High latency among clusters can cause replication jobs to run more slowly, but does not cause them to fail. For best performance, latency between the source cluster NameNode and the destination cluster NameNode should be less than 80 milliseconds. (You can test latency using the Linux ping command.) Cloudera has successfully tested replications with latency of up to 360 milliseconds. As latency increases, replication performance degrades.

If your cluster has Hive clients (Hive Gateway roles) installed on hosts with limited resources, Hive replication may use these hosts to run commands for the replication, which can cause the performance of the replication to degrade. To improve performance, you can specify the hosts (a ”white list”) to use during replication so that the lower-resource hosts are not used.

  Note: JARs for permanent UDFs are stored in HDFS at a user-defined location. If you are replicating Hive data, you should also replicate this directory.

1. Verify that your cluster conforms to a supported replication scenario.
2. If the source cluster is managed by a different Cloudera Manager server than the destination cluster, configure a peer relationship.
3. Do one of the following:
   • From the Backup tab, select Replications.
   • From the Clusters tab, go to the Hive service and select Quick Links > Replication.

   The Schedules tab of the Replications page displays.

4. Select Create New Schedule > Hive Replication.
5. Use the Source drop-down list to select the cluster with the Hive service you want to replicate.
6. Use the Destination drop-down list to select the destination for the replication. If there is only one Hive service managed by Cloudera Manager available as a destination, this is specified as the destination. If more than one Hive service is managed by this Cloudera Manager, select from among them.
7. Select a Schedule:
   • Immediate - Run the schedule Immediately.
   • Once - Run the schedule one time in the future. Set the date and time.
   • Recurring - Run the schedule periodically in the future. Set the date, time, and interval between runs.
8. Leave Replicate All checked to replicate all the Hive metastore databases from the source. To replicate only selected databases, uncheck this option and enter the database name(s) and tables you want to replicate.
   • You can specify multiple databases and tables using the plus symbol to add more rows to the specification.
   • You can specify multiple databases on a single line by separating their names with the pipe (|) character. For example: mydbname1|mydbname2|mydbname3.
   • Regular expressions can be used in either database or table fields, as described in the following table:

     Regular Expression	Result
     [\w].+	Any database or table name.
     (?!myname\b).+	Any database or table except the one named myname.
     db1|db2 [\w_]+	All tables of the db1 and db2 databases.
     db1 [\w_]+ Click the "+" button and then enter db2 [\w_]+	All tables of the db1 and db2 databases (alternate method).

9. Use the Advanced Options section to specify an export location, modify the parameters of the MapReduce job that will perform the replication, and set other options. You can select a MapReduce service (if there is more than one in your cluster) and change the following parameters:
   • Uncheck the Replicate HDFS Files checkbox to skip replicating the associated data files.
   • Uncheck the Replicate Impala Metadata checkbox to skip replicating Impala metadata. (This option is checked by default.) See Impala Metadata Replication.
   • The Force Overwrite option, if checked, forces overwriting data in the destination metastore if incompatible changes are detected. For example, if the destination metastore was modified, and a new partition was added to a table, this option forces deletion of that partition, overwriting the table with the version found on the source.
       Important: If the Force Overwrite option is not set, and the Hive replication process detects incompatible changes on the source cluster, Hive replication fails. This sometimes occurs with recurring replications, where the metadata associated with an existing database or table on the source cluster changes over time.
   • By default, Hive metadata is exported to a default HDFS location (/user/${user.name}/.cm/hive) and then imported from this HDFS file to the destination Hive metastore. In this example, user.name is the process user of the HDFS service on the destination cluster. To override the default HDFS location for this export file, specify a path in the Export Path field.
       Note: In a Kerberized cluster, the HDFS principal on the source cluster must have read, write, and execute access to the Export Path directory on the destination cluster.
   • By default, Hive HDFS data files (for example, /user/hive/warehouse/db1/t1) are replicated to a location relative to "/" (in this example, to /user/hive/warehouse/db1/t1). To override the default, enter a path in the HDFS Destination Path field. For example, if you enter /ReplicatedData, the data files would be replicated to /ReplicatedData/user/hive/warehouse/db1/t1.
   • Select the MapReduce Service to use for this replication (if there is more than one in your cluster).
   • To specify the user that should run the MapReduce job, use the Run As Username option. By default, MapReduce jobs run as hdfs. To run the MapReduce job as a different user, enter the user name. If you are using Kerberos, you must provide a user name here, and it must have an ID greater than 1000.
       Note: If you are using different principals on the source and destination clusters, the user running the MapReduce job should have read and execute permissions on the Hive warehouse directory on the source cluster. If you configure the replication job to preserve permissions, superuser privileges are required on the destination cluster.
   • Scheduler Pool - The name of a resource pool. The value you enter is used by the MapReduce Service you specified when Cloudera Manager executes the MapReduce job for the replication. The job specifies the value using one of these properties:
     • MapReduce - Fair scheduler: mapred.fairscheduler.pool
     • MapReduce - Capacity scheduler: queue.name
     • YARN - mapreduce.job.queuename
   • Log Path - An alternative path for the logs.
   • Maximum Map Slots and Maximum Bandwidth- Limits for the number of map slots and for bandwidth per mapper. The default is 100 MB.
   • Abort on Error - Whether to abort the job on an error. By selecting the check box, files copied up to that point remain on the destination, but no additional files will be copied. Abort on Error is off by default.
   • Skip Checksum Checks - Whether to skip checksum checks, which are performed by default.
     Checksums are used for two purposes:

     • To skip replication of files that have already been copied. If Skip Checksum Checks is selected, the replication job skips copying a file if the file lengths and modification times are identical between the source and destination clusters. Otherwise, the job copies the file from the source to the destination.
     • To redundantly verify the integrity of data. However, checksums are not required to guarantee accurate transfers between clusters. HDFS data transfers are protected by checksums during transfer and storage hardware also uses checksums to ensure that data is accurately stored. These two mechanisms work together to validate the integrity of the copied data.
   • Replication Strategy - Whether file replication should be static (the default) or dynamic. Static replication distributes file replication tasks among the mappers up front to achieve a uniform distribution based on file sizes. Dynamic replication distributes file replication tasks in small sets to the mappers, and as each mapper processes its tasks, it dynamically acquires and processes the next unallocated set of tasks.
   • Delete Policy - Whether files that were on the source should also be deleted from the destination directory. Options include:
     • Keep Deleted Files - Retains the destination files even when they no longer exist at the source. (This is the default.).
     • Delete to Trash - If the HDFS trash is enabled, files are moved to the trash folder.
     • Delete Permanently - Uses the least amount of space; use with caution.
   • Preserve - Whether to preserve the Block Size, Replication Count, and Permissions as they exist on the source file system, or to use the settings as configured on the destination file system. By default, settings are preserved on the source.
       Note: You must be running as a superuser to preserve permissions. Use the "Run As Username" option to ensure that is the case.
   • Alerts - Whether to generate alerts for various state changes in the replication workflow. You can alert On Failure, On Start, On Success, or On Abort (when the replication workflow is aborted).
10. Click Save Schedule.

    The replication task appears as a row in the Replications Schedule table. See Viewing Replication Schedules.

To specify additional replication tasks, select Create > Hive Replication.

  Note: If your replication job takes a long time to complete, and tables change before the replication finishes, the replication may fail. Consider making the Hive Warehouse Directory and the directories of any external tables snapshottable, so that the replication job creates snapshots of the directories before copying the files. See Using Snapshots with Replication.

The Replications Schedules page displays a row of information about each scheduled replication job. Each row also displays recent messages regarding the last time the Replication job ran.

Figure 1. Replication Schedules Table

Only one job corresponding to a replication schedule can occur at a time; if another job associated with that same replication schedule starts before the previous one has finished, the second one is canceled.

You can limit the replication jobs that are displayed by selecting filters on the left. If you do not see an expected schedule, adjust or clear the filters. Use the search box to search the list of schedules for path, database, or table names.

• While a job is in progress, the Last Run column displays a spinner and progress bar, and each stage of the replication task is indicated in the message beneath the job's row. Click the Command Details link to view details about the execution of the command.
• If the job is successful, the number of files copied is indicated. If there have been no changes to a file at the source since the previous job, then that file is not copied. As a result, after the initial job, only a subset of the files may actually be copied, and this is indicated in the success message.
• If the job fails, the icon displays.
• To view more information about a completed job, select Actions > Show History. See Viewing Replication History.

You can view historical details about replication jobs on the Replication History page.

To view the history of a replication job:

1. Select Backup > Replication Schedules to go to the Replication Schedules page.
2. Locate the row for the job.
3. Click Actions > Show History.

Figure 2. Replication History Screen (HDFS)

Figure 3. Replication History Screen (Hive, Failed Replication)

The Replication History page displays a table of previously run replication jobs with the following columns:

Table 2. Replication History Table

Column	Description
Start Time	Time when the replication job started. Click to expand the display and show details of the replication. In this screen, you can: Click the View link to open the Command Details page, which displays details and messages about each step in the execution of the command. Click to expand the display for a Step to: View the actual command string. View the Start time and duration of the command. Click the Context link to view the service status page relevant to the command. Select one of the tabs to view the Role Log, stdout, and stderr for the command. See Viewing Running and Recent Commands. Click Collect Diagnostic Data to open the Send Diagnostic Data screen, which allows you to collect replication-specific diagnostic data for this run of the schedule: Select Send Diagnostic Data to Cloudera to automatically send the bundle to Cloudera Support. You can also enter a ticket number and comments when sending the bundle. Click Collect and Send Diagnostic Data to generate the bundle and open the Replications Diagnostics Command screen. When the command finishes, click Download Result Data to download a zip file containing the bundle. (HDFS only) Link to view details on the MapReduce Job used for the replication. See Viewing and Filtering MapReduce Activities. (Dry Run only) View the number of Replicable Files. Displays the number of files that would be replicated during an actual replication. (Dry Run only) View the number of Replicable Bytes. Displays the number of bytes that would be replicated during an actual replication. Link to download a CSV file containing a Replication Report. This file lists the databases and tables that were replicated. View the number of Errors that occurred during the replication. View the number of Impala UDFs replicated. (Displays only for Hive replications where Replicate Impala Metadata is selected.) Click the link to download a CSV file containing a Download Listing. This file lists the files and directories that were replicated. Click the link to download a CSV file containing Download Status. If a user was specified in the Run As Username field when creating the replication job, the selected user displays. View messages returned from the replication job.
Duration	Amount of time the replication job took to complete.
Outcome	Indicates success or failure of the replication job.
Files Expected	Number of files expected to be copied, based on the parameters of the replication schedule.
Files Copied	Number of files actually copied during the replication.
Tables	(Hive only) Number of tables replicated.
Files Failed	Number of files that failed to be copied during the replication.
Files Deleted	Number of files that were deleted during the replication.
Files Skipped	Number of files skipped during the replication. The replication process skips files that already exist in the destination and have not changed.