What's New in MapR Data Science Refinery 1.3

Prior to the 1.3 release, if a Zeppelin interpreter is idle and using excessive resources, you must either restart or kill the interpreter to reclaim resources. Starting in 1.3, MapR Data Science Refinery terminates interpreters that have been idle for an hour. You can configure this timeout threshold. For details, see the Idle Interpreter Timeout Threshold in Understanding Zeppelin Docker Parameters.

Starting with the 1.3 release, MapR Data Science Refinery supports the Helium repository browser for enabling Zeppelin visualization packages. This provides a simpler procedure for enabling these packages. See Using Visualization Packages in Zeppelin for detailed instructions.

Starting with the MapR Data Science Refinery 1.3 release, you can store certain Zeppelin configuration files in MapR File System, which enables you to share them across multiple containers. For details, see Configuration Storage in Understanding Zeppelin Docker Parameters.

Starting with MapR Data Science Refinery 1.3, you can configure the default Drill JDBC connection URL. For more information, see Default Drill JDBC Connection URL in Understanding Zeppelin Docker Parameters.

Starting with the 1.3 release, you can build your own custom Docker image of MapR Data Science Refinery. See Building your own MapR Data Science Refinery Docker Image for more information.

Prior to the 1.3 release, Spark interpreter jobs run in YARN client mode. The interpreter now runs in cluster mode. This mode reduces Spark resource utilization on the host machine of your MapR Data Science Refinery container. See Understanding Zeppelin Interpreters for details.

In prior releases, the Livy interpreter uses separate Livy sessions for Spark, PySpark, and SparkR jobs. Starting in the 1.3 release, it uses a shared Livy session to run all Spark variations. This reduces resource utilization in your MapR cluster.

Starting with the 1.3 release, MapR Data Science Refinery runs paragraphs in a notebook sequentially rather than in parallel. This allows paragraphs to run properly when they have dependencies on earlier paragraphs in the same notebook.

Starting with the 1.3 release, you must specify ssl=true in your Hive JDBC URL when connecting to a secure MapR cluster. See Installing Custom Packages for PySpark Using Conda for an example.

Starting with the 1.3 release, you no longer can run both Python 2 and Python 3 with the Livy interpreter. You can run only one or the other. By default, the interpreter runs Python 2. To switch to Python 3, see Python Version in Understanding Zeppelin Docker Parameters.

The limitation also applies if you are installing custom Python packages. See Installing Custom Packages for PySpark Using Conda for instructions on how to install Python 2 vs Python 3 custom packages.

The DEPLOY_MODE parameter is your Kubernetes pod manifest file has been renamed to ZEPPELIN_DEPLOY_MODE. You can still use DEPLOY_MODE, but MapR Data Science Refinery 1.3 returns a warning, indicating the parameter is deprecated. See Running MapR Data Science Refinery as a Kubernetes Service for an example of a pod manifest file.

Starting with the 1.3 release, to store your notebooks in MapR File System, you no longer need to use the FUSE-based POSIX client. See Understanding Zeppelin Docker Parameters for details.