Hive/Impala Replication

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 non-Gateway roles installed on hosts with limited resources, Hive/Impala 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.

Parameters of databases, tables, partitions, and indexes are replicated by default during Hive/Impala replications.

To use BDR for Hive replication in environments where the Hive Metastore changes, such as when a database or table gets created or deleted, additional configuration is needed.

By default, BDR uses snapshot differences ("diff") to improve performance by comparing HDFS snapshots and only replicating the files that are changed in the source directory. While Hive metadata requires a full replication, the data stored in Hive tables can take advantage of snapshot diff-based replication.

To use this feature, follow these guidelines:

• The source and target clusters must be managed by Cloudera Manager 5.15.0 or higher. If the destination is Amazon S3 or Microsoft ADLS, the source cluster must be Managed by Cloudera Manager 5.15.0 or higher. Snapshot diff-based restore to S3 or ADLS is not supported
• The source and target clusters run CDH 5.15.0 or higher, 5.14.2 or higher, or 5.13.3 or higher.
• Verify that HDFS snapshots are immutable.

  In the Cloudera Manager Admin Console, go to Clusters > <HDFS cluster> > Configuration and search for Enable Immutable Snapshots.

• Do not use snapshot diff for globbed paths. It is not optimized for globbed paths.
• Set the snapshot root directory as low in the hierarchy as possible.
• Decide if you want BDR to abort on a snapshot diff failure or continue the replication. If you choose to configure BDR to continue the replication when it encounters an error, BDR performs a complete replication. Note that continuing the replication can result in a longer duration since a complete replication is performed.
• BDR performs a complete replication when one or more of the following change: Delete Policy, Preserve Policy, Target Path, or Exclusion Path.
• All paths in the replication schedule should be under a snapshottable root or should be snapshottable for the schedule to run using snapshot diff. If even one path is not snapshottable, BDR does not use snapshot diff.

• If a Hive replication schedule is created to replicate a database, ensure all the HDFS paths for the tables in that database are either snapshottable or under a snapshottable root. For example, if the database that is being replicated has external tables, all the external table HDFS data locations should be snapshottable too. Failing to do so will cause BDR to fail to generate a diff report. Without a diff report, BDR will not use snapshot diff.

1. Verify that your cluster conforms to one of the Supported Replication Scenarios.
2. If the source cluster is managed by a different Cloudera Manager server than the destination cluster, configure a peer relationship. If the source or destination is S3 or ADLS, you must configure AWS credentials or configure ADLS credentials.
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. The General tab displays.
5. Select the General tab to configure the following:
   1. Use the Name field to provide a unique name for the replication schedule.
   2. Use the Source drop-down list to select the cluster with the Hive service you want to replicate.
   3. 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.
   4. Leave Replicate All checked to replicate all the Hive 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).

   5. 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.
   6. 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.
6. Select the Resources tab to configure the following:
   • Scheduler Pool – (Optional) Enter the name of a resource pool in the field. 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
   • Maximum Map Slots and Maximum Bandwidth – Limits for the number of map slots and for bandwidth per mapper. The default is 100 MB.
   • 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.
7. Select the Advanced tab 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.
   • If both the source and destination clusters use CDH 5.7.0 or later up to and including 5.11.x, select the Replicate Impala Metadata drop-down list and select No to avoid redundant replication of Impala metadata. (This option only displays when supported by both source and destination clusters.) You can select the following options for Replicate Impala Metadata:
     • Yes – replicates the Impala metadata.
     • No – does not replicate the Impala metadata.
     • Auto – Cloudera Manager determines whether or not to replicate the Impala metadata based on the CDH version.

     To replicate Impala UDFs when the version of CDH managed by Cloudera Manager is 5.7 or lower, see Replicating Data to Impala Clusters for information on when to select this option.

   • 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.
   • 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.
   • Number of concurrent HMS connections - The number of concurrent Hive Metastore connections. These connections are used to concurrently import and export metadata from Hive.

     If you set the value to 1 or more, BDR uses multi-threading with the number of connections specified. If you set the value to 0 or fewer, BDR uses single threading and a single connection.

   • 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).
   • Log Path - An alternative path for the logs.
   • Description - A description for the replication schedule.
   • 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.
   • Skip Listing Checksum Checks - Whether to skip checksum check when comparing two files to determine whether they are same or not. If skipped, the file size and last modified time are used to determine if files are the same or not. Skipping the check improves performance during the mapper phase. Note that if you select the Skip Checksum Checks option, this check is also skipped.
   • 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.
   • Abort on Snapshot Diff Failures - If a snapshot diff fails during replication, BDR uses a complete copy to replicate data. If you select this option, the BDR aborts the replication when it encounters an error instead.
   • 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. (Not supported when replicating to S3 or ADLS.)
     • 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.
   • 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).
8. Click Save Schedule.

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

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.

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.

The Replication Schedules columns are described in the following table.

Replication Schedules Table

Column	Description
ID	An internally generated ID number that identifies the schedule. Provides a convenient way to identify a schedule. Click the ID column label to sort the replication schedule table by ID.
Name	The unique name you specify when you create a schedule.
Type	The type of replication scheduled, either HDFS or Hive.
Source	The source cluster for the replication.
Destination	The destination cluster for the replication.
Throughput	Average throughput per mapper/file of all the files written. Note that throughput does not include the following information: the combined throughput of all mappers and the time taken to perform a checksum on a file after the file is written.
Progress	The progress of the current run of the scheduled replication.
Last Run	The date and time when the replication last ran. Displays None if the scheduled replication has not yet been run. Click the date and time link to view the Replication History page for the replication. Displays one of the following icons: - Successful. Displays the date and time of the last run replication. - Failed. Displays the date and time of a failed replication. - None. This scheduled replication has not yet run. - Running. Displays a spinner and bar showing the progress of the replication. Click the Last Run column label to sort the Replication Schedules table by the last run date.
Next Run	The date and time when the next replication is scheduled, based on the schedule parameters specified for the schedule. Hover over the date to view additional details about the scheduled replication. Click the Next Run column label to sort the Replication Schedules table by the next run date.
Objects	Displays on the bottom line of each row, depending on the type of replication: Hive - A list of tables selected for replication. HDFS - A list of paths selected for replication. For example:
Actions	The following items are available from the Action button: Show History - Opens the Replication History page for a replication. See Viewing Replication History. Edit Configuration - Opens the Edit Replication Schedule page. Dry Run - Simulates a run of the replication task but does not actually copy any files or tables. After a Dry Run, you can select Show History, which opens the Replication History page where you can view any error messages and the number and size of files or tables that would be copied in an actual replication. Click Collect Diagnostic Data to open the Send Diagnostic Data screen, which allows you to collect replication-specific diagnostic data for the last 10 runs 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. Run Now - Runs the replication task immediately. Disable | Enable - Disables or enables the replication schedule. No further replications are scheduled for disabled replication schedules. Delete - Deletes the schedule. Deleting a replication schedule does not delete copied files or tables.

• 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.

Replication History Screen (HDFS)

Replication History Screen (Hive, Failed Replication)

You can use Cloudera Manager to replicate Hive/Impala data and metadata to and from Amazon S3 or Microsoft ADLS: however, you cannot replicate data from one S3 or ADLS instance to another using Cloudera Manager. You must have the appropriate credentials to access the S3 or ADLS account. In S3, you must create buckets to store the replicated files. In ADLS, you must create data lake stores.

When you replicate data to cloud storage with BDR, BDR backs up file metadata, including extended attributes and ACLs.