Concurrent Operations

Uncontrolled concurrency can lead to throttling, dropped requests, or reduced system performance. Managing concurrency effectively improves responsiveness and prevents unnecessary retries.

• Use thread pools, async workers, or background jobs to manage simultaneous operations
• Tune concurrency levels based on system capacity and observed latency
• Monitor for 429 Too Many Requests responses and reduce concurrency dynamically
• Apply tenant-specific thresholds to prevent local bottlenecks

Job Queues and Scheduling

Queues provide a buffer between demand and execution. By scheduling jobs across time, you can avoid rate limit violations and smooth traffic peaks.

• Queue inbound requests for processing in controlled intervals
• Use job priorities to sequence time-sensitive vs. deferred operations
• Isolate retries from first-attempt jobs to avoid duplication
• Align job execution with batch cycles or off-peak hours

Batching Strategies

Batching reduces the number of network roundtrips and improves throughput efficiency.

• Group multiple $p360-retrieve calls or resource queries into a single batch job
• Use FHIR batch endpoints where available
• Balance batch size against payload size to avoid timeouts
• Schedule batch execution during predictable windows to avoid contention

Rate Limiting

Health Gorilla applies tenant-level and endpoint-level rate limits. Systems that ignore these limits may experience degraded performance or blocked access.

• Monitor X-RateLimit-Limit and X-RateLimit-Remaining headers in responses
• Spread request load evenly across time to stay within allowed quotas
• Use exponential backoff and jitter for retry timing
• Tune pacing based on endpoint usage patterns and quota consumption

Error Monitoring and Retry Logic

Retries should be controlled and observable. Unbounded retries during partial failures can overwhelm systems and introduce duplicate records.

• Track request volumes, error rates, and retry behavior with logging and alerts
• Use idempotency keys to detect and reject duplicate POSTs
• Apply circuit breakers to halt retries when systems are degraded
• Audit webhook delivery and reprocessing logic to avoid re-entry storms

Summary

Reliable high-volume processing depends on proactive concurrency control, scheduled job execution, and strategic batching. Adhering to rate limits and implementing safe, observable retries protects system performance and ensures consistent data delivery at scale.