Taming P99s in OpenFGA: A Self-Tuning Strategy

Authorization systems are the silent guardians of digital infrastructure, and maintaining their performance under load is a critical engineering challenge. When OpenFGA encountered persistent high-percentile latency issues, the team embarked on a journey to build a solution that could adapt in real-time.

The result was a self-tuning strategy planner designed to automatically manage configuration parameters, moving beyond manual adjustments to a more intelligent, data-driven approach. This innovation addresses the elusive nature of P99 latency—the performance metric that matters most during peak traffic.

In distributed systems, P99 latency represents the 99th percentile of response times, meaning that 99% of requests are faster than this value. While average latency often looks healthy, P99 spikes can cause severe user experience degradation during critical moments.

For OpenFGA, a popular open-source authorization engine, managing these spikes became a persistent hurdle. Traditional tuning methods relied on static configurations and manual intervention, which proved insufficient for dynamic workloads.

The core problem involved:

• Unpredictable traffic patterns causing sudden latency increases
• Manual tuning being reactive rather than proactive
• Difficulty in identifying optimal configuration parameters
• Resource constraints during peak usage periods

Engineers realized that a more adaptive system was needed—one that could learn from past behavior and adjust accordingly.

The development of the self-tuning strategy planner centered on creating an automated feedback loop. This system continuously monitors performance metrics and adjusts OpenFGA configurations in response to observed conditions.

Key components of the planner include:

• Real-time metric collection from authorization requests
• Historical data analysis to identify patterns
• Automated parameter adjustment algorithms
• Performance validation and rollback mechanisms

By leveraging historical performance data, the planner can predict when configurations need adjustment before users experience issues. This proactive approach marks a significant shift from traditional reactive tuning methods.

The system essentially learns the "personality" of the workload, understanding how different traffic patterns affect performance and adjusting accordingly.

The implementation focuses on adaptive thresholds that change based on current system state, rather than fixed values that may become outdated as conditions evolve.

The self-tuning planner operates through a sophisticated decision engine that evaluates multiple factors simultaneously. It considers current latency, request volume, system resources, and historical patterns to make informed adjustments.

The tuning process follows these general principles:

1. Continuously collect performance metrics from the authorization layer
2. Analyze trends and identify potential bottlenecks
3. Apply configuration adjustments within safe boundaries
4. Monitor the impact of changes and refine future decisions

One of the most valuable aspects of this approach is its ability to handle edge cases that human operators might miss. The system can detect subtle patterns that indicate emerging issues, allowing for intervention before problems escalate.

Additionally, the planner maintains a safety net through automated rollback capabilities. If a configuration change leads to unexpected degradation, the system can revert to a previous stable state without manual intervention.

The implementation of the self-tuning strategy planner has transformed how OpenFGA handles performance optimization. Rather than relying on periodic manual reviews, the system now maintains consistent performance through continuous adaptation.

Notable improvements include:

• Reduced frequency of P99 latency spikes
• More consistent user experience during traffic surges
• Decreased operational overhead for engineering teams
• Enhanced ability to scale with growing demand

The automated nature of the planner allows engineering teams to focus on higher-value tasks instead of constant performance monitoring. This represents a fundamental shift in how authorization systems are maintained and optimized.

Automation doesn't replace human expertise—it amplifies it by handling routine optimization so engineers can focus on strategic challenges.

As authorization requirements continue to evolve, this self-tuning capability provides a foundation for handling increasingly complex performance scenarios.

The development of a self-tuning strategy planner for OpenFGA demonstrates the power of automation in solving complex engineering challenges. By moving from reactive manual tuning to proactive automated optimization, the system achieves more consistent performance with less human intervention.

This approach offers a blueprint for other systems facing similar P99 latency challenges. The principles of continuous monitoring, data-driven decision making, and safe automated adjustments can be applied across various distributed systems.

As organizations continue to scale their authorization infrastructure, solutions like this will become increasingly critical. The ability to maintain performance without constant manual oversight represents not just an efficiency gain, but a fundamental improvement in system reliability.