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Integrate Apache Zeppelin with Hive Warehouse Connector in Azure HDInsight

  • Grein

HDInsight Spark clusters include Apache Zeppelin notebooks with different interpreters. In this article, we focus only on the Livy interpreter to access Hive tables from Spark using Hive Warehouse Connector.

Athugasemd

This article contains references to a term that Microsoft no longer uses. When the term is removed from the software, we'll remove it from this article.

Prerequisite

Complete the Hive Warehouse Connector setup steps.

Getting started

  1. Use ssh command to connect to your Apache Spark cluster. Edit the following command by replacing CLUSTERNAME with the name of your cluster, and then enter the command:

    Windows Command Prompt 
    ssh sshuser@CLUSTERNAME-ssh.azurehdinsight.net

  2. From your ssh session, execute the following command to note the versions for hive-warehouse-connector-assembly and pyspark_hwc:

    Bash 
    ls /usr/hdp/current/hive_warehouse_connector

    Save the output for later use when configuring Apache Zeppelin.

Configure Livy

Following configurations are required to access hive tables from Zeppelin with the Livy interpreter.

Interactive Query Cluster

  1. From a web browser, navigate to https://LLAPCLUSTERNAME.azurehdinsight.net/#/main/services/HDFS/configs where LLAPCLUSTERNAME is the name of your Interactive Query cluster.

  2. Navigate to Advanced > Custom core-site. Select Add Property... to add the following configurations:

    Configuration Value
    hadoop.proxyuser.livy.groups *
    hadoop.proxyuser.livy.hosts *

  3. Save changes and restart all affected components.

Spark Cluster

  1. From a web browser, navigate to https://CLUSTERNAME.azurehdinsight.net/#/main/services/SPARK2/configs where CLUSTERNAME is the name of your Apache Spark cluster.

  2. Expand Custom livy2-conf. Select Add Property... to add the following configuration:

    Configuration Value
    livy.file.local-dir-whitelist /usr/hdp/current/hive_warehouse_connector/

  3. Save changes and restart all affected components.

Configure Livy Interpreter in Zeppelin UI (Spark Cluster)

  1. From a web browser, navigate to https://CLUSTERNAME.azurehdinsight.net/zeppelin/#/interpreter, where CLUSTERNAME is the name of your Apache Spark cluster.

  2. Navigate to livy2.

  3. Add the following configurations:

    Configuration Value
    livy.spark.hadoop.hive.llap.daemon.service.hosts @llap0
    livy.spark.security.credentials.hiveserver2.enabled true
    livy.spark.sql.hive.llap true
    livy.spark.yarn.security.credentials.hiveserver2.enabled true
    livy.superusers livy, zeppelin
    livy.spark.jars file:///usr/hdp/current/hive_warehouse_connector/hive-warehouse-connector-assembly-VERSION.jar.
    Replace VERSION with value you obtained from Getting started, earlier.
    livy.spark.submit.pyFiles file:///usr/hdp/current/hive_warehouse_connector/pyspark_hwc-VERSION.zip.
    Replace VERSION with value you obtained from Getting started, earlier.
    livy.spark.sql.hive.hiveserver2.jdbc.url Set it to the HiveServer2 Interactive JDBC URL of the Interactive Query cluster.
    spark.security.credentials.hiveserver2.enabled true

  4. For ESP clusters only, add the following configuration:

    Configuration Value
    livy.spark.sql.hive.hiveserver2.jdbc.url.principal hive/_HOST@<AAD-Domain>

    • Use ssh command to connect to your Interactive Query cluster. Look for default_realm parameter in the /etc/krb5.conf file. Replace <AAD-DOMAIN> with this value as an uppercase string, otherwise the credential cannot be found.

      hive warehouse connector AAD Domain.

  5. Save the changes and restart the Livy interpreter.

If Livy interpreter isn't accessible, modify the shiro.ini file present within Zeppelin component in Ambari. For more information, see Configuring Apache Zeppelin Security.

Running Queries in Zeppelin

Launch a Zeppelin notebook using Livy interpreter and execute the following

Python 
%livy2

import com.hortonworks.hwc.HiveWarehouseSession
import com.hortonworks.hwc.HiveWarehouseSession._
import org.apache.spark.sql.SaveMode

# Initialize the hive context
val hive = HiveWarehouseSession.session(spark).build()

# Create a database
hive.createDatabase("hwc_db",true)
hive.setDatabase("hwc_db")

# Create a Hive table
hive.createTable("testers").ifNotExists().column("id", "bigint").column("name", "string").create()

val dataDF = Seq( (1, "foo"), (2, "bar"), (8, "john")).toDF("id", "name")

# Validate writes to the table
dataDF.write.format("com.hortonworks.spark.sql.hive.llap.HiveWarehouseConnector").mode("append").option("table", "hwc_db.testers").save()

# Validate reads
hive.executeQuery("select * from testers").show()

Next steps