Trending near real-time data is a complex task, specially in distributed environments. This problem was typically
tackled in financial and transaction systems, but it now applies to its utmost in other contexts, such as hardware
monitoring in large-scale projects. Data handling requires subscription to specific data feeds that need to be
implemented avoiding replication, and rate of transmission has to be assured. On the side of the graphical client,
rendering needs to be fast enough so it may be perceived as real-time processing and display.
ALMA Common Software (ACS) provides a software infrastructure for distributed projects which may require
trending large volumes of data. For theses requirements ACS offers a Sampling System, which allows sampling
selected data feeds at different frequencies. Along with this, it provides a graphical tool to plot the collected
information, which needs to perform as well as possible.
Currently there are many graphical libraries available for data trending. This imposes a problem when trying
to choose one: It is necessary to know which has the best performance, and which combination of programming
language and library is the best decision. This document analyzes the performance of different graphical libraries
and languages in order to present the optimal environment when writing or re-factoring an application using
trending technologies in distributed systems. To properly address the complexity of the problem, a specific set of
alternative was pre-selected, including libraries in Java and Python, languages which are part of ACS. A stress
benchmark will be developed in a simulated distributed environment using ACS in order to test the trending