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  • OLTP

    Analysis of database management systems designed with a focus on OTLP (OnLine Transaction Processing) uses.

    March 12, 2017

    Introduction to SequoiaDB and SequoiaCM

    For starters, let me say:

    Also:

    Unfortunately, SequoiaDB has not captured a lot of detailed information about unpaid open source production usage.

    Read more

    December 10, 2015

    Readings in Database Systems

    Mike Stonebraker and Larry Ellison have numerous things in common. If nothing else:

    I mention the latter because there’s a new edition of Readings in Database Systems, aka the Red Book, available online, courtesy of Mike, Joe Hellerstein and Peter Bailis. Besides the recommended-reading academic papers themselves, there are 12 survey articles by the editors, and an occasional response where, for example, editors disagree. Whether or not one chooses to tackle the papers themselves — and I in fact have not dived into them — the commentary is of great interest.

    But I would not take every word as the gospel truth, especially when academics describe what they see as commercial market realities. In particular, as per my quip in the first paragraph, the data warehouse market has not yet gone to the extremes that Mike suggests,* if indeed it ever will. And while Joe is close to correct when he says that the company Essbase was acquired by Oracle, what actually happened is that Arbor Software, which made Essbase, merged with Hyperion Software, and the latter was eventually indeed bought by the giant of Redwood Shores.**

    *When it comes to data warehouse market assessment, Mike seems to often be ahead of the trend.

    **Let me interrupt my tweaking of very smart people to confess that my own commentary on the Oracle/Hyperion deal was not, in retrospect, especially prescient.

    Mike pretty much opened the discussion with a blistering attack against hierarchical data models such as JSON or XML. To a first approximation, his views might be summarized as:? Read more

    February 22, 2015

    Data models

    7-10 years ago, I repeatedly argued the viewpoints:

    Since then, however:

    So it’s probably best to revisit all that in a somewhat organized way.

    Read more

    November 30, 2014

    Thoughts and notes, Thanksgiving weekend 2014

    I’m taking a few weeks defocused from work, as a kind of grandpaternity leave. That said, the venue for my Dances of Infant Calming is a small-but-nice apartment in San Francisco, so a certain amount of thinking about tech industries is inevitable. I even found time last Tuesday to meet or speak with my clients at WibiData, MemSQL, Cloudera, Citus Data, and MongoDB. And thus:

    1. I’ve been sloppy in my terminology around “geo-distribution”, in that I don’t always make it easy to distinguish between:

    The latter case can be subdivided further depending on whether multiple copies of the data can accept first writes (aka active-active, multi-master, or multi-active), or whether there’s a clear single master for each part of the database.

    What made me think of this was a phone call with MongoDB in which I learned that the limit on number of replicas had been raised from 12 to 50, to support the full-replication/latency-reduction use case.

    2. Three years ago I posted about agile (predictive) analytics. One of the points was:

    … if you change your offers, prices, ad placement, ad text, ad appearance, call center scripts, or anything else, you immediately gain new information that isn’t well-reflected in your previous models.

    Subsequently I’ve been hearing more about predictive experimentation such as bandit testing. WibiData, whose views are influenced by a couple of Very Famous Department Store clients (one of which is Macy’s), thinks experimentation is quite important. And it could be argued that experimentation is one of the simplest and most direct ways to increase the value of your data.

    3. I’d further say that a number of developments, trends or possibilities I’m seeing are or could be connected. These include agile and experimental predictive analytics in general, as noted in the previous point, along with:? Read more

    July 14, 2014

    21st Century DBMS success and failure

    As part of my series on the keys to and likelihood of success, I outlined some examples from the DBMS industry. The list turned out too long for a single post, so I split it up by millennia. The part on 20th Century DBMS success and failure went up Friday; in this one I’ll cover more recent events, organized in line with the original overview post. Categories addressed will include analytic RDBMS (including data warehouse appliances), NoSQL/non-SQL short-request DBMS, MySQL, PostgreSQL, NewSQL and Hadoop.

    DBMS rarely have trouble with the criterion “Is there an identifiable buying process?” If an enterprise is doing application development projects, a DBMS is generally chosen for each one. And so the organization will generally have a process in place for buying DBMS, or accepting them for free. Central IT, departments, and — at least in the case of free open source stuff — developers all commonly have the capacity for DBMS acquisition.

    In particular, at many enterprises either departments have the ability to buy their own analytic technology, or else IT will willingly buy and administer things for a single department. This dynamic fueled much of the early rise of analytic RDBMS.

    Buyer inertia is a greater concern.

    A particularly complex version of this dynamic has played out in the market for analytic RDBMS/appliances.

    Otherwise I’d say:? Read more

    June 18, 2014

    Using multiple data stores

    I’m commonly asked to assess vendor claims of the kind:

    So I thought it might be useful to quickly review some of the many ways organizations put multiple data stores to work. As usual, my bottom line is:

    Horses for courses

    It’s now widely accepted that different data managers are better for different use cases, based on distinctions such as:

    Vendors are part of this consensus; already in 2005 I observed

    For all practical purposes, there are no DBMS vendors left advocating single-server strategies.

    Vendor agreement has become even stronger in the interim, as evidenced by Oracle/MySQL, IBM/Netezza, Oracle’s NoSQL dabblings, and various companies’ Hadoop offerings.

    Multiple data stores for a single application

    We commonly think of one data manager managing one or more databases, each in support of one or more applications. But the other way around works too; it’s normal for a single application to invoke multiple data stores. Indeed, all but the strictest relational bigots would likely agree:? 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

    November 10, 2013

    RDBMS and their bundle-mates

    Relational DBMS used to be fairly straightforward product suites, which boiled down to:

    Now, however, most RDBMS are sold as part of something bigger.

    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

    September 23, 2013

    Thoughts on in-memory columnar add-ons

    Oracle announced its in-memory columnar option Sunday. As usual, I wasn’t briefed; still, I have some observations. For starters:

    I’d also add that Larry Ellison’s pitch “build columns to avoid all that index messiness” sounds like 80% bunk. The physical overhead should be at least as bad, and the main saving in administrative overhead should be that, in effect, you’re indexing ALL columns rather than picking and choosing.

    Anyhow, this technology should be viewed as applying to traditional business transaction data, much more than to — for example — web interaction logs, or other machine-generated data. My thoughts around that distinction start:

    Read more

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