6 HN users recommended this talk10 months2014–2022score 11.5
Years with an HN recommendation, 2007–2026
The go-to talk showing how Erlang let a tiny team run a massive messaging system.
Rick Reed of WhatsApp explains at Erlang Factory 2014 how the service scaled to billions of messages with a very small engineering team. He covers the FreeBSD and Erlang stack, Mnesia usage, server counts and bottleneck work.
“watch this talk and imagine trying to recreate that system with only a handful of server engineers” (HN, 2017)
My other favorite "scalability" study is from WhatsApp: [this video] That's 'Billion' with a 'B': Scaling to the Next Level at WhatsApp (that walk title was create before the acquisition and was mean to imply message count, after the acquisition it got a secondary meaning). The one thing that is fascinating about it, is how small their team was compared to the volume and complexity of the operation.
Not sure why this comment saw a couple downvotes earlier. mbesto is correct: for most startups, most of the time, competitive advantage doesn't come from the underlying tech stack. To make a general statement, most things could be done similarly on any of several platforms. However, when product requirements match exceptionally well with a specialized technology, you can see things that would simply be infeasible or extremely tough using a different stack. WhatsApp + Erlang was one of those cases (watch this talk and imagine trying to recreate that system with only a handful of server engineers using any other tech: [this video] ). Discord + Elixir appears to be another. Curious if anyone has any examples that spring to mind from outside the highly concurrent messaging space.
Specialized tech enabling a handful of engineers to run huge system
One of my favorite talks is by Rick Reed about scaling Erlang at WhatsApp. What an absolute savage. He flies through an articulate and in-depth curriculum on system performance and bottleneck mitigation. The talk is called "That's 'Billion' with a 'B'" and makes for a great lunchtime watch: [this video]
In-depth system performance and bottleneck mitigation curriculum
Unrelated, but maybe interesting: WhatsApp was running Mnesia and made it work fantastically. Especially with such a small team of engineers. Here is a video of Rick Reed talking about how they did it (hint it is not a one monolithic Mnesia cluster). [this video]
Just to follow up on topic of WhatsApp and Erlang, here is a presentation given at Erlang Factory 2014 about their goals with scaling WhatsApp to billions of simultaneous users. [this video]
It's required because your phone is where your messages are stored. Whatsapp don't retain messages/media after they've been delivered to your phone, which is a compelling privacy feature for many. It's also what allows them to serve such an enormous user base with limited hardware. Their technology stack (FreeBSD/Erlang) is pretty interesting, more info here: 2014 talks by Rick Reed: [this video] [link] Slides: [link] There's likely no technical reason why you couldn't use a pc instead of a phone for users that want to use the pc as the primary client (with the phone optionally accessing the DB on the pc in the same way that the desktop client does for the phone). Perhaps they've decided that this is a small and declining market. Edit: Slides for second talk [link] 440k connections/sec, 1.1 million msgs/sec, 1 billion images/day, and that was in 2014...
Interesting tech stack with huge scale on limited hardware
Here's a great talk from Rick Reed (a former mentor of mine at Yahoo!) about how WhatsApp scaled their Erlang infrastructure: [link] Here's another talk: [this video]
Great talk on scaling WhatsApp's Erlang infrastructure
Here is the description of their stack and how Erlang helped them by Rick Reed at Erlang Factory: [this video] Here is Jamshid Mahdavi talking about their stack as well but focusing on development, testing and shipping code. Some of the stuff they do will surprise you if you've been at a shop with a large QA team and deployment pipelines with many stages. [link]
Description of the WhatsApp stack and how Erlang helped
I believe the issue is that unlike web chats and apps like Telegram, Whatsapp doesn't store conversations on their servers beyond what is required to deliver messages to the required destination device(s). This presents obvious problems with having not just a web client, but any client other than the main device, since the main device is the only store of the existing conversations. New messages sent also need to be synchronized across devices, which I presume is why it's required to keep your phone connected to the Internet. Otherwise Whatsapp would need keep conversations on their servers until they could be synced, which is very much not their model. Details on their architecture: [this video] [link]
Klarna (the European payment processor) and WhatsApp architectures are built on Mnesia. Mnesia is Erlang's built-in distributed database. Due to its age it has a few bad corner cases, that could be fixed if it just had a better backend (the front-end API is nice, well designed, supports transactions, is built-in to the lanauge). So this effort brought in the ability to have a better backend, and make Mnesia a better option as a general purpose distributed database. Here is an talk on how WhatsApp uses Mnesia: Erlang Factory 2014 - That's 'Billion' with a 'B': Scaling to the Next Level at WhatsApp [this video] Here is an example of using Mnesia: [link]
Yes, I understand, I was just wondering if Erlang would be considered a "proven language". From my (limited) Erlang experience, Erlang & Elixir have the same core functionality. Elixir has a "nicer" syntax (I prefer Erlang's syntax but most devs I've talked to like Elixir's). So if Elixir isn't proven but the concurrency model & programming paradigms make sense, maybe Erlang is a good choice. * WhatsApp Scaling to 1 Billion users in erlang: [this video] * [link]
There's a talk one of the engineers gives[1] (that someone else posted here so I'm watching it) about their architecture that's published in March of 2014, and in it he talks about ~550 servers, which includes 250 multimedia servers and 150 chat servers. The only places he mentions 16 is when he talks about the "multimedia database". I think there were 16 sharded database servers. 1: [this video]
Server counts and architecture figures from the talk
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