New financial edition eXtremeDB 6.0 feature improvement preview, extremedb6.0

New financial edition eXtremeDB 6.0 feature improvement preview, extremedb6.0

Use SQL and Python to access Vector-based functions to increase programming speed and productivity. Distributed Query Processing and RLE compression are used to process big data analysis in the capital market.

Federal Road, Washington, December October 27, 2014-McObject®Announcing eXtremeDB®Financial version 6.0 has been officially released, which is also an important upgrade of Database System Technology for capital markets. This improvement includes the use of a wide range of SQL and Python languages to access the most powerful features of the eXtremeDB financial edition, the use of declarative SQL language and quick prototyping of the Python language for development and release of productivity. Version 6.0 uses its new functions: Distributed Query Processing and professional market data compression, which solves the Big Data Challenges Faced by financial systems, it improves the efficiency of processing real-time and historical data (the data growth on the day exceeds 1 MB. "In STAC-M3 testing and other tests, the eXtremeDB financial edition has demonstrated its ultra-low latency in processing market data," said McObject co-founder and CEO. The 6.0 version has been improved on the technology of this feature. Some features improve performance, while others increase revenue, and some new features take both into account. Many major companies in the capital market have applied the improved technology of eXtremeDB financial edition 6.0. McObject is expected to help more companies solve the most difficult challenges encountered by financial computing as this upgrade enters the comprehensive release.

Graves demonstrates the specific improvements of eXtremeDB financial edition 6.0, as shown below:

Vector-based statistics implemented using SQL and Python

A notable feature of eXtremeDB is its vector-based statistical function library. This statistical function library is passed to the CPU cache to shorten the latency in analyzing market data. Version 6.0 introduces the use of its vector-based functions from the internal SQL statements, and uses its vector-based functions. SQL is a database language that is widely used in enterprise computer technology. This also greatly increases the number of developers. Their existing technologies can quickly adopt McObject database systems.

Applications written in C/C ++, Python, Java, and C # (. NET) languages can use SQL on the command line interface. You can also use SQL on your own. It has a high level of productivity in creating interactive code with the database system, which makes development work faster in some time fields that are key competitive factors. McObject video shows an actual example of using vector functions from an internal SQL statement.

EXtremeDB financial edition 6.0 also supports Python. Python is an advanced language used for Fast Algorithms (especially the original method) and supports the capital market. With Python and the eXtremeDB Dynamic Data Definition Language DDL, developers can quickly implement their ideas and quickly optimize database tables and indexes by testing code changes.

Distributed Query Processing

Using Distributed Query Processing, The eXtremeDB financial version divides the entire database and performs Distributed Query Processing through multiple servers, CPUs, or CPU cores. In some cases, the database performance is greatly improved by using multiple hosts to perform database operations in parallel.

Market Data Compression

This upgrade adds powerful Compression Algorithm functions to reduce the volume of stored market data, thus reducing storage costs and accelerating processing. This RLE compression can be applied to columnar data (that is, the 'column 'database type defined as eXtremeDB financial edition, which is usually used for transactions, quotations, and other market data) in McObject testing, after activating this function, the volume of PEN data in the Chicago option trading market volatility index (VIX) dropped to 1/4 of its pre-compressed volume, the read speed of the database has also increased by 21%. EXtremeDB financial edition 6.0 also adds the ability to compress non-columnar structured data.

Improvement of Vector-based statistical function library

EXtremeDB financial edition 6.0 uses a variety of methods to improve its vector-based function libraries, including:

• Many new features, including a full family of haxi aggregation functions, can
• Increase the unordered sequence (that is, not filtered by timestamp or fixed interval ).
• Create a sequence from an input string, as shown in the following example: Create a sequence named "Bidding": "insert value ('{1.0, 1.1, 1.2, 1.1 }')"
• Use a new SQL SELECT statement keyword to convert columnar data (that is, time series data, such as market data, to traditional relational databases, based on the row format.