OLTP
Analysis of database management systems designed with a focus on OTLP (OnLine Transaction Processing) uses.
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 | 3 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.)
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
The Clustrix story
After my recent post, the Clustrix guys raised their hands and briefed me. Takeaways included: Read more
| Categories: Application areas, Clustrix, Emulation, transparency, portability, Games and virtual worlds, MySQL, NoSQL, OLTP, Parallelization, Solid-state memory | 8 Comments |
Read-your-writes (RYW), aka immediate, consistency
In which we reveal the fundamental inequality of NoSQL, and why NoSQL folks are so negative about joins.
Discussions of NoSQL design philosophies tend to quickly focus in on the matter of consistency. “Consistency”, however, turns out to be a rather overloaded concept, and confusion often ensues.
In this post I plan to address one essential subject, while ducking various related ones as hard as I can. It’s what Werner Vogel of Amazon called read-your-writes consistency (a term to which I was actually introduced by Justin Sheehy of Basho). It’s either identical or very similar to what is sometimes called immediate consistency, and presumably also to what Amazon has recently called the “read my last write” capability of SimpleDB.
This is something every database-savvy person should know about, but most so far still don’t. I didn’t myself until a few weeks ago.
Considering the many different kinds of consistency outlined in the Werner Vogel link above or in the Wikipedia consistency models article — whose names may not always be used in, er, a wholly consistent manner — I don’t think there’s much benefit to renaming read-your-writes consistency yet again. Rather, let’s just call it RYW consistency, come up with a way to pronounce “RYW”, and have done with it. (I suggest “ree-ooh”, which evokes two syllables from the original phrase. Thoughts?)
Definition: RYW (Read-Your-Writes) consistency is achieved when the system guarantees that, once a record has been updated, any attempt to read the record will return the updated value.
| Categories: Amazon and its cloud, NoSQL, OLTP, Parallelization, Theory and architecture | 24 Comments |
ITA Software and Needlebase
Rumors are flying that Google may acquire ITA Software. I know nothing of their validity, but I have known about ITA Software for a while. Random notes include:
- ITA Software builds huge OLTP systems that it runs itself on behalf of airlines.
- Very, very unusually, ITA Software builds these huge OLTP systems in LISP.
- ITA Software is an Oracle shop (see Dan Weinreb’s comment).
- ITA Software is run by a techie (again, see Dan Weinreb’s comment).
- ITA Software has an interesting screen-scraping/web ETL project called Needlebase
ITA’s software does both price/reservation lookup/checking and reservation-making. I’ve had trouble keeping it straight, but I think the lookup is ITA’s actual business, and the reservation-making is ITA’s Next Big Thing. This is one of the ultimate federated-transaction-processing applications, because it involves coordinating huge OLTP systems run, in some cases, by companies that are bitter competitors with each other. Network latencies have to allow for intercontinental travel of the data itself.
Indeed, airline reservation systems are pretty much the OLTP ultimate in themselves. As the story goes, transaction monitors were pretty much invented for airline reservation systems in the 1960s.
A really small project for ITA Software is Needlebase. I stopped by ITA to look at Needlebase in January, and what it is is a very smart and hence interesting screen-scraping system. The idea is people publish database information to the web, and you may want to look at their web pages and recover the database records it is based on. Applications of this to the airline industry, which has 100s of 1000s of price changes per day — and I may be too low by one or two orders of magnitude when I say that — should be fairly obvious. ITA Software has aspirations of applying Needlebase to other sectors as well, or more precisely having users who do so. Last I looked, ITA hadn’t put significant resources behind stimulating Needlebase adoption — but Google might well change that.
Edit: I just re-found an old characterization of (some of) what ITA Software does by — who else? — Dan Weinreb:
I am working on our new product, an airline reservation system. It’s an online transaction-processing system that must be up 99.99% of the time, maintaining maximum response time (e.g. on www.aircanada.com). It’s a very, very complicated system. The presentation layer is written in Java using conventional techniques. The business rule layer is written in Common Lisp; about 500,000 lines of code (plus another 100,000 or so of open source libraries). The database layer is Oracle RAC. We operate our own data centers, some here in Massachusetts and a disaster-recovery site in Canada (separate power grid).
Related links
- ITA Software and Needlebase websites
- More about LISP 🙂
| Categories: Data integration and middleware, EAI, EII, ETL, ELT, ETLT, Google, OLTP, Oracle | 5 Comments |
Notes on the evolution of OLTP database management systems
The past few years have seen a spate of startups in the analytic DBMS business. Netezza, Vertica, Greenplum, Aster Data and others are all reasonably prosperous, alongside older specialty product vendors Teradata and Sybase (the Sybase IQ part). OLTP (OnLine Transaction Processing) and general purpose DBMS startups, however, have not yet done as well, with such success as there has been (MySQL, Intersystems Cache’, solidDB’s exit, etc.) generally accruing to products that originated in the 20th Century.
Nonetheless, OLTP/general-purpose data management startup activity has recently picked up, targeting what I see as some very real opportunities and needs. So as a jumping-off point for further writing, I thought it might be interesting to collect a few observations about the market in one place. These include:
- Big-brand OLTP/general-purpose DBMS have more “stickiness” than analytic DBMS.
- By number, most of an enterprise’s OLTP/general-purpose databases are low-volume and low-value.
- Most interesting new OLTP/general-purpose data management products are either MySQL-based or NoSQL.
- It’s not yet clear whether MySQL will prevail over MySQL forks, or vice-versa, or whether they will co-exist.
- The era of silicon-centric relational DBMS is coming.
- The emphasis on scale-out and reducing the cost of joins spans the NoSQL and SQL-based worlds.
- Users’ instance on “free” could be a major problem for OLTP DBMS innovation.
I shall explain. Read more
Akiban highlights
Akiban responded quickly to my complaints about its communication style, and I chatted for a couple of hours with senior Akiban techies Ori Herrnstadt, Peter Beaman and Jack Orenstein. It’s still early days for Akiban product development, so some details haven’t been determined yet, and others I just haven’t yet pinned down. Still, I know a lot more than I did a day ago. Highlights of my talk with Akiban included: Read more
| Categories: Akiban, MySQL, Object, OLTP, Software as a Service (SaaS) | 4 Comments |
The Naming of the Foo
Let’s start from some reasonable premises. Read more
| Categories: Data models and architecture, Database diversity, Hadoop, MapReduce, MarkLogic, NoSQL, OLTP, Theory and architecture | 37 Comments |
Cassandra and the NoSQL scalable OLTP argument
Todd Hoff put up a provocative post on High Scalability called MySQL and Memcached: End of an Era? The post itself focuses on observations like:
- Facebook invented and is adopting Cassandra.
- Twitter is adopting Cassandra.
- Digg is adopting Cassandra.
- LinkedIn invented and is adopting Voldemort.
- Gee, it seems as if the super-scalable website biz has moved beyond MySQL/Memcached.
But in addition, he provides a lot of useful links, which DBMS-oriented folks such as myself might have previously overlooked. Read more