Your timeout issues are caused by inefficient API query patterns and lack of caching. Here’s how to systematically address all three problem areas.
API Query Optimization: You’re making 1000+ individual API calls to load a single dashboard - this is the root cause of timeouts. Redesign your data fetching strategy to batch queries. Instead of per-widget API calls, implement a single bulk telemetry query that fetches data for all 200 devices at once:
// Pseudocode - Optimized data fetching:
1. On dashboard load, identify all devices needed across all widgets
2. Make single API call: GET /api/v1/devices/telemetry?deviceIds=dev1,dev2,...,dev200
3. Store response in client-side state management (Redux/Context)
4. Each widget reads its required data from shared state
5. Refresh every 30-60 seconds with same bulk query
This reduces API calls from 1000 to 1, eliminating network overhead and backend load. If the response is too large, paginate by device groups (50 devices per call = 4 total calls). Ensure your API endpoint supports bulk queries with comma-separated device IDs or POST body with device list.
Caching Strategies: Implement multi-level caching to prevent redundant queries. Client-side caching with 30-second TTL means dashboard refreshes don’t trigger new API calls if data is fresh. Use browser sessionStorage to cache responses:
const cachedData = sessionStorage.getItem('telemetry_cache');
const cacheTime = sessionStorage.getItem('cache_timestamp');
if (cachedData && (Date.now() - cacheTime < 30000)) {
// Use cached data
} else {
// Fetch fresh data and update cache
}
For server-side caching, implement Redis or ElastiCache to cache frequent queries for 15-30 seconds. This reduces load on IoT Core and improves response times for all users. Consider implementing a WebSocket connection for real-time updates instead of polling - this eliminates repeated API calls entirely.
CloudWatch Latency Monitoring: Set up detailed CloudWatch monitoring for your dashboard API. Create custom metrics tracking query execution time, response size, and error rates. Monitor P95 and P99 latencies to identify outlier queries:
aws cloudwatch put-metric-data \
--namespace Dashboard/API \
--metric-name QueryLatency \
--value $duration \
--dimensions Endpoint=telemetry,DeviceCount=200
Create CloudWatch alarms when P95 latency exceeds 5 seconds or when timeout rate exceeds 5%. Use CloudWatch Logs Insights to analyze slow queries and identify patterns - certain device types, time ranges, or data volumes may correlate with timeouts. Add query timing logs in your API code to pinpoint bottlenecks (database query vs data transformation vs network).
Additionally, optimize your database queries if you’re fetching from a data store. Ensure indexes exist on deviceId and timestamp fields. Use query explain plans to verify index usage. Consider pre-aggregating frequently accessed metrics in a materialized view or separate table. The combination of bulk queries, aggressive caching, and proper monitoring will eliminate your timeout issues and make your dashboard reliable even during peak load.