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.
HardTechnical
0 practiced
For a workload of high-frequency metrics sampled at 1 kHz per series with many boolean (0/1) values, recommend a compression algorithm and block/chunk size to minimize storage while keeping recent-query latency low. Explain why you chose that algorithm and how chunk size affects memory and decompression cost.
HardTechnical
0 practiced
Write a SQL (or pseudocode) plan for an online schema migration that adds a new indexed label (e.g., region) to an existing time-series table without downtime. Explain steps to avoid blocking writes and to backfill historical data incrementally while keeping queries correct.
EasyTechnical
0 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.
HardTechnical
0 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
0 practiced
Explain how WAL (write-ahead log) retention and recovery interact with point-in-time recovery for a TSDB. How would you manage WAL retention for a system with heavy write rates and limited disk, while keeping an acceptable RPO?
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