Explain Pre-computation vs On-Demand.
Pre-computation vs On-demand refers to the choice between computing results in advance (and storing them for fast retrieval) or calculating them dynamically when requested.
When to Use
Use pre-computation for repetitive, high-latency operations where low response time is critical—like pre-rendering dashboards or caching popular queries. Use on-demand when results change frequently or data freshness outweighs latency—like user-specific analytics or ad recommendations.
Example
Netflix pre-encodes videos in multiple formats (pre-computation) for instant playback. Generating encodings during playback (on-demand) would be slower but save space.
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Why Is It Important
Choosing between the two affects latency, cost, and data freshness. Pre-computation improves speed and user experience, while on-demand ensures real-time accuracy with less storage overhead.
Interview Tips
In interviews, frame this as a compute vs storage trade-off.
Mention caching, freshness, and workload type. Use examples like materialized views or newsfeed generation to show practical understanding.
Trade-offs
- Pre-computation: + low latency, – higher storage, possible staleness.
- On-demand: + real-time accuracy, – higher compute cost, slower response.
Pitfalls
- Over-precomputing rarely used data wastes resources.
- On-demand under load can cause latency spikes.
- Failing to refresh precomputed caches can lead to stale results.
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