This is where I spent most of my time and probably where I lost points.
Start by clarifying requirements (scale, latency, retention, consistency) and then present a high-level architecture covering data model, ingestion (pull and push), storage, and query. Dive into trade-offs for each component, emphasizing backpressure and high throughput, and conclude with monitoring and failure handling.
Pro tip: Demonstrate maturity by discussing how to handle backpressure end-to-end, including client-side throttling, server-side queueing, and load shedding, and relate it to real-world systems like Prometheus and StatsD.
Ask questions to understand scale (metrics per second, number of services), latency requirements, retention policies, and consistency needs. This ensures the design meets actual needs.
Describe how metrics are represented with names, labels/tags, and types (counter, gauge, histogram). Discuss cardinality challenges and indexing strategies.
Outline both pull-based (e.g., scraping) and push-based (e.g., agents) ingestion. Explain how to achieve high throughput with batching, compression, and partitioning.
Detail mechanisms like client-side rate limiting, server-side queues, load shedding, and retries with exponential backoff. Discuss how to monitor and alert on backpressure.
Choose a time-series database (e.g., Prometheus, InfluxDB) and explain storage optimizations (downsampling, rollups) and query capabilities for aggregation and alerting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: data volume, write/read patterns, query types, and retention needs. Then propose a time-series database (e.g., TimescaleDB, InfluxDB) or a custom solution using columnar storage and time-based partitioning. Explain compression techniques like delta encoding and Gorilla compression, and describe a tiered storage architecture with hot data in fast storage and cold data in object storage, ensuring seamless querying across tiers.
Pro tip: Emphasize that compression and tiering decisions should be driven by access patterns and SLAs; for example, keep recent data uncompressed for fast writes, compress older data, and move infrequently accessed data to cheaper storage after a defined period.
Ask about data volume, ingestion rate, query patterns (e.g., real-time vs. historical), retention period, and budget constraints to tailor the solution.
Select a time-series optimized database or design a custom schema using columnar storage and time-based partitioning to efficiently handle writes and range queries.
Use techniques like delta-of-delta encoding for timestamps, Gorilla compression for floating-point values, and dictionary encoding for labels to reduce storage footprint.
Implement hot storage (e.g., SSD-backed) for recent data and cold storage (e.g., object storage) for older data, with automated policies to move data based on age or access frequency.
Provide a unified query interface that transparently accesses both tiers, using metadata and indexes to minimize latency for cold data retrieval.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about a PromQL-style query layer mostly.
Start by clarifying the requirements: what types of aggregations, downsampling intervals, and label filters are needed, and who the users are (e.g., internal dashboards, external API consumers). Then propose a query language or interface that balances expressiveness, performance, and ease of use, such as a PromQL-like language or a REST API with query parameters. Finally, discuss trade-offs and justify your choice based on scalability and maintainability.
Pro tip: Mention that you would expose both a high-level query language for ad-hoc analysis and a lower-level API for programmatic access, and emphasize the importance of query optimization and pushdown to the storage layer to avoid full scans.
Ask about the expected query patterns, data volume, latency requirements, and user expertise to determine the right interface.
Consider options like a PromQL-like language, SQL with time-series extensions, a REST API with query parameters, or a GraphQL interface, and weigh their pros and cons.
Select one interface (e.g., a PromQL-like language) and explain how it supports aggregations, downsampling, and label filtering with examples.
Discuss how queries will be parsed, optimized, and executed efficiently, including indexing, pushdown, and caching strategies.
Acknowledge limitations of your choice and suggest how to extend or complement it (e.g., adding a UI or SDK) as needs evolve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Forgot to mention deduplication at first and had to backtrack when they asked about noisy alerting.
Start by clarifying requirements and scale, then walk through the alerting pipeline from metric ingestion to notification. Emphasize how each component (thresholds, SLOs, silencing, deduplication, routing) fits together to reduce noise and ensure actionable alerts.
Pro tip: Highlight the importance of alert quality over quantity: define alerts based on user-impacting symptoms (e.g., SLO burn rates) rather than raw resource metrics, and always include runbook links in alerts to speed up resolution.
Ask about the scale (metrics per second, number of services), existing monitoring stack, and team structure. This informs design choices like using a managed service vs. building in-house.
Cover threshold-based alerts (static and dynamic) and SLO tracking using error budgets and burn rates. Explain how to define SLOs and derive alerts from them.
Describe silencing (maintenance windows, manual silences) and deduplication (grouping similar alerts, using fingerprints). Mention how to handle flapping and alert storms.
Explain routing based on severity, service ownership, and on-call schedules. Discuss integration with tools like PagerDuty, Slack, and email, and the importance of escalation policies.
Talk about monitoring the alerting system itself, testing alerts, and continuously refining thresholds and SLOs based on feedback and postmortems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a sharding approach keyed on tenant ID plus metric name.
Start by clarifying the system's requirements and constraints, then structure your answer around the three pillars: high availability, horizontal scalability, and multi-tenant isolation. For each pillar, explain specific techniques and trade-offs, and tie them together by showing how they interact (e.g., how isolation choices affect scalability).
Pro tip: Acknowledge that multi-tenant isolation often involves trade-offs between cost, performance, and security; showing awareness of these trade-offs and proposing a hybrid approach (e.g., shared infrastructure with logical isolation) demonstrates maturity.
Ask about expected scale, SLAs, tenant size distribution, and compliance needs to tailor your answer. This shows you don't jump to solutions without understanding the problem.
Discuss redundancy at every layer (e.g., multi-AZ deployments, load balancing, failover, health checks) and how you'd handle failures gracefully. Mention specific patterns like circuit breakers and retries with backoff.
Explain how you'd design stateless services, use partitioning/sharding, and leverage auto-scaling. Highlight the importance of a shared-nothing architecture and asynchronous processing.
Compare isolation levels: shared database with tenant ID, schema-per-tenant, database-per-tenant, and hybrid. Discuss trade-offs in terms of cost, complexity, security, and performance.
Show how HA, scalability, and isolation interact. For example, database-per-tenant simplifies isolation but complicates scaling; propose a balanced solution and mention monitoring and testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Cardinality is one of those things that sounds boring until it kills your system.
Start by acknowledging the problem's two facets: cardinality explosion from label combinations and quota enforcement. Then, outline a multi-layered strategy: prevention via label design and aggregation, detection via monitoring, and enforcement via quotas at ingestion and query time. Emphasize trade-offs between flexibility and cost, and how you'd balance them.
Pro tip: Mention that cardinality explosion often stems from unbounded label values (e.g., user IDs, request IDs) and that the best fix is to prevent them at the source by enforcing label policies and using relabeling. Also, highlight that quotas should be enforced at multiple levels (tenant, service, metric) with graceful degradation to avoid disrupting critical monitoring.
Explain how cardinality explosion occurs (e.g., high-cardinality labels like user IDs, dynamic values) and its impact on storage, query performance, and cost.
Describe techniques like label whitelisting, relabeling, dropping unnecessary labels, and using aggregation to reduce cardinality before storage.
Outline how to track cardinality metrics per tenant/service and set alerts for anomalies to detect explosions early.
Detail quota mechanisms: per-tenant/service limits on active series, ingestion rate, and query load, with enforcement points (e.g., at the collector, gateway, or storage layer).
Explain strategies like rate limiting, dropping samples, or degrading service (e.g., sampling) to maintain system stability while notifying tenants.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Write path I described as: agent scrape or push to ingest gateway, fan out to a write-ahead log, then async flush to time-series storage.
Start by clarifying the system's requirements and assumptions, then describe the write and read paths step-by-step, explicitly covering failure handling and consistency guarantees. Use a concrete example (e.g., a key-value store or social media feed) to ground your explanation and demonstrate trade-off analysis.
Pro tip: Always tie consistency guarantees to business requirements—e.g., 'For this use case, eventual consistency is acceptable because...'—and mention how you'd monitor and alert on consistency violations in production.
Ask about scale, latency, durability, and consistency needs to frame your design. State assumptions explicitly (e.g., 'Assume we need strong consistency for writes but can tolerate eventual consistency for reads').
Walk through the flow from client to storage: ingestion, validation, replication, and acknowledgment. Explain how you handle failures at each stage (e.g., retries, idempotency, quorum writes).
Explain how reads are served: from cache, replicas, or primary; how you handle stale data, read repair, and failures (e.g., fallback to primary, retries with backoff).
Cover failure scenarios: node crashes, network partitions, disk failures. Describe mechanisms like replication, quorum, anti-entropy, and circuit breakers.
Define the consistency model (e.g., strong, eventual, causal) and justify it based on requirements. Discuss trade-offs with latency, availability, and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.