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This article mainly introduces "what are the improvements in the function of the financial version of eXtremeDB 6.0". In the daily operation, I believe that many people have doubts about the improvement of the function of the financial version of eXtremeDB 6.0. the editor consulted all kinds of materials and sorted out the simple and easy-to-use operation methods. I hope it will be helpful to answer the questions of" what improvements are there in the financial version of eXtremeDB 6.0"? Next, please follow the editor to study!

Access vector-based functions through SQL and Python to improve programming speed and productivity. Distributed query processing and RLE compression are used to deal with big data's analysis in the capital market.

Federal Road, Washington, October 27, 2014-McObject ®has announced the official release of eXtremeDB ®Financial version 6.0.This is also a major upgrade to the technology of database systems for capital markets. This improvement includes using the widely used SQL and Python languages to access the most powerful features of eXtremeDB Financial Edition, and developing in the declarative SQL language and the Python language of rapid prototyping to release productivity. Version 6.0 uses its new functions: distributed query processing and professional market data compression to solve the big data challenges faced by the financial system and improve the efficiency of processing real-time and historical data (data growth of the day is more than 1 trillion). McObject co-founder and CEO said: "in STAC-M3 testing and other tests, eXtremeDB Financial has demonstrated its ultra-low latency in processing market data. Version 6.0 makes technical improvements to this feature, with some features improving performance, others increasing revenue, and some new features doing both. Many large companies in the capital markets have used the improved technology of eXtremeDB Financial version 6.0. McObject hopes that as this upgrade enters a full release, it will help more companies solve the most difficult challenges encountered in financial computing.

Graves shows specific improvements to eXtremeDB Financial version 6.0, as follows:

Realization of Vector-based Statistical function by using SQL and Python

A striking feature of eXtremeDB is its vector-based statistical function library. This statistical library is passed to the CPU cache to reduce the delay in analyzing market data. Version 6.0 introduces the function of using its vector-based functions from the inherent SQL, and SQL is an almost widely used database language in enterprise computer technology. This has also greatly increased the number of developers whose existing technology can quickly adopt McObject's database system.

Applications written in the languages of C _ Python, Java, and C # (. Net) can use SQL through the command-line interface, as well as itself. It has a high level of productivity in creating code that interacts with database systems, which makes development work faster in areas where time is a key competitive factor. McObject video shows a practical example of using vector-based functions from within SQL.

EXtremeDB Financial version 6. 0 also supports Python. Python is a high-level language for fast algorithms (especially prototypes) that supports capital markets. Using Python, coupled with eXtremeDB's 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, eXtremeDB financial version divides the entire database and processes distributed queries through multiple servers, CPU or CPU cores. In some cases, the performance of the database will be greatly improved by using multiple hosts to perform database operations in parallel.

Market data compression

This upgrade adds a powerful compression algorithm to reduce the size of stored market data so as to reduce storage costs and speed up processing. This run length coding (RLE) compression can be applied to columnar data (defined as the eXtremeDB Financial version of the 'column' database type, which is usually used for trading, quotes, and other market data). After activating this function, the Chicago options Exchange Market volatility Index (VIX) data volume was reduced to 1/4 of its pre-compressed volume, and the database read speed increased by 21%. EXtremeDB Financial version 6. 0 also adds the ability to compress non-cylindrical structure data.

Improvement of Statistical function Library based on Vector

EXtremeDB Financial version 6.0 improves its vector-based function library in a number of ways, including:

Many new features, including a whole family of Hashi aggregation, which can

Improve unordered sequences (that is, they are not filtered by timestamps and fixed time intervals).

Create a sequence from an input string, such as the following example: create a sequence called "bidding": "insert quoted value ('{1.0, 1.1, 1.2, 1.1}')"

Through a new SQL SELECT statement keyword, columnar data (i.e. time series data such as market data, traditional relational database based on row format.

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