Sliding Windowhard70 minData Engineer

Metrics Streaming Window Anomaly

Data Engineer signal: sliding window + frequency map in a metric dependency graph context. This is a ProdMatch-owned data engineer drill, framed as a May 2026 Ola Analytics Platform simulation, not a copied platform question.

Company context

Ola · Analytics Platform

Freshness

May 2026

Product surface

metric dependency graph

ProdMatch interview simulation based on product-team patterns; not a claim of a real company question.

Question

For metric dependency graph, process a timestamp-ordered stream of metrics. Find the longest contiguous window where at most k distinct risk labels appear and the total severity stays under budget.

Input

Array of events {label, severity} and integers k, budget.

Output

Maximum window length satisfying both constraints.

Constraints

1 <= events.length <= 300000
1 <= k <= 50
Severity values are positive integers.

Concepts

stream windows
watermarks
lineage DAGs
sliding window
frequency map
stream processing

labels: A,B,A,C with k=2 and budget=7 -> longest valid window length is 3

Approach

Try framing your own approach first. The 30 seconds you think before peeking is where learning happens.

Clean Solution

Reveal the approach first.

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Common Mistakes

Update the answer only after the window is valid.
Avoid recomputing the whole window on every move.

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