What Is the Role of Machine Learning in Databases?
(This article was authored by Sanjay Krishnan, Zongheng Yang, Joe Hellerstein, and Ion Stoica.) What is the role of machine learning in the design and implementation of a modern database system? This question has sparked considerable recent introspection in the data management community, and the epicenter of this debate is the core database problem of query optimization, where the database system finds the best physical execution path for an SQL query. The au courant research direction, inspired by trends in Computer Vision, Natural Language Processing, and Robotics, is to apply deep learning; let the database learn the value of each execution strategy by executing different query plans repeatedly (an homage to Google’s robot “arm farm”) rather through a pre-programmed analytical…
A History of Postgres
(crossposted from databeta.wordpress.com) The ACM began commissioning a series of reminiscence books on Turing Award winners. Thanks to hard work by editor Michael Brodie, the first one is Mike Stonebraker’s book, which just came out. I was asked to write the chapter on Postgres. I was one of the large and distinguished crew of grad students on the Postgres project, so this was fun. ACM in its wisdom decided that these books would be published in a relatively traditional fashion—i.e. you have to pay for them. The publisher, Morgan-Claypool, has this tip for students and ACM members: Please note that the Bitly link goes to a landing page where Students, ACM Members, and Institutions who have access to the ACM…
An Overview of the CALM Theorem
For folks who care about what’s possible in distributed computing: Peter Alvaro and I wrote an introduction to the CALM Theorem and subsequent work that is now up on arXiv. The CALM Theorem formally characterizes the class of programs that can achieve distributed consistency without the use of coordination. — Joe Hellerstein (Cross-posted from databeta.wordpress.com.) I spent a good fraction of my academic life in the last decade working on a deeper understanding of how to program the cloud and other large-scale distributed systems. I was enormously lucky to collaborate with and learn from amazing friends over this period in the BOOM project, and see our work picked up and extended by new friends and colleagues. Our research was motivated by…
SQL Query Optimization Meets Deep Reinforcement Learning
We show that deep reinforcement learning is successful at optimizing SQL joins, a problem studied for decades in the database community. Further, on large joins, we show that this technique executes up to 10x faster than classical dynamic programs and 10,000x faster than exhaustive enumeration. This blog post introduces the problem and summarizes our key technique; details can be found in our latest preprint, Learning to Optimize Join Queries With Deep Reinforcement Learning. SQL query optimization has been studied in the database community for almost 40 years, dating all the way back from System R’s classical dynamic programming approach. Central to query optimization is the problem of join ordering. Despite the problem’s rich history, there is still a continuous stream…
Going Fast and Cheap: How We Made Anna Autoscale
Background: In an earlier blog post, we described a system called Anna, which used a shared-nothing, thread-per-core architecture to achieve lightning-fast speeds by avoiding all coordination mechanisms. Anna also used lattice composition to enable a rich variety of coordination-free consistency levels. The first version of Anna blew existing in-memory KVSes out of the water: Anna is up to 700x faster than Masstree, an earlier state-of-the-art research KVS, and up to 800x faster than Intel’s “lock-free” TBB hash table. You can find the previous blog post here and the full paper here. We refer to that version of Anna as “Anna v0.” In this post, we describe how we extended the fastest KVS in the cloud to be extremely cost-efficient and…
Anna: A Crazy Fast, Super-Scalable, Flexibly Consistent KVS 🗺
This article cross-posted from the DataBeta blog. There’s fast and there’s fast. This post is about Anna, a key/value database design from our team at Berkeley that’s got phenomenal speed and buttery smooth scaling, with an unprecedented range of consistency guarantees. Details are in our upcoming ICDE18 paper on Anna. Conventional wisdom (or at least Jeff Dean wisdom) says that you have to redesign your system every time you scale by 10x. As researchers, we asked the counter-cultural question: what would it take to build a key-value store that would excel across many orders of magnitude of scale, from a single multicore box to the global cloud? Turns out this kind of curiosity can lead to a system with pretty interesting practical…