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Apache Kafka is a distributed event store and stream-processing platform. It is an open-source system developed by the Apache Software Foundation written in Java and Scala . The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Raft is a consensus algorithm designed as an alternative to the Paxos family of algorithms. It was meant to be more understandable than Paxos by means of separation of logic, but it is also formally proven safe and offers some additional features. [1]
According to computer scientist Eric Brewer of the University of California, Berkeley, the theorem first appeared in autumn 1998. [9] It was published as the CAP principle in 1999 [10] and presented as a conjecture by Brewer at the 2000 Symposium on Principles of Distributed Computing (PODC). [11]
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.
Replication in computing refers to maintaining multiple copies of data, processes, or resources to ensure consistency across redundant components. This fundamental technique spans databases, file systems, and distributed systems, serving to improve availability, fault-tolerance, accessibility, and performance. [1]
Controlled replication under scalable hashing or CRUSH [28] is an extension to RUSH [29] that improves upon rendezvous hashing by constructing a tree where a pseudo-random function (hash) is used to navigate down the tree to find which node is ultimately responsible for a given key. It permits perfect stability for adding nodes; however, it is ...
Automated workload management, data replication, server recovery, query optimization, and storage optimization. Native integration with open source big data technologies like Apache Kafka and Apache Spark. Support for standard programming interfaces, including ODBC, JDBC, ADO.NET, and OLEDB.
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.