PACELC theorem

In theoretical computer science, the PACELC theorem is an extension to the CAP theorem. It states that in case of network partitioning (P) in a distributed computer system, one has to choose between availability (A) and consistency (C) (as per the CAP theorem), but else (E), even when the system is running normally in the absence of partitions, one has to choose between latency (L) and consistency (C).

Overview

PACELC builds on the CAP theorem. Both theorems describe how distributed databases have limitations and tradeoffs regarding consistency, availability, and partition tolerance. PACELC however goes further and states that another trade-off also exists: this time between latency and consistency, even in absence of partitions, thus providing a more complete portrayal of the potential consistency tradeoffs for distributed systems.[1]

A high availability requirement implies that the system must replicate data. As soon as a distributed system replicates data, a tradeoff between consistency and latency arises.

The PACELC theorem was first described by Daniel J. Abadi from Yale University in 2010 in a blog post,[2] which he later formalized in a paper in 2012.[1] The purpose of PACELC is to address his thesis that "Ignoring the consistency/latency tradeoff of replicated systems is a major oversight [in CAP], as it is present at all times during system operation, whereas CAP is only relevant in the arguably rare case of a network partition."

Database PACELC ratings

Database PACELC ratings are from [3]

  • The default versions of DynamoDB, Cassandra, Riak and Cosmos DB are PA/EL systems: if a partition occurs, they give up consistency for availability, and under normal operation they give up consistency for lower latency.
  • Fully ACID systems such as VoltDB/H-Store and Megastore are PC/EC: they refuse to give up consistency, and will pay the availability and latency costs to achieve it. BigTable and related systems such as HBase are also PC/EC.
  • Cosmos DB supports five tunable consistency levels that allow for tradeoffs between C/A during P, and L/C during E. Cosmos DB never violates the specified consistency level, so it’s formally CP.
  • MongoDB can be classified as a PA/EC system. In the baseline case, the system guarantees reads and writes to be consistent.
  • PNUTS is a PC/EL system.
  • Hazelcast IMDG and indeed most in-memory data grids are an implementation of a PA/EC system; Hazelcast can be configured to be EL rather than EC.[4]
DDBS P+A P+C E+L E+C
DynamoDB Yes Yes[lower-alpha 1]
Cassandra Yes Yes[lower-alpha 1]
Cosmos DB Yes Yes
Riak Yes Yes[lower-alpha 1]
VoltDB/H-Store Yes Yes
Megastore Yes Yes
BigTable/HBase Yes Yes
MongoDB Yes Yes
PNUTS Yes Yes
Hazelcast IMDG[5] Yes Yes Yes

See also

Notes

  1. 1 2 3 Dynamo, Cassandra, and Riak have user-adjustable settings to control the LC tradeoff.[3]

References

  1. 1 2 Abadi, Daniel J. "Consistency Tradeoffs in Modern Distributed Database System Design" (PDF). Yale University.
  2. ↑ Abadi, Daniel J. (2010-04-23). "DBMS Musings: Problems with CAP, and Yahoo's little known NoSQL system". Retrieved 2016-09-11.
  3. 1 2 "Consistency Tradeoffs in Modern Distributed Database System Design" slide summary by Arinto Murdopo, Research Engineer
  4. ↑ Abadi, Daniel (2017-10-08). "DBMS Musings: Hazelcast and the Mythical PA/EC System". DBMS Musings. Retrieved 2017-10-20.
  5. ↑ Abadi, Daniel (2017-10-08). "DBMS Musings: Hazelcast and the Mythical PA/EC System". DBMS Musings. Retrieved 2017-10-20.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.