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
33 practiced
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
36 practiced
You detect a hot partition in Kafka: one partition has extremely high ingress and consumer lag vs others. Programmatically how would you detect such hot partitions, and what safe remediation steps (partition reassignment, key salting, adding partitions, producer-side changes) would you take to minimize downtime and ordering disruption?
HardTechnical
31 practiced
Implement consistent hashing with virtual nodes and support for weighted nodes in Java or C++. The implementation should support addNode(nodeId, weight), removeNode(nodeId), and getNode(key) operations efficiently. Provide code or detailed pseudocode and explain time and space complexity and how virtual nodes reduce load imbalance.
MediumTechnical
31 practiced
Implement checkpointing logic for a simple stateful operator in Python that maintains per-key sums. Provide pseudocode for processing events, snapshotting state atomically to durable storage (e.g., S3), and restoring state on restart. Discuss consistency guarantees and trade-offs for checkpoint frequency.
HardTechnical
38 practiced
For a pipeline processing 100M events/day, propose a cost-optimized architecture using spot instances, autoscaling, and tiered storage. Include expected cost-saving techniques, risks associated with preemptible compute (spot), strategies for handling preemptions safely, and data retention tiering (hot/warm/cold) with lifecycle policies.
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