• Couchbase

    Discussion of Couchbase (the company formed from the merger of Membase and CouchOne) and its products, most of which will also be branded as Couchbase.

    October 15, 2015

    Basho and Riak

    Basho was on my (very short) blacklist of companies with whom I refuse to speak, because they have lied about the contents of previous conversations. But Tony Falco et al. are long gone from the company. So when Basho’s new management team reached out, I took the meeting.

    For starters:

    Basho’s product line has gotten a bit confusing, but as best I understand things the story is:

    Technical notes on some of that include:? Read more

    October 15, 2015

    Couchbase 4.0 and related subjects

    I last wrote about Couchbase in November, 2012, around the time of Couchbase 2.0. One of the many new features I mentioned then was secondary indexing. Ravi Mayuram just checked in to tell me about Couchbase 4.0. One of the important new features he mentioned was what I think he said was Couchbase’s “first version” of secondary indexing. Obviously, I’m confused.

    Now that you’re duly warned, let me remind you of aspects of Couchbase timeline.

    Technical notes on Couchbase 4.0 — and related riffs ?? — start: Read more

    March 28, 2014

    NoSQL vs. NewSQL vs. traditional RDBMS

    I frequently am asked questions that boil down to:

    The details vary with context — e.g. sometimes MySQL is a traditional RDBMS and sometimes it is a new kid — but the general class of questions keeps coming. And that’s just for short-request use cases; similar questions for analytic systems arise even more often.

    My general answers start:

    In particular, migration away from legacy DBMS raises many issues:? Read more

    December 8, 2013

    DataStax/Cassandra update

    Cassandra’s reputation in many quarters is:

    This has led competitors to use, and get away with, sales claims along the lines of “Well, if you really need geo-distribution and can’t wait for us to catch up — which we soon will! — you should use Cassandra. But otherwise, there are better choices.”

    My friends at DataStax, naturally, don’t think that’s quite fair. And so I invited them — specifically Billy Bosworth and Patrick McFadin — to educate me. Here are some highlights of that exercise.

    DataStax and Cassandra have some very impressive accounts, which don’t necessarily revolve around geo-distribution. Netflix, probably the flagship Cassandra user — since Cassandra inventor Facebook adopted HBase instead — actually hasn’t been using the geo-distribution feature. Confidential accounts include:

    DataStax and Cassandra won’t necessarily win customer-brag wars versus MongoDB, Couchbase, or even HBase, but at least they’re strongly in the competition.

    DataStax claims that simplicity is now a strength. There are two main parts to that surprising assertion. 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

    July 2, 2013

    Notes and comments, July 2, 2013

    I’m not having a productive week, part of the reason being a hard drive crash that took out early drafts of what were to be last weekend’s blog posts. Now I’m operating from a laptop, rather than my preferred dual-monitor set-up. So please pardon me if I’m concise even by comparison to my usual standards.

    *Basic and unavoidable ETL (Extract/Transform/Load) of course excepted.

    **I could call that ABC (Always Be Comparing) or ABT (Always Be Testing), but they each sound like – well, like The Glove and the Lions.

    April 1, 2013

    Some notes on new-era data management, March 31, 2013

    Hmm. I probably should have broken this out as three posts rather than one after all. Sorry about that.

    Performance confusion

    Discussions of DBMS performance are always odd, for starters because:

    But in NoSQL/NewSQL short-request processing performance claims seem particularly confused. Reasons include but are not limited to:

    MongoDB and 10gen

    I caught up with Ron Avnur at 10gen. Technical highlights included: 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

    November 19, 2012

    Couchbase 2.0

    My clients at Couchbase checked in.

    The big changes in Couchbase 2.0 versus the previous (1.8.x) version are:

    Couchbase 2.0 is upwards-compatible with prior versions of Couchbase (and hence with Memcached), but not with CouchDB.

    Technology notes on Couchbase 2.0 include: Read more

    November 19, 2012

    Incremental MapReduce

    My clients at Cloudant, Couchbase, and 10gen/MongoDB (Edit: See Alex Popescu’s comment below) all boast the feature incremental MapReduce. (And they’re not the only ones.) So I feel like making a quick post about it. For starters, I’ll quote myself about Cloudant:

    The essence of Cloudant’s incremental MapReduce seems to be that data is selected only if it’s been updated since the last run. Obviously, this only works for MapReduce algorithms whose eventual output can be run on different subsets of the target data set, then aggregated in a simple way.

    These implementations of incremental MapReduce are hacked together by teams vastly smaller than those working on Hadoop, and surely fall short of Hadoop in many areas such as performance, fault-tolerance, and language support. That’s a given. Still, if the jobs are short and simple, those deficiencies may be tolerable.

    A StackOverflow thread about MongoDB’s version of incremental MapReduce highlights some of the implementation challenges.

    But all practicality aside, let’s return to the point that incremental MapReduce only works for some kinds of MapReduce-based algorithms, and consider how much of a limitation that really is. Looking at the Map steps sheds a little light: Read more

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