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
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?
MediumTechnical
0 practiced
Design partitioning, clustering, and storage strategies for a star-schema analytics warehouse (fact_sales, dim_customer, dim_product) to optimize queries that filter by date and customer region. Discuss partition keys, clustering columns, file format and size, and trade-offs between query latency and storage cost.
MediumTechnical
0 practiced
Explain replication and load balancing choices for stateful stream processors to maintain throughput and high availability. Compare active-passive vs active-active replication, checkpointing vs synchronous replication of state, and the implications for consistency, failover time, and performance.
MediumSystem Design
0 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.
HardTechnical
0 practiced
Design a pipeline that ensures exactly-once semantics when writing processed events to an external relational DB (e.g., PostgreSQL) from a stream processor (e.g., Kafka Streams or Flink). Cover failure modes, transaction boundaries, the outbox pattern, idempotent writes, two-phase commit, and the performance implications of each approach.
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