Memory-centric data management
Analysis of technologies that manage data entirely or primarily in random-access memory (RAM). Related subjects include:
- Oracle TimesTen
- solidDB
- QlikTech
- SAP‘s BI Accelerator
- Exasol
- Solid-state memory as a replacement for disk
Comments on the analytic DBMS industry and Gartner’s Magic Quadrant for same
This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is out.* I shall now comment, just as I did on the 2010, 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants, to varying extents. To frame the discussion, let me start by saying:
- In general, I regard Gartner Magic Quadrants as a bad use of good research.
- Illustrating the uselessness of — or at least poor execution on — the overall quadrant metaphor, a large majority of the vendors covered are lined up near the line x = y, each outpacing the one below in both of the quadrant’s dimensions.
- I find fewer specifics to disagree with in this Gartner Magic Quadrant than in previous year’s versions. Two factors jump to mind as possible reasons:
- This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is somewhat less ambitious than others; while it gives as much company detail as its predecessors, it doesn’t add as much discussion of overall trends. So there’s less to (potentially) disagree with.
- Merv Adrian is now at Gartner.
- Whatever the problems may be with Gartner’s approach, the whole thing comes out better than do Forrester’s failed imitations.
*At the time of this posting, I don’t yet have a link. However, I expect that to change quickly, and I plan to edit this paragraph accordingly. If nothing else, I hope people will drop links into the comment thread.
Specific company comments, roughly in line with Gartner’s rough single-dimensional rank ordering, include: Read more
Terminology: Data mustering
I find myself in need of a word or phrase that means bring data together from various sources so that it’s ready to be used, where the use can be analysis or operations. The first words I thought of were “aggregation” and “collection,” but they both have other meanings in IT. Even “data marshalling” has a specific meaning different from what I want. So instead, I’ll go with data mustering.
I mean for the term “data mustering” to encompass at least three scenarios:
- Integrated (relational) data warehouse.
- Big bit bucket.
- Big bit stream.
Let me explain what I mean by each. Read more
| Categories: Complex event processing (CEP), Data warehousing, Investment research and trading, Sybase, Teradata | 10 Comments |
Some big-vendor execution questions, and why they matter
When I drafted a list of key analytics-sector issues in honor of look-ahead season, the first item was “execution of various big vendors’ ambitious initiatives”. By “execute” I mean mainly:
- “Deliver products that really meet customers’ desires and needs.”
- “Successfully convince them that you’re doing so …”
- “… at an attractive overall cost.”
Vendors mentioned here are Oracle, SAP, HP, and IBM. Anybody smaller got left out due to the length of this post. Among the bigger omissions were:
- salesforce.com (multiple subjects).
- SAS HPA.
- The evolution of Hadoop.
StreamBase LiveView — push-based real-time BI
My clients at StreamBase are coming out with a new product line called LiveView, and I agreed they could launch it via this blog. Key points about StreamBase LiveView Version 1.0 include:
- LiveView is a business intelligence and alerting suite built on/in the rest of StreamBase’s technology, meant to operate on streaming data.
- LiveView is positioned by StreamBase as having a true push event-driven architecture rather than pull/poll.
- StreamBase LiveView is designed to query in-memory data and then have the results change in real time as the data set changes.
- The LiveView user interface is a rapidly changing work in progress.
- LiveView has other Version 1 limitations as well
- LiveView is targeted squarely at StreamBase’s financial trading core market until some of the Version 1 limitations are lifted.
The basic StreamBase LiveView pipeline goes something like: Read more
| Categories: Business intelligence, Complex event processing (CEP), Data warehousing, Memory-centric data management, StreamBase | 1 Comment |
StreamBase catchup
While I was cryptic in my general CEP/streaming catchup, I’ll say a bit more regarding StreamBase in particular. At the highest level, non-technically:
- StreamBase once planned to conquer the world.
- However, StreamBase really only sold effectively in the financial trading and intelligence markets.
- StreamBase retrenched, focusing almost exclusively on the financial trading market.
- With StreamBase LiveView, StreamBase is expanding from embedded operational analytics to do (also operational) business intelligence as well.
- StreamBase is hopeful that, perhaps starting with Version 2 or so, LiveView will be successful outside the financial trading market.
| Categories: Complex event processing (CEP), Investment research and trading, Parallelization, StreamBase | 2 Comments |
Very brief CEP/streaming catchup
When I agreed to launch the StreamBase LiveView product via DBMS 2, I planned to catch up on the whole CEP/streaming area first. Due to the power and internet outages last week, that didn’t entirely happen. So I’ll do a bit of that now, albeit more cryptically than I hoped and intended.
- The upshot of my what to call CEP thread in August was that “streaming” and “event processing” are not the same concept, but it so happens that they have the most traction where they intersect. That said, I both observe and endorse an apparent shift from “event” to “stream” as the core of the terminology, in a reversal of my opinion of several years ago.
- IBM continues to throw a lot of resources at its System S/ InfoSphere Streams product, but I haven’t heard yet of much marketplace success. That said, I believe IBM is still pretty serious about Streams, as one would expect from an effort whose code name so cheekily references System R. In particular, Streams shows up prominently on IBM’s top-level analytic architecture slide.
- Sybase recently released its ESP (Event Stream Processor) 5.0, which it says is the full merger of the Aleri and Coral8 predecessors. You can still get Sybase ESP without buying into the full Sybase RAP stack, and Sybase has no plans to change that.
- Sybase has discontinued all the business intelligence types of products Aleri and Coral8 were developing. Rather, Sybase is OEMing Panopticon, which it reports has been well received. Other than the discontinuation of the BI efforts, there seem to be few Aleri or Coral8 features missing from the merged Sybase ESP product.
- Truviso continues to be out of the picture.
- I have more to say about StreamBase separately.
- I have more to say about Sybase and IBM, which I’ll get to when I can.
- I have nothing new on Progress Apama. I also know little about any of the open source efforts.
Meanwhile, if you want to see technically nitty-gritty posts about the CEP/streaming area, you may want to look at my CEP/streaming coverage circa 2007-9, based on conversations with (among others) Mike Stonebraker, John Bates, and Mark Tsimelzon.
| Categories: Business intelligence, Complex event processing (CEP), IBM and DB2, StreamBase, Sybase, Truviso | 1 Comment |
The database architecture of salesforce.com, force.com, and database.com
salesforce.com, force.com, and database.com use exactly the same database infrastructure and architecture. That’s the good news. The bad news is that salesforce.com is somewhat obscure about technical details, for reasons such as:
- A long-ago marketing decision to not give infrastructure details, so as to convey a “Don’t worry; we’ll take care of everything” message.
- Even so, a long-ago and perhaps now-regretted marketing decision to disclose and even exaggerate salesforce.com’s reliance on Oracle, as part of an early-days attempt to prove salesforce was using enterprise-class technology.
- A desire to hide the recipe for salesforce.com’s secret sauce.
- Force of habit — I’m not sure salesforce even knows how to tell its technical story with any clarity.
Actually, salesforce.com has moved some kinds of data out of Oracle that previously used to be stored there. Besides Oracle, salesforce uses at least a file system and a RAM-based data store about which I have no details. Even so, much of salesforce.com’s data is stored in Oracle — a single instance of Oracle, which it believes may be the largest instance of Oracle in the world.
| Categories: Data models and architecture, Market share and customer counts, Memory-centric data management, OLTP, Object, Oracle, Software as a Service (SaaS), salesforce.com | 13 Comments |
Renaming CEP … or not
One of the less popular category names I deal with is “Complex Event Processing (CEP)”. The word “complex” looks weird, and many are unsure about the “event processing” part as well. CEP does have one virtue as a name, however — it’s concise.
The other main alternative is to base the name on “stream processing” instead.* The CEP-or-whatever industry is split between these choices, with StreamBase currently favoring “CEP” (despite its company name), IBM emphatically favoring “stream”, and Sybase seemingly trying to have things both ways.
*And then, of course, there is “event stream processing”, regarding which please see below.
| Categories: Complex event processing (CEP), StreamBase | 24 Comments |
Couchbase technical update
My Couchbase business update with Bob Wiederhold was very interesting, but it didn’t answer much about the actual Couchbase product. For that, I talked with Dustin Sallings. We jumped around a lot, and some important parts of the Couchbase product haven’t had their designs locked down yet anyway. But here’s at least a partial explanation of what’s up.
memcached is a way to cache data in RAM across a cluster of servers and have it all look logically like a single memory pool, extremely popular among large internet companies. The Membase product — which is what Couchbase has been selling this year — adds persistence to memcached, an obvious improvement on requiring application developers to write both to memcached and to non-transparently-sharded MySQL. The main technical points in adding persistence seem to have been:
- A persistent backing store (duh), namely SQLite.
- A change to the hashing algorithm, to avoid losing data when the cluster configuration is changed.
Couchbase is essentially Membase improved by integrating CouchDB into it, with the main changes being:
- Changing the backing store to CouchDB (duh). This will be in the first Couchbase release.
- Adding cross data center replication on CouchDB’s consistency model. This will not, I believe, be in the first Couchbase release.
- Offering CouchDB’s programming and query interfaces as an option. So far as I can tell, this will be implemented straightforwardly in the first Couchbase release, with elegance planned for later down the road.
Let’s drill down a bit into Membase/Couchbase clustering and consistency. Read more
| Categories: Cache, Clustering, Couchbase, Memory-centric data management, MySQL, Parallelization, Solid-state memory, memcached | 6 Comments |
McObject and eXtremeDB
I talked with McObject yesterday. McObject has two product lines, both of which are something like in-memory DBMS — eXtremeDB, which is the main one, and Perst. McObject has been around since at least 2003, probably has no venture capital, and probably has a very low double-digit number of employees.*
*I could be wrong in those guesses; as small companies go, McObject is unusually prone to secrecy games.
As best I understand:
- eXtremeDB is something like an in-memory object-oriented DBMS, designed to be embeddable.
- However, much as with Objectivity and other old-school OODBMS, eXtremeDB winds up being more of a toolkit with which to build DBMS than a full DBMS.
- eXtremeDB has a few indexing schemes. The main one is good old B-trees. One customer wanted Patricia tries, so they’re in there. (Perhaps not coincidentally, solidDB relies on Patricia tries.) At least one wanted R-trees, so they’re in there too.
- eXtremeDB has long had the option of persistent logs.
- eXtremeDB newly has a hybrid memory-centric option, in which you can have more data in the database than fits into RAM.
- eXtremeDB newly has multi-master two-phase-commit clustering.
My guess three years ago that eXtremeDB might emerge as an alternative to solidDB seems to have been borne out. McObject CEO Steve Graves says that the core of McObject’s business is OEMs, in sectors such as telecom equipment and defense/aerospace. That’s exactly solidDB’s traditional market, except that solidDB got acquired by IBM and deemphasized it.
I’ve said before that if I were starting a SaaS effort — and it wasn’t just focused on analytics — I’d look at using a memory-centric OODBMS. Perhaps eXtremeDB is worth looking at in such scenarios.