Handling Large Scale Data and Time Series Data Questions
Design for efficient storage and querying of massive datasets. Understand time-series data patterns (metrics, logs), specialized solutions like InfluxDB or TimescaleDB, and archiving strategies for historical data.
MediumSystem Design
55 practiced
Design an ingestion pipeline that can sustain 200,000 metric samples per second (sustained), supports 30-day hot retention, and allows real-time dashboards. Outline components (instrumentation, buffering, kafka/streaming, TSDB), how you'd handle partitioning/batching, backpressure, and durability.
EasyTechnical
54 practiced
Explain hot (real-time) vs cold (archival) storage for time-series data. Give examples of technologies you would use for each tier, describe typical access patterns, and explain how you would move data between tiers without breaking queries.
MediumTechnical
95 practiced
Compare sharding strategies for time-series data: sharding by time, by series (metric+labels hash), and by metric name. For each approach, explain operational complexity, query routing cost, rebalancing, and hot-shard risks. Which would you pick for a multi-tenant monitoring platform and why?
EasyTechnical
54 practiced
Explain chunking (or chunk-size) in TSDBs and why chunk size matters for both ingestion and query performance. Include effects on compaction, memory usage, parallel reads, and number of files/objects in object storage.
HardTechnical
61 practiced
Describe how you would instrument and test disaster recovery (DR) for a global time-series platform. Outline DR test scenarios (regional outage, data corruption, full-cluster rebuild), the test plan, validation queries and metrics to verify success, and how often to run these tests.
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