Setting up Hadoop integration¶
DSS is able to connect to a Hadoop cluster and to:
- Read and write HDFS datasets
- Run Hive queries and scripts
- Run Impala queries
- Run Pig scripts
- Run preparation recipes on Hadoop
In addition, if you setup Spark integration, you can:
- Run most visual recipes on Spark
- Run SparkSQL queries
- Run PySpark, SparkR and Spark Scala scripts
- Train & use Spark MLLib models
- Run machine learning scoring recipes on Spark
DSS supports the following Hadoop distributions:
- Cloudera’s Distribution Including Apache Hadoop (CDH) versions 5.5 to 5.11 (see CDH-specific notes)
- Hortonworks Data Platform (HDP) versions 2.3 to 2.6 (see HDP-specific notes)
- Amazon EMR versions 4.7, 4.8, 5.3 and 5.4 (Experimental) (see EMR-specific notes)
- MapR Converged Data Platform versions 4.1 to 5.2 (see MapR-specific notes)
Running Data Preparation recipes on Hadoop is only supported if the cluster runs Java 7 or later, which might not be the case for older installations.
The host running DSS should have client access to the cluster (it can, but it does not need to host any cluster role like a datanode).
Getting client access to the cluster normally involves installing:
- the Hadoop client libraries (Java jars) matching the Hadoop distribution running on the cluster.
- the Hadoop configuration files so that client processes (including DSS) can find and connect to the cluster.
Both of the above operations are typically best done through your cluster manager interface, by adding the DSS machine to the set of hosts managed by the cluster manager, and configuring “client” or “gateway” roles for it. If not possible, installing the client libraries usually consists in installing software packages from your Hadoop distribution, and the configuration files can be typically be downloaded from the cluster manager interface, or simply copied from another server connected to the cluster. See the documentation of your cluster distribution.
The above should be done at least for the HDFS and Yarn/MapReduce subsystems, and optionally for Hive and Pig if you plan to use these with DSS.
You may also need to setup a writable HDFS home directory for DSS (typically “/user/dataiku”) if you plan to store DSS datasets in HDFS,
You need to have access to one or several writable Hive metastore database (default “dataiku”) so that DSS can create Hive table definitions for the datasets it creates on HDFS.
You must have a running Hiveserver2 server.
Several Hive security modes are supported. See DSS and Hive for more information.
First, test that the machine running DSS has proper Hadoop connectivity.
A prerequisite is to have the “hadoop” binary in your PATH. To test it, simply run:
It should display version information for your Hadoop distribution.
You can check HDFS connectivity by running the following command from the DSS account:
hdfs dfs -ls / # Or the following alternate form for older installations, and MapR distributions hadoop fs -ls /
If you want to run Hive recipes using “Hive CLI” mode, you need a properly configured “hive” command line client for the DSS user account (available in the PATH).
You can check Hive connectivity by running the following command from the DSS account:
hive -e "show databases"
If it succeeds, and lists the databases declared in your global Hive metastore, your Hive installation is correctly set up for DSS to use it.
This is only required if you intend on using the “Hive CLI” mode of Hive recipes. For more information on Hive recipe modes, see Hive execution engines
If your Hadoop cluster has Kerberos security enabled, please don’t follow these instructions. Head over to Connecting to secure clusters.
If your Hadoop cluster does not have security (Kerberos) enabled, DSS automatically checks for Hadoop connectivity at installation time, and automatically configures Hadoop integration if possible. You don’t need to perform the
dsadmin install-hadoop-integration step. You still need to perform Hive and Impala configuration, though.
You can configure or reconfigure DSS Hadoop integration at any further time:
- Go to the DSS data directory
- Stop DSS:
- Run the setup script
- Restart DSS
You should reconfigure Hadoop integration using the above procedure whenever your cluster installation changes, such as after an upgrade of your cluster software.
To test HDFS connectivity, try to create an HDFS dataset:
If the Hadoop HDFS button does not appear, Data Science Studio has not properly detected your Hadoop installation.
You can then select the “hdfs_root” connection (which gives access to the whole HDFS hierarchy) and click the Browse button and verify that you can see your HDFS data.
Upon first setup of DSS Hadoop integration, two HDFS connections are defined: “hdfs_root” for read-only access to the entire HDFS filesystem, “hdfs_managed” to store DSS-generated datasets. You can edit these default connections, in particular their HDFS root path and default Hive database name, to match your installation. You can delete them or define additional ones as needed.
For more information, see Setup a new HDFS connection
For DSS to be able to read and write Hive table definitions, you must setup the host of your HiveServer2.
Go to Administration > Settings > Hadoop, enter the host name of your HiveServer2, and save settings.
For more information, see DSS and Hive
If you want to run Hive recipes using “Hive CLI” mode, you also need a properly configured “hive” command line client for the DSS user account.
If your Hadoop cluster has Impala, you need to configure the impalad hosts.
Go to Administration > Settings > Hadoop, enable Impala, enter the list of Impala servers (if none is set, then the localhost will be used), and save settings.
For more information, see DSS and Impala.
To run Pig recipes, you need to have the Pig packages installed on the host running Data Science Studio. It is required to have the “pig” command in the PATH.
If Pig was already installed when you installed Data Science Studio, connectivity to Pig is automatically set up.
If you installed Pig after installing Data Science Studio, you need to perform a new setup. See Setting up DSS Hadoop integration