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This makes it possible for multiple users on multiple machines to share files and storage resources. Distributed file systems differ in their performance, mutability of content, handling of concurrent writes, handling of permanent or temporary loss of nodes or storage, and their policy of storing content.
State-based CRDTs (also called convergent replicated data types, or CvRDTs) are defined by two types, a type for local states and a type for actions on the state, together with three functions: A function to produce an initial state, a merge function of states, and a function to apply an action to update a state.
Some examples include: MapR File System (MapR-FS), Ceph-FS, Fraunhofer File System (BeeGFS), Lustre File System, IBM General Parallel File System (GPFS), and Parallel Virtual File System. MapR-FS is a distributed file system that is the basis of the MapR Converged Platform, with capabilities for distributed file storage, a NoSQL database with ...
For example, due to the nature of Java, the IDL-Java mapping is rather straightforward and makes usage of CORBA very simple in a Java application. This is also true of the IDL to Python mapping. The C++ mapping requires the programmer to learn datatypes that predate the C++ Standard Template Library (STL). By contrast, the C++11 mapping is ...
In this approach, a client requests and receives permission from multiple servers in order to read and write a replicated data. As an example, suppose in a distributed file system, a file is replicated on N servers. To update a file, a client must send a request to at least N/2 + 1 in order to make their agreement to perform an update. After ...
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Concurrent programming languages and multiprocessor programs must have a consistency model (also known as a memory model). The consistency model defines rules for how operations on computer memory occur and how results are produced. One of the first consistency models was Leslie Lamport's sequential consistency model. Sequential consistency is ...
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.