Reddit Migrates Comment Back End from Python to Go(old.reddit.com) |
Reddit Migrates Comment Back End from Python to Go(old.reddit.com) |
A few weeks ago I was evaluating Hibernate for Java again, since my product is expected to support 2 different databases next year. In the end I decided to keep the codebase ORM free, because it’s much easier to me to directly debug our SQL queries than trying to find what and why Hibernate does exactly. I think I’m done using ORMs for the foreseeable future.
now there's better tools like sqlc etc that have orm ergonomics without the performance hit.
at times just use nosql
Using Python for a backend system to "scale" really is just pure cope and was unscalable in the first place and in the long run as Reddit just found out. They knew they needed a lot more than just fake optimizations from an interpreted language to improve the performance and a Golang rewrite unsurprisingly solved those issues.
This once again clearly shows that other than in the prototyping stage of an MVP, it really makes no sense to scale with backends written in these interpreted languages in 2025.
Switching to safe, highly performant mature languages such as Golang tells you that it not only generally improves performance, but correctly handles concurrency which Python has always struggled at, especially at scale which is why in Reddit's case, the race conditions now revealed themselves more clearly before the rewrite.
They picked the tech that was available and mature at the time, and enabled them to scale for 20 years (to 100M+ DAUs + IPO) - seems like a pretty good choice to me.
You know which other platform was built on Python? Youtube.
Python isn't a bad choice if you're building [certain kinds of] billion-dollar businesses.
If you want to build an app that's easy to maintain, Python is a bad choice because of it's dynamic typing and struggles around tooling. If you want to build an app that's performant enough to scale, Python is a bad choice because Python. If you want to build an app that will get you a website ASAP, Python is still not the best choice, because other languages have larger batteries-included frameworks.
In 2005, even PHP would have been a better choice and would probably still be performant enough to run today.
Today, the story is much worse. Python has use cases! Experimentation, anything science related, "throwaway" code. Applications is just not one of the use cases IMO.
Instagram, which is significantly bigger than Reddit, disagrees.
Now we don't have details on what the comments service in Reddit entails - maybe it does indeed do a lot of CPU-intensive processing, in which case moving to Golang will definitely help.
But maybe it's also just a trivial "read from DB, spit out JSON", in which case the bottleneck will always be the DB, and "scalability" is just an excuse to justify the work.
The fact this is part of a move off a "legacy" system to "modern" "microservices" suggests there's a huge amount of developers having fun and are incentivized to justify continuing getting paid to have fun replacing a perfectly functioning system, rather than an actual hard blocker to scalability that can't be solved in a simpler way like by throwing more hardware at it.
I don't think it suggests that at all. This is their press release, so of course they're going to spin it that way.
I been saying it for almost 10yr, go is the future for backends.
For example Python is struggling to reach real time performance decoding RLL/MFM data off of ancient 40 year old hard drives (https://github.com/raszpl/sigrok-disk). 4GHz CPU and I cant break 500KB/s in a simple loop:
for i in range(len(data)):
decoder.shift = ((decoder.shift << data[i]) + 1) & 0xffffffffff
decoder.shift_index += data[i]
if decoder.shift_index >= 16:
decoder.shift_index -= 16
decoder.shift_byte = (decoder.shift >> decoder.shift_index) & 0x5555
decoder.shift_byte = (decoder.shift_byte + (decoder.shift_byte >> 1)) & 0x3333
decoder.shift_byte = (decoder.shift_byte + (decoder.shift_byte >> 2)) & 0x0F0F
decoder.shift_byte = (decoder.shift_byte + (decoder.shift_byte >> 4)) & 0x00FFI maintain a critical service written in Python and hosted in AWS and with about 40 containers it can do 1K requests/sec with good reliability. But we see issues with http libraries and systemic pressure within the service.
Modern hardware is incredibly fast, so if you wait for said scale it may never actually happen. It's likely someone will win the push for a rewrite based on politics rather than an actual engineering constraint, which I suspect happened here.
Not saying it's ideal but it's a solved problem and Python is eating good in terms of quality dataframe libraries.
self.shift_index -= 16
shift_byte = (self.shift >> self.shift_index) & 0x5555
shift_byte = (shift_byte + (shift_byte >> 1)) & 0x3333
shift_byte = (shift_byte + (shift_byte >> 2)) & 0x0F0F
self.shift_byte = (shift_byte + (shift_byte >> 4)) & 0x00FF
but only for exactly 2-4 milliseconds per 1 million pulses :) Declaring local variable in a tight loop forces Python into a cycle of memory allocations and garbage collection negative potential gains :(
SWAR : 0.288 seconds -> 0.33 MiB/s
SWAR local : 0.284 seconds -> 0.33 MiB/s
This whole snipped is maybe what 50-100 x86 opcodes? Native code runs at >100MB/s while Python 3.14 struggles around 300KB/s. Python 3.4 (Sigrok hardcoded requirement) is even worse: SWAR : 0.691 seconds -> 0.14 MiB/s
SWAR local : 0.648 seconds -> 0.14 MiB/s
You can try your luck https://github.com/raszpl/sigrok-disk/tree/main/benchmarks I will appreciate Pull requests if anyone manages to speed this up. I give up at ~2 seconds per one RLL HDD track.This is what I get right now decoding single tracks on i7-4790 platform:
fdd_fm.sr 0.9385 seconds
fdd_mfm.sr 1.4774 seconds
fdd_fm.sr 0.8711 seconds
fdd_mfm.sr 1.2547 seconds
hdd_mfm_RQDX3.sr 1.9737 seconds
hdd_mfm_RQDX3.sr 1.9749 seconds
hdd_mfm_AMS1100M4.sr 1.4681 seconds
hdd_mfm_WD1003V-MM2.sr 1.8142 seconds
hdd_mfm_WD1003V-MM2_int.sr 1.8067 seconds
hdd_mfm_EV346.sr 1.8215 seconds
hdd_rll_ST21R.sr 1.9353 seconds
hdd_rll_WD1003V-SR1.sr 2.1984 seconds
hdd_rll_WD1003V-SR1.sr 2.2085 seconds
hdd_rll_WD1003V-SR1.sr 2.2186 seconds
hdd_rll_WD1003V-SR1.sr 2.1830 seconds
hdd_rll_WD1003V-SR1.sr 2.2213 seconds
HDD_11tracks.sr 17.4245 seconds <- 11 tracks, 6 RLL + 5 MFM interpreted as RLL
HDD_11tracks.sr 12.3864 seconds <- 11 tracks, 6 RLL + 5 MFM interpreted as MFMThat's a really low rate in my world.
I write software handling a couple of million of messages per second on a single core on a single machine
You don't end up seeing these kinds of complaints about Ruby backends and Ruby is the same order of magnitude in terms of speed.