InfluxData has debutednew capabilities targeted at speeding the visualization and analysis of structured application and system events captured via log files. Now by correlating business, server and application metrics with structured logs, InfluxData provides faster and more precise problem investigation and root-cause analysis capabilities.

This release expands the functionality of the InfluxData platform with support for high-speed parsing and ingestion using the syslog protocol, custom log parsing, and pre- built log visualization components. InfluxData believes in a metrics first approach that provides developers and operators with the means to easily ingest, correlate, and visualize all time series data, including data from infrastructure metrics (applications, databases, systems, containers), business metrics and now log events.

Each log message represents an event in time and the same metadata that accompanies metrics can be used to pinpoint the valuable contextual information contained within those files. By starting with metrics and their associated metadata, operators and developers can rapidly understand where and how to interrogate the large volumes of event data contained within logs without performing expensive search queries. This reduces much of the guesswork and prior knowledge required to sift through log data that is typically present when using logs as the initial and primary source of anomaly detection.

With InfluxData, users can uniquely capture metadata at the collection point, allowing the developer to map elements across systems and supplement additional information when and where required, providing consistency and richness to the logs being transmitted via the syslog protocol. It provides an improved workflow for log visualization within the same environment where they have constructed metrics dashboards, which allows a developer to easily analyze the captured log events for a specific time interval and narrow data down by the important metadata elements, such as host, application, subsystem, and more. Starting the problem investigation at the specific time interval and with a specific set of metadata highlighted by captured metrics reduces process noise and allows developers to identify the root cause faster.

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