From scraping chaos to analytics: finally a stable hourly pipeline
Hit a milestone this week with my web scraping infrastructure:
it’s finally stable enough (hourly runs) that I can stop firefighting
and start building analytics + visualizations to improve it
The last two weeks (real story)
Over the past couple of weeks, I’ve been constantly tweaking the architecture to balance load across:
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the database
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3rd party services (especially Perplexity)
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the scraper itself
Every time something improved, something else would get overloaded.
Replit did a great job suggesting fixes after each failure, but the pattern was clear:
some part of the system would always become the bottleneck
The breakthrough
Instead of pushing harder, we dialed everything back and introduced gating:
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limiting request rates
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controlling concurrency
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smoothing load across components
That got us to a point where:
the pipeline could run on schedule without crashing
Then the direction flipped
Once things were stable, we could finally do the opposite:
gradually increase load
increase scraping throughput
expand event discovery
…but without breaking stability
This is where analytics became essential
To scale safely, we needed visibility into the system.
The key question:
Where do we have unused capacity?
Example visualization
The chart below shows scraping activity over time:
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Blue = scheduled runs
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Orange = pipeline / ad-hoc scraping
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Red markers = error events (often tied to overload conditions like Perplexity limits)
What this reveals
Two really important things:
1. Whitespace (unused capacity)
There are clear gaps between spikes where:
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the scraper is idle
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services are underutilized
This is where we can safely add more scraping
2. Overload signals
The red error markers tend to cluster around spikes in activity:
especially when pushing Perplexity too hard
This gives us a visual way to:
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detect rate limits
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tune concurrency
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avoid cascading failures
What I’m building now
Using these visuals to guide:
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where to increase load
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where to back off
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how to balance across services
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how to maximize event discovery without errors
Why this feels like a turning point
Before:
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reactive
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constant failures
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unclear bottlenecks
Now:
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controlled
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observable
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tunable
My takeaway
You don’t really “have” a data pipeline until:
it’s stable enough to push harder safely
and observable enough to know where to push
Curious how others approach this phase
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How do you detect unused capacity in distributed systems?
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Any good ways to visualize rate limits / third-party constraints?
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Do you scale gradually or in larger steps?
Feels like this is where the real optimization work begins.