University:
Amherst College
Course:
COSC 211 | Data Structures
Academic year:

2023
Views:

472

Pages:

3
Author:

5prinzg1s

110 PACELC Theorem (New) Let's learn about the PACELC theorem and its usage. Background We cannot avoid partition in a distributed system, therefore, according to the CAP theorem, a distributed system should choose between consistency or availability. ACID (Atomicity, ** Consistency, Isolation, Durability) databases, such as RDBMSs like MySQL, Oracle, and ** Microsoft SQL Server, chose consistency (refuse response if it cannot check with peers), *** *** *** while BASE (Basically Available, Soft-state, Eventually consistent) databases, such as *** ** ** NoSQL databases like MongoDB, Cassandra, and Redis, chose availability (respond with *** *** local data without ensuring it is the latest with its peers). One place where the CAP theorem is silent is what happens when there is no network * partition? What choices does a distributed system have when there is no partition? * Solution The PACELC theorem states that in a system that replicates data: **~ ~** *** ** ⠀ *** if there is a partition ('P'), a distributed system can tradeoff between availability and *** ** ** ** * *** * * *** ** consistency (i.e., 'A' and 'C'); *** *** ** *** ** ** ** ** else ('E'), when the system is running normally in the absence of partitions, the system *** ** ** * * * * * * can tradeoff between latency ('L') and consistency ('C'). ** * * ** ** ** ** *** The first part of the theorem (PAC) is the same as the CAP theorem, and the ELC is the extension. *** The whole thesis is assuming we maintain high availability by replication. *** *** *** * ** So, when there is a failure, CAP theorem prevails. *** But if not, we still have to consider the tradeoff between consistency and latency of a *** *** *** replicated system. Examples **~* Dynamo and Cassandra are PA/EL systems: * * * ~ ** They choose availability over consistency when a partition occurs; *** *** **~* *** ~~ ~~ * * otherwise, they choose lower latency. *** *** *** BigTable and HBase are PC/EC systems: * * * ~ ** They will always choose consistency, giving up availability and lower latency. *** **~ *** *** *** ~~ ~~ ~~ ~~ MongoDB can be considered PA/EC (default configuration): ~ ** ** MongoDB works in a primary/secondaries configuration. ** *** *** In the default configuration, all writes and reads are performed on the *** *** *** primary. As all replication is done asynchronously (from primary to *** *** *** secondaries), when there is a network partition in which primary is lost or becomes isolated * * on the minority side, there is a chance of losing data that is unreplicated to secondaries, hence there is a loss of consistency during partitions. * ~~ ~~* Therefore it can be concluded that in the case of a network partition, * * MongoDB chooses availability, but otherwise guarantees consistency. ** *** ** *** *** *** *** Alternately, when MongoDB is configured to write on majority replicas *** * * * * and read from the primary, it could be categorized as PC/EC. * * ** **

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