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In Logging, there is a common fact: more applications means more data to handle. Running services at scale in a distributed environment brings exciting challenges for data management, but with the volume of data increasing there is a necessity to ship this data faster, but a few ones realize the side effect: high resource consumption. On implementing a logging pipeline, pre-processing of the data is mandatory, a simple example of this is Kubernetes metadata enrichment for every log record, but more data means more computing time, the same cost applies when delivering to the final storage or cloud service. In this session, we will do a deep dive into our journey of performance challenges that we faced in the Fluent Bit project around Network I/O + TLS, filesystem buffers, routing, and multiplexing for high throughput. We will share how did we go from 5k/sec to more than 30k/sec using a single-core CPU using purely design improvements and taking the most of Linux OS interfaces.
Eduardo is an entrepreneur and Software Engineer. He is one of Fluentd project maintainers and creator of Fluent Bit, a lightweight Logs, Metrics, and Traces processor. Prev: founder at Calyptia, now at Chronosphere.
