Advanced Caching and Data Pipeline Design Questions
Distributed caching, cache coherency, specialized stores (search engines, column stores, time-series databases). Data pipeline architecture: batch processing, stream processing, ETL design. Understanding Lambda and Kappa architectures.
MediumTechnical
0 practiced
Implement a sliding window rate limiter in Python that allows N requests per user per minute using Redis sorted sets as the backing store. Provide pseudo-code for allow_request(user_id) and explain how the ZADD, ZREMRANGEBYSCORE, and ZCOUNT operations are used to enforce the window and scale safely.
MediumSystem Design
0 practiced
You manage a microservices platform where multiple services cache shared user-profile data. Design an invalidation strategy so that when the profile changes in service A it is updated in caches maintained by services B and C without causing thundering herd or race conditions. Consider event ordering, retries, and network partitions.
HardSystem Design
0 practiced
Architect a streaming analytics platform that must process 1M events/sec with end-to-end processing latency under 200ms for most pipelines and exactly-once semantics for financial metrics. Explain choices for message bus, stream processor, state backend, checkpointing, partitioning, and how to ensure exactly-once sinks.
HardTechnical
0 practiced
Design a schema evolution strategy for Kafka-based pipelines using Avro/Protobuf with a schema registry. Explain how to support compatible additive changes, deprecations, and breaking changes, and how consumers should be designed to handle versioned messages and unknown fields.
MediumSystem Design
0 practiced
You must architect a high-throughput analytics ingestion path from mobile devices using a combination of edge ingestion, Kafka, and batch/stream processors. Explain how you would handle unreliable network clients, schema evolution on device events, and caching to reduce repeated writes for duplicate telemetry.
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