DBMS product categories
Analysis of database management technology in specific product categories. Related subjects include:
Teradata, Aster Data, and Teradata/Aster
Teradata is acquiring Aster Data. Naturally, the deal is being presented with a Treaty of Tordesillas kind of positioning — Teradata does X, Aster Data does Y, and everybody looks forward to having X and Y in the same product portfolio. That said, my initial positioning and product strategy thoughts on the Teradata/Aster combination go something like this. Read more
| Categories: Analytic technologies, Aster Data, Columnar database management, Data warehouse appliances, Data warehousing, Database compression, RDF and graphs, Specific users, Teradata | 9 Comments |
How about “Short Request Processing”?
While my other terminology posts seem to have gone pretty well, the Internet Request Processing name is proving a bit problematic. People seem pretty cool with the “request processing” part, but there are issues with the modifier, including:
- “Internet” doesn’t really cover everything.
- “Network” in practice sounds too low-level, and is also too general.
- “Online” is also too general.
So how about just going with “short”? OLTP requests are inherently short. “GET” and “SET” are certainly short. 🙂 In general, queries that do not involve JOINs are probably short requests. Analytic queries, however, are generally not short. Even better, all that can apply to the syntax and the execution time alike. 🙂
Please note that I’m focused more here on describing use cases than products. Whether products generally used to do one kind of thing can also be stretched to do another — e.g., complex analytics hardwired into a Cassandra application — is not my primary concern.
| Categories: NoSQL, OLTP | 11 Comments |
Terminology: Internet Request Processing (IRP)
As I observed previously, we need a term that means “like OLTP but not necessarily transactional”, to help describe a category of use cases that can reasonably be addressed by NoSQL or scale-out SQL systems alike.* So here’s a candidate phrase: Internet Request Processing (IRP). If we use that, I’ll call Schooner, Cassandra, Couchbase , et al. IRP DBMS, while other people will probably call them IRP databases.
*Consider, for example, the overlapping use cases for Schooner, dbShards, ScaleBase, Couchbase, and DataStax/Cassandra.
In my proposed terminology, an internet request processing (IRP) use case is one in which: Read more
| Categories: NoSQL, OLTP | 8 Comments |
Membase and CouchOne merged to form Couchbase
Membase, the company whose product is Membase and whose former company name is Northscale, has merged with CouchOne, the company whose product is CouchDB and whose former name is Couch.io. The result (product and company) will be called Couchbase. CouchDB inventor Damien Katz will join the Membase (now Couchbase) management team as CTO. Couchbase can reasonably be regarded as a document-oriented NoSQL DBMS, a product category I not coincidentally posted about yesterday.
In essence, Couchbase will be CouchDB with scale-out. Alternatively, Couchbase will be Membase with a richer programming interface. The Couchbase sweet spot is likely to be: Read more
| Categories: Application areas, Cache, Couchbase, CouchDB, Market share and customer counts, memcached, NoSQL, Open source, Parallelization, Solid-state memory | 2 Comments |
The Continuent Tungsten MySQL replication story
To the consternation of its then-CEO, I wrote very little about my then-client Continuent. However, when I knew Schooner’s recent announcement was coming, I reached out to other MySQL scale-out vendors too. I’ve already posted accordingly about CodeFutures (the dbShards guys) and ScaleBase. Now it’s late-responding Continuent’s turn.
Actually, what I’m mainly going to do is quote a very long email that Continuent’s current CEO/former CTO Robert Hodges sent me, and which I lightly edited. Read more
| Categories: Continuent, Data integration and middleware, dbShards and CodeFutures, MySQL, Open source, Parallelization, Schooner Information Technology | 9 Comments |
Comments on the Gartner 2010/2011 Data Warehouse Database Management Systems Magic Quadrant
Edit: Comments on the February, 2012 Gartner Magic Quadrant for Data Warehouse Database Management Systems — and on the companies reviewed in it — are now up.
The Gartner 2010 Data Warehouse Database Management Systems Magic Quadrant is out. I shall now comment, just as I did to varying degrees on the 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants.
Note: Links to Gartner Magic Quadrants tend to be unstable. Please alert me if any problems arise; I’ll edit accordingly.
In my comments on the 2008 Gartner Data Warehouse Database Management Systems Magic Quadrant, I observed that Gartner’s “completeness of vision” scores were generally pretty reasonable, but their “ability to execute” rankings were somewhat bizarre; the same remains true this year. For example, Gartner ranks Ingres higher by that metric than Vertica, Aster Data, ParAccel, or Infobright. Yet each of those companies is growing nicely and delivering products that meet serious cutting-edge analytic DBMS needs, neither of which has been true of Ingres since about 1987. Read more
Exadata notes
It’s been a while since I penetrated Oracle’s tight message control and actually talked with them about Exadata. But Doug Henschen wrote a good article about Exadata based on an Andy Mendelsohn webcast. I agree with almost all of it. At first I was a little surprised that Exadata’s emphasis shift from data warehousing to OLTP/generic consolidation hasn’t gone more quickly, but on the other hand:
- On the data warehouse side Exadata can alleviate screaming pain points.
- In OLTP consolidation, Exadata mainly can save money. (Yes, I just said a product from Oracle can save customers money, and I meant it. You may stop laughing at any time.)
Doug did overstate when he said that columnar architectures give 100X or more compression. That doesn’t happen. Yes, columnar compression can be >10X in a variety of use cases, while pre-Exadata Oracle index bloat can approach 10X at times; but even if you’re counting that way I doubt there are many instances in which it actually multiplies out to >100.
In other Exadata news, the long-standing observation that Oracle doesn’t like to do on-site Exadata POCs still holds true. A couple of existing Oracle users — one rather well-known — recently told me that Oracle won’t let them text Exadata except on Oracle premises. In one case, this is a deal-breaker keeping Exadata from being considered for a purchase, and Oracle still won’t budge.
Finally, I’m pretty sure that this “new” Softbank Teradata replacement Oracle has been touting since September as competitive evidence — which Doug’s article also references — isn’t quite what it sounds like. I believe Teradata’s version of the story, which somewhat edited goes like this: Read more
| Categories: Benchmarks and POCs, Columnar database management, Data warehouse appliances, Database compression, Exadata, Oracle, Teradata | 26 Comments |
Schooner — flash-based, now software-only, and very fast
Last October I wrote about Schooner Information Technology, which made flash-based appliances, for MySQL, memcached, or persistent memcached. Schooner sold those appliances to close to 20 customers, but even so decided software-only was a better way to go.
Schooner’s core value proposition is that one Schooner box with flash does the job of a lot of MySQL or NoSQL boxes with hard drives. Highlights of the Schooner story — of which you can find more detail at the Schooner website — now include: Read more
| Categories: Clustering, memcached, MySQL, OLTP, Schooner Information Technology, Solid-state memory | 4 Comments |
ScaleBase, another MPP OLTP quasi-DBMS
Liran Zelkha of ScaleBase raised his hand on Twitter. It turns out ScaleBase has a story rather similar to that of CodeFutures/dbShards. That is:
- Like dbShards, ScaleBase is a proxy that looks to the application like a scale-out DBMS, but routes work to multiple servers running MySQL against different shards of the database. Other DBMS beyond MySQL are planned, but PostgreSQL — which dbShards supports — did not get mentioned.
- Sharding is done at configuration time, and is transparent to the application. You want to shard the big tables and replicate the small ones, because if you join two sharded tables, performance can be slow. ScaleBase may have more of a configuration-advisor wizard than dbShards does.
- Each shard is replicated to a mirror, in a high-availability way.
- You can use ScaleBase across multiple data centers, but there’s little or no magic to overcome the performance issues that would arise in many use cases.
- Much like dbShards, ScaleBase supports three kinds of sharding — hash, list, and range.
- ScaleBase currently has no support whatsoever for stored procedures, which is slightly less than dbShards has.
- Liran stresses that ScaleBase looks even to management tools — e.g. TOAD — like a single DBMS.
- ScaleBase runs on EC2 and private cloud.
Our talk didn’t get deeply technical, and I don’t know exactly how ScaleBase’s replication works. But a website reference to a small transaction log in a distributed cache does sound, while not identical to the dbShards approach, at least directionally similar.
ScaleBase is a year or so old, with about 6 people, based in the Boston area despite strong Israeli roots. ScaleBase has raised a round of venture capital; I didn’t ask for details.
Liran says that ScaleBase is in closed beta, with some production users, at least one of whom has over 100 database servers.
| Categories: Clustering, dbShards and CodeFutures, MySQL, OLTP, Parallelization, ScaleBase, Transparent sharding | 10 Comments |
dbShards update
I talked yesterday with Cory Isaacson of CodeFutures, and hence can follow up on my previous post about dbShards. dbShards basics include:
- dbShards gives you, in effect, an MPP DBMS based on MySQL or PostgreSQL, meant for OLTP (OnLine Transaction Processing). dbShards always did distributed queries, and now does distributed transactions as well.
- dbShards works by sharding the database and automagically sending work to the correct shard.
- For safety, dbShards of course replicates each shard. Contrary to what I said in the previous post, the replication method is not log-shipping.
- At this time, dbShards only works in a single data center.
- dbShards can handle any SQL that would work through, say, a JDBC driver, and is not particularly sensitive to data type. However, dbShards’ stored procedure support is iffy — if a procedure touches data in more than one shard, it simply fails.
One dbShards customer writes 1/2 billion rows on a busy day, and serves 3-4,000 pages per second, naturally with multiple queries per page. This is on a 32-node cluster, with uninspiring hardware, in the cloud. The database has 16 shards, aggregating 128 virtual shards. I forgot to ask how big the database actually is. Overall, dbShards is up to a dozen or so signed customers, half of whom are in production or soon will be.
dbShards’ replication scheme works like this: Read more
| Categories: Clustering, dbShards and CodeFutures, MySQL, OLTP, Parallelization, Transparent sharding | 9 Comments |