Start by clarifying requirements and scale (ingestion rate, query patterns, retention, tenant count) to scope the design. Then propose a layered architecture: a write-optimized ingestion pipeline, a columnar time-series storage engine with label indexing, and a query layer with caching and downsampling. Emphasize trade-offs around consistency, cost, and isolation, and tie choices back to Okta's identity/security context.
Pro tip: Anchor your design around a proven open-source time-series database (e.g., Prometheus, Thanos, Cortex, or VictoriaMetrics) and explain how you'd extend it for multi-tenancy and Okta-specific needs, rather than designing from scratch. This shows pragmatism and deep familiarity with real-world systems.
Ask about ingestion volume (e.g., millions of samples/sec), query types (range, instant, aggregation), retention periods, tenant count, and isolation requirements. Establish SLAs for latency and durability.
Propose a distributed write path: ingestion gateways for auth/rate-limiting, a message queue (e.g., Kafka) for buffering and decoupling, and stream processors for validation, enrichment, and routing to storage shards.
Choose a time-series storage engine (e.g., TSDB with columnar compression) and design label-based indexing (inverted index or series map). Discuss sharding by tenant/time, replication for durability, and tiered storage for long-term retention.
Build a query service that parses label queries, prunes shards, and executes distributed scans with aggregation pushdown. Add caching (query results, series metadata) and downsampling for long-range queries.
Explain isolation models (shared-nothing vs. shared storage with tenant IDs), resource quotas, and security. Discuss trade-offs: consistency vs. availability, cost vs. performance, and operational complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through a querier layer that fans out to store gateways per shard and merges results.
Start by clarifying the query path requirements and constraints, then walk through the fan-out, deduplication, and security layers in a logical order. Emphasize trade-offs and how you would validate the design with metrics and testing.
Pro tip: Highlight that deduplication should happen as early as possible in the query pipeline to reduce network and processing overhead, and that cross-tenant security must be enforced at every layer, not just at the API gateway.
Ask about query patterns, data volume, shard count, replication factor, latency SLAs, and tenant isolation requirements to scope the problem.
Determine how to route queries to relevant shards, handle partial failures, and aggregate results efficiently, possibly using a scatter-gather pattern with timeouts and retries.
Choose a deduplication method (e.g., unique IDs, hashing, or versioning) and decide where to apply it (per-shard, at the aggregator, or both) to avoid duplicate results.
Ensure tenant context is propagated through the query path, enforce access control at each layer (API, query planner, storage), and consider encryption or row-level security.
Compare consistency vs. latency, cost vs. performance, and explain how you would test the design (e.g., chaos engineering, load testing, security audits).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Cardinality explosion I'd actually thought about before so this part went better.
Start by acknowledging that cardinality explosion and long-term storage costs are inherent to high-resolution telemetry systems, then systematically break down the challenges and propose mitigation strategies. Frame your answer around trade-offs between granularity, cost, and query performance, and tie it back to Okta's scale and reliability needs.
Pro tip: Show you understand that cardinality explosion isn't just a storage problem—it degrades query performance and increases memory usage in time-series databases. Mention specific techniques like metric relabeling, aggregation, and downsampling, and note that Okta likely uses a combination of open-source and custom solutions.
Explain how high-dimensional labels (e.g., user IDs, request IDs, IP addresses) and high-frequency events can cause a combinatorial explosion in time-series data. Mention that in Okta's context, per-tenant or per-user metrics can quickly multiply.
Describe how cardinality explosion leads to increased memory and disk usage, slower queries, and higher costs. Note that long-term storage of high-resolution data exacerbates this, especially if retention policies are not tiered.
Suggest techniques like limiting label dimensions, using aggregation at ingest (e.g., rollups), and employing cardinality limits or dynamic sampling. Mention tools like Prometheus relabeling, VictoriaMetrics, or Thanos for handling high cardinality.
Discuss tiered storage (hot/warm/cold), downsampling older data, and using cheaper object storage (e.g., S3) with compression. Highlight the trade-off between resolution and cost, and propose retention policies based on data value.
Emphasize that Okta handles millions of users and requires high availability, so any solution must be scalable, cost-effective, and not compromise on observability. Mention the need for monitoring cardinality itself and alerting on anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.