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.
EasyTechnical
56 practiced
List and briefly explain common compression techniques used by time-series databases (for example: gorilla/delta-of-delta, run-length encoding, lz4, zstd). For each technique, note the trade-offs between compression ratio, CPU cost, and query latency.
HardSystem Design
52 practiced
Design an automated compaction and retention system for a high-cardinality metrics platform. The system should balance query latency, storage cost, and ingestion throughput. Describe compaction triggers, scheduling during low-load windows, data tiering, and how compaction impacts availability.
MediumTechnical
60 practiced
You're migrating 3 years of metrics from InfluxDB to ClickHouse while wanting minimal downtime for dashboards and alerts. Outline a migration plan that includes data export, transformation, validation, incremental sync, dual-writing or side-by-side testing, and final cutover with rollback strategy.
HardTechnical
67 practiced
Write a Python program (pseudocode acceptable) that monitors incoming metric series counts and detects anomalous growth in cardinality growth rate. Upon detection, the program should automatically identify the top offending label combinations and trigger a safe throttling action (for example, apply a relabeling rule or notify owners). Explain false-positive avoidance and rate-limits for automation.
MediumTechnical
67 practiced
How does indexing differ between columnar analytics stores (e.g., ClickHouse) and purpose-built TSDBs (e.g., InfluxDB, Prometheus/TSDB)? Which index types or data-structures would you use to optimize time-range aggregations, tag lookups, and high-cardinality filters?
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