Use Oozie with Hadoop to define and run a workflow on Linux-based HDInsight

Learn how to use Apache Oozie to define a workflow that uses Hive and Sqoop, and then run the workflow on a Linux-based HDInsight cluster.

Apache Oozie is a workflow/coordination system that manages Hadoop jobs. It is integrated with the Hadoop stack, and it supports Hadoop jobs for Apache MapReduce, Apache Pig, Apache Hive, and Apache Sqoop. It can also be used to schedule jobs that are specific to a system, like Java programs or shell scripts


Another option for defining workflows with HDInsight is Azure Data Factory. To learn more about Azure Data Factory, see Use Pig and Hive with Data Factory.


Before you begin this tutorial, you must have the following:

Example workflow

The workflow used in this document contains two actions. Actions are definitions for tasks, such as running Hive, Sqoop, MapReduce, or other process:

Workflow diagram

  1. A Hive action runs a HiveQL script to extract records from the hivesampletable included with HDInsight. Each row of data describes a visit from a specific mobile device. The record format appears similar to the following:

     8       18:54:20        en-US   Android Samsung SCH-i500        California     United States    13.9204007      0       0
     23      19:19:44        en-US   Android HTC     Incredible      Pennsylvania   United States    NULL    0       0
     23      19:19:46        en-US   Android HTC     Incredible      Pennsylvania   United States    1.4757422       0       1

    The Hive script used in this document counts the total visits for each platform (such as Android or iPhone,) and stores the counts to a new Hive table.

    For more information about Hive, see Use Hive with HDInsight.

  2. A Sqoop action exports the contents of the new Hive table to a table in an Azure SQL database. For more information about Sqoop, see Use Hadoop Sqoop with HDInsight.


For supported Oozie versions on HDInsight clusters, see What's new in the Hadoop cluster versions provided by HDInsight.

Create the working directory

Oozie expects resources required for a job to be stored in the same directory. This example uses wasbs:///tutorials/useoozie. Use the following command to create this directory, and the data directory that holds the new Hive table created by this workflow:

hdfs dfs -mkdir -p /tutorials/useoozie/data

The -p parameter caused all directories in the path to be created if they do not already exist. The data directory is used to hold data used by the useooziewf.hql script.

Also run the following command, which ensures that Oozie can impersonate your user account when running Hive and Sqoop jobs. Replace USERNAME with your login name:

sudo adduser USERNAME users

If you receive an error that the user is already a member of users, you can just ignore it.

Add a database driver

Since this workflow uses Sqoop to export data to SQL Database, you must provide a copy of the JDBC driver used to talk to SQL Database. Use the following command to copy it to the working directory:

hdfs dfs -put /usr/share/java/sqljdbc_4.1/enu/sqljdbc*.jar /tutorials/useoozie/

If your workflow used other resources, such as a jar containing a MapReduce application, you would need to add these as well.

Define the Hive query

Use the following steps to create a HiveQL script that defines a query, which is used in an Oozie workflow later in this document.

  1. Connect to the cluster using SSH. The following command is an example of using the ssh command. Replace USERNAME with the SSH user for the cluster. Replace CLUSTERNAME with the name of the HDInsight cluster.


    For more information, see Use SSH with HDInsight.

  2. From the SSH connection, use the following command to create a new file:

     nano useooziewf.hql
  3. Once the nano editor opens, use the following as the contents of the file:

     DROP TABLE ${hiveTableName};
     CREATE EXTERNAL TABLE ${hiveTableName}(deviceplatform string, count string) ROW FORMAT DELIMITED
     INSERT OVERWRITE TABLE ${hiveTableName} SELECT deviceplatform, COUNT(*) as count FROM hivesampletable GROUP BY deviceplatform;

    There are two variables used in the script:

    • ${hiveTableName}: contains the name of the table to be created

    • ${hiveDataFolder}: contains the location to store the data files for the table

      The workflow definition file (workflow.xml in this tutorial) passes these values to this HiveQL script at run time

  4. Press Ctrl-X to exit the editor. When prompted, select Y to save the file, then use Enter to use the useooziewf.hql file name.

  5. Use the following commands to copy useooziewf.hql to wasbs:///tutorials/useoozie/useooziewf.hql:

     hdfs dfs -put useooziewf.hql /tutorials/useoozie/useooziewf.hql

    These commands store the useooziewf.hql file on the Azure Storage account associated with this cluster, which preserves the file even if the cluster is deleted. This allows you to save money by deleting clusters when they aren't in use, while maintaining your jobs and workflows.

Define the workflow

Oozie workflows definitions are written in hPDL (an XML Process Definition Language). Use the following steps to define the workflow:

  1. Use the following statement to create and edit a new file:

     nano workflow.xml
  2. Once the nano editor opens, enter the following as the file contents:

     <workflow-app name="useooziewf" xmlns="uri:oozie:workflow:0.2">
         <start to = "RunHiveScript"/>
         <action name="RunHiveScript">
         <hive xmlns="uri:oozie:hive-action:0.2">
         <ok to="RunSqoopExport"/>
         <error to="fail"/>
         <action name="RunSqoopExport">
         <sqoop xmlns="uri:oozie:sqoop-action:0.2">
         <ok to="end"/>
         <error to="fail"/>
         <kill name="fail">
         <message>Job failed, error message[${wf:errorMessage(wf:lastErrorNode())}] </message>
         <end name="end"/>

    There are two actions defined in the workflow:

    • RunHiveScript: This is the start action, and runs the useooziewf.hql Hive script

    • RunSqoopExport: This exports the data created from the Hive script to SQL Database using Sqoop. This only runs if the RunHiveScript action is successful.


      For more information about Oozie workflow and using workflow actions, see Apache Oozie 4.0 documentation (for HDInsight version 3.0) or Apache Oozie 3.3.2 documentation (for HDInsight version 2.1).

      Note that the workflow has several entries, such as ${jobTracker}, that is replaced by values you use in the job definition later in this document.

      Also note the <archive>sqljdbc4.jar</arcive> entry in the Sqoop section. This instructs Oozie to make this archive available for Sqoop when this action runs.

  3. Use Ctrl-X, then Y and Enter to save the file.

  4. Use the following command to copy the workflow.xml file to /tutorials/useoozie/workflow.xml:

     hdfs dfs -put workflow.xml /tutorials/useoozie/workflow.xml

Create the database

Follow the steps in the Create a SQL Database document to create a new database. When creating the database, use oozietest as the database name. Also make a note of the name used for the database server, as this is needed in the next section.

Create the table


There are many ways to connect to SQL Database to create a table. The following steps use FreeTDS from the HDInsight cluster.

  1. Use the following command to install FreeTDS on the HDInsight cluster:

     sudo apt-get --assume-yes install freetds-dev freetds-bin
  2. Once FreeTDS has been installed, use the following command to connect to the SQL Database server you created previously:

     TDSVER=8.0 tsql -H <serverName> -U <sqlLogin> -P <sqlPassword> -p 1433 -D oozietest

    You receive output similar to the following:

     locale is "en_US.UTF-8"
     locale charset is "UTF-8"
     using default charset "UTF-8"
     Default database being set to oozietest
  3. At the 1> prompt, enter the following lines:

     CREATE TABLE [dbo].[mobiledata](
     [deviceplatform] [nvarchar](50),
     [count] [bigint])
     CREATE CLUSTERED INDEX mobiledata_clustered_index on mobiledata(deviceplatform)

    When the GO statement is entered, the previous statements are evaluated. This creates a new table named mobiledata that is written to by Sqoop.

    Use the following to verify that the table has been created:

     SELECT * FROM information_schema.tables

    You should see output similar to the following:

     oozietest       dbo     mobiledata      BASE TABLE
  4. Enter exit at the 1> prompt to exit the tsql utility.

Create the job definition

The job definition describes where to find the workflow.xml, as well as other files used by the workflow (such as useooziewf.hql.) It also defines the values for properties used within the workflow and associated files.

  1. Use the following command to get the full WASB address to default storage. This is used in the configuration file in a moment:

     sed -n '/<name>fs.default/,/<\/value>/p' /etc/hadoop/conf/core-site.xml

    This should return information similar to the following:


    If the HDInsight cluster uses Azure Storage as the default storage, the <value> element contents begin with wasbs://. If Azure Data Lake Store is used instead, it begins with adl://.

    Save the contents of the <value> element, as it is used in the next steps.

  2. Use the following command to get FQDN of the cluster headnode. This is used for the JobTracker address for the cluster. This is used in the configuration file in a moment:

     hostname -f

    This returns information similar to the following:

     The port used for the JobTracker is 8050, so the full address to use for the JobTracker is ``.
  3. Use the following to create the Oozie job definition configuration:

     nano job.xml
  4. Once the nano editor opens, use the following as the contents of the file:

     <?xml version="1.0" encoding="UTF-8"?>
    • Replace all instances of wasbs:// with the value you received earlier for default storage.


      If the path is a wasb path, you must use the full path. Do not shorten it to just wasb:///.

    • Replace JOBTRACKERADDRESS with the JobTracker/ResourceManager address you received earlier.

    • Replace YourName with your login name for the HDInsight cluster.
    • Replace serverName, adminLogin, and adminPassword with the information for your Azure SQL Database.

      Most of the information in this file is used to populate the values used in the workflow.xml or ooziewf.hql files (such as ${nameNode}.)


      The entry defines where to find the workflow.xml file, which contains the workflow ran by this job.

  5. Use Ctrl-X, then Y and Enter to save the file.

Submit and manage the job

The following steps use the Oozie command to submit and manage Oozie workflows on the cluster. The Oozie command is a friendly interface over the Oozie REST API.


When using the Oozie command, you must use the FQDN for the HDInsight headnode. This FQDN is only accessible from the cluster, or if the cluster is on an Azure Virtual Network, from other machines on the same network.

  1. Use the following to obtain the URL to the Oozie service:

     sed -n '/<name>oozie.base.url/,/<\/value>/p' /etc/oozie/conf/oozie-site.xml

    This returns information similar to the following:


    The portion is the URL to use with the Oozie command.

  2. Use the following to create an environment variable for the URL, so you don't have to type it for every command:

     export OOZIE_URL=http://HOSTNAMEt:11000/oozie

    Replace the URL with the one you received earlier.

  3. Use the following to submit the job:

     oozie job -config job.xml -submit

    This loads the job information from job.xml and submits it to Oozie, but does not run it.

    Once the command completes, it should return the ID of the job. For example, 0000005-150622124850154-oozie-oozi-W. This is used to manage the job.

  4. View the status of the job using the following command. Enter the job ID returned by the previous command:

     oozie job -info <JOBID>

    This returns information similar to the following.

     Job ID : 0000005-150622124850154-oozie-oozi-W
     Workflow Name : useooziewf
     App Path      : wasbs:///tutorials/useoozie
     Status        : PREP
     Run           : 0
     User          : USERNAME
     Group         : -
     Created       : 2015-06-22 15:06 GMT
     Started       : -
     Last Modified : 2015-06-22 15:06 GMT
     Ended         : -
     CoordAction ID: -

    This job has a status of PREP, which indicates that it was submitted, but has not been started yet.

  5. Use the following to start the job:

     oozie job -start JOBID

    If you check the status after this command, it is in a running state, and information is returned for the actions within the job.

  6. Once the task completes successfully, you can verify that the data was generated and exported to the SQL Database table by using the following commands:

     TDSVER=8.0 tsql -H <serverName> -U <adminLogin> -P <adminPassword> -p 1433 -D oozietest

    At the 1> prompt, enter the following:

     SELECT * FROM mobiledata

    The information returned is similar to the following:

     deviceplatform  count
     Android 31591
     iPhone OS       22731
     proprietary development 3
     RIM OS  3464
     Unknown 213
     Windows Phone   1791
     (6 rows affected)

For more information on the Oozie command, see Oozie Command Line Tool.


The Oozie REST API allow you to build your own tools that work with Oozie. The following are HDInsight specific information about using the Oozie REST API:

  • URI: The REST API can be accessed from outside the cluster at

  • Authentication: You must authenticate to the API using the cluster HTTP account (admin,) and password. For example:

      curl -u admin:PASSWORD

For more information on using the Oozie REST API, see Oozie Web Services API.

Oozie Web UI

The Oozie Web UI provides a web-based view into the status of Oozie jobs on the cluster. It allows you to view job status, the job definition, configuration, a graph of the actions in the job, and logs for the job. You can also view details for actions within a job.

To access the Oozie Web UI, use the following steps:

  1. Create an SSH tunnel to the HDInsight cluster. For information on how to do this, see Use SSH Tunneling to access Ambari web UI, ResourceManager, JobHistory, NameNode, Oozie, and other web UI's.

  2. Once a tunnel has been created, open the Ambari web UI in your web browser. The URI for the Ambari site is Replace CLUSTERNAME with the name of your Linux-based HDInsight cluster.

  3. From the left side of the page, select Oozie, then Quick Links, and finally Oozie Web UI.

    image of the menus

  4. The Oozie Web UI defaults to displaying running Workflow Jobs. To see all workflow jobs, select All Jobs.

    All jobs displayed

  5. Select a job to view more information about the job.

    Job info

  6. From the Job Info tab, you can see basic job information, as well as the individual actions within the job. Using the tabs at the top you can view the Job Definition, Job Configuration, access the Job Log or view a Directed Acyclic Graph (DAG) of the job.

    • Job Log: Select the GetLogs button to get all logs for the job, or use the Enter Search Filter field to filter logs

      Job log

    • JobDAG: The DAG is a graphical overview of the data paths taken through the workflow

      Job DAG

  7. Selecting one of the actions from the Job Info tab brings up information for the action. For example, select the RunHiveScript action.

    Action info

  8. You can see details for the action, including a link to the Console URL, which can be used to view JobTracker information for the job.

Scheduling jobs

The coordinator allows you to specify a start, end, and occurrance frequency for jobs so that they can be scheduled for certain times.

To define a schedule for the workflow, use the following steps:

  1. Use the following to create a new file named coordinator.xml:

     nano coordinator.xml

    Use the following as the contents of the file:

     <coordinator-app name="my_coord_app" frequency="${coordFrequency}" start="${coordStart}" end="${coordEnd}" timezone="${coordTimezone}" xmlns="uri:oozie:coordinator:0.4">

    Note the ${...} variables; these are replaced by values in the job definition at run-time. The variables are:

    • ${coordFrequency}: Time between running instances of the job.

    • ${coordStart}: The job start time.

    • ${coordEnd}: The job end time.

    • ${coordTimezone}: Coordinator jobs are in a fixed time zone with no daylight savings time (typically represented by using UTC). This time zone is referred as the "Oozie processing timezone".

    • ${wfPath}: The path to the workflow.xml.

  2. Use Ctrl-X, then Y and Enter to save the file.

  3. Use the following command to copy the file to the working directory for this job:

     hadoop fs -put coordinator.xml /tutorials/useoozie/coordinator.xml
  4. Use the following to modify the job.xml file:

     nano job.xml

    Make the following changes:

    • Change <name></name> to <name>oozie.coord.application.path</name>. This instructs Oozie to run the coordinator file instead of the workflow file.

    • Add the following, which sets a variable used in the coordinator.xml to point to the location of the workflow.xml:


      Replace the wasbs:// text with the value used in other entries in the job.xml file.

    • Add the following, which define the start, end, and frequency to use for the coordinator.xml file:


      These set the start time to 12:00PM on February 7th, 2017, the end time to February 9th, 2017, and the interval for running this job daily. The frequency is in minutes, so 24 hours x 60 minutes = 1440 minutes. Finally, the timezone is set to UTC.

  5. Use Ctrl-X, then Y and Enter to save the file.

  6. To run the job, use the following command:

     oozie job -config job.xml -run

    This submits and starts the job.

  7. If you visit the Oozie Web UI and select the Coordinator Jobs tab, you see information similar to the following:

    coordinator jobs tab

    Note the Next Materialization entry; this is when the job runs next.

  8. Similar to the earlier workflow job, selecting the job entry in the web UI displays information on the job:

    Coordinator job info

    Note that this only shows successful runs of the job, not individual actions within the scheduled workflow. To see that, select one of the Action entries. This displays information similar to that retrieved for the earlier workflow job.

    Action info


When troubleshooting problems with Oozie jobs, the Oozie UI is very helpful as it allows you to easily view both Oozie logs, as well as links to JobTracker logs for MapReduce tasks such as Hive queries. In general, the pattern for troubleshooting should be:

  1. View the job in Oozie Web UI.

  2. If there is an error or failure for a specific action, select the action to see if the Error Message field provides more information on the failure.

  3. If available, use the URL from the action to view more details (such as JobTracker logs,) for the action.

The following are specific errors you may encounter, and how to resolve them.

JA009: Cannot initialize cluster

Symptoms: The job status changes to SUSPENDED. Details for the job shows the RunHiveScript status as START_MANUAL. Selecting the action displays the following error message:

JA009: Cannot initialize Cluster. Please check your configuration for map

Cause: The WASB addresses used in the job.xml file do not contain the storage container or storage account name. The WASB address format must be wasbs://

Resolution: Change the WASB addresses used by the job.

JA002: Oozie is not allowed to impersonate <USER>

Symptoms: The job status changes to SUSPENDED. Details for the job shows the RunHiveScript status as START_MANUAL. Selecting the action shows the following error message:

JA002: User: oozie is not allowed to impersonate <USER>

Cause: Current permission settings do not allow Oozie to impersonate the specified user account.

Resolution: Oozie is allowed to impersonate users in the users group. Use the groups USERNAME to see the groups that the user account is a member of. If the user is not a member of the users group, use the following command to add the user to the group:

sudo adduser USERNAME users

It may take several minutes before HDInsight recognizes that the user has been added to the group.

Launcher ERROR (Sqoop)

Symptoms: The job status changes to KILLED. Details for the job shows the RunSqoopExport status as ERROR. Selecting the action shows the following error message:

Launcher ERROR, reason: Main class [org.apache.oozie.action.hadoop.SqoopMain], exit code [1]

Cause: Sqoop is unable to load the database driver required to access the database.

Resolution: When using Sqoop from an Oozie job, you must include the database driver with the other resources (such as the workflow.xml,) used by the job.

You must also reference the archive containing the database driver from the <sqoop>...</sqoop> section of the workflow.xml.

For example, for the job in this document, you would use the following steps:

  1. Copy the sqljdbc4.1.jar file to the /tutorials/useoozie directory:

     hdfs dfs -put /usr/share/java/sqljdbc_4.1/enu/sqljdbc41.jar /tutorials/useoozie/sqljdbc41.jar
  2. Modify the workflow.xml to add the following on a new line above </sqoop>:


Next steps

In this tutorial, you learned how to define an Oozie workflow and how to run an Oozie job. To learn more about working with HDInsight, see the following articles: