In a consistent system the view of the data is atomic at the all time. 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. Consistency: All nodes can see the same data at the same time. Financial System : Consistent & Available Chat Applications : Consistent & Partition tolerant Cache : Redis – Consistent & partition tolerant CAP – Consistency, Availability, Partition Tolerance. Because of this, Redis Cluster implements neither true availability nor consistency of the CAP theorem. AP in CAP Theorem. True consistency is given up in favor of performance. CAP Theorem for data stores has been studied pretty well. Under network partitioning a database can either provide consistency (CP) or availability (AP). 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). Defining CAP Terminology. As such, it was designed from the ground up with the major value additions to Redis in mind: performance and a strong data model. Consistency – All your data servers have the same data, so you can query any server in the system and get the exact same data. How is CAP theorem used in the field of distributed system databases? cap theorem states that any database system can only attain two out of following states which is consistency, availability and partition tolerance. The DNS, MongoDB, Redis are the example of CP systems. A distributed system is any network structure that consists of autonomous systems that are connected using a distribution node. AP – Possibility of Non-Consistent. CAP Theorem Consistency. Distributed Systems - The CAP Theorem. The CAP Theorem Published by Eric Brewer in 2000, the theorem is a set of basic requirements that describe any distributed system. You’ll often hear about the CAP theorem which specifies some kind of an upper limit when designing distributed systems. An AP system delivers availability and partition tolerance at the expense of consistency. You can only achieve 2 feature out of 3. ... HBase, Redis, MongoDB etc., AP System. Use Cases. 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. 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. ... Redis, PostgreSQL, Neo4J(they don’t distribute data) consistent and partition tolerant (CP): MongoDB and HBase. This proves CAP theorem. Before we deep dive into the concepts, let us try to understand the distribution system. The CAP Theorem You cannot build a general data store that is continually available, sequentially consistent and tolerant to any partition failures. 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