Design an Intelligent Observability Platform — System Design Interview Practice

Interview prompt

Design collect metrics, logs, and traces, correlate topology, detect anomalies, and query incidents so users can ingest and investigate telemetry reliably at scale.

• Define the source of truth for retained telemetry and alert history and make retries idempotent.
• Use bounded, partitioned state to meet 10M telemetry events per second across thousands of services and dashboard query p95 <=2s.
• Separate the critical request path from aggregation, indexing, anomaly detection, and retention.
• Explain consistency, failure recovery, authorization, observability, and a degraded mode.

Requirements and scale assumptions

• Support the core workflow to ingest and investigate telemetry.
• Expose status, results, and freshness appropriate to collect metrics, logs, and traces, correlate topology, detect anomalies, and query incidents.
• Support authorization, validation, updates, deletion, and recovery semantics.
• Meet dashboard query p95 <=2s under normal load.
• Scale to 10M telemetry events per second across thousands of services without a single hot key or unbounded synchronous work.
• Do not lose committed state; make retries and duplicate events safe.
• Degrade safely when downstream workers, caches, or external dependencies fail.
• 10M telemetry events per second across thousands of services
• Partition by the primary tenant, user, item, or geographic key and isolate hot partitions.
• Keep serving state bounded; retain raw events or durable records for replay and auditing.

• Peak scale: 10M telemetry events per second across thousands of services — Capacity assumption that drives partitioning and backpressure.
• Latency target: dashboard query p95 <=2s — User-facing budget for the primary request or read path.
• Durable boundary: Committed before async — The source of truth is retained telemetry and alert history.
• Async boundary: At-least-once workers — Keep aggregation, indexing, anomaly detection, and retention off the synchronous path.

Key entities

• SourcePartitionsourceId, partitionId, cursor, schemaVersion, watermark, status

  Replayable observability platform source evidence and ingestion cursor.

• SchemaVersiondatasetId, version, compatibility, owner, effectiveAt, status

  Governed observability platform contract used to validate producers and consumers.

• ProcessingRunrunId, inputWatermark, checkpoint, qualityStatus, codeVersion, status

  Checkpointed observability platform processing attempt with quality and lineage metadata.

• AnalyticalDatasetdatasetId, partition, watermark, schemaVersion, qualityStatus, location

  Curated observability platform serving partition with freshness and quality state.

Data flow

1. 1. Register sources and contractsThe observability platform catalog records owners, schemas, compatibility rules, retention, lineage, and partitioning before data is accepted.
2. 2. Ingest with backpressureConnectors checkpoint observability platform source cursors, validate schema and deduplication keys, and slow producers when downstream capacity is exhausted.
3. 3. Process event time with checkpointsStream or batch engines compute observability platform transformations using watermarks, late-data policy, state checkpoints, and deterministic code versions.
4. 4. Publish quality-gated datasetsOnly observability platform outputs that pass completeness, freshness, validity, and privacy checks become visible to analytical consumers.
5. 5. Serve, replay, and reconcileConsumers read bounded partitions with freshness metadata while operators replay failed observability platform ranges and compare output checksums.

Deep dives and trade-offs

• Schema evolution and data qualityVersion observability platform contracts and make compatibility rules explicit for every producer and consumer. Quarantine malformed partitions instead of poisoning the whole dataset. Track row counts, null rates, duplicates, distribution changes, and policy violations by partition.
• Watermarks, late data, and exactly-once effectsUse source cursors and event-time watermarks for observability platform progress, not wall-clock assumptions. Make checkpoints, output keys, and sink commits retry-safe under at-least-once delivery. Document how late events revise windows, aggregates, or snapshots.
• Replay, lineage, and costKeep immutable observability platform raw evidence and code or schema versions so failed outputs can be reproduced. Separate hot serving storage from cold retention and cap replay concurrency. Measure freshness, backlog, compute cost, storage growth, and quality-gate failure rate.
• Streaming versus batchUse streaming for freshness-critical observability platform paths and batch for backfills, compaction, and expensive recomputation. Forcing every workload into streaming makes state, replay, and cost harder to operate.
• Raw retention versus curated-only storageRetain enough immutable raw evidence for replay, audit, and correction, then tier or expire it according to policy. Without raw evidence, a bad transformation can require an unreproducible emergency fix.
• Central warehouse versus domain-owned datasetsCentralize governance and discovery while letting domain owners own contracts and quality signals. A single team owning every transformation becomes a delivery bottleneck and hides data ownership.