New eXtremeDB Financial Edition Version 6.0 Ramps Up Database System's Speed, Scalability and Development Simplicity - Seite 2

eXtremeDB Financial Edition 6.0 also adds support for Python, a high-level language favored in capital markets because of its fast time-to-deployment, particularly for tasks such as rapid prototyping. Using Python alongside eXtremeDB Financial Edition's dynamic database definition language (DDL) capability, developers can implement their ideas quickly, and optimize rapidly by testing changes to code, database tables and indexes.

Distributed query processing.

With this new capability, eXtremeDB Financial Edition partitions a database and distributes query processing across multiple servers, CPUs and/or CPU cores. Performance is accelerated -- dramatically, in some cases -- via parallel execution of database operations and by harnessing the capabilities of many host computers rather than just one.

Market data compression.

The upgrade adds a powerful new compression algorithm for reducing the size of stored market data, to reduce storage costs and accelerate processing. This run-length encoding (RLE) compression can be applied to columnar data (i.e. fields defined as eXtremeDB Financial Edition's 'sequence' data type used for trades, quotes and other market data). In McObject's tests, activating this feature reduced Chicago Board Options Exchange Market Volatility Index (VIX) tick data to one-quarter of its pre-compression size, and increased speed in reading the database by 21%. (eXtremeDB Financial Edition 6.0 also adds a feature for compressing non-columnar data.)

Improvements to vector-based statistical function library.

eXtremeDB Financial Edition 6.0 improves its vector-based function library in additional ways, including:

• Many new functions, including a family of hash aggregate functions that lend efficiency in processing sequences that are unordered (i.e. not sorted by timestamp or fixed interval)
• The ability to create a sequence from an input string, as in the following example, which creates the sequence named "bid": "insert into Quote (bid) values ('{1.0, 1.1, 1.2, 1.1}')"
• The ability, via a new SQL SELECT statement keyword, to "flatten" the results of queries on columnar data (i.e. time series data such as market data) into a row-based format that is more familiar to users of traditional relational database systems