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You should implement strong API rate-limit protections before creating an integration with JetBrains products (like IntelliJ, PyCharm, or Space) to avoid informational flooding to their endpoints or hitting throttling limits. This is particularly true if your application is syncing data, loading metadata, and/or automating builds. It's better to think of rate-limiting as a strategic and defensive strategy.
Begin with imposing client-side throttling — limit outbound requests per token or per user session using a token bucket or leaky bucket algorithm. You can implement some maximum, such as 60 requests/min per user or you can implement a more dynamic approach such as increase limit until the JetBrains's API returns 429 Too Many Requests. If a Retry-After header is returned, always respect that. On your backend service, there should be per-tenant and per-ip quotas to avoid abuse and runaway scripts.
Incorporate step back and retry logic with exponential delays so that your system intrinsically slows under load rather than retrying excessively. Use circuit breakers to disable some non-critical syns under API error spikes. Logging is important, so track performance metrics like average request rate, throttled request rate, retry success rate, etc., and somehow export it into your metrics stack (Datadog, Grafana, or what have you).
Lastly, to prevent throttles from cascading between tenants, isolate rate-limit pools by API key if your integration supports several JetBrains products or accounts. It's similar to driving on cruise control in that the objective is to drive so smoothly that you don't even sense the brakes applying.
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