Mastering Fluent Bit: Ultimate Guide to Backpressure Troubleshooting and Optimizing
Fluent Bit is a powerful and efficient telemetry agent, but relying on defaults without optimizing its configuration environment can lead to trouble with backpressure - caused by heavy loads, network congestion, or misconfigurations can degrade performance, lead to data loss, and destabilize systems. What many engineers don’t realize is how early backpressure issues can be detected and fixed before escalating into costly outages!
Backed by years of experience supporting Fluent Bit across enterprise environments, this session shares production-tested tactics ensuring pipeline performance at scale. We’ll break down the mechanics of Fluent Bit backpressure - what causes it and how to catch it early with detection, debugging, and dialing in config strategies. Attendees leave with playbooks for solving backpressure problems with Fluent Bit in your system. An extra bonus, attendees are given a hands-on, self-paced, workshop to explore at https://o11y-workshops.gitlab.io/workshop-fluentbit.
Inside the Telemetry Data Plane: Constraints, Tradeoffs, and Scale
Modern telemetry systems often struggle not because of missing features, but because of hidden constraints in how data is buffered, scheduled, and moved through the system. This session explores the practical realities of building a telemetry data plane that must operate under extreme throughput, tight latency budgets, and strict resource limits.
Using real-world experience from developing a high-performance open source telemetry agent, we’ll examine how design tradeoffs around buffering, concurrency, and I/O shape system behavior at scale. Topics include user-space serialization strategies, adaptive buffering models, memory-mapped persistence, and multithreaded I/O coordination, along with how these choices interact with core Linux primitives such as epoll, asynchronous I/O, and zero-copy techniques.
Rather than focusing on APIs or products, this talk dives into the mechanics and constraints that determine whether a telemetry system remains predictable under load. The discussion is grounded in production lessons learned from operating at billions of events per minute and highlights patterns that apply broadly to collectors, agents, and streaming systems.
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