In addition to these features, DistCp is also highly scalable and can handle large volumes of data without any issues. Support for Incremental Copying: DistCp has the ability to copy only the changes made to a file, which can help reduce the amount of data that needs to be transferred. Support for Different File Systems: DistCp can copy data between clusters running different file systems, such as HDFS and S3.Ħ. Resume Capability: DistCp has the ability to resume copying from where it left off in case of failure or interruption.ĥ. Support for Compression: DistCp can compress data during the copying process, which can help reduce the amount of data that needs to be transferred.Ĥ. Preserving File Attributes: DistCp preserves file attributes such as permissions, timestamps, and ownership during the copying process.ģ. Parallel Copying: DistCp uses MapReduce to copy data in parallel, which makes it fast and efficient.Ģ. Some of the features of DistCp include:ġ.
In this blog, we will explore the features of DistCp and its benefits in transferring data between clusters.ĭistCp uses MapReduce to copy data between clusters, and it provides a number of features that make it a powerful tool for data transfer.
It is an integral part of the Hadoop ecosystem and is used to efficiently transfer large volumes of data between clusters. DistCp, short for Distributed Copy, is a tool used for transferring data between Hadoop clusters.
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