June 7, 2007

StreamBase and Truviso

StreamBase is a decently-established startup, possibly the largest company in its area. Truviso, in the process of changing its name from Amalgamated Insight, has a dozen employees, one referenceable customer, and a product not yet in general availability. Both have ambitious plans for conquering the world, based on similar stories. And the stories make a considerable amount of sense.

Both companies’ core product is a memory-centric SQL engine designed to execute queries without ever writing data to disk. Of course, they both have persistence stories too — Truviso by being tightly integrated into open-source PostgreSQL, StreamBase more via “yeah, we can hand the data off to a conventional DBMS.” But the basic idea is to route data through a whole lot of different in-memory filters, to see what queries it satisfies, rather than executing many queries in sequence against disk-based data.

The basic paradigm for filtering – certainly in Truviso’s case, and I think in StreamBase’s as well – is from the columnar/bitmap school. A record is a vector or a set of vectors. A query or filter is a vector or set of vectors. So take some dot products, see where you are, and you’ll know if the query is satisfied.

Not coincidentally, I would think, this is very similar to the approach taken by various memory-centric BI vendors, such as QlikTech, or SAP in its BI Accelerator. It’s also the approach taken by text indexers. Indeed, the first product I ever heard of of this kind was actually in the text area, from Verity, a decade ago, notwithstanding that Verity had a very small team for sophisticated DBMS types of things. (The whole kernel group was 6-7 people.) And also perhaps not coincidentally, StreamBase reports that despite a lack of text processing primitives, a lot of the use of their technology is for text filtering, I presume in national-security kinds of applications.

The core market for this stuff is financial trading, where the rule of thumb is that a complete query, decision, and transaction has to be done in 30 milliseconds, and database lookup gets to use less than 10% of that time. (Hence the memory-centric requirement; there’s simply no way to search a disk usefully in less than 4-7 milliseconds.) That’s also where Progress Apama (which has a rules-based approach to the same problems) is focused.

The two other obvious and traditional markets for memory-centric technology are national security and telecommunications. (E.g., there’s no prize for guessing which three industries are called out on StreamBase’s website.) But it goes further than that. For example,

Adopting this technology is easier than one might at first think. StreamBase has a nice Eclipse-based development tool. Truviso has some packaged BI/visualization. StreamBase notes that one use of its technology is preprocessing input streams for existing or conventional applications. Truviso claims to make ETL easier as well, although I confess to so far only having seen their smoke on that subject, and not yet detected the associated fire.

One point StreamBase noted is that this preprocessing is not just for speed/latency. Especially in the intelligence business, the raw data is huge. Vast amounts of data reduction are an absolute requirement. As we go to civilian deployments of widespread sensor technology – RFID, GPS/presence, whatever – similar needs may well arise.

I could and probably at some point will say a lot more on these subjects. There are technical details (e.g., Truviso fondly claims to have a more complete product, despite being a much smaller company that’s been in business for a much shorter length of time, apparently because it bundles in more SQL of the nature suited for disk-centric systems). There’s how this all fits into my thesis on the disruption/reinvention of BI, and what’s still lacking – some more personal alert/KPI management, guys, if you please! And so on. Please stay tuned.

And please let me know what you think based on what you know today.


8 Responses to “StreamBase and Truviso”

  1. Mark Palmer on June 8th, 2007 7:29 am

    Hi Curt – One point of clarification – StreamBase, indeed is one of the startups in the complex event processing (CEP) space. However, analysts who have quantitatively tracked the market and vendors consistently rank them to be the 4th or 5th largest vendor in terms of customers and revenue, not the largest. I realize you said “possibly” the largest vendor, and it is true that the market is small (less than about $70M) and challenging to measure. That said, most estimates (AITE, IDC, Bloor) are that Progress Apama and TIBCO Business Events the largest commercial vendors by a factor or 3-5X, followed by a group of KX Systems, Coral8, Aleri Labs, and StreamBase – potentially in that order. KX is a bit of an odd duck, since it’s more of a database than pure CEP vendor, but they do lay claim to doing CEP.

    Hope this helps,

    – Mark Palmer, General Manager, Progress Apama

  2. Curt Monash on June 8th, 2007 8:22 pm

    Hi Mark!

    If I understood them correctly, StreamBase seems to think that those figures are quite inaccurate and/or obsolete.

    But I’ll let them defend such claims here themselves, reveal revenue/size metrics, etc., if they choose.



  3. Text Technologies»Blog Archive » Event stream processors active in text filtering on July 26th, 2007 7:08 am

    […] on StreamBase can be found over on DBMS2, e.g. in this post or this […]

  4. Tim Bass on August 5th, 2007 7:44 am

    Hi Curt,

    StreamBase is an interesting company in the CEP/ESP space; however, SteamBase typically inflates their market position in public statements. SteamBase markets CEP and ESP with terms like “the largest” and “the leading” – but the true fact of the matter is that the StreamBase market share is significantly far behind market leaders Progress Apama and TIBCO.

    Yours faithfully, Tim

  5. Curt Monash on August 6th, 2007 12:40 pm

    I WOULD be interested in seeing what definitions they use when they make those claims …



  6. Bill Hobbib on August 7th, 2007 12:12 am

    We are pleased to see so much interest in StreamBase once again and appreciate the
    visibility from several of our competitors. I would like to clarify a few points
    for those posting who seem to be confused. Regarding performance and statements
    about our leadership, in every performance-based vendor ‘bakeoff’ performed by
    customers over the past several years, StreamBase has never lost to a competitor.
    Never. If those posting to this blog perhaps are unaware of those situations,
    I would be glad to detail them for you privately. The customers have told us our
    throughput was orders of magnitude faster than the competitors with lower latency,
    so we are indeed the leader in the segment of CEP which is considered
    “high performance CEP”, as we have stated publicly. As we have stated in industry
    forums, we would be glad to work with an independent third party on a
    performance-based benchmark test so there is a basis for comparing the performance
    of all of the vendors, but this idea has not gotten traction to date.

    In terms of revenue, as we have said in the past, we are a privately-held company,
    and as such, we do not publicly disclose our revenues. Therefore, any estimates of
    our revenues on the part of competitors or others are conjecture. The revenues
    of the CEP product lines of larger companies have also not been disclosed publicly.
    In the field of pure-play software companies whose only product line is event
    processing software, based on our best estimates of contracts won and revenues,
    StreamBase is indeed probably the largest pure-play in the business.

    I hope this helps clarify any confusion on the part of our competitors.

  7. Curt Monash on August 7th, 2007 1:35 am

    OK. Is everybody happy now with the claims about relative market revenue rank?


  8. DBMS2 — DataBase Management System Services » Blog Archive » Mike Stonebraker on financial stream processing on April 25th, 2008 12:06 am

    […] my call with Truviso and blog post referencing same, I had the chance to discuss stream processing with Mike Stonebraker, who among […]

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