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How We Built Uber’s Highest Query per Second Service Using Go

Resurfaced independently across 3 calendar years, with breakout response in 2 of them.

submissions
7
submitters
7
observed span
2016–2019
peak thread · 129 comments
187 pts
latest 20+ return · 2019-11-14
185 pts

Submission timeline

2007–2026

One slot for every year since HN launched. Height is that year's peak points; orange marks a 100+ point or 50+ comment breakout. Select a bar to open its strongest thread.

First comments on top threads

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I don't think I've learned anything from this article. It's interesting how quickly the keywords pick up on HN (Uber and Go). More than 3 years ago we've implemented a reverse geocoder web-service that indexed complete Census TIGER dataset. The service handled over 15K reqs/sec on a 2011 macbook, doing exact in polygon search(no approximations). We implemented an optimized (for in polygon search) R-tree data structure for the lookups. Assuming Uber's geofence lookups would rely on a much smaller dataset…

emrekzd·187-point thread·

This is a very inefficient implementation. Really, just poor quality work overall, as anyone with even a basic understanding of spatial indexing would know that an R-tree would be many times faster, as illustrated here: https://medium.com/@buckhx/unwinding-uber-s-most-efficient-s...

woeirua·185-point thread·

An excellent response that I remember from the last time this article made the rounds: https://medium.com/@buckhx/unwinding-uber-s-most-efficient-s...

The first top-level comment from each of the four biggest threads, in HN’s own order. Excerpts are shortened; open a comment for full context.

Breakout years
2

100+ points or 50+ comments

Total points
417

reference only — not used in Hall rules or ranking

Total comments
250

reference only — not used in Hall rules or ranking

Every submission