Showing posts from April, 2022

Anna: A Key-Value Store For Any Scale

This paper (ICDE'18) introduces Anna, a CALM / CRDT implementation of a distributed key-value system both at the data structure level as well as system architecture and transaction protocol levels. Anna is a partitioned, multi-mastered key-value system that achieves high performance and elasticity via wait-free execution and coordination-free consistency. Anna employs coordination-free actors that perform state update via merge of lattice-based composite data structures. I love the strongly opinionated introduction of this paper. This is what papers should be about: opinionated, challenging conventions, making bets, and doing hypothesis testing in the small. Conventional wisdom says that software designed for one scale point needs to be rewritten when scaling up by 10x. Anna sets out to disprove this by showing how a key-value storage (KVS) system can be architected to scale across many orders of magnitude. (Spoiler Anna can give you only upto causal consistency, but cannot pro

An Empirical Evaluation of In-Memory MultiVersion Concurrency Control

This is a closely related paper to the "Evaluation of Distributed Concurrency Control" paper we just covered . This paper is also from VDLB'17, and also has Andy Pavlo as an author . This time we zoom-in on the multiversion concurrency control protocols, and investigate their performances on a single node database management system (DBMS) with in-memory multicore execution. (It is worth reminding that Andy's database courses, both the intro and advanced courses , are on YouTube and are worth checking to refresh your database knowledge.) Although multiversion concurrency control (MVCC) is an old idea (going back to 1970s), it is used today by almost every major relational DBMS as shown in Table1.  In a single-version system transactions always overwrite a tuple with new information whenever they update it. In contrast, multiversioning allows read-only transactions to access older versions of tuples without preventing read-write transactions from generating newer vers

An Evaluation of Distributed Concurrency Control

This VLDB'17 paper investigates the effects of distributed concurrency control protocols on performance of distributed transactional databases. They evaluate six protocols: two-phase locking with NO_WAIT and WAIT_DIE flavors, timestamp ordering (TIMESTAMP), multi-version concurrency control (MVCC), optimistic concurrency control (OCC), and a deterministic protocol Calvin. For the evaluations, they use an in-memory distributed database evaluation framework called Deneva ( ), providing an apples-to-apples comparison between each protocol. This is a delightful paper. After we covered several distributed databases in recent posts , this paper helps us consider the performance implications of concurrency control schemes they use and how they would fare under different workloads. The paper is very well written and easy to follow. In my summary I lifted a lot of the text from the paper with little editing. The six transaction protocols considered The pape

Calvin: Fast Distributed Transactions for Partitioned Database Systems

The Calvin paper appeared in Sigmod 2012. I had read this paper before, and seen it covered in a reading group, but I never got to write a post about it, and I'm remedying that today. I think the name Calvin comes as a pun on the predestination doctrine in Calvinism . Calvin is all about pre-ordering transactions in logs in batches, and then deterministically executing this log in the same order in replicas. The key technical feature that allows Calvin scalability in the face of distributed transactions is a deterministic ordering/locking mechanism that enables the elimination of distributed commit protocols. Since the schedulers get a good look of which transactions are coming next in this log, they can  perform optimizations by pulling some independent transactions locally and executing them in parallel in worker partitions with no ill effect. Calvin achieves good performance thanks to the batching and optimizations pulled by the schedulers by looking ahead to transactions in th

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