The recent increase in interest for batch analytics has resulted in extensive use of distributed frameworks such as Hadoop and Dryad. Batch analytics—as the name suggests, perform many computations on large volumes of data.
That is, large quantities of data are ingested once and read many times mostly in large chunks, which is characterized as write-once read- many (WORM) workload. The storage part of these distributed frameworks (say, HDFS in Hadoop) use ﬁle systems such as ext4 or XFS as native object stores to store objects as ﬁles in individual nodes of the distributed system. These general purpose ﬁle systems were designed with broader goals such as POSIX-compliance, optimal performance for a wide range of ﬁle size, user friendliness, etc. However, most of these features are not required for a native object store in distributed ﬁle systems.
WORMStore is a light weight object store that is designed exclusively for use in distributed systems for WORM workload. WORMStore provides interesting advantages such as the ability to prefetch large objects, small metadata to data ratio, media aware data/metadata placement, etc. As WORMStore is log-structured, it provides the ability to recover upon failure. Our experiments show that WORMStore provides a 28% increase in the read throughput per node in a Hadoop cluster.
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