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(Editor's note: This post is cross-posted on Fauna's blog, where Daniel Abadi serves as an adviser. Fauna is is a serverless, cloud database system that is built using the Calvin scalable distributed data store technology from Daniel Abadi's research lab.) In several recent posts, we discussed two ways to trade off correctness for performance in database systems. In particular, I wrote two posts (
The two-phase commit protocol (2PC) has been used in enterprise software systems for over three decades. It has been an an incredibly impactful protocol for ensuring atomicity and durability of transactions that access data in multiple partitions or shards. It is used everywhere --- both in older “venerable” distributed systems, database systems, and file systems such as Oracle, IBM DB2, PostgreSQ
(Spanner vs. Calvin, Part 2) [TL;DR I wrote a post in 2017 that discussed Spanner vs. Calvin that focused on performance differences. This post discusses another very important distinction between the two systems: the subtle differences in consistency guarantees between Spanner (and Spanner-derivative systems) vs. Calvin.] The CAP theorem famously states that it is impossible to guarantee both con
Introduction In 2012, two research papers were published that described the design of geographically replicated, consistent, ACID compliant, transactional database systems. Both papers criticized the proliferation of NoSQL database systems that compromise replication consistency and transactional support, and argue that it is very possible to build extremely scalable, geographically replicated sys
(This post is coauthored by Alexander Thomson and Daniel Abadi) In the last decade, database technology has arguably progressed furthest along the scalability dimension. There have been hundreds of research papers, dozens of open-source projects, and numerous startups attempting to improve the scalability of database technology. Many of these new technologies have been extremely influential---some
As 24/7 availability becomes increasingly important for modern applications, database systems are frequently replicated in order to stay up and running in the face of database server failure. It is no longer acceptable for an application to wait for a database to recover from a log on disk --- most mission-critical applications need immediate failover to a replica. There are several important trad
Oracle is the clear market leader in the commercial database community, and therefore it is critical for any member of the database community to pay close attention to the new product announcements coming out of Oracle’s annual Open World conference. The sheer size of Oracle’s sales force, entrenched customer base, and third-party ecosystem instantly gives any new Oracle product the potential for
The problems with ACID, and how to fix them without going NoSQL (This post is coauthored by Alexander Thomson and Daniel Abadi) It is a poorly kept secret that NoSQL is not really about eliminating SQL from database systems (e.g., see these links). Rather, systems such as Bigtable, HBase, Hypertable, Cassandra, Dynamo, SimpleDB (and a host of other key-value stores), PNUTS/Sherpa, etc. are mostly
Problems with CAP, and Yahoo’s little known NoSQL system Over the past few weeks, in my advanced database system implementation class I teach at Yale, I’ve been covering the CAP theorem, its implications, and various scalable NoSQL systems that would appear to be influenced in their design by the constraints of CAP. Over the course of my coverage of this topic, I am convinced that CAP falls far sh
[Warning: the ParAccel TPC-H results referred to in this post have since been challenged by a competitor and found to be in violation of certain TPC rules (I cannot find any public disclosure of which specific rules were violated). These results have since been removed from the TPC-H Website, as of Sep 24th 2009.] At SIGMOD last week, I was chatting with Mike Stonebraker (chatting might be the wro
I recently came across a paper entitled “MapReduce Online” written by Tyson Condie, Neil Conway, Peter Alvaro, Joe Hellerstein, Khaled Elmeleegy, and Russell Sears at Berkeley (University of California). Since I’m very interested in Hadoop-related research (see my group’s work on HadoopDB) and this Berkeley group have historically produced reliably good research papers, I immediately downloaded th
If you have a short attention span see the shorter blog post. If you have a large attention span, see the complete 12-page paper. There are two undeniable trends in analytical data management. First, the amount of data that needs to be stored and processed is exploding. This is partly due to the increased automation with which data can be produced (more business processes are becoming digitized),
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