Sybase
Analysis of Sybase and its various product lines, such as Sybase IQ. Related subjects include:
- Data warehousing
- Columnar database management systems
- (in Text Technologies) Sybase’s Answers Anywhere language-response technology
- (in Software Memories) Historical notes about Sybase
Mike Stonebraker on database compression — comments
In my opinion, the key part of Mike Stonebraker’s fascinating note on data compression was (emphasis mine):
The standard wisdom in most row stores is to use block compression. Hence, a storage block is compressed using a single technique (say Lempel-Ziv or dictionary). The technique chosen then compresses all the attributes in all the columns which occur on the block. In contrast, Vertica compresses a storage block that only contains one attribute. Hence, it can use a different compression scheme for each attribute. Obviously a compression scheme that is type-specific will beat an implementation that is “one size fits all”.
It is possible for a row store to use a type-specific compression scheme. However, if there are 50 attributes in a record, then it must remember the state for 50 type-specific implementations, and complexity increases significantly.
In addition, all row stores we are familiar with decompress each storage block on access, so that the query executor processes uncompressed tuples. In contrast, the Vertica executor processes compressed tuples. This results in better L2 cache locality, less main memory copying and generally much better performance.
Of course, any row store implementation can rewrite their executor to run on compressed data. However, this is a rewrite – and a lot of work.
| Categories: Columnar database management, Data warehousing, Database compression, Sybase, Vertica Systems | 8 Comments |
The end of the single-server DBMS vendor
For all practical purposes, there are no DBMS vendors left advocating single-server strategies. Oracle was the last one, but it just acquired in-memory data management vendor TimesTen, which will be used as a cache in front of high-performance Oracle databases. (It will also continue to be sold for stand-alone uses, especially in the financial trading and defense/intelligence markets.)
IBM’s Viper is a server-and-a-half story, with lots of integration over a dual-server (one relational, one native XML) base. IBM also is moving aggressively in data integration/federation, with Ascential and many other acquisitions. It also sells a broad range of database products itself, including two DB2s, several Informix products, and so on.
Microsoft also has a multi-server strategy. In its case, relational, text, and MOLAP storage are more separate than in Oracle’s or even IBM’s products; again, there’s a thick layer of technology on top integrating them. An eventual move to native XML storage will, one must imagine, be handled in the same way.
Smaller vendors Sybase and Progress also offer multiple DBMS each.
Teradata is a pretty big player with only one DBMS — but it’s specialized for data warehousing. Teradata is the first to tell you you should use something else for your classical transaction processing.
The Grand Unified Integrated Database theory is, so far as I can tell, quite dead. Some people just refuse to admit that fact.