Towards robust distributed systems (abstract)

By Eric A. Brewer. ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, 2000

862 citations recorded by the source corpus.

Current distributed systems, even the ones that work, tend to be very fragile: they are hard to keep up, hard to manage, hard to grow, hard to evolve, and hard to program. In this talk, I look at several issues in an attempt to clean up the way we think about these systems. These issues include the fault model, high availability, graceful degradation, data consistency, evolution, composition, and autonomy. These are not (yet) provable principles, but merely ways to think about the issues that simplify design in practice. They draw on experience at Berkeley and with giant-scale systems built at Inktomi, including the system that handles 50% of all web searches.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/cf1c70afbd942ff34595052c3438dc3f50a90167

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Citations in the corpus

Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “• CAP Option: CAP theorem [163], [164] reveals that a shared data system can only choose at most two of three properties: consistency, availability, and tolerance to partitions.”
OLTP through the looking glass, and what we found there (2008, background): “In particular, in distributed Internet applications, eventual consistency is often favored over transactional consistency [ Bre00 , DHJ+07].”
The Quest for Scalable Blockchain Fabric: Proof-of-Work vs. BFT Replication (2015, neutral): “However, BFT and state-machine replication protocols in general are often challenged for their scalability in terms of number of nodes (replicas) [8], and have not been throughly tested in this aspect critical to blockchain.”
A Survey of Large Scale Data Management Approaches in Cloud Environments (2011, background): “CAP theorem [45], [46] shows that a shared-data system can only choose at most two out of three properties: Consistency (all records are the same in all replicas), Availability (all replicas can accept updates or inserts), and tolerance to Partitions (the system still functions when distributed repl”