Before we deep dive into the concepts, let us try to understand the distribution system. Financial System : Consistent & Available Chat Applications : Consistent & Partition tolerant Cache : Redis – Consistent & partition tolerant ... MongoDB, Redis, AppFabric Caching, and MemcacheDB. Distributed Systems - The CAP Theorem. The essential idea being, out of Consistency, Availability and Partition-Tolerance, a data store technology can choose either of two at any point in time. Because of this, Redis Cluster implements neither true availability nor consistency of the CAP theorem. Consistency: All nodes can see the same data at the same time. This proves CAP theorem. In the event of a network partition, they can become unable to respond to certain types of queries (for example, in a Mongo replica set you flag slaveok to false for reads). CAP – Consistency, Availability, Partition Tolerance. This perfectly fits well for data store technologies. ... HBase, Redis, MongoDB etc., AP System. You’ll often hear about the CAP theorem which specifies some kind of an upper limit when designing distributed systems. How is CAP theorem used in the field of distributed system databases? A distributed system is any network structure that consists of autonomous systems that are connected using a distribution node. In a consistent system the view of the data is atomic at the all time. AP – Possibility of Non-Consistent. ... Redis, PostgreSQL, Neo4J(they don’t distribute data) consistent and partition tolerant (CP): MongoDB and HBase. CAP theorem: CAP theorem is just the observation we made above. As such, it was designed from the ground up with the major value additions to Redis in mind: performance and a strong data model. cap theorem states that any database system can only attain two out of following states which is consistency, availability and partition tolerance. AP in CAP Theorem. Example Cassandra chose A & P while Redis chose C & P, SQL Server went with C & A. Defining CAP Terminology. Let’s get some basic definitions out of the way so we can be on the same page as we move forward talking about this theorem. The CAP Theorem Published by Eric Brewer in 2000, the theorem is a set of basic requirements that describe any distributed system. Use Cases. An AP system delivers availability and partition tolerance at the expense of consistency. CAP Theorem for data stores has been studied pretty well. Note that a DB running on a single node under a some number of requests and duration execution time will … CAP Published by Eric Brewer in 2000, the theorem is a set of basic requirements that describe any distributed system like: NoSQL Cassandra, MongoDB, CouchDB. CAP Theorem Consistency. You can only achieve 2 feature out of 3. Consistency – All your data servers have the same data, so you can query any server in the system and get the exact same data. 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