A table view that scrolls fine with 10 rows can lock up with 137 rows if the data changes about 30 times a second. That is the story behind a short first-person account by Suprie, a Senior iOS Engineer, on DEV Community. It describes a real-time trading screen that froze at peak load. The fix wasn’t a new framework. It was finding where repeated “small” work had piled up. This article walks through that account and what it does and doesn’t show.
What happened
The author’s WebSocket-driven feature first showed 10 price levels. It was later expanded to show every available level, which is between 80 and 137 depending on the stock. At peak trading hours the view froze and users couldn’t scroll. The problem appeared only under peak load, so it got past QA. The page is dated “Sep 27”. The opened page shows no year, and search-result metadata gives 2026.
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The author gives an illustrative before-and-after workload: 5 levels at 30 updates per second is manageable, while 100 levels at 30 updates per second produced what the author calls “UI death”. This is the author’s description of the workload, not a measured benchmark.
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Attempt one: Diffable Data Source
The author first used Diffable Data Source with UUID-backed items. The stream, however, delivered the entire list of price levels on every update, not a snapshot of only the changed items. Diffing a full replacement against the previous list was extra work. In this case the author found that tableView.reloadData() was faster.
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The author’s summary of the design lesson: “diffable data source is great for partial updates, not full replacements.” That is advice drawn from this one case. It doesn’t show that reloadData() is generally better.
The real bottleneck: repeated work on the main thread
Profiling pointed elsewhere. Computed properties were running on the main thread. They colored up and down prices, formatted numbers and handled localization. Each is cheap for one row. Multiplied across 80 to 137 rows and about 30 updates a second, they were not.
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The fix was to do those calculations on background threads while converting the WebSocket response into view data. The view then only read values that were already prepared. The author reports that scrolling stayed smooth during peak trading hours after this change.
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| Factor | Question to ask | In this account |
|---|---|---|
| Update shape | Does the server send partial changes or complete replacements? | Complete lists, so diffing added work |
| Work per update | How much formatting, coloring or localization runs per row? | Substantial, and on the main thread |
| Frequency | How many updates arrive per second? | About 30 per second in the author’s example |
| Thread | Is repeated preparation done on the main thread? | Moved to background before building view data |
What the account does not establish
- It is a single short account. It gives no reproducible code, device or build configuration, profiler trace or timing numbers.
- No external study, controlled benchmark or independent test backs it.
- The figures (10 levels, 80–137 levels, 5 versus 100 levels at 30 updates per second) describe this workload only. They aren’t industry statistics.
Takeaways for your own screen
- Count both rows and update frequency. Cost scales with the product of the two.
- Check what your data source actually sends. If every update is a full replacement, an incremental diff may not pay for itself, so measure it.
- Profile with realistic peak load, not the quiet case QA tends to see.
- Move formatting, color decisions and localization out of cell and view code. Compute them off the main thread when you build view models.
The author’s own line sums it up: “‘Cheap’ operations aren’t cheap at scale – profile before you assume.”
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