Kx Technical Whitepaper: C API for kdb+

20 Jul 2017 | , ,
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By Jeremy Lucid

In its traditional financial domain, and across an increasingly broad range of industries, one of the main strengths of kdb+ is its flexibility in integrating and communicating with external systems. This adoption-enhancing feature is facilitated through a number of interfaces, including C and Java APIs, ODBC support, HTTP and WebSockets.

In the latest Kx technical whitepaper, C API for kdb+, it is illustrated how the C API can be used to enable a C program to interact with a kdb+ process, and thereby leverage the real-time streaming and processing strengths of kdb+. The paper includes multiple working code samples, made available for reference and reuse on GitHub,  which cover a broad range of common use cases – including publishing data to, and consuming data from, kdb+ instances.

For the complete white paper go here.


kdb+ FFI

Kdb+ FFI: Access external libraries more easily from q

22 Nov 2017 | ,

Following on from the hugely popular Python library and interface embedPy and PyQ, Kx has released an FFI as part of the Fusion for kdb+ interfaces. As with embedPy and PyQ, this FFI is open-sourced under the Apache 2 license.
The kdb+ FFI is a foreign function interface library for loading and calling dynamic libraries from q code. It has been adapted and expanded upon from a library originally written by Alex Belopolsky of Enlightenment Research. With the kdb+ FFI you can now call your favorite C/C++ libraries directly from q without the overhead of having to compile shared objects and load into q using the 2: command.

Kdb+ interface to R

Kdb+ Interface to R

2 Aug 2017 | , , ,

Kx recently introduced an updated interface to R from kdb+. R is a development environment commonly used for data analysis and visualizing data. When interfacing R with kdb+ we can think of kdb+ as a database behind R which can be used to store huge amounts of data, and we can easily extract this data using the language q.

In this blog, Kx engineer Louise Totten demonstrates how to utilize the strengths of both R and kdb+/q to perform statistical analyses using real world examples.