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hardSystem Design

How would you design an analytics/metrics pipeline?

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01

Understand the problem

Outline a data pipeline.

analyticspipeline
02

Attempt it yourself

Sketch your approach before reading the solution — that's what interviews test.

Nudge consolestandby

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03

Study the solution

Step 1: Outline use cases and constraints

Gather requirements and scope the problem. Ask questions to clarify use cases and constraints. Discuss assumptions.

Use cases

We'll scope the problem to handle only the following use cases

  • User performs core action described in How would you design an an

Solution ready — 2 min read

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04

Read the code

Tumbling-window aggregation
Run Playground
from collections import defaultdict

# (timestamp_seconds, metric) event stream
events = [
    (0, "page_view"), (5, "page_view"), (61, "page_view"),
    (62, "click"), (119, "page_view"),
]
WINDOW = 60   # seconds per tumbling window

agg = defaultdict(int)
for ts, metric in events:
    window = ts // WINDOW                 # bucket the event into its window
    agg[(window, metric)] += 1

for (window, metric), count in sorted(agg.items()):
    print("minute", window, metric, "=", count)
05

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