Data Pipeline Scalability and Performance Questions
Design data pipelines that meet throughput and latency targets at large scale. Topics include capacity planning, partitioning and sharding strategies, parallelism and concurrency, batching and windowing trade offs, network and I O bottlenecks, replication and load balancing, resource isolation, autoscaling patterns, and techniques for maintaining performance as data volume grows by orders of magnitude. Include approaches for benchmarking, backpressure management, cost versus performance trade offs, and strategies to avoid hot spots.
MediumTechnical
0 practiced
Implement an asyncio-based batching producer in Python. The API should provide 'async send(msg)' which enqueues messages and flushes them when batch size reaches N or when a timer T elapses. Provide 'await close()' which flushes remaining messages and stops background tasks. Show thread-safety considerations and how to apply backpressure to callers.
MediumTechnical
0 practiced
Compare autoscaling patterns for stream processing jobs: reactive HPA (based on CPU/lag), partition/reshard-based scaling, and predictive scaling using traffic forecasts. For stateful jobs explain implications of scaling (state migration, checkpointing) and propose an autoscaling design that balances responsiveness and stability.
HardSystem Design
0 practiced
Design a streaming system that supports complex stateful windowed joins with a working set of hundreds of GB. Discuss state backend selection (RocksDB vs in-memory), compaction and TTL strategies to bound state size, checkpoint frequency, scaling state across nodes, restore/warm-start behavior, and operational practices for backups and restores.
HardTechnical
0 practiced
Design a robust autoscaling policy for stream processing that handles flash crowds while minimizing state migration overhead. Include predictive forecasting for expected load, reactive thresholds, reserved buffer capacity, cooldown periods, and approaches to reshard stateful jobs with minimal downtime.
EasyTechnical
0 practiced
Define the roles of online and offline feature stores. For a model that retrains daily but requires features fresher than 1 second for inference, propose an architecture that supports both online low-latency reads and offline reproducible training datasets. Discuss ingestion, storage formats, consistency, and versioning.
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