MapR FS

MapR FS Features
Developer(s)MapR
Full nameMapR FS
Introduced2011 with Linux
Structures
Directory contentsB-tree
File allocationMulti-level B-tree
Limits
Max volume sizeunlimited
Max file size16 EiB
Max no. of filesunlimited
Features
File system
permissions
Standard Unix, Access Control expressions
Transparent
compression
Yes
Transparent
encryption
Yes
Other
Supported
operating systems
Linux

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.[2][3] In addition to file-oriented access, MapR FS supports access to tables and message streams using the Apache HBase and Apache Kafka APIs, as well as via a document database interface.

First released in 2010,[4] MapR FS is now typically described as the MapR Converged Data Platform due to the addition of tabular and messaging interfaces. The same core technology is, however, used to implement all of these forms of persistent data storage and all of the interfaces are ultimately supported by the same server processes. To distinguish the different capabilities of the overall data platform, the term MapR FS is used more specifically to refer to the file-oriented interfaces, MapR DB or MapR JSON DB is used to refer to the tabular interfaces and MapR Streams is used to describe the message streaming capabilities.

MapR FS is a cluster filesystem that provides uniform access from files to other objects such as tables used as universal namespace accessible from any client of the system. Access control is also provided for files, tables and streams used as access control expressions, which is an extension of the more common (and limited) access control list that allow permissions from composed lists of allowed users or groups, but boolean instead allow combinations of user id and groups.

  1. ^ Brennan, Bob. "Flash Memory Summit". youtube. Samsung. Retrieved June 21, 2016.
  2. ^ Dunning, Ted; Friedman, Ellen (January 2015). "Chapter 3: Understanding the MapR Distribution for Apache Hadoop". Real World Hadoop (First ed.). Sebastopol, CA: O'Reilly Media, Inc. pp. 23–28. ISBN 978-1-491-92395-5. Retrieved June 21, 2016.
  3. ^ Perez, Nicolas. "How MapR improves our productivity and simplifies our design". Medium. Medium. Retrieved June 21, 2016.
  4. ^ "MapR 1.0 Release Notes". MapR Documentation. MapR. Retrieved June 21, 2016.