Using a custom MapR security configuration in the mapr.login.conf file (optional) - Cloud - 8.0

MapR
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ジョブデザインと開発 > ジョブデザイン > Hadoopディストーション > MapR
Last publication date
2023-09-14

If the default security configuration of your MapR cluster has been changed, you need to configure the Job to be executed to take this custom security configuration into account.

MapR specifies its security configuration in the mapr.login.conf file located in /opt/mapr/conf of the cluster. For further information about this configuration file and the Java service it uses behind, see mapr.login.conf and JAAS.

If no change has been made in the mapr.login.conf file, skip this procedure.

About this task

To configure your Job, you need to define the related parameters in the Basic settings tab and the Advanced settings tab of the Component view of the component you want your Job to use to connect to MapR.

If you are using a MapReduce Job, you need to define the related parameters in the Hadoop configuration tab of the Job.

If you are designing a Spark Job, you need to do the related configuration in the Spark configuration tab of the Job.

Proceed as follows to do the configuration:

Procedure

  1. Verify what has been changed about this mapr.login.conf file.
    You should be able to obtain the related information from the administrator or the developer of your MapR cluster.
  2. If the location of the MapR configuration files has been changed to somewhere else in the cluster, that is to say, the MapR Home directory has been changed, select the Set the MapR Home directory check box and enter the new Home directory. Otherwise, leave this check box clear and the default Home directory is used.
  3. If the login module to be used in the mapr.login.conf file has been changed, select the Specify the Hadoop login configuration check box and enter the module to be called from the mapr.login.conf file. Otherwise, leave this check box clear and the default login module is used.
    For example, enter kerberos to call the hadoop_kerberos module or hybrid to call the hadoop_hybrid module.