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The CAP Theorem You cannot build a general data store that is continually available, sequentially consistent and tolerant to any partition failures. cap theorem states that any database system can only attain two out of following states which is consistency, availability and partition tolerance. You can only achieve 2 feature out of 3. Note that a DB running on a single node under a some number of requests and duration execution time will … Example Cassandra chose A & P while Redis chose C & P, SQL Server went with C & A. Simply put, the CAP theorem demonstrates that any distributed system cannot guaranty C, A, and P simultaneously, rather, trade-offs must be made at a point-in-time to achieve the level of performance and availability required for a specific task. The DNS, MongoDB, Redis are the example of CP systems. Under network partitioning a database can either provide consistency (CP) or availability (AP). 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. As such, it was designed from the ground up with the major value additions to Redis in mind: performance and a strong data model. Use Cases. 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. In a consistent system the view of the data is atomic at the all time. True consistency is given up in favor of performance. Financial System : Consistent & Available Chat Applications : Consistent & Partition tolerant Cache : Redis – Consistent & partition tolerant How is CAP theorem used in the field of distributed system databases? You’ll often hear about the CAP theorem which specifies some kind of an upper limit when designing distributed systems. ... MongoDB, Redis, AppFabric Caching, and MemcacheDB. 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. This perfectly fits well for data store technologies. 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. CAP Theorem Consistency. ... Redis, PostgreSQL, Neo4J(they don’t distribute data) consistent and partition tolerant (CP): MongoDB and HBase. CAP – Consistency, Availability, Partition Tolerance. AP in CAP Theorem. Before we deep dive into the concepts, let us try to understand the distribution system. A distributed system is any network structure that consists of autonomous systems that are connected using a distribution node. 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). ... HBase, Redis, MongoDB etc., AP System. AP – Possibility of Non-Consistent. At any given point of time, if there are series of operation happened and state of the data is changed, any query being served post the change should have modified data. Distributed Systems - The CAP Theorem. An AP system delivers availability and partition tolerance at the expense of consistency. CAP theorem: CAP theorem is just the observation we made above. CAP Theorem for data stores has been studied pretty well. This proves CAP theorem. Consistency – All your data servers have the same data, so you can query any server in the system and get the exact same data. Defining CAP Terminology. 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