I started with the write path since the throughput constraint was the obvious anchor.
Start by clarifying requirements and scale (e.g., number of services, metrics per second, retention, query patterns). Then propose a distributed architecture with separate ingestion, storage, and query layers, using techniques like sharding, replication, and tiered storage. Finally, discuss trade-offs and optimizations for low-latency dashboards and alerts.
Pro tip: Emphasize the importance of a columnar time-series database (e.g., Bigtable, Monarch-like) and pre-aggregation for dashboards, while ensuring high availability and fault tolerance across data centers.
Ask about the number of services, metrics volume (e.g., millions of data points per second), retention policies, query latency SLAs, and global distribution needs.
Propose a scalable ingestion layer with load balancers, message queues (e.g., Kafka) for buffering, and regional collectors to handle high throughput and avoid data loss.
Select a time-series database (e.g., Bigtable, Monarch) with a schema optimized for writes and reads, using sharding by metric name and time, and replication across data centers.
Design a query layer with caching and pre-aggregation for dashboards, and a separate alerting system that evaluates rules on recent data with low latency.
Discuss consistency vs. availability, cost vs. performance, and techniques like downsampling, tiered storage, and edge aggregation to reduce load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was basically a follow-up but felt like its own question.
Start by clarifying the scale, latency, and consistency requirements, then propose a multi-region architecture with replication and federation. Discuss trade-offs between consistency models (e.g., eventual vs. strong) and how they affect query results and system design.
Pro tip: Emphasize that consistency requirements are often per-query or per-use-case, not global; propose a tunable consistency model to balance latency and accuracy. Also, mention the importance of monitoring replication lag and providing staleness indicators to users.
Ask about data volume, query patterns, latency SLAs, and consistency needs (e.g., real-time vs. analytical). This shapes the replication and federation strategy.
Choose between synchronous vs. asynchronous replication, and decide on data partitioning and replication topology (e.g., star, mesh). Consider using a global metric store with regional replicas.
Implement a query federation layer that routes queries to regional replicas and merges results. Handle partial failures and timeouts gracefully.
Discuss CAP theorem implications: strong consistency increases latency and reduces availability during partitions; eventual consistency improves performance but may return stale data. Propose tunable consistency (e.g., quorum reads/writes) and explain how to surface staleness to users.
Cover monitoring replication lag, handling region failures, and cost implications. Suggest mechanisms like read-repair or anti-entropy for eventual consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about time-based rollups, keeping raw data for maybe 24 hours then aggregating to minute-level, then hour-level.
Start by clarifying the scale and requirements (e.g., data volume, query patterns, retention, accuracy needs). Then propose a multi-tiered strategy combining downsampling, aggregation, and retention policies, balancing storage savings with query performance and analytical needs. Finally, discuss implementation details like rollup pipelines, time-series databases, and trade-offs.
Pro tip: Emphasize that downsampling should be driven by query patterns and business needs, not just storage costs—preserving high-resolution data for recent periods and critical metrics while aggressively downsampling older or less critical data. Also mention the importance of monitoring and iterating on sampling strategies.
Ask about data volume, query patterns, latency requirements, retention policies, and accuracy needs to tailor the solution.
Propose storing raw data for a short period, then downsampling to coarser resolutions (e.g., 1-minute, 5-minute, 1-hour) for longer retention, possibly using different storage tiers.
Discuss aggregation methods (avg, min, max, percentiles) and sampling techniques (uniform, reservoir, adaptive) based on metric type and query needs.
Describe how to build pipelines (e.g., using stream processing or batch jobs) to compute and store downsampled data, ensuring idempotency and handling late data.
Explain how to route queries to appropriate resolution, use caching, and leverage time-series databases with downsampling support (e.g., Prometheus, TimescaleDB).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.