Distribution-specific Notes

Connecting to a CDH 5.13 Quickstart VM

Start and configure the Quickstart VM

1. Download the Cloudera Quickstart VM (version 5.13) from the Cloudera website.

2. Import the OVA packaged VM to your virtualization environment (Virtualbox and VMware are covered in this guide).

3. It is strongly recommended to upgrade to Java 1.8 on the single-node cluster provided by the VM. Otherwise, the execution of Single Process Pushdown and Apply Model operators will fail.

   You can take the following steps only if no clusters or Cloudera management services have been started yet. For the full upgrading process, read Cloudera’s guide.

   Upgrading to Java 1.8:

   • Start the VM.
   • Download and unzip JDK 1.8 – preferrably jdk1.8.0_60 – to /usr/java/jdk1.8.0_60.

   • Add the following configuration line to /etc/default/cloudera-scm-server:

       export JAVA_HOME=/usr/java/jdk1.8.0_60
     

   • Launch Cloudera Express (or Enterprise trial version).

   • Open a web browser, and log in to Cloudera Manager (quickstart.cloudera:7180) using cloudera/cloudera as credentials. Navigate to Hosts / quickstart.cloudera / Configuration. In Java Home Directory field, enter

       /usr/java/jdk1.8.0_60
     

   • On the home page of Cloudera Manager, (re)start the Cloudera QuickStart cluster and Cloudera Management Service as well.

4. If you are using Virtualbox, make sure that the VM is shut down, and set the type of the primary network adapter from NAT to Host-only. The VM will work only with this setting in a Virtualbox environment.

5. Start the VM and wait for the boot to complete. A browser with some basic information will appear.

6. Edit your local hosts file (on your host operating system, not inside the VM) and add the following line (replace <vm-ip-address> with the IP address of the VM):

   <vm-ip-address> quickstart.cloudera

Setup the connection in RapidMiner Studio

1. Click on New Connection button and choose Add Connection Manually

2. Set Hadoop username to hive. (As an alternative, you can set both Hadoop username and Username on Hive tab to your own user.)

3. Add quickstart.cloudera as NameNode Address

4. Add quickstart.cloudera as Resource Manager Address

5. Add quickstart.cloudera as Hive Server Address

6. Select Cloudera Hadoop (CDH5) as Hadoop version

7. Add the following entries to the Advanced Hadoop Parameters:

   Key	Value
   dfs.client.use.datanode.hostname	true

   (This parameter is not required when using the Import Hadoop Configuration Files option):

   Key	Value
   mapreduce.map.java.opts	-Xmx256m

8. Select the appropriate Spark Version (this should be Spark 1.6 if you want use the VM’s built-in Spark assembly jar) and set the Assembly Jar Location to the following value:

   local:///usr/lib/spark/lib/spark-assembly.jar

Connecting to a 2.6.3+ Sandbox VM

Start and configure the Sandbox VM

1. Download the Hortonworks Sandbox VM for VirtualBox (version 2.6.3+) from the Hortonworks website.

2. Import the OVA packaged VM to your virtualization environment (Virtualbox is covered in this guide).

3. Start the VM. After powering it on, you have to select the first option from the boot menu, then wait for the boot to complete.

4. Log in to the VM. You can do this by switching to the login console (Alt+F5), or even better via SSH on localhost port 2122. It is important to note that there are 2 exposed SSH ports on the VM, one belongs to the VM itself (2122), while the other (2222) belongs to a Docker container running inside the VM. The username is root, the password is hadoop for both.

5. Enable remote access to the DataNode, JobHistory Server and YARN services by running the following commands as root on the VM:

    iptables -t nat -A DOCKER ! -i docker0 -p tcp --dport 50010 -j DNAT --to-destination 172.17.0.2:50010
    iptables -t nat -A DOCKER ! -i docker0 -p tcp --dport 10020 -j DNAT --to-destination 172.17.0.2:10020
    iptables -t nat -A DOCKER ! -i docker0 -p tcp --dport 8030 -j DNAT --to-destination 172.17.0.2:8030
    iptables -t nat -A DOCKER ! -i docker0 -p tcp --dport 8025 -j DNAT --to-destination 172.17.0.2:8025
    iptables -A DOCKER -d 172.17.0.2/32 ! -i docker0 -o docker0 -p tcp -m tcp --dport 50010 -j ACCEPT
    iptables -A DOCKER -d 172.17.0.2/32 ! -i docker0 -o docker0 -p tcp -m tcp --dport 10020 -j ACCEPT
    iptables -A DOCKER -d 172.17.0.2/32 ! -i docker0 -o docker0 -p tcp -m tcp --dport 8030 -j ACCEPT
    iptables -A DOCKER -d 172.17.0.2/32 ! -i docker0 -o docker0 -p tcp -m tcp --dport 8025 -j ACCEPT
   

6. To make these settings persistent after reboot, execute the following:

    echo "iptables -t nat -A DOCKER ! -i docker0 -p tcp --dport 50010 -j DNAT --to-destination 172.17.0.2:50010" >> /root/start_scripts/start_sandbox.sh
    echo "iptables -t nat -A DOCKER ! -i docker0 -p tcp --dport 10020 -j DNAT --to-destination 172.17.0.2:10020" >> /root/start_scripts/start_sandbox.sh
    echo "iptables -t nat -A DOCKER ! -i docker0 -p tcp --dport 8030 -j DNAT --to-destination 172.17.0.2:8030" >> /root/start_scripts/start_sandbox.sh
    echo "iptables -t nat -A DOCKER ! -i docker0 -p tcp --dport 8025 -j DNAT --to-destination 172.17.0.2:8025" >> /root/start_scripts/start_sandbox.sh
    echo "iptables -A DOCKER -d 172.17.0.2/32 ! -i docker0 -o docker0 -p tcp -m tcp --dport 50010 -j ACCEPT" >> /root/start_scripts/start_sandbox.sh
    echo "iptables -A DOCKER -d 172.17.0.2/32 ! -i docker0 -o docker0 -p tcp -m tcp --dport 10020 -j ACCEPT" >> /root/start_scripts/start_sandbox.sh
    echo "iptables -A DOCKER -d 172.17.0.2/32 ! -i docker0 -o docker0 -p tcp -m tcp --dport 8030 -j ACCEPT" >> /root/start_scripts/start_sandbox.sh
    echo "iptables -A DOCKER -d 172.17.0.2/32 ! -i docker0 -o docker0 -p tcp -m tcp --dport 8025 -j ACCEPT" >> /root/start_scripts/start_sandbox.sh
   

7. These changes only made sure that the referenced ports of the Docker container are accessible on the respective ports of the VM. Since the network adapter of the VM is attached to NAT, these ports are not accessible from your local machine. To make them available you have to add the port forwarding rules listed below to the VM. In VirtualBox you can find these settings under Machine / Settings / Network / Adapter 1 / Advanced / Port Forwarding.

   Name	Protocol	Host Ip	Host Port	Guest Ip	Guest Port
   datanode	TCP	127.0.0.1	50010		50010
   jobhistory	TCP	127.0.0.1	10020		10020
   resourcescheduler	TCP	127.0.0.1	8030		8030
   resourcetracker	TCP	127.0.0.1	8025		8025

8. Edit your local hosts file (on your host operating system, not inside the VM), add sandbox.hortonworks.com and sandbox-hdp.hortonworks.com to your localhost entry. At the end it should look something like this:

   127.0.0.1 localhost sandbox.hortonworks.com sandbox-hdp.hortonworks.com

9. Reset Ambari access. Use an SSH client to login to localhost as root, this time using port 2222! (For example, on OS X or Linux, use the command ssh root@localhost -p 2222, password: hadoop)

   • (At first login you have to set a new root password, do it and remember it.)
   • Run ambari-admin-password-reset as root user.
   • Provide a new admin password for Ambari.
   • Run ambari-agent restart.

10. Open the Ambari website: http://sandbox.hortonworks.com:8080

    • Login with admin and the password you chose in the previous step.
    • Navigate to the YARN / Configs / Memory configuration page.
    • Set the node’s memory to at least 7 GB and click Save. Please note that Ambari may recommend to modify some related properties automatically. If so, untick tez.runtime.io.sort.mb to keep its original value.
    • Navigate to the Hive / Configs / Advanced configuration page.
    • Add the hive.security.authorization.sqlstd.confwhitelist.append setting as a new property to both Custom hive-site and Custom hiveserver2-site. The value should be the following (it must contain no whitespaces): radoop\.operation\.id|mapred\.job\.name|hive\.warehouse\.subdir\.inherit\.perms|hive\.exec\.max\.dynamic\.partitions|hive\.exec\.max\.dynamic\.partitions\.pernode|spark\.app\.name

    

    • Save the configuration and restart all affected services.

11. For enabling Spark operators, you should copy the assembly JAR to the HDFS on the Sandbox machine. Use an SSH client to login to sandbox.hortonworks.com as root using port 2222. Copy the jar to the HDFS:

     sudo -u hdfs hadoop fs -mkdir -p /user/spark
     sudo -u hdfs hadoop fs -put /usr/hdp/current/spark-client/lib/spark-assembly-*.jar /user/spark/spark-assembly.jar
    

    If you know the exact name and location of the assembly jar on the VM, this step can be skipped.

Setup the connection in RapidMiner Studio

1. Click on New Connection button and choose Add Connection Manually

2. Set Hadoop username to hive. (As an alternative, you can set both Hadoop username and Username on Hive tab to your own user.)

3. Add sandbox-hdp.hortonworks.com as NameNode Address

4. Add sandbox-hdp.hortonworks.com as Resource Manager Address

5. Add sandbox-hdp.hortonworks.com as Hive Server Address

6. Select Hortonworks HDP 2.x as Hadoop version

7. Set the Resource Manager Port to 8032.

8. Add the following entry to the Advanced Hadoop Parameters:

   Key	Value
   dfs.client.use.datanode.hostname	true

   (This parameter is not required when using the Import Hadoop Configuration Files option):

   Key	Value
   mapreduce.map.java.opts	-Xmx256m

9. Select the appropriate Spark Version (this should be Spark 1.6 if you want use the VM’s built-in Spark assembly jar) and set Assembly Jar Location to the following value:

   hdfs:///user/spark/spark-assembly.jar

   You can provide the local path of the JAR with the local:// prefix.

It is highly recommended to use New Connection / Import from Cluster Manager option to create the connection directly from the configuration retrieved from Cloudera Manager. If you do not have a Cloudera Manager account that has access to the configuration, an administrator should be able to Download Client Configuration. Using the client configuration files, choose New Connection / Import Hadoop Configuration Files to create the connection from those files.

If security is enabled on the cluster, make sure you check Configuring Apache Sentry authorization section of the Hadoop Security chapter.

Configuring Spark

If you are using Spark 1.6 version you may need to select Spark 1.6 (CDH) for more recent Cloudera Hadoop releases and Spark 1.6 for older releases. Select any of them and then run the Spark job test (enable only this test in Full Test… / Customize…) that automatically detects the proper version for you. Please choose the setting that this test recommends.

Using any other Spark version should be straightforward.

The following describes setup for HDP 2.2.6, 2.3.0, 2.3.2, 2.3.4, 2.4.0 and 2.5.0. Setup for other HDP versions should be similar.

Configuring the cluster

If there are restrictions on Hive commands on your cluster (for example, SQL Standard Based Hive Authorization is enabled on it), then the change of certain properties through HiveServer2 must be explicitly enabled. This is required if you get the following error message when running a Full Test in RapidMiner Radoop: Cannot modify radoop.operation.id at runtime. In this case a property must be added on the Ambari interface to resolve this issue.

• Login to the Ambari interface.
• Navigate to the Hive / Configs / Advanced configuration page
• Add the hive.security.authorization.sqlstd.confwhitelist.append setting as a new property to both Custom hive-site and Custom hiveserver2-site. The value should the following (it must contain no whitespaces): radoop\.operation\.id|mapred\.job\.name|hive\.warehouse\.subdir\.inherit\.perms|hive\.exec\.max\.dynamic\.partitions|hive\.exec\.max\.dynamic\.partitions\.pernode|spark\.app\.name

• Save the configuration and restart the proposed services.

For a more detailed explanation, see the Hadoop security section.

To enable Spark operators in RapidMiner Radoop, make the following changes in the Connection Settings dialog:

1. Select the appropriate Spark Version option in the Spark Settings. If Spark is installed with Ambari, the Spark Version depends on the cluster’s HDP version.

   HDP version	Spark assembly JAR location
   2.6.x	Spark 1.6 or Spark 2.1 / Spark 2.2
   2.5.x	Spark 1.6 or Spark 2.0
   2.4.x	Spark 1.6
   2.3.4	Spark 1.5
   2.3.2	Spark 1.4 or below
   2.3.0	Spark 1.4 or below
   2.2.6	Spark 1.4 or below

2. Set the Assembly Jar Location / Spark Archive path to point to the Spark location on your cluster. The following table contains the default local locations depending on your HDP version. Refer to your Hadoop administrator if the specified path does not seem to work.

   HDP version	Spark 1.x assembly JAR location	Spark 2.x archive path
   2.6.x	local:///usr/hdp/current/spark-client/lib/spark-hdp-assembly.jar	local:///usr/hdp/current/spark2-client/jars/
   2.5.0	local:///usr/hdp/current/spark-client/lib/spark-hdp-assembly.jar
   2.4.2	local:///usr/hdp/current/spark-client/lib/spark-assembly.jar
   2.4.0	local:///usr/hdp/current/spark-client/lib/spark-assembly-1.6.0.2.4.0.0-169-hadoop2.7.1.2.4.0.0-169.jar
   2.3.4	local:///usr/hdp/current/spark-client/lib/spark-assembly-1.5.2.2.3.4.0-3485-hadoop2.7.1.2.3.4.0-3485.jar
   2.3.2	local:///usr/hdp/current/spark-client/lib/spark-assembly-1.4.1.2.3.2.0-2950-hadoop2.7.1.2.3.2.0-2950.jar
   2.3.0	local:///usr/hdp/current/spark-client/lib/spark-assembly-1.3.1.2.3.0.0-2557-hadoop2.7.1.2.3.0.0-2557.jar
   2.2.6	local:///usr/hdp/current/spark-client/lib/spark-assembly-1.2.1.2.2.6.0-2800-hadoop2.6.0.2.2.6.0-2800.jar

Notes on security

If you receive a permission error during connection Full Test, verify that:

• The /user/<hadoop_username> directory exists on the HDFS and is owned by <hadoop_username>. (If the Hadoop username setting is empty, the client OS username is used.)
• The <hadoop_username> has write privileges on /user/history directory on the HDFS.

SQL Standard Based Hive Authorization may require that the user running HiveServer2 owns the files and directories loaded into Hive. This can disrupt the normal operation of RapidMiner Radoop. In case of a permission error, consult your Hadoop administrator.

Connecting to an Azure HDInsight 3.6 cluster using Radoop Proxy

As of this writing RapidMiner Radoop supports 3.5 and 3.6 versions of Azure HDInsight, a cloud-based Hadoop service that is built upon Hortonworks Data Platform (HDP) distribution. If RapidMiner Radoop does not run inside the Azure network, there are a couple of options for the networking setup. A solution like Azure ExpressRoute or a VPN can simplify the setup. However, if those options are not available, the HDInsight clusters can be accessed using Radoop Proxy, which coordinates all the communication between RapidMiner Studio and the cluster resources. Since this setup is the most complex, this guides assumes this scenario, feel free to skip steps that are not required because of an easier networking setup.

For a proper networking setup, a RapidMiner Server instance (with Radoop Proxy enabled) should be installed on an additional machine that is located in the same virtual network as the cluster nodes. The following guide provides the necessary steps for establishing a proxied connection to an HDInsight cluster.

Starting an HDInsight cluster

If you already have an HDInsight cluster running in the Azure network, skip these steps entirely.

1. Create a new Virtual network for all the network resources that will be created during cluster setup. The default Address space and Subnet address range may be suitable for this purpose. Use the same Resource group for all resources that are created during the whole cluster setup procedure.

2. Use the Custom (size, settings, apps) option instead of Quick create for creating the cluster. Choose Spark cluster type with Linux operating system, and the latest Spark version supported by Radoop, which is Spark 2.2.0 (HDI 3.6) as of this writing. Fill all the required login credential fields. Select the previously defined Resource group.

   

3. Choose the Primary storage type of the cluster. You may specify additional storage accounts as well.

   • Azure Storage : Provide a new or already existing Storage account and a Default container name. You may connect to as many Azure Storage accounts as needed.
   • Data Lake Store : Provide a Data Lake Store account. Make sure that the root path exists and the associated Service principal has adequate privileges for accessing the chosen Data Lake Store and path. Please note that a Service principal can be re-used for other cluster setups as well. For this purpose, it is recommended to save the Certificate file and the Certificate password for future reference. Once a Service principal is chosen, the access rights for any Data Lake Stores can be configured via this single Service principal object.

4. Configure the Cluster size of your choice.

5. On Advanced settings tab, choose the previously created Virtual network and Subnet.

6. After getting through all the steps of the wizard, create the cluster. After it has started, please find the private IPs and private domain names of the master nodes. You will need to copy these to your local machine. This step is required because some domain name resolutions need to take place on the client (RapidMiner Studio) side. The easiest way to do this is by copying it from one of the cluster nodes. Navigate to the dashboard of your HDInsight cluster, and select the SSH + Cluster login option. Choose any item from the Hostname selector. On Linux and Mac systems you can use the ssh command appearing below the selector. On Windows systems you will have to extract the hostname and the username from the command, and use PuTTY to connect to the host. The password is the one you provided in step 2. Once you are connected, view the contents of the /etc/hosts file of the remote host, for example by issuing the following command: cat /etc/hosts. Copy all the entries with long, generated hostnames. Paste them into the hosts file of your local machine, which is available at the following location:

   • For Windows systems: Windows\system32\drivers\etc\hosts
   • For Linux and Mac systems: /etc/hosts

Starting RapidMiner Server and Radoop Proxy

1. Create a new RapidMiner Server virtual machine in Azure. For this you will need to select the “Create a resource” option and search the Marketplace for RapidMiner Server. Select the BYOL version which best matches your Studio version. Press Create and start configuring the virtual machine. Provide the Basic settings according to your taste, but make sure that you use the previously configured Resource group and the same Location as for your cluster. Click Ok, then select a virtual machine size with at least 10GB of RAM. Configure optional features. It is essential that the same Virtual network and Subnet are selected in the Network settings as the ones used for the cluster. All other settings may remain unchanged. Check the summary, then click Create.

2. Once the VM is started, you still need to wait a few minutes for RapidMiner Server to start. The easiest way to validate this is to open (Public IP address of the VM):8080 in your browser. Once that page loads, you can log in with admin username and the name of your VM in Azure as password. You will immediately be asked for a valid license key. A free license is perfectly fine for this purpose. If your license is accepted you can close this window, you will not need it anymore.

Setting up the connection in RapidMiner Studio

First, create a Radoop Proxy Connection for the newly installed Radoop Proxy (described here in Step 1). The needed properties are:

For setting up a new Radoop connection to an Azure HDInsight 3.6 cluster, we strongly recommend to choose Import from Cluster Manager option, as it offers by far the easiest way to make the connection work correctly. This section describes the Cluster Manager import process. The Cluster Manager URL should be the base URL of the Ambari interface web page (e.g. https://radoopcluster.azurehdinsight.net). You can easily access it by clicking Ambari Views on the cluster dashboard.

After the connection is imported, most of the required settings are filled automatically. In most cases, only the following properties have to be provided manually:

You will also need to configure your storage credentials, which is described by the Storage credentials setup section. If you want to connect to a premium cluster you will need to follow the steps in the Connecting to a Premium cluster section. Once you completed these steps, you can click OK on the Connection Settings dialog, and save your connection.

Storage credentials setup

An HDInsight cluster can have more storage instances attached, which may even have different storage types (Azure Storage and Data Lake Store). For accessing them, the related credentials must be provided in Advanced Hadoop Parameters table. The following sections clarify the type of credentials needed, and how they can be acquired.

Data Lake Store credentials

As above mentioned, a single Active Directory service principal object can be attached to the cluster. This controls the access rights towards Data Lake Store(s). Obviously, only one Data Lake Store can take the role of the primary storage. In order to enable Radoop to access a Data Lake Store through this principal, the following Advanced Hadoop Parameters have to be specified:

Key	Value
dfs.adls.oauth2.access.token.provider.type	ClientCredential
dfs.adls.oauth2.refresh.url	OAuth 2.0 Token Endpoint address
dfs.adls.oauth2.client.id	Service principal application ID
dfs.adls.oauth2.credential	Service principal access key

You can acquire all of these values under Azure Active Directory dashboard (available at the service list of the main Azure Portal). Click App registrations on the dashboard, then look for the needed values as follows:

• For OAuth 2.0 Token Endpoint address, go to Endpoints, and copy the value of OAuth 2.0 Token Endpoint.
• On App registrations page, choose the Service principal associated with your HDInsight cluster, and provide the value of Application ID as Service principal application ID.
• Click Keys. Generate a new key by entering a name and an expiry date, and replace the value of Service principal access key with the generated password.

Finally, go to the HDInsight cluster main page, and click Data Lake Store access in the menu. Provide the value of Service Principal Object ID as Hadoop Username.

Connecting to a Premium cluster (having Kerberos enabled)

If you have set up or have a Premium HDInsight cluster (subscription required), some additional connection settings are required for Kerberos-based authentication.

• Configuring Kerberos authentication section describes general Kerberos-related settings.
• As for all Hortonworks distribution based clusters, you also have to apply a Hive setting (hive.security.authorization.sqlstd.confwhitelist.append) described in this section. Please note that a Hive service restart will be needed.
• We strongly advise to use Import from Cluster Manager option for creating a Radoop connection to the Kerberized cluster. The import process covers some necessary changes in Advanced Hadoop Parameters that are required for the connection to work as expected.

Connecting to EMR from another network

RapidMiner Radoop supports connecting to an EMR cluster using SOCKS proxy, VPN or Radoop Proxy. The following steps will guide you, how to start and configure an EMR cluster to use with RapidMiner Radoop and how to setup the networking and the Radoop connection to connect to the cluster. The steps required for a VPN setup can be found in a separated collapse box below.

Starting an EMR cluster

Skip these steps if you already have an EMR cluster running that has Java 8.

1. Use the advanced options for creating the cluster. Select EMR release 4.4 or newer. Make sure that Hadoop, Hive and Spark are selected for installation.

2. Add the following configuration in order to configure Java 8:

    [
        {
            "Classification": "hadoop-env",
            "Configurations": [
                {
                    "Classification": "export",
                    "Configurations": [],
                    "Properties": {
                        "JAVA_HOME": "/usr/lib/jvm/java-1.8.0"
                    }
                }
            ],
            "Properties": {}
        },
        {
            "Classification": "spark-env",
            "Configurations": [
                {
                    "Classification": "export",
                    "Configurations": [],
                    "Properties": {
                        "JAVA_HOME": "/usr/lib/jvm/java-1.8.0"
                    }
                }
            ],
            "Properties": {}
        }
    ]
   

3. After getting through all the steps of the wizard, create the cluster. If it has been started, the private IPs and private domain names of the EC2 instances should be available.

Configure the cluster

Using an SSH access, configure the cluster as follows. Log into the master node using the hadoop username and private key set during the launch. Execute the following commands to create the necessary staging directories:

hadoop fs -mkdir -p /tmp/hadoop-yarn/staging/history
hadoop fs -chmod -R 777 /tmp/hadoop-yarn
hadoop fs -mkdir /user
hadoop fs -chmod 777 /user

Setup networking

If you can install RapidMiner Server into an edge node on the EMR cluster, we strongly recommend to use Radoop Proxy. As a second option, you can use SOCKS proxy to connect to your EMR cluster. See the Networking Setup section for information on starting a SOCKS proxy and an SSH tunnel. Please open the SSH tunnel and the SOCKS proxy. The third option is to use a VPN connection. Please initiate the VPN connection to the cluster.

Setup the connection in RapidMiner Studio

When using the SOCKS proxy, the standard SOCKS proxy connection (described in Networking Setup) should be used with the following settings:

To use Spark operators in RapidMiner Radoop, enable them by selecting the appropriate Spark Version option in the Spark Settings. Then provide the path to the Spark libraries:

• If you have added Spark 1.x on the cluster creation page, upload the Spark assembly jar to HDFS:

  hadoop fs -mkdir -p /user/spark
  hadoop fs -put /usr/lib/spark/lib/spark-assembly.jar /user/spark
  

  Set hdfs:///user/spark/spark-assembly.jar as Assembly Jar Location.

• For Spark 2.x versions, the best practice is to upload the compressed Spark jar files to HDFS. This can easily be done by issuing the following commands:

  cd /usr/lib/spark/jars
  zip -r /tmp/spark.zip .
  hadoop fs -mkdir -p /user/spark
  hadoop fs -put /tmp/spark.zip /user/spark
  

  Set hdfs:///user/spark/spark.zip as Spark Archive path.

Radoop supports MapR 5.x for both RapidMiner Studio and RapidMiner Server. Note that MapR support on Server requires RapidMiner Server version 8.1 or later.

Host Settings

1. Studio and Server Job Agents must be running on host machines with MapR 5.x client installed and connected.

   • Follow instructions at Installing the MapR Client.
   • Set MAPR_HOME system environment variable properly (required on Windows, if not set on OS X or Linux will default to /opt/mapr).
   • On Windows, edit $MAPR_HOME/hadoop/hadoop-x.y.z/etc/hadoop/core-site.xml to configure the UID, GID and user name of the cluster user that will be used to access the cluster, see Configuring MapR Client User on Windows. Setting the spoofed user information is also required for MapR client version 6.0.

   • To confirm the host machine is connected, user should be able to perform the following command and get back a valid result. Acquiring a ticket via maprlogin may be required before running the command in case of a secure cluster.

     hadoop fs -ls /

2. If your HiveServer2 instance is secured by MapR Security, you need to do additional setup for Hive access. If it is not, this step can be skipped. Copy the jars according to the MapR JDBC Connections docs to a common directory on the host machine. See below an example of the list of files for Hive 2.1.1. Note that the files may differ on your environment.

Radoop Connection Setup

1. Click on New Connection button and choose Add Connection Manually

2. Choose MapR 5.x as Hadoop version.

3. Enter ResourceManager Address, JobHistory Server Address, and Hive Address fields.

4. Review default port settings in JobHistory Server Port and Hive Port fields.

5. If your HiveServer2 instance is secured by MapR Security, choose Custom HiveServer 2 in Hive Version pulldown box. If security is not enabled, you can use select HiveServer2 (Hive 0.13 or newer) and ignore the following 2 steps.

6. In Custom Hive Lib Directory select the directory where jars were copied to in Host Settings step 2.

7. For the JDBC URL Postfix textfield if your HiveServer2 instance is Secured by MapR Security, enter auth=maprsasl;saslQop=auth-conf
   if setup with SSL see Hive SSL Setup in Notes

8. For clusters using MapR Security, check Enable MapR Security.

9. Select the Spark Version and Assembly Jar Location or Spark Archive according to the installed Spark version on the cluster. See an example below for setting up Spark 2.1 if that is installed on the cluster.

   

10. On Windows, add the following Advanced Spark Parameters entry. Here we assume that the MAPR_HOME on the cluster is /opt/mapr, please change the value if this is not the case.

    Key	Value
    spark.driver.extraClassPath	/opt/mapr/hadoop/hadoop-2.7.0/share/hadoop/mapreduce/*

Notes

Spark 2.1 Setup

Spark 2.1 should be shipped with MapR 5.x. Otherwise, see Install Spark on Yarn in MapR documentation for installation instructions for Spark.

1. In Spark Version pulldown select Spark 2.1.

2. In Spark Archive (or libs) path textfield enter local:///opt/mapr/spark/spark-2.1.0/jars.

• Spark Resource Allocation Policy is defaulted to Dynamic Resource Allocation. If the cluster is not configured for this, log entries of InvalidAuxServiceException: The auxService:spark_shuffle does not exist will appear in the logs for given Spark jobs. In this case, either change cluster to enable Dynamic Resource Allocation see MapR - Enabling Dynamic Allocation in Apache Spark or change to different Resource Allocation Policy on the Radoop connection (e.g. to Static, Heuristic Configuration).

Connecting to an IBM Open Platform (IOP) cluster with default settings usually works without any special setting on the Connection Settings dialog. Select IBM Open Platform 4.1+ as Hadoop version, and provide the appropriate address fields. If the SQL Standard Based Hive Authorization is enabled for your cluster or any unexpected error occurs, please refer to the Hortonworks Data Platform description.