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Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to store and organize large amounts of data.Originally developed at the U.S. National Center for Supercomputing Applications, it is supported by The HDF Group, a non-profit corporation whose mission is to ensure continued development of HDF5 technologies and the continued accessibility of data stored in HDF.
Some researchers have made a functional and experimental analysis of several distributed file systems including HDFS, Ceph, Gluster, Lustre and old (1.6.x) version of MooseFS, although this document is from 2013 and a lot of information are outdated (e.g. MooseFS had no HA for Metadata Server at that time).
Some of the distributed parallel file systems use an object storage device (OSD) (in Lustre called OST) for chunks of data together with centralized metadata servers. BeeGFS is a hardware-independent parallel file system that features distributed metadata and striping of files across multiple targets, such as NVMe devices or logical volumes.
Apache, GridGain Systems Apache 2.0: Apache Jackrabbit: Key-value & Hierarchical & Document Yes Yes Yes Yes likely Java: Apache, Roy Fielding, Day Software: Apache 2.0: Berkeley DB/Dbm 1.x Key-value Yes No No No No C: old school Various Berkeley DB Sleepycat/Oracle Berkeley DB 5.x Key-value Yes Yes Yes Yes No C, C++, or Java dbm, Sleepycat/Oracle
Hadoop Distributed File System is a distributed file system that handles large data sets running on commodity hardware (Ishengoma, 2013). It is used to scale a single Apache Hadoop cluster to hundreds (and even thousands) of nodes. HDFS is one of the major components of Apache Hadoop, the others being MapReduce and YARN.
Hypertable runs on top of a distributed file system such as the Apache HDFS, GlusterFS or the CloudStore Kosmos File System (KFS). It is written almost entirely in C++ as the developers believed it had significant performance advantages over Java. [1] Hypertable software was originally developed at the company Zvents before 2008.
Its file storage capability is compatible with the Apache Hadoop Distributed File System (HDFS) API but with several design characteristics that distinguish it from HDFS. Among the most notable differences are that MapR-FS is a fully read/write filesystem with metadata for files and directories distributed across the namespace, so there is no ...
The MapR File System (MapR FS) is a clustered file system that supports both very large-scale and high-performance uses. [1] MapR FS supports a variety of interfaces including conventional read/write file access via NFS and a FUSE interface, as well as via the HDFS interface used by many systems such as Apache Hadoop and Apache Spark.