Data models and architecture
Discussion of issues in data modeling, and whether databases should be consolidated or loosely coupled. Related subjects include:
Toward a NoSQL taxonomy
I talked Friday with Dwight Merriman, founder of 10gen (the MongoDB company). He more or less convinced me of his definition of NoSQL systems, which in my adaptation goes:
NoSQL = HVSP (High Volume Simple Processing) without joins or explicit transactions
Within that realm, Dwight offered a two-part taxonomy of NoSQL systems, according to their data model and replication/sharding strategy. I’d be happier, however, with at least three parts to the taxonomy:
- How data looks logically on a single node
- How data is stored physically on a single node
- How data is distributed, replicated, and reconciled across multiple nodes, and whether applications have to be aware of how the data is partitioned among nodes/shards. Read more
| Categories: Cassandra, Data models and architecture, NoSQL, Parallelization, RDF and graphs, Structured documents, Theory and architecture | 13 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
| Categories: Cassandra, Data models and architecture, NoSQL, OLTP, Open source, Parallelization, Specific users, Theory and architecture | 16 Comments |
Interesting trends in database and analytic technology
My project for the day is blogging based on my “Database and analytic technology: State of the union” talk of a few days ago. (I called it that because of when it was given, because it mixed prescriptive and descriptive elements, and because I wanted to call attention to the fact that I cover the union of database and analytic technologies – the intersection of those two sectors is an area of particular focus, but is far from the whole of my coverage.)
One section covered recent/ongoing/near-future trends that I thought were particularly interesting, including: Read more
Intersystems Cache’ highlights
I talked with Robert Nagle of Intersystems last week, and it went better than at least one other Intersystems briefing I’ve had. Intersystems’ main product is Cache’, an object-oriented DBMS introduced in 1997 (before that Intersystems was focused on the fourth-generation programming language M, renamed from MUMPS). Unlike most other OODBMS, Cache’ is used for a lot of stuff one would think an RDBMS would be used for, across all sorts of industries. That said, there’s a distinct health-care focus to Intersystems, in that:
- MUMPS, the original Intersystems technology, was focused on health care.
- The reasons Intersystems went object-oriented have a lot to do with the structure of health-care records.
- Intersystems’ biggest and most visible ISVs are in the health-care area.
- Intersystems is actually beginning to sell an electronic health records system called TrakCare around the world (but not in the US, where it has lots of large competitive VARs).
Note: Intersystems Cache’ is sold mainly through VARs (Value-Added Resellers), aka ISVs/OEMs. I.e., it’s sold by people who write applications on top of it.
So far as I understand – and this is still pretty vague and apt to be partially erroneous – the Intersystems Cache’ technical story goes something like this: Read more
| Categories: Data models and architecture, Emulation, transparency, portability, Health care, Intersystems and Cache', Mid-range, Object, OLTP, Sybase, Theory and architecture | 8 Comments |
The legit part of the NoSQL idea
I’ve written some snarky things about the “NoSQL” concept – or at least the moniker. (Carl Olofson’s term “non-schematic databases” seems less bad.) Yet I’m actually favorable about the increasing use of SQL alternatives. Perhaps I should pull those thoughts together. Read more
| Categories: Data models and architecture, Database diversity, Hadoop, NoSQL, Theory and architecture | 21 Comments |
NoSQL Q and A
Neal Leavitt is writing an article for IEEE on NoSQL. So he’s circulated a long list of questions, encouraging people to answer as many or few as they choose. Unfortunately, most of the questions are technically meaningless, in that they implicitly rely on the false assumption that there is such a thing as a single or at least reasonably well-defined NoSQL technology. (I imagine most of his questions are really about key-value stores.) Nonetheless, I took a crack at a number of them before getting bored. Anybody else want to pitch in too? Read more
| Categories: Data models and architecture, Database diversity, NoSQL, Theory and architecture | 10 Comments |
Oracle and Vertica on compression and other physical data layout features
In my recent post on Exadata pricing, I highlighted the importance of Oracle’s compression figures to the discussion, and the uncertainty about same. This led to a Twitter discussion featuring Greg Rahn* of Oracle and Dave Menninger and Omer Trajman of Vertica. I also followed up with Omer on the phone. Read more
| Categories: Columnar database management, Data models and architecture, Data warehousing, Database compression, Oracle, Theory and architecture, Vertica Systems | 14 Comments |
HadoopDB
Despite a thoughtful heads-up from Daniel Abadi at the time of his original posting about HadoopDB, I’m just getting around to writing about it now. HadoopDB is a research project carried out by a couple of Abadi’s students. Further research is definitely planned. But it seems too early to say that HadoopDB will ever get past the “research and oh by the way the code is open sourced” stage and become a real code line — whether commercialized, open source, or both.
The basic idea of HadoopDB is to put copies of a DBMS at different nodes of a grid, and use Hadoop to parcel work among them. Major benefits when compared with massively parallel DBMS are said to be:
- Open/cheap/free
- Query fault-tolerance
- The related concept of tolerating node degradation that isn’t an outright node failure.
HadoopDB has actually been built with PostgreSQL. That version achieved performance well below that of a commercial DBMS “DBX”, where X=2. Column-store guru Abadi has repeatedly signaled his intention to try out HadoopDB with VectorWise at the nodes instead. (Recall that VectorWise is shared-everything.) It will be interesting to see how that configuration performs.
The real opportunity for HadoopDB, however, in my opinion may lie elsewhere. Read more
Introduction to the XLDB and SciDB projects
Before I write anything else about the overlapping efforts known as XLDB and SciDB, I probably should explain and disambiguate what they are as best I can. XLDB was organized and still is run by guys who want to solve a scientific problem in eXtremely Large DataBase Management, most especially Jacek Becla of SLAC (the organization previously known as Stanford Linear Accelerator Center). Becla’s original motivation was that he needs a DBMS to manage what will be 55 petabytes of raw image data and 100 petabytes of astronomical data total for LSST (Large Synoptic Survey Telescope). Read more