Clustering

Analysis of products and issues in database clustering. Relates subjects include:

August 7, 2014

Actian Vector Hadoop Edition

I have a small blacklist of companies I won’t talk with because of their particularly unethical past behavior. Actian is one such; they evidently made stuff up about me that Josh Berkus gullibly posted for them, and I don’t want to have conversations that could be dishonestly used against me.

That said, Peter Boncz isn’t exactly an Actian employee. Rather, he’s the professor who supervised Marcin Zukowski’s PhD thesis that became Vectorwise, and I chatted with Peter by Skype while he was at home in Amsterdam. I believe his assurances that no Actian personnel sat in on the call. :)

In other news, Peter is currently working on and optimistic about HyPer. But we literally spent less than a minute talking about that

Before I get to the substance, there’s been a lot of renaming at Actian. To quote Andrew Brust,

… the ParAccel, Pervasive and Vectorwise technologies are being unified under the Actian Analytics Platform brand. Specifically, the ParAccel technology … is being re-branded Actian Matrix; Pervasive’s technologies are rechristened Actian DataFlow and Actian DataConnect; and Vectorwise becomes Actian Vector.

and

Actian … is now “one company, with one voice and one platform” according to its John Santaferraro

The bolded part of the latter quote is untrue — at least in the ordinary sense of the word “one” — but the rest can presumably be taken as company gospel.

All this is by way of preamble to saying that Peter reached out to me about Actian’s new Vector Hadoop Edition when he blogged about it last June, and we finally talked this week. Highlights include:  Read more

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 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

March 23, 2014

Wants vs. needs

In 1981, Gerry Chichester and Vaughan Merlyn did a user-survey-based report about transaction-oriented fourth-generation languages, the leading application development technology of their day. The report included top-ten lists of important features during the buying cycle and after implementation. The items on each list were very similar — but the order of the items was completely different. And so the report highlighted what I regard as an eternal truth of the enterprise software industry:

What users value in the product-buying process is quite different from what they value once a product is (being) put into use.

Here are some thoughts about how that comes into play today.

Wants outrunning needs

1. For decades, BI tools have been sold in large part via demos of snazzy features the CEO would like to have on his desk. First it was pretty colors; then it was maps; now sometimes it’s “real-time” changing displays. Other BI features, however, are likely to be more important in practice.

2. In general, the need for “real-time” BI data freshness is often exaggerated. If you’re a human being doing a job that’s also often automated at high speed — for example network monitoring or stock trading — there’s a good chance you need fully human real-time BI. Otherwise, how much does a 5-15 minute delay hurt? Even if you’re monitoring website sell-through — are your business volumes really high enough that 5 minutes matters much? eBay answered “yes” to that question many years ago, but few of us work for businesses anywhere near eBay’s scale.

Even so, the want for speed keeps growing stronger. :)

3. Similarly, some desires for elastic scale-out are excessive. Your website selling koi pond accessories should always run well on a single server. If you diversify your business to the point that that’s not true, you’ll probably rewrite your app by then as well.

4. Some developers want to play with cool new tools. That doesn’t mean those tools are the best choice for the job. In particular, boring old SQL has merits — such as joins! — that shiny NoSQL hasn’t yet replicated.

5. Some developers, on the other hand, want to keep using their old tools, on which they are their employers’ greatest experts. That doesn’t mean those tools are the best choice for the job either.

6. More generally, some enterprises insist on brand labels that add little value but lots of expense. Yes, there are many benefits to vendor consolidation, and you may avoid many headaches if you stick with not-so-cutting-edge technology. But “enterprise-grade” hardware failure rates may not differ enough from “consumer-grade” ones to be worth paying for.

Read more

March 17, 2014

Notes and comments, March 17, 2014

I have ever more business-advice posts up on Strategic Messaging. Recent subjects include pricing and stealth-mode marketing. Other stuff I’ve been up to includes:

The Spark buzz keeps increasing; almost everybody I talk with expects Spark to win big, probably across several use cases.

Disclosure: I’ll soon be in a substantial client relationship with Databricks, hoping to improve their stealth-mode marketing. :D

The “real-time analytics” gold rush I called out last year continues. A large fraction of the vendors I talk with have some variant of “real-time analytics” as a central message.

Basho had a major change in leadership. A Twitter exchange ensued. :) Joab Jackson offered a more sober — figuratively and literally — take.

Hadapt laid off its sales and marketing folks, and perhaps some engineers as well. In a nutshell, Hadapt’s approach to SQL-on-Hadoop wasn’t selling vs. the many alternatives, and Hadapt is doubling down on poly-structured data*/schema-on-need.

*While Hadapt doesn’t to my knowledge use the term “poly-structured data”, some other vendors do. And so I may start using it more myself, at least when the poly-structured/multi-structured distinction actually seems significant.

WibiData is partnering with DataStax, WibiData is of course pleased to get access to Cassandra’s user base, which gave me the opportunity to ask why they thought Cassandra had beaten HBase in those accounts. The answer was performance and availability, while Cassandra’s traditional lead in geo-distribution wasn’t mentioned at all.

Disclosure: My fingerprints are all over that deal.

In other news, WibiData has had some executive departures as well, but seems to be staying the course on its strategy. I continue to think that WibiData has a really interesting vision about how to do large-data-volume interactive computing, and anybody in that space would do well to talk with them or at least look into the open source projects WibiData sponsors.

I encountered another apparently-popular machine-learning term — bandit model. It seems to be glorified A/B testing, and it seems to be popular. I think the point is that it tries to optimize for just how much you invest in testing unproven (for good or bad) alternatives.

I had an awkward set of interactions with Gooddata, including my longest conversations with them since 2009. Gooddata is in the early days of trying to offer an all-things-to-all-people analytic stack via SaaS (Software as a Service). I gather that Hadoop, Vertica, PostgreSQL (a cheaper Vertica alternative), Spark, Shark (as a faster version of Hive) and Cassandra (under the covers) are all in the mix — but please don’t hold me to those details.

I continue to think that computing is moving to a combination of appliances, clusters, and clouds. That said, I recently bought a new gaming-class computer, and spent many hours gaming on it just yesterday.* I.e., there’s room for general-purpose workstations as well. But otherwise, I’m not hearing anything that contradicts my core point.

*The last beta weekend for The Elder Scrolls Online; I loved Morrowind.

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

July 20, 2013

The refactoring of everything

I’ll start with three observations:

As written, that’s probably pretty obvious. Even so, it’s easy to forget just how pervasive the refactoring is and is likely to be. Let’s survey some examples first, and then speculate about consequences. Read more

June 6, 2013

Dave DeWitt responds to Daniel Abadi

A few days ago I posted Daniel Abadi’s thoughts in a discussion of Hadapt, Microsoft PDW (Parallel Data Warehouse)/PolyBase, Pivotal/Greenplum Hawq, and other SQL-Hadoop combinations. This is Dave DeWitt’s response. Emphasis mine.

Read more

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