Adjust Temporal worker concurrency, poller, and rate limit settings for throughput.
npx skills add https://github.com/therealbill/mynet --skill worker-tuning
Please help me install this Agent Skill. Skill: worker-tuning Source: https://github.com/therealbill/mynet/tree/main/timelord/skills/worker-tuning Command: npx skills add https://github.com/therealbill/mynet --skill worker-tuning
Temporal workers are often bottlenecked by suboptimal concurrency settings and poller configurations, leading to reduced throughput and higher latency in task processing. This guide helps operators identify bottlenecks and apply targeted tuning to improve performance and reliability.
Start by assessing your workload characteristics and adjust concurrency and pollers according to the guidelines to optimize throughput.
High-intent search queries and answers about installing and using this skill.
Tune Temporal worker throughput by adjusting MaxConcurrentActivityExecutionSize, MaxConcurrentWorkflowTaskExecutionSize, and poller counts based on whether your workload is CPU-bound or I/O-bound. This maximizes task processing speed while maintaining safe defaults and stability.
Temporal worker bottlenecks are caused by suboptimal concurrency settings and poller configurations. Identifying these misconfigurations allows you to apply targeted rate limits and scaling patterns to reduce task processing latency and improve reliability.
The best way to scale Temporal workers for high-throughput task queues is applying targeted poller and rate-limiting strategies alongside horizontal and vertical scaling patterns. This approach balances MaxConcurrentWorkflowTaskExecutionSize and MaxConcurrentActivityExecutionSize for peak performance.
Yes, you can apply safe defaults and rollback options when tuning Temporal worker configurations. This approach maintains stability across diverse deployments by validating MaxConcurrentActivityExecutionSize and poller adjustments before committing them permanently.
Configure MaxConcurrentActivityExecutionSize for I/O-bound workloads in Temporal by increasing the concurrency limit and adjusting poller rates. This allows the worker to handle more simultaneous I/O tasks, preventing bottlenecks and maximizing throughput without exhausting CPU resources.
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