In-memory DBMS
Analysis of memory-centric OLTP DBMS. Related subjects include:
Object-oriented database management systems (OODBMS)
There seems to be a fair amount of confusion about object-oriented database management systems (OODBMS). Let’s start with a working definition:
An object-oriented database management system (OODBMS, but sometimes just called “object database”) is a DBMS that stores data in a logical model that is closely aligned with an application program’s object model. Of course, an OODBMS will have a physical data model optimized for the kinds of logical data model it expects.
If you’re guessing from that definition that there can be difficulties drawing boundaries between the application, the application programming language, the data manipulation language, and/or the DBMS — you’re right. Those difficulties have been a big factor in relegating OODBMS to being a relatively niche technology to date.
Examples of what I would call OODBMS include: Read more
| Categories: Cache, In-memory DBMS, Intersystems and Cache', Memory-centric data management, Objectivity and Infinite Graph, OLTP, Software as a Service (SaaS), Starcounter | 15 Comments |
Starcounter high-speed memory-centric object-oriented DBMS, coming soon
Since posting recently about Starcounter, I’ve had the chance to actually talk with the company (twice). Hence I know more than before. 
Starcounter:
- Has been around as a company since 2006.
- Has developed memory-centric object-oriented DBMS technology that has been OEMed by a few application software companies (especially in bricks-and-mortar retailing and in online advertising).
- Is planning to actually launch an OODBMS product sometime this summer.
- Has 14 employees (most or all of whom are in Sweden, which is also where I think Starcounter’s current customers are centered).
- Is planning to shift emphasis soon to the US market.
Starcounter’s value propositions are programming ease (no object/relational impedance mismatch) and performance. Starcounter believes its DBMS has 100X the performance of conventional DBMS at short-request transaction processing, and 10X the performance of other memory-centric and/or object-oriented DBMS (e.g. Oracle TimesTen, or Versant). That said, Starcounter has not yet tested VoltDB. Starcounter does not claim performance much beyond that of disk-based DBMS on analytic tasks such as aggregations.
The key technical aspect to Starcounter is integration between the DBMS and the virtual machine, so that the same copy of the data is accessed by both the DBMS and the application program, without any movement or transformation being needed. (Starcounter isn’t aware of any other object-oriented DBMS that work this way.) Transient and persistent data are handled in the same way, seamlessly.
Other Starcounter technical highlights include: Read more
| Categories: Data models and architecture, In-memory DBMS, Memory-centric data management, Object, OLTP, Starcounter, Theory and architecture | 3 Comments |
What Starcounter may be up to
Starcounter seems to be offering an in-memory object-based/object-oriented/whatever short-request DBMS that also talks SQL. I haven’t been briefed at this point, and hence don’t have detail beyond what’s on their rather breathless web site. I’m guessing this isn’t an H-Store/VoltDB architecture, but rather something more like what Workday runs.
Most of the crunch I found on the Starcounter website (emphasis mine) is:
Let’s say that it is possible to make a database that is 10,000 times faster than what you use today. It would then be possible for your computer language objects to live inside the database from the very beginning. From the first { Customer a = new Customer(); }. The objects could live in the database, not as a copy, but as both database object and a Java or C# object at the same time. The database would transparently be your heap. The time it would take to save your object to the database would be reduced to nothing.
If such a database existed, you could say goodbye to caches and the duality of business objects, the database objects/rows and the complexity that follows. The speed would be amazing. Goodbye to time consuming scale-out solutions. Actually, you would be able to say good bye to the databases as you know them. You only need your simple objects.
Such a technology would be the ultimate NoSQL database. But what if the ultimate NoSQL database had SQL support, ACID, checkpoints and recovery and other enterprise features? Your pure, clean objects would then become the fastest and most powerful database in the world.
Beside that, other clues to what Starcounter is doing include references to Hibernate and to the declining cost of RAM.
| Categories: In-memory DBMS, Memory-centric data management, Starcounter | 7 Comments |
Some quick notes on HP-Vertica
HP is acquiring Vertica. Read more
| Categories: Complex event processing (CEP), In-memory DBMS, Investment research and trading, Memory-centric data management, StreamBase, VoltDB and H-Store | 12 Comments |
Notes and links October 22, 2010
A number of recent posts have had good comments. This time, I won’t call them out individually.
Evidently Mike Olson of Cloudera is still telling the machine-generated data story, exactly as he should be. The Information Arbitrage/IA Ventures folks said something similar, focusing specifically on “sensor data” …
… and, even better, went on to say: Read more
Some interesting links
In no particular order: Read more
| Categories: Business intelligence, EnterpriseDB and Postgres Plus, Fun stuff, Hadoop, Humor, In-memory DBMS, MapReduce, Memory-centric data management, Open source, Oracle, SAP AG | 2 Comments |
Details and analysis of the VoltDB argument
Todd Hoff (High Scalability blog) posted a lengthy examination of the case and use cases for VoltDB. That excellent post, in turn, is based on a Mike Stonebraker* webinar for VoltDB, for which the slide deck is happily available. It’s all nicely consistent with what I wrote about VoltDB last month, in connection with its launch. Read more
| Categories: In-memory DBMS, Michael Stonebraker, OLTP, Parallelization, Theory and architecture, VoltDB and H-Store | 2 Comments |
VoltDB finally launches
VoltDB is finally launching today. As is common for companies in sectors I write about, VoltDB — or just “Volt” — has discovered the virtues of embargoes that end 12:01 am. Let’s go straight to the technical highlights:
- VoltDB is based on the H-Store technology, which I wrote about in February, 2009. Most of what I said about H-Store then applies to VoltDB today.
- VoltDB is a no-apologies ACID relational DBMS, which runs entirely in RAM.
- VoltDB has rather limited SQL. (One example: VoltDB can’t do SUMs in SQL.) However, VoltDB guy Tim Callaghan (Mark Callaghan’s lesser-known but nonetheless smart brother) asserts that if you code up the missing functionality, it’s almost as fast as if it were present in the DBMS to begin with, because there’s no added I/O from the handoff between the DBMS and the procedural code. (The data’s in RAM one way or the other.)
- VoltDB’s Big Conceptual Performance Story is that it does away with most locks, latches, logs, etc., and also most context switching.
- In particular, you’re supposed to partition your data and architect your application so that most transactions execute on a single core. When you can do that, you get VoltDB’s performance benefits. To the extent you can’t, you’re in two-phase-commit performance land. (More precisely, you’re doing 2PC for multi-core writes, which is surely a major reason that multi-core reads are a lot faster in VoltDB than multi-core writes.)
- VoltDB has a little less than one DBMS thread per core. When the data partitioning works as it should, you execute a complete transaction in that single thread. Poof. No context switching.
- A transaction in VoltDB is a Java stored procedure. (The early idea of Ruby on Rails in lieu of the Java/SQL combo didn’t hold up performance-wise.)
- Solid-state memory is not a viable alternative to RAM for VoltDB. Too slow.
- Instead, VoltDB lets you snapshot data to disk at tunable intervals. “Continuous” is one of the options, wherein a new snapshot starts being made as soon as the last one completes.
- In addition, VoltDB will also spool a kind of transaction log to the target of your choice. (Obvious choice: An analytic DBMS such as Vertica, but there’s no such connectivity partnership actually in place at this time.)
Further quick SAP/Sybase reactions
Raj Nathan of Sybase has been calling around to chat quickly about the SAP/Sybase deal and related matters. Talking with Raj didn’t change any of my initial reactions to SAP’s acquisition of Sybase. I also didn’t bother Raj with too many hard questions, as he was clearly in call-and-reassure mode, reaching out to customers and influencers alike.
That said, Read more
Quick reactions to SAP acquiring Sybase
SAP is acquiring Sybase. On the conference call SAP said Sybase would be run as a separate division of SAP (no surprise). Most of the focus was on Sybase’s mobile technology, which is forecast at >$400 million in 2010 revenues (which would be 30%ish of the total). My quick reactions include: Read more