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.
HardSystem Design
0 practiced
Design a highly available multi-region time-series storage system that supports cross-region reads and writes, provides DR, and guarantees sub-200ms read latency for recent data from the user's closest region. Outline replication model, consistency choices, routing, and how you'd handle failover.
MediumTechnical
0 practiced
A sudden producer-side bug sent a flood of unique label combinations for a critical metric, causing OOMs in your query nodes and extreme storage growth. Describe immediate mitigation steps to stabilize the system, and then outline longer-term fixes to prevent recurrence.
MediumSystem Design
0 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.
MediumSystem Design
0 practiced
Design a backup and restore strategy for a petabyte-scale time-series deployment that must maintain 99.99% availability. Consider snapshotting, incremental backups, object storage, retention, restore-time objectives (RTO), and restore-point objectives (RPO). Describe trade-offs and how to test restores.
MediumTechnical
0 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.
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