hadoop distcp

The hadoop distcp command is a tool used for large inter- and intra-cluster copying.

It uses MapReduce to effect its distribution, error handling and recovery, and reporting. It expands a list of files and directories into input to map tasks, each of which will copy a partition of the files specified in the source list.


hadoop [ Generic Options ] distcp
    [-p [rbugp] ]
    [-i ]
    [-log ]
    [-m ]
    [-overwrite ]
    [-update  ]
    [-f <URI list> ]
    [-filelimit <n> ]
    [-sizelimit <n> ]
    [-delete ]


Command Options

The following command options are supported for the hadoop distcp command:




Specify the source URL.


Specify the destination URL.

-p [rbugp]

Preserve r: replication number b: block size u: user g: group p: permission -p alone is equivalent to -prbugp. Modification times are not preserved. When you specify -update, status updates are not synchronized unless the file sizes also differ.


Ignore failures. As explained in the below, this option will keep more accurate statistics about the copy than the default case. It also preserves logs from failed copies, which can be valuable for debugging. Finally, a failing map will not cause the job to fail before all splits are attempted.

-log <logdir>

Write logs to <logdir>. The hadoop distcp command keeps logs of each file it attempts to copy as map output. If a map fails, the log output will not be retained if it is re-executed.

-m <num_maps>

Maximum number of simultaneous copies. Specify the number of maps to copy data. Note that more maps may not necessarily improve throughput. See Map Sizing.


Overwrite destination. If a map fails and -i is not specified, all the files in the split, not only those that failed, will be recopied. As discussed in the Overwriting Files Between Clusters, it also changes the semantics for generating destination paths, so users should use this carefully.


Overwrite if <source> size is different from <destination> size. As noted in the preceding, this is not a "sync" operation. The only criterion examined is the source and destination file sizes; if they differ, the source file replaces the destination file. See Updating Files Between Clusters.

-f <URI list>

Use list at <URI list> as source list. This is equivalent to listing each source on the command line. The value of <URI list> must be a fully qualified URI.

-filelimit <n>

Limit the total number of files to be <= n. See Symbolic Representations.

-sizelimit <n>

Limit the total size to be <= n bytes. See Symbolic Representations.


Delete the files existing in the <destination> but not in <source>. The deletion is done by FS Shell.

Generic Options

The hadoop distcp command supports the following generic options: -conf <configuration file>, -D <property=value>, -fs <local|file system URI>, -jt <local|jobtracker:port>, -files <file1,file2,file3,...>, -libjars <libjar1,libjar2,libjar3,...>, and -archives <archive1,archive2,archive3,...>. For more information on generic options, see Generic Options.

Symbolic Representations

The parameter <n> in -filelimit and -sizelimit can be specified with symbolic representation. For example,

  • 1230k = 1230 * 1024 = 1259520
  • 891g = 891 * 1024^3 = 956703965184

Map Sizing

The hadoop distcp command attempts to size each map comparably so that each copies roughly the same number of bytes. Note that files are the finest level of granularity, so increasing the number of simultaneous copiers (i.e. maps) may not always increase the number of simultaneous copies nor the overall throughput.

If -m is not specified, distcp will attempt to schedule work for min (total_bytes / bytes.per.map, 20 * num_task_trackers) where bytes.per.map defaults to 256MB.

Tuning the number of maps to the size of the source and destination clusters, the size of the copy, and the available bandwidth is recommended for long-running and regularly run jobs.


For all of the below examples, the cluster name must be specified in the mapr-clusters.conf configuration file.

Basic inter-cluster copying

The hadoop distcp command is most often used to copy files between clusters:

hadoop distcp maprfs://cluster1/foo \

The command in the example expands the namespace under /foo/bar on cluster1 into a temporary file, partitions its contents among a set of map tasks, and starts a copy on each NodeManager node from cluster1 to cluster2. Note that the hadoop distcp command expects absolute paths.

Only those files that do not already exist in the destination are copied over from the source directory.

Updating files between clusters

Use the hadoop distcp -update command to synchronize changes between clusters.

$ hadoop distcp -update maprfs://cluster1/foo maprfs://cluster2/bar/foo

Files in the /foo subtree are copied from cluster1 to cluster2 only if the size of the source file is different from that of the size of the destination file. Otherwise, the files are skipped over.

Note that using the -update option changes distributed copy interprets the source and destination paths making it necessary to add the trailing /foo subdirectory in the second cluster.

Overwriting files between clusters

By default, distributed copy skips files that already exist in the destination directory, but you can overwrite those files using the -overwrite option. In this example, multiple source directories are specified:

$ hadoop distcp -overwrite maprfs://cluster1/foo/a \
maprfs://cluster1/foo/b \

As with using the -update option, using the -overwrite changes the way that the source and destination paths are interpreted by distributed copy: the contents of the source directories are compared to the contents of the destination directory. The distributed copy aborts in case of a conflict.

Migrating Data from HDFS to MapR-FS

The hadoop distcp command can be used to migrate data from an HDFS cluster to a MapR-FS where the HDFS cluster uses the same version of the RPC protocol as that used by MapR. For a discussion, see Copying Data from Apache Hadoop.

$ hadoop distcp namenode1:50070/foo maprfs:///bar

You must specify the IP address and HTTP port (usually 50070) for the namenode on the HDFS cluster.