Clustering
Analysis of products and issues in database clustering. Relates subjects include:
I’m collecting data points on NoSQL and HVSP adoption
I was asked to do a magazine article on NoSQL, where by “NoSQL” is meant “whatever they talk about at NoSQL conferences.” By now the number of publications planning to run the article is up to 2, the deadline is next week and, crucially, it has been agreed that I may talk about HVSP in general, NoSQL and SQL alike.
It also is understood that, realistically, I can’t be expected to know and mention the very latest news for all the many products in the categories. Even so, I think this would be fine time to check just where NoSQL and HVSP adoption stand. Here is most of what I know, or links to same; it would be great if you guys would contribute additional data in the comment thread.
In the NoSQL area: Read more
Teradata, Xkoto Gridscale (RIP), and active-active clustering
Having gotten a number of questions about Teradata’s acquisition of Xkoto, I leaned on Teradata for an update, and eventually connected with Scott Gnau. Takeaways included:
- Teradata is discontinuing Xkoto’s existing product Gridscale, which Scott characterized as being too OLTP-focused to be a good fit for Teradata. Teradata hopes and expects that existing Xkoto Gridscale customers won’t renew maintenance. (I’m not sure that they’ll even get the option to do so.)
- The point of Teradata’s technology + engineers acquisition of Xkoto is to enhance Teradata’s active-active or multi-active data warehousing capabilities, which it has had in some form for several years.
- In particular, Teradata wants to tie together different products in the Teradata product line. (Note: Those typically all run pretty much the same Teradata database management software, except insofar as they might be on different releases.)
- Scott rattled off all the plausible areas of enhancement, with multiple phrasings – performance, manageability, ease of use, tools, features, etc.
- Teradata plans to have one or two releases based on Xkoto technology in 2011.
Frankly, I’m disappointed at the struggles of clustering efforts such as Xkoto Gridscale or Continuent’s pre-Tungsten products, but if the DBMS vendors meet the same needs themselves, that’s OK too.
The logic behind active-active database implementations actually seems pretty compelling: Read more
| Categories: Clustering, Continuent, Data warehousing, Solid-state memory, Teradata, Theory and architecture, Xkoto | 5 Comments |
Gear6 seems to have failed in the memcached market too
As previously noted, I’ve briefly cut back on blogging (and research) due to some family health issues. The first casualty was a post about memcached. One of the two companies to be featured were my new clients at Northscale. The other was Gear6. What they had in common was:
- Both Northscale and Gear6 offered distributions of memcached.
- Both Northscale and Gear6 also wanted to sell persistent versions of memcached — in essence, simple DBMS with the memcached API in place of a substantial DML (Data Manipulation Language).
| Categories: Clustering, NoSQL, Northscale, memcached | 1 Comment |
Memcached-based company NorthScale launches
NorthScale, a start-up based around memcached, has just launched, two weeks after the Todd Hoff’s post arguing the MySQL/memcached combo is passe’. NorthScale wouldn’t necessarily argue with Todd, arguing that what you really should use instead is NorthScale’s combo of memcached and Membase, a memcached-like DBMS …
… or something like that. I don’t intend to write seriously about NorthScale until I have a better idea of what Membase is.
In the mean time,
- VentureBeat put up a solid post on NorthScale’s company history and so on
- Om Malik bought into the NorthScale memcached pitch
- TechCrunch has a low-quality post about NorthScale (although it wasn’t as error-riddled as the same author’s post about nStein, which Seth Grimes properly blasted)
| Categories: Cache, Clustering, NoSQL, Northscale, Parallelization, memcached | Leave a Comment |
Boston Big Data Summit keynote outline
Last month, Bob Zurek asked me to give a talk on “Big Data”, where “big” is anything from a few terabytes on up, then moderate a panel on cloud computing. We agreed that I could talk just from notes, without slides. So, since I have them typed up, I’m posting them below.
Martin Kersten on issues in scientific data management
Martin Kersten emailed a response to my post on issues in scientific data management. With his permission, I’ve lightly edited it, and am posting it below. Read more
| Categories: Analytic technologies, Clustering, Parallelization, SciDB, Scientific research | 3 Comments |
Xkoto Gridscale highlights
I talked yesterday with cofounders Albert Lee and Ariff Kassam of Xkoto. Highlights included: Read more
| Categories: Clustering, IBM and DB2, Market share, Microsoft and SQL*Server, Parallelization, Pricing, Xkoto | 15 Comments |
Continuent on clustering
Robert Hodges, CTO of my client Continuent, put up a blog post laying out his and Continuent’s views on database clustering. Continuent offers Tungsten, its third try at database clustering technology, targeted at MySQL, PostgreSQL, and perhaps Oracle. Unlike Continuent’s more ambitious. second-generation product, Tungsten offers single-master replication, which in Robert’s view allows for great ease of deployment and administration (he likes the phrase “bone-simple”).
The downside to Continuent Tungsten ’s stripped down architecture is that it doesn’t solve the most extreme performance scale-out problems. Instead, Continuent focuses on the other big benefits of keeping your data in more than one place, namely high availability and data loss prevention (i.e., backup).
Continuent has been around for a number of years, starting out in Finland but now being based in Silicon Valley. For most purposes, however, it’s reasonable to think of Continuent and Tungsten as start-up efforts.
As you might guess from the references to Finland and MySQL, Continuent’s products are open source, or at least have open source versions. I’m still a little fuzzy as to which features are open sourced and which are not. For that matter, I’m still unclear as to Tungsten’s feature list overall …
| Categories: Clustering, Continuent, MySQL, Open source, PostgreSQL | 2 Comments |
What are the best choices for scaling Postgres?
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.
| Categories: Cache, Clustering, Data mart outsourcing, EnterpriseDB and Postgres Plus, In-memory DBMS, Memory-centric data management, MySQL, OLTP, Open source, Parallelization, PostgreSQL | 30 Comments |
Exadata and Oracle Database Machine parallelization clarified
Some kind Oracle development managers have reached out and helped me better understand where Oracle does or doesn’t stand in query and analytic parallelization. This post supersedes prior discussions of the subject over the past week. Read more
| Categories: Clustering, Data warehouse appliances, Data warehousing, Exadata, Oracle, Parallelization | 10 Comments |