In-memory DBMS

Analysis of memory-centric OLTP DBMS. Related subjects include:

June 16, 2013

Webinar Wednesday, June 26, 1 pm EST — Real-Time Analytics

I’m doing a webinar Wednesday, June 26, at 1 pm EST/10 am PST called:

             Real-Time Analytics in the Real World

The sponsor is MemSQL, one of my numerous clients to have recently adopted some version of a “real-time analytics” positioning. The webinar sign-up form has an abstract that I reviewed and approved … albeit before I started actually outlining the talk. ;)

Our plan is:

*MemSQL is debuting pretty high in my rankings of content sponsors who are cool with vendor neutrality. I sent them a draft of my slides mentioning other tech vendors and not them, and they didn’t blink.

In other news, I’ll be in California over the next week. Mainly I’ll be visiting clients — and 2 non-clients and some family — 10:00 am through dinner, but I did set aside time to stop by GigaOm Structure on Wednesday. I have sniffles/cough/other stuff even before I go. So please don’t expect a lot of posts until I’ve returned, rested up a bit, and also prepared my webinar deck.

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

February 13, 2013

It’s hard to make data easy to analyze

It’s hard to make data easy to analyze. While everybody seems to realize this — a few marketeers perhaps aside — some remarks might be useful even so.

Many different technologies purport to make data easy, or easier, to an analyze; so many, in fact, that cataloguing them all is forbiddingly hard. Major claims, and some technologies that make them, include:

*Complex event/stream processing terminology is always problematic.

My thoughts on all this start:  Read more

December 2, 2012

Are column stores really better at compression?

A consensus has evolved that:

Still somewhat controversial is the claim that:

A strong plausibility argument for the latter point is that new in-memory analytic data stores tend to be columnar — think HANA or Platfora; compression is commonly cited as a big reason for the choice. (Another reason is that I/O bandwidth matters even when the I/O is from RAM, and there are further reasons yet.)

One group that made the in-memory columnar choice is the Spark/Shark guys at UC Berkeley’s AMP Lab. So when I talked with them Thursday (more on that another time, but it sounds like cool stuff), I took some time to ask why columnar stores are better at compression. In essence, they gave two reasons — simplicity, and speed of decompression.

In each case, the main supporting argument seemed to be that finding the values in a column is easier when they’re all together in a column store. Read more

November 29, 2012

Notes on Microsoft SQL Server

I’ve been known to gripe that covering big companies such as Microsoft is hard. Still, Doug Leland of Microsoft’s SQL Server team checked in for phone calls in August and again today, and I think I got enough to be worth writing about, albeit at a survey level only,

Subjects I’ll mention include:

One topic I can’t yet comment about is MOLAP/ROLAP, which is a pity; if anybody can refute my claim that ROLAP trumps MOLAP, it’s either Microsoft or Oracle.

Microsoft’s slides mentioned Yahoo refining a 6 petabyte Hadoop cluster into a 24 terabyte SQL Server “cube”, which was surprising in light of Yahoo’s history as an Oracle reference.

Read more

November 5, 2012

Do you need an analytic RDBMS?

I can think of seven major reasons not to use an analytic RDBMS. One is good; but the other six seem pretty questionable, niche circumstances excepted, especially at this time.

The good reason to not have an analytic RDBMS is that most organizations can run perfectly well on some combination of:

Those enterprises, however, are generally not who I write for or about.

The six bad reasons to not have an analytic RDBMS all take the form “Can’t some other technology do the job better?”, namely:

Read more

August 20, 2012

In-memory, (hybrid) memory-centric DBMS — three analytic glossary draft entries

These are three closely-related draft entries for the DBMS2 analytic glossary. Please comment with any ideas you have for their improvement!

1. We coined the term memory-centric data management to comprise several kinds of technology that manage data in RAM (Random Access Memory), including:

Related link

2. An in-memory DBMS is a DBMS designed under the assumption that substantially all database operations will be performed in RAM (Random Access Memory). Thus, in-memory DBMS form a subcategory of memory-centric data management systems.

Ways in which in-memory DBMS are commonly different from those that query and update persistent storage include: Read more

July 17, 2012

Why I recommend avoiding Kognitio

Since my recent post about Kognitio, things have gotten worse. The company is insistently pushing the marketing message that Kognitio has always been an in-memory product, and at one point went so far as to publicly pretend that I had agreed.

I do not agree. Yes, it’s fair to say — as I did in 2008 — that Kognitio is very RAM-centric, but that’s not at all the same thing. In particular:

The truth is that Kognitio offers a disk-based DBMS that has long been worked on by a small team. I believe that the team really has put considerable effort into how Kognitio uses RAM. But there’s no basis to give Kognitio credit for being “really” in-memory vs. a variety of other analytic RDBMS alternatives. And a row-based product that doesn’t currently offer compression is at a large disadvantage versus, say, columnar products that already do.*

*Columnar systems don’t clobber row-based ones in-memory as extremely as they do in some disk-based use cases. But even in-memory it’s good not to have to move around data that isn’t relevant to your query.

Until Kognitio gets at least somewhat more honest in its marketing, I recommend avoiding Kognitio like the plague. It’s simply not a big enough company to buy from unless you have some level of trust in the management team.

July 2, 2012

Introduction to Yarcdata

Cray’s strategy these days seems to be:

At the moment, the main diversifications are:

The last of the three is what Cray subsidiary Yarcdata is all about. Read more

June 18, 2012

Introduction to MemSQL

I talked with MemSQL shortly before today’s launch. MemSQL technology basics are:

MemSQL’s performance claims include:

MemSQL company basics include: Read more

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