md" suffix. You can read those text files directly if you want. Start with index.md.

The markdown code can be compiled to HTML using the Jekyll tool. To use the jekyll command, you will need to have Jekyll installed. The easiest way to do this is via a Ruby Gem, see the jekyll installation instructions. Compiling the site with Jekyll will create a directory called _site containing index.html as well as the rest of the compiled files.

You can modify the default Jekyll build as follows:

# Skip generating API docs (which takes a while)
$ SKIP_SCALADOC=1 jekyll build
# Serve content locally on port 4000
$ jekyll serve --watch
# Build the site with extra features used on the live page
$ PRODUCTION=1 jekyll build

Pygments

We also use pygments (http://pygments.org) for syntax highlighting in documentation markdown pages, so you will also need to install that (it requires Python) by running sudo easy_install Pygments.

To mark a block of code in your markdown to be syntax highlighted by jekyll during the compile phase, use the following sytax:

{% highlight scala %}
// Your scala code goes here, you can replace scala with many other
// supported languages too.
{% endhighlight %}

API Docs (Scaladoc and Epydoc)

You can build just the Spark scaladoc by running sbt/sbt doc from the SPARK_PROJECT_ROOT directory.

Similarly, you can build just the PySpark epydoc by running epydoc --config epydoc.conf from the SPARK_PROJECT_ROOT/pyspark directory. Documentation is only generated for classes that are listed as public in __init__.py.

When you run jekyll in the docs directory, it will also copy over the scaladoc for the various Spark subprojects into the docs directory (and then also into the _site directory). We use a jekyll plugin to run sbt/sbt doc before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc. The jekyll plugin also generates the PySpark docs using epydoc.

NOTE: To skip the step of building and copying over the Scala and Python API docs, run SKIP_API=1 jekyll.