This question has a lot of layers and I spent probably too long on the API contract before getting to the interesting parts.
Start by clarifying requirements and constraints, then outline a high-level design using parallel calls with timeouts and fallbacks. Dive into failure handling strategies and latency optimizations, and finally discuss trade-offs and monitoring.
Pro tip: Emphasize that partial failures should be handled by returning degraded but useful responses, and that the p95 latency target requires careful timeout budgeting and possibly hedged requests.
Ask about the expected response format, criticality of each service, and acceptable degradation. Confirm the p95 latency target and whether it's end-to-end or per-service.
Propose an aggregator service that fans out requests to the three downstream services in parallel using async I/O or a thread pool. Combine responses into a single payload.
Define per-service timeouts and fallbacks (e.g., cached data, default values, or omitting the field). Use a circuit breaker to avoid cascading failures and return partial results with appropriate status codes.
Set aggressive timeouts (e.g., 250ms) to stay under p95 target. Consider hedged requests for tail latency, connection pooling, and keeping the aggregator stateless for scalability.
Discuss consistency vs availability trade-offs. Propose metrics (latency, error rates per service) and logging for debugging. Mention testing strategies like chaos engineering.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a wrapper object per service, something like a status field and a nullable data field.
Start by acknowledging that partial failures are inevitable in distributed systems and should be explicitly represented in the API response schema. Propose a consistent envelope structure that separates successful data from error details, and discuss trade-offs between granularity and simplicity. Emphasize that the schema should allow clients to distinguish between complete success, partial success, and total failure.
Pro tip: Consider idempotency and retry semantics: if a downstream service fails, the client may need to retry only the failed parts. Design the schema to include a retryable flag or a list of failed operations so clients can act intelligently without re-fetching everything.
Propose a top-level structure that includes a status field (e.g., 'success', 'partial', 'failure') and separate sections for data and errors. This makes the overall outcome explicit.
For each downstream service, include an object with its own status, data (if successful), and error details (if failed). Use a consistent format across services.
Provide actionable error information such as error codes, messages, and whether the error is transient or permanent. This helps clients decide whether to retry.
Compare approaches: a flat list of errors vs. nested per-service results; always returning 200 OK vs. using HTTP status codes like 207 Multi-Status. Explain when each is appropriate.
Explain how clients should interpret the schema, including how to merge partial data, display errors, and retry failed operations. Mention idempotency keys if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Per-service timeouts with a shared deadline context propagated across all three calls.
Start by clarifying the aggregator's requirements and the downstream services' characteristics, then propose a layered strategy: set timeouts based on latency percentiles, use retries with exponential backoff and jitter, and implement circuit breakers to prevent cascading failures. Emphasize trade-offs between latency, reliability, and resource usage, and discuss how to monitor and adjust these settings.
Pro tip: Mention that you'd set timeouts slightly above the p99 latency of each downstream service and use retry budgets to cap total retries, preventing retry storms. Also, highlight the importance of idempotency keys for safe retries.
Ask about the aggregator's SLA, expected traffic, and the criticality of each downstream service. Understand whether calls are idempotent and what the failure modes are.
For each downstream service, analyze historical latency data (e.g., p95, p99) and set timeouts slightly above the p99 to avoid premature failures while ensuring slow calls don't block the aggregator.
Retry only on transient errors (e.g., timeouts, 5xx) with exponential backoff and jitter to avoid thundering herd. Limit retries to 2-3 attempts and use a retry budget to cap total retries across the system.
Use circuit breakers to stop calling a failing service after a threshold, preventing cascading failures. Define fallback responses (e.g., cached data, defaults) to maintain partial functionality.
Instrument metrics (latency, error rates, retry counts) and set up alerts. Continuously review and adjust timeout and retry settings based on observed behavior and changing service characteristics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through per-service error rate metrics, latency histograms, and distributed tracing with a span per downstream call.
Start by clarifying the aggregator's role and critical dependencies, then propose a layered observability strategy covering metrics, logs, and traces with specific examples. For reliability, explain how circuit breakers prevent cascading failures and bulkheads isolate resource pools, and tie both to concrete failure scenarios and trade-offs.
Pro tip: Emphasize that observability should drive actionable alerts and that circuit breakers and bulkheads must be tuned based on SLOs and load testing—not just added blindly. Mention that you'd validate resilience patterns with chaos experiments to ensure they work under real failure conditions.
Ask about the aggregator's dependencies, traffic patterns, and SLOs to ground your answer. Identify likely failure modes such as slow downstream services, partial outages, and traffic spikes.
Outline metrics (latency, error rates, saturation, dependency health), structured logs with correlation IDs, and distributed tracing to pinpoint bottlenecks. Suggest dashboards and alerts tied to SLOs.
Describe how circuit breakers detect failures and open to fail fast, preventing cascading failures. Discuss configuration (thresholds, timeouts, half-open state) and fallback strategies.
Explain how bulkheads isolate resources (e.g., thread pools, connection pools) per dependency to contain failures. Give an example of limiting concurrent calls to a slow service so it doesn't exhaust shared resources.
Address trade-offs like added complexity, latency from circuit breaker timeouts, and resource overhead from bulkheads. Mention testing via chaos engineering and tuning based on observed behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Stateless service so horizontal scaling is straightforward.
Start by clarifying the aggregator's role and expected scale, then walk through the architecture layer by layer, identifying potential bottlenecks at each stage. Emphasize horizontal scaling, caching, and asynchronous processing, and discuss trade-offs between consistency and availability. Conclude with monitoring and auto-scaling strategies to handle high volume.
Pro tip: Quantify bottlenecks with rough numbers (e.g., QPS, latency) to show practical experience, and mention how you'd validate assumptions with load testing and metrics. This demonstrates a data-driven approach and maturity.
Ask about expected request volume, latency SLAs, data consistency needs, and aggregator's role (e.g., fan-out to multiple services). This ensures your answer is tailored to the actual scenario.
Outline the aggregator's components: load balancer, stateless service instances, cache, downstream services, and data stores. Explain how requests flow through the system.
Discuss horizontal scaling of stateless services, caching (e.g., Redis) for repeated queries, asynchronous processing (e.g., message queues) for non-critical tasks, and database sharding/read replicas.
Walk through each layer: network I/O, CPU, memory, downstream service limits, database contention, and cache misses. Explain how each could become a bottleneck under high load.
Suggest solutions like rate limiting, circuit breakers, backpressure, auto-scaling, and comprehensive monitoring (metrics, tracing) to detect and address bottlenecks proactively.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.