Displaying 1 to 6 from 6 results

membase - distributed key-value database

  •    C

Membase is an distributed, key-value database management system optimized for storing data behind interactive web applications. These applications must service many concurrent users, creating, storing, retrieving, aggregating, manipulating and presenting data in real-time. Supporting these requirements, membase processes data operations with quasi-deterministic low latency and high sustained throughput.

Apache Geode - Distributed, In-memory Database for Scale-Out Applications

  •    Java

Apache Geode is distributed, in-memory database for scale-out applications. All data is stored in-memory for low latency. Performance scales linearly as nodes are added. Data is distributed automatically between nodes to optimize performance. Clusters fail-over to other nodes in case of failures, and rebalance remaining resources. Geode servers can be configured to talk memcached protocol.

redis - Baidu Ksarch Redis - a production solution of redis cluster

  •    

This is a Redis Cluster Controller to control the state of the cluster nodes and provide http api and tools(both command line and web ui). It is production-ready and used at baidu.com, with a scale of 2000+ machines.




KVRaftDB - a distributed Key/Value Database based on Raft. MIT-6.824

  •    Go

A replicated service achieves fault tolerance by storing complete copies of its state (i.e., data) on multiple replica servers. Replication allows the service to continue operating even if some of its servers experience failures (crashes or a broken or flaky network). The challenge is that failures may cause the replicas to hold differing copies of the data. Raft manages a service's state replicas, and in particular it helps the service sort out what the correct state is after failures. Raft implements a replicated state machine. It organizes client requests into a sequence, called the log, and ensures that all the replicas agree on the contents of the log. Each replica executes the client requests in the log in the order they appear in the log, applying those requests to the replica's local copy of the service's state. Since all the live replicas see the same log contents, they all execute the same requests in the same order, and thus continue to have identical service state. If a server fails but later recovers, Raft takes care of bringing its log up to date. Raft will continue to operate as long as at least a majority of the servers are alive and can talk to each other. If there is no such majority, Raft will make no progress, but will pick up where it left off as soon as a majority can communicate again.





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