Concise Server-Wide Causality Management for Eventually Consistent Data Stores
Large scale distributed data stores rely on optimistic replication to scale and remain highly available in the face of network partitions. Managing data without coordination results in eventually consistent data stores that allow for concurrent data updates. These systems often use anti-entropy mechanisms (like Merkle Trees) to detect and repair divergent data versions across nodes. However, in practice hash-based data structures are too expensive for large amounts of data and create too many false conflicts.
Another aspect of eventual consistency is detecting write conflicts. Logical clocks are often used to track data causality, necessary to detect causally concurrent writes on the same key. However, there is a non-negligible metadata overhead per key, which also keeps growing with time, proportional with the node churn rate. Another challenge is deleting keys while respecting causality: while the values can be deleted, per-key metadata cannot be permanently removed without coordination.
We introduce a new causality management framework for eventually consistent data stores, that leverages node logical clocks (Bitmapped Version Vectors) and a new key logical clock (Dotted Causal Container) to provides advantages on multiple fronts: 1) a new efficient and lightweight anti-entropy mechanism; 2) greatly reduced per-key causality metadata size; 3) accurate key deletes without permanent metadata.
KeywordsDistributed Systems Key-Value Stores Eventual Consistency Causality Logical Clocks Anti-Entropy
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- 2.DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Vosshall, P., Vogels, W.: Dynamo: amazon’s highly available key-value store. In: ACM SIGOPS Operating Systems Review, vol. 41, pp. 205–220 (2007)Google Scholar
- 4.Golding, R.A.: Weak-consistency group communication and membership. Ph.D. thesis, University of California Santa Cruz (1992)Google Scholar
- 5.Johnson, P.R., Thomas, R.H.: The maintenance of duplicate databases. Internet Request for Comments RFC 677, Information Sciences Institute (1976)Google Scholar
- 6.Karger, D., Lehman, E., Leighton, T., Panigrahy, R., Levine, M., Lewin, D.: Consistent hashing and random trees. In: Proceedings of the Twenty-Ninth Annual ACM Symposium on Theory of Computing, pp. 654–663. ACM (1997)Google Scholar
- 7.Klophaus, R.: Riak core: building distributed applications without shared state. In: ACM SIGPLAN Commercial Users of Functional Programming. ACM (2010)Google Scholar
- 12.Merkle, R.C.: A certified digital signature. In: Brassard, G. (ed.) Advances in Cryptology - CRYPTO 1989. LNCS, vol. 435, pp. 218–238. Springer, Heidelberg (1990), http://dl.acm.org/citation.cfm?id=118209.118230
- 13.Parker Jr., D.S., Popek, G., Rudisin, G., Stoughton, A., Walker, B., Walton, E., Chow, J., Edwards, D.: Detection of mutual inconsistency in distributed systems. IEEE Transactions on Software Engineering, 240–247 (1983)Google Scholar
- 15.Wuu, G., Bernstein, A.: Efficient solutions to the replicated log and dictionary problems. In: Symp. on Principles of Dist. Comp. (PODC), pp. 233–242 (1984)Google Scholar