Theory and architecture
Analysis of design choices in databases and database management systems. Related subjects include:
- Any subcategory
- Database diversity
- Explicit support for specific data types
- (in Text Technologies) Text search
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 |
Comments on the 2011 Forrester Wave for Enterprise Data Warehouse Platforms
The Forrester Wave: Enterprise Data Warehouse Platforms, Q1 2011 is now out,* hot on the heels of the Gartner Magic Quadrant. Unfortunately, this particular Forrester Wave is riddled with inaccuracy. Read more
| Categories: Analytic technologies, Columnar database management, Data warehousing, EMC, Exadata, Greenplum, Netezza, Oracle, Pricing, SAP AG, Sybase, Teradata, Vertica Systems | 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 |
Notes on document-oriented NoSQL
When people talk about document-oriented NoSQL or some similar term, they usually mean something like:
Database management that uses a JSON model and gives you reasonably robust access to individual field values inside a JSON (JavaScript Object Notation) object.
Or, if they really mean,
The essence of whatever it is that CouchDB and MongoDB have in common.
well, that’s pretty much the same thing as what I said in the first place. 🙂
Of the various questions that might arise, three of the more definitional ones are:
- Why JSON rather than XML?
- What’s with this fluidity between the terms “document” and “object”?
- Are you serious about the lack of joins?
Let me take a crack at each. Read more
| Categories: CouchDB, MapReduce, MarkLogic, MongoDB, NoSQL, Object, Structured documents | 16 Comments |
Columnar compression vs. column storage
I’m getting the increasing impression that certain industry observers, such as Gartner, are really confused about columnar technology. (I further suspect that certain vendors are encouraging this confusion, as vendors commonly do.) So here are some basic points.
A simple way to think about the difference between columnar storage and columnar (or any other kind of) compression is this:
- Columnar storage is a reference to how data is grouped together on disk (or in solid-state memory).
- (Columnar) compression is a reference to whether the actual data is on disk, or whether you save space by storing some smaller substitute for the actual data.
Specifically, if data in a relational table is grouped together according to what row it’s in, then the database manager is called “row-based” or a “row store.” If it’s grouped together according to what column it’s in, then the database management system is called “columnar” or a “column store.” Increasingly, row-based and columnar storage are being hybridized.
There are two main kinds of compression — compression of bit strings and more intelligent compression of actual data values. Compression of actual data values can reasonably be called “columnar,” in that different columns of data can be compressed in different ways, often depending only on the data in that column.* Read more
| Categories: Columnar database management, Data warehousing, Database compression, Exadata, Vertica Systems | 21 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 |
Cassandra company DataStax (formerly Riptano) is on track
Riptano, the Cassandra company, has changed its name to DataStax. DataStax has opened headquarters in Burlingame and hired some database-experienced folks – notably Ben Werther from Greenplum and Michael Weir from ParAccel, with Zenobia Godschalk (who worked with Aster Data) somewhere in the outside PR mix. Other than that, what’s new at DataStax is pretty much what could have been expected based on what DataStax folks said last spring.
Most notably, DataStax is introducing a software offering, whose full name is DataStax OpsCenter for Apache Cassandra. DataStax OpsCenter for Apache Cassandra seems to be, in essence, a monitoring tool for Cassandra clusters, with a bit of capacity planning bundled in. (If there are any outright operations parts to DataStax OpsCenter, they got overlooked in our conversation.)* Read more
| Categories: Cassandra, DataStax, Market share and customer counts, NoSQL, Specific users, Telecommunications | 1 Comment |
Choices in analytic computing system design
When I posted a long list of architectural options for analytic DBMS, I left a couple of IOUs in for missing parts. One was in the area of what is sometimes called advanced-analytics functionality, which roughly speaking means aspects of analytic database management systems that are not directly related to conventional* SQL queries.
*Main examples of “conventional” = filtering, simple aggregrations.
The point of such functionality is generally twofold. First, it helps you execute analytic algorithms with high performance, due to reducing data movement and/or executing the analytics in parallel. Second, it helps you create and execute sophisticated analytic processes with (relatively) little effort.
For now, I’m going to refer to an analytic RDBMS that has been extended by advanced-analytics functionality as an analytic computing system, rather than as some kind of “platform,” although I suspect the latter term is more likely to wind up winning. So far, there have been five major categories of subsystem or add-on module that contribute to making an analytic DBMS a more fully-fledged analytic computing system:
- SQL extensions. Examples include SQL-2003 analytics (notably windowing), or vendor-specific temporal functionality.
- A framework for UDFs (User-Defined Functions) to further extend SQL. At its core, a relational DBMS is a big SQL interpreter. SQL, while powerful, only does a limited number of things. User-Defined Functions are new predicates in the SQL language that do additional things.
- An execution engine for analytic processes that is less coupled to the SQL engine than a pure UDF framework might be. The two main approaches are MapReduce (e.g. Aster Data) and general C++ libraries (Netezza, ParAccel).
- Libraries of pre-built analytic processes. Commonly included are statistics, (other machine learning), general linear algebra, and Monte Carlo analysis. Some of these functions are fully parallelized (perhaps tens per vendor). Others just play nicely with the vendor’s execution framework, in that a separate copy can be run on each node (up to thousands per vendor, for those who bring in open source statistics libraries).
- Development tools such as integrated development environments (IDEs). Aster keeps trying to convince me that having built a nice Eclipse IDE is a major competitive differentiation.