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.
MediumSystem Design
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Design a resource isolation strategy for running multiple data pipelines with different SLAs on the same Kubernetes cluster. Consider cgroups/requests/limits, node pools, QoS classes, priority classes, pod disruption budgets, admission control, and how to prevent noisy-neighbor interference while guaranteeing resources for critical pipelines.
MediumTechnical
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Implement an exponential backoff with full jitter retry utility in JavaScript/TypeScript: async function retry(fn, maxRetries, baseMs). Include a cap on maximum delay, randomized jitter, and reset behavior. Provide code and explain why jitter is important in high-concurrency retry storms.
HardTechnical
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As the tech lead, the analytics team needs interactive query latencies under 2 seconds but current warehouse queries average 30 seconds. Propose architecture changes across ingestion, storage, and query layers (materialized views, OLAP engines, streaming aggregates, caching, schema changes) to reduce latency while controlling cost. Prioritize changes and explain trade-offs.
HardSystem Design
0 practiced
Design a backpressure protocol for two microservices communicating over gRPC to stream large event payloads. Define message-level flow control (credit tokens), how the client and server behave under saturation, fallback behaviors (reject, queue, degrade), and how you'd instrument and test the protocol.
EasyTechnical
0 practiced
List the core metrics you would monitor for a large-scale data pipeline (batch and streaming) to detect performance and scalability issues. For each metric explain why it matters, an example alert threshold, and what a sustained anomaly typically indicates about system health.
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