In-memory DBMS
Analysis of memory-centric OLTP DBMS. Related subjects include:
Groovy Corp puts out a ridiculous press release
I knew Groovy Corp’s press release today would be bad, as it was pitched in advance as being about an awe-inspiring benchmark. That part met my very low expectations, emphasizing how the Groovy SQL Switch massively outperformed MySQL* in a benchmark, and how this supposedly shows the Groovy SQL Switch would outperform every other competitive RDBMS by at least similar margins.
*While a few use cases are exceptions, being “better than MySQL” for a DBMS is basically like being “better than Pabst Blue Ribbon” for a beer. Unless price is your top consideration, why are you even making the comparison?
Even worse, the press release, from its subhead and very first sentence, emphasizes the claim “the Groovy SQL Switch’s ability to significantly outperform relational databases.” As CEO Joe Ward quickly agreed by email, that’s not accurate. As you would expect from the “SQL” in its name, the Groovy SQL Switch is just as relational as the products it’s being contrasted to. Unfortunately for Joe, who I gather aspires to edit it to say something more sensible, the press release is out already in multiple places.
More favorably, Renee Blodgett has a short, laudatory post about Groovy, with some kind of embedded video.
| Categories: Groovy Corporation, In-memory DBMS, Memory-centric data management, MySQL, OLTP | 17 Comments |
What are the best choices for scaling Postgres?
March, 2011 edit: In its quaintness, this post is a reminder of just how fast Short Request Processing DBMS technology has been moving ahead. If I had to do it all over again, I’d suggest they use one of the high-performance MySQL options like dbShards, Schooner, or both together. I actually don’t know what they finally decided on in that area. (I do know that for analytic DBMS they chose Vertica.)
I have a client who wants to build a new application with peak update volume of several million transactions per hour. (Their base business is data mart outsourcing, but now they’re building update-heavy technology as well. ) They have a small budget. They’ve been a MySQL shop in the past, but would prefer to contract (not eliminate) their use of MySQL rather than expand it.
My client actually signed a deal for EnterpriseDB’s Postgres Plus Advanced Server and GridSQL, but unwound the transaction quickly. (They say EnterpriseDB was very gracious about the reversal.) There seem to have been two main reasons for the flip-flop. First, it seems that EnterpriseDB’s version of Postgres isn’t up to PostgreSQL’s 8.4 feature set yet, although EnterpriseDB’s timetable for catching up might have tolerable. But GridSQL apparently is further behind yet, with no timetable for up-to-date PostgreSQL compatibility. That was the dealbreaker.
The current base-case plan is to use generic open source PostgreSQL, with scale-out achieved via hand sharding, Hibernate, or … ??? Experience and thoughts along those lines would be much appreciated.
Another option for OLTP performance and scale-out is of course memory-centric options such as VoltDB or the Groovy SQL Switch. But this client’s database is terabyte-scale, so hardware costs could be an issue, as of course could be product maturity.
By the way, a large fraction of these updates will be actual changes, as opposed to new records, in case that matters. I expect that the schema being updated will be very simple — i.e., clearly simpler than in a classic order entry scenario.
The Groovy SQL Switch
I’ve now had a chance to talk with Groovy Corporation CEO Joe Ward, and can add to what Groovy advisor Tony Bain wrote about Groovy Corp and its SQL Switch DBMS. Highlights include: Read more
| Categories: Groovy Corporation, In-memory DBMS, Memory-centric data management, OLTP | 2 Comments |
Groovy Corp
Groovy Corp sent over a press release and apparently suggested I write about the company’s wonderfulness immediately. This was without any kind of briefing. I don’t do that kind of thing.
However, a Twitter check revealed that Tony Bain is familiar with Groovy Corp and the Groovy SQL Switch (apparently they started out in Australia, where he lives and works, and he evidently knows the guys). Tony’s take, in summary, is (emphasis mine):
- They are an in memory RDBMS
- They have worked with Intel to architect from the ground up for large multi processor concurrency
- Initially they are launching as a multi-core appliance
- They claim 200,000 sql operations per second from a single box
- They are proprietary (not built on MySQL or any other open source database) which means they have had a lot of control around their architecture
- They are a pretty cool company with some interesting people
There’s a little more detail at the above link.
| Categories: DBMS product categories, Groovy Corporation, In-memory DBMS, Memory-centric data management, OLTP | 3 Comments |
Hasso Plattner calls for in-memory OLTP column stores
Former SAP CEO Hasso Plattner has written a paper called A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database, in association with a SIGMOD keynote address.* The approach Plattner advocates is an MPP in-memory column store, presumably somewhat akin to SAP’s frequently renamed Business Warehouse Accelerator/Business Intelligence Accelerator/BWA/BIA/Son-of-TREX technology. There also are strong similarities to the MPP in-memory row store project H-Store/VoltDB, although I don’t know whether Plattner would go so far as to adopt the H-Store view that all transactions should run in stored procedures. Unsurprisingly, SAP applications are used as the OLTP paradigm throughout.
*Thanks to Dave Kellogg for tipping me off to Plattner’s paper. I only went to two SIGMOD sessions, neither of which was Plattner’s. Nobody actually mentioned Plattner’s talk to me when I was down at SIGMOD.
Perhaps the most interesting part is Plattner’s claim that what’s demanding about OLTP isn’t database updating per se, but rather maintaining aggregates for quick-response analytics. In his main example of that point, Plattner proposes a real-life “more than 18” table schema, of which 2 are base tables, and (most of?) the rest are materialized views that his proposed database architecture dispenses with (because analytic performance is sufficiently good without them). Thus, Plattner’s core columnar argument seemingly is
columnar –> natively fast analytics –> no need to maintain aggregates –> much lower update burden.
That said — if Plattner’s paper contained a clear statement of how much more expensive it is to insert or update a single row in a columnar vs. row-based system, I overlooked it. Instead, Plattner seems to be arguing that the volume of base-table updates is low enough that — whatever it may be — column-store update overhead is an acceptable price to pay. (At one point he claims that only 5% of the data inserted in a financial application ever gets changed.) That may actually be true in a financial accounting system, but seems more questionable in a sufficiently large application that gets its updates from automatic devices, or from the consumer web.
Other highlights include: Read more
H-Store is now VoltDB
I’ve always honored more of an NDA about the H-Store project and its commercialization than I really felt obligated to, given how freely information was being bandied about to others. I’m still doing so. 🙂
But I think I’ll at least say that the H-Store project is now named VoltDB. The VoltDB website names two individuals — Mike Stonebraker and Andy Palmer — both of whom are founders of Vertica. Job listings on the site are for field engineer and trainer, but not developer, so that suggests something about the project’s/product’s maturity level.
If you have an extreme OLTP need, you should talk to VoltDB. If you don’t have access to Mike or Andy directly, I can hook you up with a key VoltDB marketing/outreach guy. Price may not be as much of a barrier as you’d initially fear.
If anybody from VoltDB wants to be less cloak-and-daggery and say more in the comment thread, I’d be pleased.
And yes — an open-secret working name for H-Store/VoltDB was, for a while, “Horizontica.”
| Categories: In-memory DBMS, Memory-centric data management, OLTP, Vertica Systems, VoltDB and H-Store | 15 Comments |
CSQL: Yet another in-memory DBMS for caching
A few of you care about obscure in-memory DBMS products. Well, I was just e-mailed about another one, apparently called CSQL or CSQLcache. As of now, CSQL has a SourceForge website, a Wikipedia entry, and a blog.
One interesting thing on that blog is a taxonomy of caches — Level 1 cache, Level 2 cache, RAM, disk, etc., with some approximate figures for lookup times. Edit: However, Kevin Closson emailed me to say it’s way out of date. Stay tuned to his blog for more on the subject.
| Categories: Cache, In-memory DBMS, Memory-centric data management | 3 Comments |
Ordinary OLTP DBMS vs. memory-centric processing
A correspondent from China wrote in to ask about products that matched the following application scenario: Read more
| Categories: In-memory DBMS, McObject, Memory-centric data management, OLTP, Oracle TimesTen, solidDB | 7 Comments |
Another round of discussion on in-memory OLTP data management
Oracle Exadata was pre-teased as “Extreme performance.” Some incorrect speculation shortly before the announcement focused on the possibility of OLTP without disk, which clearly would speed things up a lot. I interpret that in part as being wishful thinking. 🙂
The most compelling approach I’ve seen to that problem yet is H-Store, which however makes some radical architectural assumptions. One point I didn’t stress in my earlier posts, but which turned out to be a deal-breaker for one early tire-kicker, is that to use H-Store you have to be able to shoehorn each transaction into its own stored procedure. Depending on how intricate your logic is, that might make it hard to port an existing app to H-Store.
Even for new apps, it could get in the way of some things you might want to do, such as rule-based processing. And that could be a problem. A significant fraction of the highest-performance OLTP apps are customer-facing, and customer-facing apps are one of the biggest areas where rule-based processing comes into play.
| Categories: In-memory DBMS, Memory-centric data management, OLTP, VoltDB and H-Store | 3 Comments |
Exasol technical briefing
It took 5 ½ months after my non-technical introduction, but I finally got a briefing from Exasol’s technical folks (specifically, the very helpful Mathias Golombek and Carsten Weidmann). Here are some highlights: Read more