Theory and architecture

Analysis of design choices in databases and database management systems. Related subjects include:

January 3, 2014

Notes on memory-centric data management

I first wrote about in-memory data management a decade ago. But I long declined to use that term — because there’s almost always a persistence story outside of RAM — and coined “memory-centric” as an alternative. Then I relented 1 1/2 years ago, and defined in-memory DBMS as

DBMS designed under the assumption that substantially all database operations will be performed in RAM (Random Access Memory)

By way of contrast:

Hybrid memory-centric DBMS is our term for a DBMS that has two modes:

  • In-memory.
  • Querying and updating (or loading into) persistent storage.

These definitions, while a bit rough, seem to fit most cases. One awkward exception is Aerospike, which assumes semiconductor memory, but is happy to persist onto flash (just not spinning disk). Another is Kognitio, which is definitely lying when it claims its product was in-memory all along, but may or may not have redesigned its technology over the decades to have become more purely in-memory. (But if they have, what happened to all the previous disk-based users??)

Two other sources of confusion are:

With all that said, here’s a little update on in-memory data management and related subjects.

And finally,

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

December 5, 2013

Vertica 7

It took me a bit of time, and an extra call with Vertica’s long-time R&D chief Shilpa Lawande, but I think I have a decent handle now on Vertica 7, code-named Crane. The two aspects of Vertica 7 I find most interesting are:

Other Vertica 7 enhancements include:

Overall, two recurring themes in our discussion were:

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

October 30, 2013

Splunk strengthens its stack

I’m a little shaky on embargo details — but I do know what was in my own quote in a Splunk press release that went out yesterday. :)

Splunk has been rolling out a lot of news. In particular:

I imagine there are some operationally-oriented use cases for which Splunk instantly offers the best Hadoop business intelligence choice available. But what I really think is cool is Splunk’s schema-on-need story, wherein:

That highlights a pretty serious and flexible vertical analytic stack. I like it.

October 30, 2013

Glassbeam instantiates a lot of trends

Glassbeam checked in recently, and they turn out to exemplify quite a few of the themes I’ve been writing about. For starters:

Glassbeam basics include:

All Glassbeam customers except one are SaaS/cloud (Software as a Service), and even that one was only offered a subscription (as oppose to perpetual license) price.

So what does Glassbeam’s technology do? Glassbeam says it is focused on “machine data analytics,” specifically for the “Internet of Things”, which it distinguishes from IT logs.* Specifically, Glassbeam sells to manufacturers of complex devices — IT (most of its sales so far ), medical, automotive (aspirational to date), etc. — and helps them analyze “phone home” data, for both support/customer service and marketing kinds of use cases. As of a recent release, the Glassbeam stack can: Read more

October 24, 2013

JSON in Teradata

I coined the term schema-on-need last month. More precisely, I coined it while being briefed on JSON-in-Teradata, which was announced earlier this week, and is slated for availability in the first half of 2014.

The basic JSON-in-Teradata story is as you expect:

JSON virtual columns are referenced a little differently than ordinary physical columns are. Thus, if you materialize a virtual column, you have to change your SQL. If you’re doing business intelligence through a semantic layer, or otherwise have some kind of declarative translation, that’s probably not a big drawback. If you’re coding analytic procedures directly, it still may not be a big drawback — hopefully you won’t reference the virtual column too many times in code before you decide to materialize it instead.

My Bobby McFerrin* imitation notwithstanding, Hadapt illustrates a schema-on-need approach that is slicker than Teradata’s in two ways. First, Hadapt has full SQL transparency between virtual and physical columns. Second, Hadapt handles not just JSON, but anything represented by key-value pairs. Still, like XML before it but more concisely, JSON is a pretty versatile data interchange format. So JSON-in-Teradata would seem to be useful as it stands.

*The singer in the classic 1988 music video Don’t Worry Be Happy. The other two performers, of course, were Elton John and Robin Williams.

October 10, 2013

Aster 6, graph analytics, and BSP

Teradata Aster 6 has been preannounced (beta in Q4, general release in Q1 2014). The general architectural idea is:

There’s much more, of course, but those are the essential pieces.

Just to be clear: Teradata Aster 6, aka the Teradata Aster Discovery Platform, includes HDFS compatibility, native MapReduce and ways of invoking Hadoop MapReduce on non-Aster nodes or clusters — but even so, you can’t run Hadoop MapReduce within Aster over Aster’s version of HDFS.

The most dramatic immediate additions are in the graph analytics area.* The new SQL-Graph is supported by something called BSP (Bulk Synchronous Parallel). I’ll start by observing (and some of this is confusing):

Use cases suggested are a lot of marketing, plus anti-fraud.

*Pay no attention to Aster’s previous claims to do a good job on graph — and not only via nPath — in SQL-MR.

So far as I can infer from examples I’ve seen, the semantics of Teradata Aster SQL-Graph start:

Within those functions, the core idea is:  Read more

September 29, 2013

ClearStory, Spark, and Storm

ClearStory Data is:

I think I can do an interesting post about ClearStory while tap-dancing around the still-secret stuff, so let’s dive in.

ClearStory:

To a first approximation, ClearStory ingests data in a system built on Storm (code name: Stormy), dumps it into HDFS, and then operates on it in a system built on Spark (code name: Sparky). Along the way there’s a lot of interaction with another big part of the system, a metadata catalog with no code name I know of. Or as I keep it straight:

Read more

September 24, 2013

JSON in DB2

There’s a growing trend for DBMS to beef up their support for multiple data manipulation languages (DMLs) or APIs — and there’s a special boom in JSON support, MongoDB-compatible or otherwise. So I talked earlier tonight with IBM’s Bobbie Cochrane about how JSON is managed in DB2.

For starters, let’s note that there are at least four strategies IBM could have used.

IBM’s technology choices are of course influenced by its use case focus. It’s reasonable to divide MongoDB use cases into two large buckets:

IBM’s DB2 JSON features are targeted at the latter bucket. Also, I suspect that IBM is generally looking for a way to please users who enjoy working on and with their MongoDB skills.  Read more

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