Abstract
The ATLAS Trigger and Data Acquisition (TDAQ) infrastructure is responsible for filtering and transferring ATLAS experimental data from detectors to mass storage systems. It relies on a large, distributed computing system composed of thousands of software applications running concurrently. In such a complex environment, information sharing is fundamental for controlling applications behavior, error reporting and operational monitoring. During data taking, the streams of messages sent by applications and data published via information services are constantly monitored by experts to verify the correctness of running operations and to understand problematic situations. To simplify and improve system analysis and errors detection tasks, we developed the TDAQ Analytics Dashboard, a web application that aims to collect, correlate and visualize effectively this real time flow of information. The TDAQ Analytics Dashboard is composed of two main entities that reflect the twofold scope of the application. The first is the engine, a Java service that performs aggregation, processing and filtering of real time data stream and computes statistical correlation on sliding windows of time. The results are made available to clients via a simple web interface supporting SQL-like query syntax. The second is the visualization, provided by an Ajax-based web application that runs on client's browser. The dashboard approach allows to present information in a clear and customizable structure. Several types of interactive graphs are proposed as widgets that can be dynamically added and removed from visualization panels. Each widget acts as a client for the engine, querying the web interface to retrieve data with desired criteria. In this paper we present the design, development and evolution of the TDAQ Analytics Dashboard. We also present the statistical analysis computed by the application in this first period of high energy data taking operations for the ATLAS experiment.
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