Spark HBase is built using [Apache Maven](http://maven.apache.org/).

I. Clone and build Huawei-Spark/Spark-SQL-on-HBase

II. Go to the root of the source tree

III. Build without testing

IV. Build and run test suites against a HBase minicluster, from Maven.

The easiest way to start using Spark HBase is through the Scala shell:

First, add the spark-hbase jar to the SPARK_CLASSPATH in the $SPARK_HOME/conf directory, as follows:

Then go to the spark-hbase installation directory and issue

A successfull message is as follows:

You are using Spark SQL on HBase!!!

HBaseSQLContext available as hsqlContext.

To run a python script, the PYTHONPATH environment should be set to the "python" directory of the Spark-HBase installation. For example,

Note that the shell commands are not included in the Zip file of the Spark release. They are for developers' use only for this version of 1.0.0. Instead, users can use "$SPARK_HOME/bin/spark-shell --packages Huawei-Spark/Spark-SQL-on-HBase:1.0.0" for SQL shell or "$SPARK_HOME/bin/pyspark --packages Huawei-Spark/Spark-SQL-on-HBase:1.0.0" for Pythin shell.

Testing first requires [building Spark HBase](#building-spark). Once Spark HBase is built ...

Run all test suites from Maven:

Run a single test suite from Maven, for example:

We use IntelliJ IDEA for Spark HBase development. You can get the community edition for free and install the JetBrains Scala plugin from Preferences > Plugins.

6. When you run the scala test, sometimes you will get out of memory exception. You can increase your VM memory usage by the following setting, for example:

You can also make those setting to be the default by setting to the "Defaults -> ScalaTest".