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  • VoltDB and H-Store

    Analysis of OLTP DBMS research project H-Store and its commercialization VoltDB. Related subjects include:

    June 8, 2014

    Optimism, pessimism, and fatalism — fault-tolerance, Part 2

    The pessimist thinks the glass is half-empty.
    The optimist thinks the glass is half-full.
    The engineer thinks the glass was poorly designed.

    Most of what I wrote in Part 1 of this post was already true 15 years ago. But much gets added in the modern era, considering that:

    And so there’s been innovation in numerous cluster-related subjects, two of which are:

    Distributed database consistency

    When a distributed database lives up to the same consistency standards as a single-node one, distributed query is straightforward. Performance may be an issue, however, which is why we have seen a lot of:

    But in workloads with low-latency writes, living up to those standards is hard. The 1980s approach to distributed writing was two-phase commit (2PC), which may be summarized as:? Read more

    May 6, 2014

    Notes and comments, May 6, 2014

    After visiting California recently, I made a flurry of posts, several of which generated considerable discussion.

    Here is a catch-all post to complete the set.? Read more

    May 1, 2014

    MemSQL update

    I stopped by MemSQL last week, and got a range of new or clarified information. For starters:

    On the more technical side: Read more

    November 8, 2013

    Comments on the 2013 Gartner Magic Quadrant for Operational Database Management Systems

    The 2013 Gartner Magic Quadrant for Operational Database Management Systems is out. “Operational” seems to be Gartner’s term for what I call short-request, in each case the point being that OLTP (OnLine Transaction Processing) is a dubious term when systems omit strict consistency, and when even strictly consistent systems may lack full transactional semantics. As is usually the case with Gartner Magic Quadrants:

    Anyhow:? Read more

    April 14, 2013

    Introduction to Deep Information Sciences and DeepDB

    I talked Friday with Deep Information Sciences, makers of DeepDB. Much like TokuDB — albeit with different technical strategies — DeepDB is a single-server DBMS in the form of a MySQL engine, whose technology is concentrated around writing indexes quickly. That said:

    *For reasons that do not seem closely related to product reality, DeepDB is marketed as if it supports “unstructured” data today.

    Other NewSQL DBMS seem “designed for big data and the cloud” to at least the same extent DeepDB is. However, if we’re interpreting “big data” to include multi-structured data support — well, only half or so of the NewSQL products and companies I know of share Deep’s interest in branching out. In particular:

    Edit: MySQL has some sort of an optional NoSQL interface, and hence so presumably do MySQL-compatible TokuDB, GenieDB, Clustrix, and MemSQL.

    Also, some of those products do not today have the transparent scale-out that Deep plans to offer in the future.

    Read more

    January 5, 2013

    NewSQL thoughts

    I plan to write about several NewSQL vendors soon, but first here’s an overview post. Like “NoSQL”, the term “NewSQL” has an identifiable, recent coiner — Matt Aslett in 2011 — yet a somewhat fluid meaning. Wikipedia suggests that NewSQL comprises three things:

    I think that’s a pretty good working definition, and will likely remain one unless or until:

    To date, NewSQL adoption has been limited.

    That said, the problem may lie more on the supply side than in demand. Developing a competitive SQL DBMS turns out to be harder than developing something in the NoSQL state of the art.

    Read more

    April 7, 2012

    Many kinds of memory-centric data management

    I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:

    Getting more specific than that is hard, however, because:

    Consider, for example, some of the in-memory data management ideas kicking around. Read more

    July 15, 2011

    Soundbites: the Facebook/MySQL/NoSQL/VoltDB/Stonebraker flap, continued

    As a follow-up to the latest Stonebraker kerfuffle, Derrick Harris asked me a bunch of smart followup questions. My responses and afterthoughts include:

    Continuing with that discussion of DBMS alternatives:

    And while we’re at it — going schema-free often makes a whole lot of sense. I need to write much more about the point, but for now let’s just say that I look favorably on the Big Four schema-free/NoSQL options of MongoDB, Couchbase, HBase, and Cassandra.

    July 14, 2011

    An odd claim attributed to Mike Stonebraker

    This post has a sequel.

    Last week, Mike Stonebraker insulted MySQL and Facebook’s use of it, by implication advocating VoltDB instead. Kerfuffle ensued. To the extent Mike was saying that non-transparently sharded MySQL isn’t an ideal way to do things, he’s surely right. That still leaves a lot of options for massive short-request databases, however, including transparently sharded RDBMS, scale-out in-memory DBMS (whether or not VoltDB*), and various NoSQL options. If nothing else, Couchbase would seem superior to memcached/non-transparent MySQL if you were starting a project today.

    *The big problem with VoltDB, last I checked, was its reliance on Java stored procedures to get work done.

    Pleasantries continued in The Register, which got an amazing-sounding quote from Mike. If The Reg is to be believed — something I wouldn’t necessarily take for granted — Mike claimed that he (i.e. VoltDB) knows how to solve the distributed join performance problem.? Read more

    May 23, 2011

    Traditional databases will eventually wind up in RAM

    In January, 2010, I posited that it might be helpful to view data as being divided into three categories:

    I won’t now stand by every nuance in that post, which may differ slightly from those in my more recent posts about machine-generated data and poly-structured databases. But one general idea is hard to dispute:

    Traditional database data — records of human transactional activity, referred to as “Human/Tabular data above” — will not grow as fast as Moore’s Law makes computer chips cheaper.

    And that point has a straightforward corollary, namely:

    It will become ever more affordable to put traditional database data entirely into RAM.? Read more

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