Production Twitter on one machine? 100Gbps NICs and NVMe are fast

Production Twitter on one machine? 100Gbps NICs and NVMe are fast(thume.ca)

776 points by trishume 3 years ago | 477 comments

I'm going to preface this criticism by saying that I think exercises like this are fun in an architectural/prototyping code-golf kinda way.

However, I think the author critically under-guesses the sizes of things (even just for storage) by a reasonably substantial amount. e.g.: Quote tweets do not go against the size limit of the tweet field at Twitter. Likely they are embedding a tweet reference in some manner or other in place of the text of the quoted tweet itself but regardless a tweet takes up more than 280 unicode characters.

Also, nowhere in the article are hashtags mentioned. For a system like this to work you need some indexing of hashtags so you aren't doing a full scan of the entire tweet text of every tweet anytime someone decides to search for #YOLO. The system as proposed is missing a highly critical feature of the platform it purports to emulate. I have no insider knowledge but I suspect that index is maybe the second largest thing on disk on the entire platform, apart from the tweets themselves.

trishume 3 years ago | |

Quote tweets I'd do as a reference and they'd basically have the cost of loading 2 tweets instead of one, so increasing the delivery rate by the fraction of tweets that are quote tweets.

Hashtags are a search feature and basically need the same posting lists as for search, but if you only support hashtags the posting lists are smaller. I already have an estimate saying probably search wouldn't fit. But I think hashtag-only search might fit, mainly because my impression is people doing hashtag searches are a small fraction of traffic nowadays so the main cost is disk, not sure though.

I did run the post by 5 ex-Twitter engineers and none of them said any of my estimates were super wrong, mainly just brought up additional features and things I didn't discuss (which I edited into the post before publishing). Still possible that they just didn't divulge or didn't know some number they knew that I estimated very wrong.

BeefWellington 3 years ago | | |

I think the difficult part would be that tagging and indexing the relationship between a single tweet and all of its component hashtags (which you would then likely want metrics on to avoid needing to count indexes on, etc.) is where it would really start to inflate.

Another poster dug into some implementation details that I'm not going to go into. I think you could shoehorn it into an extremely large server alongside the rest of your project but then you're looking at processing overhead and capacity management around the indexes themselves starting to become a more substantial part of processing power. Consider that for each tweet you need to break out what hashtags are in it, create records, update indexes, and many times there's several hashtags in a given tweet.

When I last ran analytics on the firehose data (ca. 2015/16) I saw something like 20% of all tweets had 3 or more hashtags. I only remember this fact because I built a demo around doing that kind of analytics. That may have changed over time obviously, however without that kind of information we don't have a good guesstimate even of what storage and index management there looks like. I'd be curious if the former Twitter engineers you polled worked on the data storage side of things. Coming at it from the other end of things, I've met more than a few application engineers who genuinely have no clue how much work a DBA (or equivalent) does to get things stored and indexed well and responsively.

sulam 3 years ago | | |

You’re missing metadata in your size estimates.

busymom0 3 years ago | | |

I don’t think that hashtags are a search only feature. In the posts themselves, the hashtags are clickable to view other tweets. I don’t think that qualifies as a search.

vidarh 3 years ago | |

Biggest problem with this is the lack of considering analytics.twitter.com and ads.twitter.com. Twitter stores event data about everything that happens to a tweet, and lets you target ads with a lot of precision.

While some of those writes may well be acceptable to lose, letting you write to caches, effectively you need to assume there are more analytics events triggering writes to something than there are tweet views.

wtallis 3 years ago | | |

A Twitter-like service that fits on a single server could probably get by with the reduced revenue that comes with not offering obsessively fine-grained analytics and ad targeting.

teaearlgraycold 3 years ago | | |

Analytics data can easily be 90% of your data.

mrtksn 3 years ago | |

Twitter's "linked" tweets seems to be strangely unattached from context.

What I mean is, Twitter seems to be processing data based on whatever it is in the tweet and doesn't maintain some grand coherent database.

So I changed my Twitter handle and opened a new account with my original Twitter handle and to my surprise, I was receiving notifications of engagement with tweets my old account sent previously.

I also heard that a method for spamming Twitter trending topics is to send tweets and delete them quickly.

My impression is that Twitter is big on real time processing. They definitely don't search the entire database for #YOLO tweets, instead they seem to be searching the almost-live stuff and some archived stuff(probably ranked and saved as noteworthy tweets or something).

madeofpalk 3 years ago | | |

Old stuff on twitter is weird. Tweets seem to eventually forget that You specifically liked a tweet, and will allow you to like a tweet again, and the like count will reflect you liking it twice.

Xeoncross 3 years ago | |

> I think the author critically under-guesses the sizes of things (even just for storage) by a reasonably substantial amount.

While true, and not to take away from the parent comment, I've noticed that the size of things is often partially the result of scaling out horizontally. Most companies I've worked at end up with a lot of duplicate records as each subsystem might want a copy or to cache a copy.

vidarh 3 years ago | | |

This is indeed a problem, and one of the reasons to be careful about knowing you can fit things in one machine before taking that approach, because the moment you're forced to move to a distributed model it's rarely one machine to two but one machine to a dozen coupled with a major rearchitecturing effort at just the wrong moment.

It's often fine to start without a fully decoupled system (net present value of the time and money needed to scale out might be far too high), but you need to know whether or not it's likely to come and what to look for so you can start preparing in time.

zxcvbn4038 3 years ago | |

This is absolutely the sort of thing I wish more developers did - and I think the good ones already do. Most of what you find in blogs will work just fine at 1 request per second (OMG! 1M Hits!!) or 10 requests per second (and I think someone did post their “how I scaled to 10 million hits per month” blog to Hacker News once which sounds impressive until you do the math) but when you get into thousands of requests per second you really do need to understand the network stack, the different storage tiers, your choice of algorithms, how to interact with CDNs, etc. a lot more then any blog will tell you.

When interviewing developers I always ask them what is the largest public web site they ever worked on and then probe about performance issues they encountered and how they resolved them in order gague how far along they are in their skill development.

I would never plan to run a production service on a single server just because coordinating changes in the active dataset among two or more production servers often changes your design significantly, and you want to plan for that because the consumer grade hardware we all use has a nasty habit of not working after power cycles (which still tends to be the most strain a system goes through, even in a world of SSD storage).

api 3 years ago | |

I didn't get the impression that this would duplicate the entire functionality of Twitter, just what amounts to the MVP functionality. If you are only talking about the MVP it's at least somewhat plausible with a lot of careful engineering and highly efficient data manipulation.

Adding images, videos, other large attachments, rich search, and all the advertising and billing and analytics stuff would blow this out of the water, but... maybe not by as much as people think...? I would not be surprised if a very performance-engineered version of Twitter could run on a few dozen racks full of beefy machines like this with HPC-grade super-fast fabric interconnects.

I have a strong sense that most large scale systems are way less efficient than what's possible. They trade development ease, modularity, and velocity for performance by using a lot of microservices, flabby but easy and flexible protocols (e.g. JSON over HTTP), slow dynamic languages, and layers of abstraction that could be integrated a lot more tightly.

Of course that may be a rational trade-off if velocity, flexibility, and labor costs and logistics matter more than hardware, power, or data center floor space costs.

BeefWellington 3 years ago | | |

    I didn't get the impression that this would duplicate the entire functionality of Twitter, just what amounts to the MVP functionality. If you are only talking about the MVP it's at least somewhat plausible with a lot of careful engineering and highly efficient data manipulation.

I agree mostly. Where I differ in that I would argue that hashtags were THE thing that Twitter is most known for but that could be a perspective from having been on the platform for forever and a day and I recognize not everyone may make that same association anymore.

dspillett 3 years ago | |

The hashtag index is not going to be any bigger than the tweets storage though, and may be significantly smaller, so this part is not if by an order of magnitude (even a binary one). Assuming something like a common SQL database is used for storage there would be a tags table (one row per unique tag, tag string plus a numeric identifier, indexed by both which bloats the size a bit but it'll still be small) and a link table (one row per tag in a message, containing the tag and message ids). Even if using 64-bit IDs because you don't want to fall over at 2 thousand million messages (or 4, if your DB supports unsigned types or you start at MININT instead of zero or one) then that structure is going to consume about 32 bytes per tag per message (plus some static overheads and a little more for non-leaf index pages of course). In theory this could be the same size as the messages table or even larger (if most messages contain many small tags), but in practise it is going to be significantly smaller.

Yes, this would be big enough to need specifically factoring into a real implementation design. But it would not be big enough to invalidate the proposed idea so I understand leaving it off, at least initially, to simplify the thought experiment.

Similarly to support a message responding to, or quoting, a single other you only need one or two NULLable ID references, 16 bytes per message, which will likely be dwarfed by the message text in the average case. Given it likely makes sense to use something like SQL Server's compression options for data like this the average load imposed will be much smaller than 16 bytes/message.

We are fiddling, fairly insignificantly, measurable but to massively, with constants a & b in O(a+bN) here, so the storage problem is still essentially of order O(N) [where N is total length of the stored messages].

chippiewill 3 years ago | |

> I have no insider knowledge but I suspect that index is maybe the second largest thing on disk on the entire platform, apart from the tweets themselves.

I'd probably go as far to say that the indexes _generally_ at twitter could be larger than the tweets

gorgoiler 3 years ago | |

Isn’t a hashtag just another kind of user account — an account from which anyone can post?

The data structures for the @BeefWellington timeline of tweets and the one for the #BeefWellington timeline of tweets could look roughly the same.

bithaze 3 years ago | | |

Hashtags aren't like user accounts, no - they're strings that are part of a tweet. In theory, a separate data structure shouldn't be needed since you can just search the full text of tweets, but in practice, I don't know how that scales for the number of all-time tweets.

walrus01 3 years ago | |

> I have no insider knowledge but I suspect that index is maybe the second largest thing on disk on the entire platform

I really wonder how much of a challenge this is and how much it occupies, not even talking about disk, but continuing the theoretical exercise in the linked URL, you can get 1U size servers with 2TB of RAM these days.

knorker 3 years ago | |

I would be surprised if indexes were not larger than the raw tweets.

Text search, hashtag index, some structured data for popular tweets, etc...

In order to deliver search results I would not be surprised if tweets are duplicated/denormalized, for quick search/lookup.

redbell 3 years ago | |

> I think the author critically under-guesses the sizes of things (even just for storage) by a reasonably substantial amount.

I want to add another concept that may impact, considerably, the storage, which is "threads". I'm not sure what is the percentage of threads/tweets but what I consider an important factor is that threads do not have a maximum number of characters.

aetimmes 3 years ago |

(Disclaimer: ex-Twitter SRE)

> There’s a bunch of other basic features of Twitter like user timelines, DMs, likes and replies to a tweet, which I’m not investigating because I’m guessing they won’t be the bottlenecks.

Each of these can, in fact, become their own bottlenecks. Likes in particular are tricky because they change the nature of the tweet struct (at least in the manner OP has implemented it) from WORM to write-many, read-many, and once you do that, locking (even with futexes or fast atomics) becomes the constraining performance factor. Even with atomic increment instructions and a multi-threaded process model, many concurrent requests for the same piece of mutable data will begin to resemble serial accesses - and while your threads are waiting for their turn to increment the like counter by 1, traffic is piling up behind them in your network queues, which causes your throughput to plummet and your latency to skyrocket.

OP also overly focuses on throughput in his benchmarks, IMO. I'd be interested to see the p50/p99 latency of the requests graphed against throughput - as you approach the throughput limit of an RPC system, average and tail latency begin to increase sharply. Clients are going to have timeout thresholds, and if you can't serve the vast majority of traffic in under that threshold consistently (while accounting for the traffic patterns of viral tweets I mentioned above) then you're going to create your own thundering herd - except you won't have other machines to offload the traffic to.

jameshart 3 years ago |

Getting everything onto one machine works great until... it no longer fits on one machine.

You add another feature and it requires a little bit more RAM, and another feature that needs a little bit more, and.. eventually it doesn't all fit.

Now you have to go distributed.

And your entire system architecture and all your development approaches are built around the assumptions of locality and cache line optimization and all of a sudden none of that matters any more.

Or you accept that there's a hard ceiling on what your system will ever be able to do.

This is like building a video game that pushes a specific generation of console hardware to its limit - fantastic! You got it to do realtime shadows and 100 simultaneous NPCs on screen! But when the level designer asks if they can have water in one level you have to say 'no', there's no room to add screenspace reflections, the console can't handle that as well. And that's just a compromise you have to make, and ship the game with the best set of features you can cram into that specific hardware.

You certainly could build server applications that way. But it feels like there's something fundamental to how service businesses operate that pushes away from that kind of hyperoptimized model.

TacticalCoder 3 years ago |

TFA, to me, touches about something I've wondered about a very long time ago: what are the implications of CPU and storage growing at much faster rates than human population?

Back in the 486 days you wouldn't be keeping, in RAM, data about every single human on earth (let's take "every single human on earth" as the maximum number of humans we'll offer our services to with on our hypothetical server). Nowadays keeping in RAM, say, the GPS coordinates of every single human on earth (if we had a mean to fetch the data) is doable. On my desktop. In RAM.

I still don't know what the implications are.

But I can keep the coordinates of every single humans on earth in my desktop's RAM.

Let that sink in.

P.S: no need to nitpick if it's actually doable on my desktop today. That's not the point. If it's not doable today, it'll be doable tomorrow.

sethev 3 years ago |

John Carmack tweeted something that made me noodle on this too:

>It is amusing to consider how much of the world you could serve something like Twitter to from a single beefy server if it really was just shuffling tweet sized buffers to network offload cards. Smart clients instead of web pages could make a very large difference. [1]

Very interesting to see the idea worked out in more detail.

[1] https://twitter.com/id_aa_carmack/status/1350672098029694998

threeseed 3 years ago | |

> just shuffling tweet sized buffers to network offload cards

Except that's not what it is doing at all.

It assembles all the Tweets internally, applies an ML model to produce a finalised response to the user.

nimish 3 years ago | | |

Great, staple a few ML accelerators to your NIC. Nvidia sells them! You could build an entire supercomputer style setup 100% optimized for Twitter data movement and computation with COTS hardware IMO.

I strongly doubt that entire datacenters need to be used if and only if Twitter obsessively optimized for hardware usage efficiency over everything else. In reality they don't and make some pretty big compromises to actually get stuff built. Hardware is cheap, people are not.

seritools 3 years ago | | |

> if it really was [which it isn't]

PaulDavisThe1st 3 years ago | | |

which arrives at a browser running Tweak New Twitter as a browser extension, and that strips the response to just what the user actually wants to see. Effficiency!

varjag 3 years ago | |

Isn't that what an OPA sorta kinda does.

drewg123 3 years ago |

How much bandwidth does Twitter use for images and videos? Less than 1.4Tb/s globally? If so, we could probably fit that onto a second machine. We can currently serve over 700Gb/s from a dual-socket Milan based server[1]. I'm still waiting for hardware, but assuming there are no new bottlenecks, that should directly scale up to 1.4Tb/s with Genoa and ConnectX-7, given the IO pathways are all at least twice the bandwidth of the previous generation.

There are storage size issues (like how big is their long tail; quite large I'd imagine), but its a fun thing to think about.

[1] https://people.freebsd.org/~gallatin/talks/euro2022.pdf

cortesoft 3 years ago | |

It is way more than 1.4TBs a second globally.

_zoltan_ 3 years ago | | |

in this specific discussion it's very important to use Tb and TB correctly.

xyzzy123 3 years ago | | |

I wonder how much is api traffic and how much is assets & images.

JosephRedfern 3 years ago | |

I suppose that in practice you'd need to consider burst bandwidth and not just 95/99 percentiles.

quickthrower2 3 years ago | | |

… HN thread that reinvents CDN …

habibur 3 years ago |

He will be up for surprise.

HTTP with connection: keep-open can serve 100k req/sec. But that's for one client being served repeatedly over 1 connection. And this is the inflated number that's published in webserver benchmark tests.

For more practical down to earth test, you need to measure performance w/o keep-alive. Request per second will drop to 12k / sec then.

And that's for HTTP without encryption or ssl handshake. Use HTTPS and watch it fall down to only 400 req / sec under load test [ without connection: keep-alive ].

That's what I observer.

summerlight 3 years ago |

I think many people in this thread are making the mistake of ignoring evolutionary factors in system engineering. If a system doesn't need to adopt or change, lots of things can be much more efficient, easier and simpler, likely the order of 10x~100x. But you gotta appreciate that we're all paid because we need to swap wheels on running trains (or even engines in flying airplanes). A large fraction of demand for redundancy, introspection, abstraction and generalization comes from this.

Why do we want to apply ML at the cost of a significant fleet cost increase? Because it can make the overall system consistently perform against external changes via generalization, thus the system can evolve more cheaply. Why do we want to implement a complex logging layer although it doesn't bring direct gains on system performance? Because you need to inspect the system to understand its behavior and find out where it needs to change. The list can go on and I can give you hundreds of reasons why we need all these apparently unnecessary complexities and overheads can be important for systems' longevity.

I don't deny the existence of accidental complexities (probably Twitter can become 2~3x simpler and cheaper given sufficient eng resource and time), but in many cases you probably won't be able to confidently say if some overheads are accidental or essential since system engineering is essentially a highly predictive/speculative activity. To make this happen, you gotta have a precise understanding of how the system "currently works" to make a good bet rather than re-imagination of the system with your own wish list of how the system "should work". There's a certain value on the latter option, but it's usually more constructive to build an alternative rather than complaining about the existing system. This post is great since the author actually tried to build something to prove its possibility, this knowledge could turn out to be valuable for other Twitter alternatives later on.

jasonhansel 3 years ago |

If you really wanted to run Twitter on one machine at any cost, wouldn't an IBM mainframe be much more practical?

You can even run Linux on them now. The specs he cites would actually be fairly small for a mainframe, which can reach up to 40TB of memory.

I'm not saying this is a good idea, but it seems better than what the OP proposes.

varunkmohan 3 years ago |

Good analysis. Obviously, this doesn't handle cases like redundancy and doesn't handle some of other critical workloads the company has. However, it does show how much real compute bloat these companies actually have - https://twitter.com/petrillic/status/1593686223717269504 where they use 24 million vcpus and spend 300 million a month on cloud.

PragmaticPulp 3 years ago | |

> However, it does show how much real compute bloat these companies actually have

No, it doesn’t. It’s a fun exercise in approaching Twitter as an academic exercise. It ignores all of the real-world functionality that makes it a business rather than a toy.

A lot of complicated businesses are easy to prototype out if you discard all requirements other than the core feature. In the real world, more engineering work often goes to ancillary features that you never see as an end user.

varunkmohan 3 years ago | | |

Genuinely asking, why do you think Twitter needs 24 million vcpus to run?

This is not apples to apples but Whatsapp is a product that entirely ran on 16 servers at the time of acquisition (1.5 billion users). It really begs the question why Twitter uses so much compute if there are companies that have operated significantly more efficiently. Twitter was unprofitable during acquisition and spent around half their revenue on compute, maybe some of these features were not really necessary (but were just burning money)?

judge2020 3 years ago | |

On the other hand, Twitter does (or did) handle over 450 million monthly active users (based on stats websites), with a target for 315 monetizable daily active users (based on their earnings calls pre-privatization). Handling that amount of concurrency and beaming millions of tweets a day to home feeds and notifications is going to be logistically hard.

WJW 3 years ago | | |

Is that 315 million monetizable DAUs? That sounds like a lot if the total is only 450 MAU. OTOH, 315k DAU seems like it wouldn't be enough to pay the bills.

therealdrag0 3 years ago | | |

Anyone know if MAU includes API clients? Cuz there’s a lot of apps built on top of twitter that pull data right? Thats add a lot of traffic too.

varunkmohan 3 years ago | | |

Posted this on a comment above but systems like Whatsapp likely sent an insane amount of data as well but used only 16 servers over 1.5 billion users at time of acquisition. Modern NICs can handle millions of requests a second - I still feel there is a lot of excess here.

agilob 3 years ago |

The title reminded me about this https://www.phoronix.com/news/Netflix-NUMA-FreeBSD-Optimized (2019) and this 2 years later: https://papers.freebsd.org/2021/eurobsdcon/gallatin-netflix-... (2021)

syoc 3 years ago | |

Latest version: http://nabstreamingsummit.com/wp-content/uploads/2022/05/202... (2022)

PragmaticPulp 3 years ago |

Very cool exercise. I enjoyed reading it.

I see a lot of comments here assuming that this proves something about Twitter being inefficient. Before you jump to conclusions, take a look at the author’s code: https://github.com/trishume/twitterperf

Notably absent are things like serving HTTP, not to even mention HTTPS. This was a fun exercise in algorithms, I/O, and benchmarking. It wasn’t actually imitating anything that resembles actual Twitter or even a usable website.

trishume 3 years ago | |

Which I think I'm perfectly clear about in the blog post. The post is mostly about napkin math systems analysis, which does cover HTTP and HTTPS.

I'm now somewhat confident I could implement this if I tried, but it would take many years, the prototype and math is to check whether there's anything that would stop me if I tried and be a fun blog post about what systems are capable of.

I've worked on a team building a system to handle millions of messages per second per machine, and spending weeks doing math and building performance prototypes like this is exactly what we did before we built it for real.

PragmaticPulp 3 years ago | | |

> Which I think I'm perfectly clear about in the blog post.

Of course. I was commenting here to counter all of the comments declaring that this proves Twitter doesn’t need all of their servers, etc.

It’s a fun article, but the comments here interpreting it as proving something about Twitter engineers being bad are kind of depressing.

mgaunard 3 years ago |

All web and cloud technologies are inherently inefficient, and most programmers don't know networking or even how hardware works sufficiently well to optimize for high througput and low-latency.

There was an article just yesterday about how Jane Street had developed an internal exchange way faster than any actual exchange by building it from the ground up, thinking about how the hardware works and how agents can interact with it.

Modern software like Slack or Twitter are just reinventing what IRC or BBS did in the past, and those were much leaner, more reliable and snappier than their modern counterparts, even if they didn't run at the same scale.

It wouldn't be surprising at all that you could build something equivalent to Twitter on just one beefy machine, maybe two for redundancy.

drowsspa 3 years ago | |

I'm always reminded of that tweet from @SwiftOnSecurity https://mobile.twitter.com/SwiftOnSecurity/status/1485822027...

> Once you understand your computer has 16 cores running at 3GHz and yet doesn't boot up in .2 nanoseconds you understand everything they have taken from you.

With their infinite VC money at their disposal, and with their programmers having 100 GHz machines with thousands of cores, 128 TB of RAM and FTL internet connections, tech companies don't really have any incentive to actually reduce bloat.

Edit: it's still quite sad. I feel like we had languages with a way better future, and more promising programming architectures, back in the 80s.

Nextgrid 3 years ago | | |

It's less about the lack of incentive to reduce bloat and more about the incentive to create bloat in order to justify one's position and pad the resume for the next positions.

kaba0 3 years ago | | |

Booting has fundamental hardware limits, though. Will that harddrive or whatever really reply all in 0.2 ns?

therealdrag0 3 years ago | |

It’s “easy” to optimize for speed when you build from the ground up with no real customers or feature requirements that you can’t just conveniently ignore.

saagarjha 3 years ago | |

> It wouldn't be surprising at all that you could build something equivalent to Twitter on just one beefy machine, maybe two for redundancy.

The blog post kind of gets a very cut-down version of Twitter running on a single machine. Actual Twitter absolutely would not work.

samsquire 3 years ago |

I recommend this table of latency figures for any software engineer:

https://gist.github.com/jboner/2841832

Essentially IO is expensive except within a datacenter but even in a data center, you can do a lot of loop iterations in a hot loop in the time it takes to ask a server for something.

There is a whitepaper which talks about the raw throughput and performance of single core systems outperforming scalable systems. These should be required reading of those developing distributed systems.

http://www.frankmcsherry.org/assets/COST.pdf A summary: http://dsrg.pdos.csail.mit.edu/2016/06/26/scalability-cost/

SilverBirch 3 years ago |

I think one of the under-estimated interesting points of twitter as a business is that this is the core. Yes, Twitter is 140 characters, it's got "300m users" which is probably 5m real heavy users. So yes, you could do a lot of "140 characters, a few tweets per person, few million users" on very little hardware. But that's why Twitters a shit business!

How much RAM did your advertising network need? Becuase that is what makes twitter a business! How are you building your advertiser profiles? Where are you accounting for fast roll out of a Snapchat/Instagram/BeReal/Tiktok equivalent? Oh look, your 140 characters just turned into a few hundreds megs of video that you're going to transcode 16 different ways for Qos. Ruh Roh!

How are your 1,000 engineers going to push their code to production on one machine?

Almost always the answer to "do more work" or "buy more machines" is "buy more machines".

All I'm saying is I'd change it to "Toy twitter on one machine" not Production.

reacharavindh 3 years ago | |

The author claimed early on, and very clearly that this was a fun exercise of thought and engineering rather than saying “Look this is how Twitter should be run”. After all this is Hacker News. Such exercises, and engaging other hackers to pick something out of there is how we progress(and get our tickles). So, may be instead think about how one could tackle the advertising/indexing needs in a similar fashion(could it be done in just another server? 5 more servers?)..

SilverBirch 3 years ago | | |

Yeah I completely get that, but I think a lot of hacker news tends to think of companies as the sum of their engineering resources, rather than what they really are. Which is weird, because it's hosted by YC which is meant to be the polar opposite of that. The point of my comment was that the decision making that lead to Twitter's design actually make sense when you understand the business model behind them. It's not good enough to just run a webpage.

Nextgrid 3 years ago | |

If it actually takes you a single machine to run this, you don't really need an advertising network to fund it. Out of 5M users (let alone the theoretical 300M) there will be enough people who'd be happy to pay for verification or an exclusive badge on their profile.

> How are your 1,000 engineers going to push their code to production on one machine?

That might actually be the reason why Twitter barely keeps afloat. 1k engineers for a product that's already built and hasn't fundamentally changed nor evolved in years makes me wonder what business value those engineers actually provide.

jiggawatts 3 years ago |

Something I've found a lot modern IT architects seem to ignore is "write amplification" or the equivalent effect for reads.

If you have a 1 KB piece of data that you need to send to a customer, ideally that should require less than 1 KB of actual NIC traffic thanks to HTTP compression.

If processing that 1 KB takes more than 1 KB of total NIC traffic within and out of your data centre, the you have some level of amplification.

Now, for writes, this is often unavoidable because redundancy is pretty much mandatory for availability. Whenever there's a transaction, an amplification factor of 2-3x is assumed for replication, mirroring, or whatever.

For reads, good indexing and data structures within a few large boxes (like in the article) can reduce the amplification to just 2-3x as well. The request will likely need to go through a load balancer of some sort, which amplifies it, but that's it.

So if you need to process, say, 10 Gbps of egress traffic, you need a total of something like 30 Gbps at least, but 50 Gbps for availability and handling of peaks.

What happens in places like Twitter is that they go crazy with the microservices. Every service, every load balancer, every firewall, proxy, envoy, NAT, firewall, and gateway adds to the multiplication factor. Typical Kubernetes or similar setups will have a minimum NIC data amplification of 10x on top of the 2-3x required for replication.

Now multiply that by the crazy inefficient JSON-based protocols, the GraphQL, an the other insanity layered on to "modern" development practices.

This is how you end up serving 10 Gbps of egress traffic with terabits of internal communications. This is how Twitter apparently "needs" 24 million vCPUs to host text chat.

Oh, sorry... text chat with the occasional postage-stamp-sized, potato quality static JPG image.

bitbckt 3 years ago |

Regarding tweet distribution, I was one of the folks who built the first scalable solution to this problem at Twitter (called Haplocheirus). We used the Yahoo “Feeding Frenzy” design, pushing tweets through a Redis-backed caching layer.

Feel free to continue using that (historically-correct) answer in interviews. :P

justapassenger 3 years ago |

Saying this is production Twitter is like saying that rsync is a Dropbox.

betaby 3 years ago | |

rsync is way better than Dropbox for large volumes / high speed file transfers . So, yes.

Cyph0n 3 years ago |

More like “barebones, in-memory, English-only Twitter clone on one machine”.

Edit: Still a nice writeup!

Dylan16807 3 years ago | |

What strikes you as English-only about it?

Cyph0n 3 years ago | | |

My bad - not English, but ASCII. The assumed max tweet size is in bytes rather than (UTF) characters.

halfmatthalfcat 3 years ago | | |

As in the instance will only be tuned to serve one language?

Tepix 3 years ago |

The new EPYC servers can be filled with 6TB of RAM and 96 cores per socket. Fun times.

kissgyorgy 3 years ago |

I feel like people writing posts like this never worked in a big team at a big company on a big project. It is so obviously impossible to do this and Twitter has so many more features users will never even see, but sure, re-implement it in a couple hundred lines of Rust and Twitter will be saved...

threeseed 3 years ago | |

HN unfortunately has a lot of people like George Hotz.

They are knowledgeable to a certain level but they simply aren't great engineers who almost always are humble, cautious, thoughtful and respectful of the intentions behind what other engineers build.

Anyone who thinks they can jump in and replace any tech stack without an extensive deep dive of the business requirements, design decisions, cost constraints, resource limitations etc that drove the choices deserves the pain and unemployment that inevitably follows.

LastTrain 3 years ago | |

Every intern proposes something cute like this in my org, every year.

ricardobeat 3 years ago |

> super high performance tiering RAM+NVMe buffer managers which can access the RAM-cached pages almost as fast as a normal memory access are mostly only detailed and benchmarked in academic papers

Isn't this exactly what modern key value stores like RocksDB, LMDB etc are built for?

morphle 3 years ago |

Why not a single FPGA with 100Gbps ethernet or pcie with NVM attached? Around $5K for the hardware and $5K for the traffic per month. The software would be a bit trickier to write, but you now get 100x performance for the same price

keewee7 3 years ago |

In the coming years we will probably see a lot of complicated microservice architectures be replaced by well-designed and optimized Rust (and modern C++) monoliths that use simple replication to scale horizontally.

pixl97 3 years ago | |

Replication and simple never belong in the same sentence. DNS which is one of the simplest replication systems I know of has its own complex failure modes.

qaq 3 years ago | | |

It can be outsourced complexity as in just using horizontally scalable database like Spanner, CockroachDB etc.

hinkley 3 years ago | |

Yes, but it won't be because they're better, it'll be because they're different.

Like last time, and the time before that, and the time before that, and the time before that.

Hasu 3 years ago | | |

You're right, no progress has ever been made in software, no new ideas are better than any old ideas, and it's fads all the way down. The only difference between software today and software in 1980 is that today's software is hip and software from 1980 is square.

I understand the frustration with flavor of the week "best practices" and the constant churn of frameworks and ideas, but software engineering as a practice IS moving forward. The difficulty is separating the good ideas (CI/CD, for example) from the trends (TDD all the things all the time) ahead of time.

Nextgrid 3 years ago | |

You don't even need Rust or highly optimised code. Just moving the existing code from "vCPUs" and networked storage to real CPUs with direct-attach NVME storage will be enough for most purposes. (btw you can do that now, just get yourself a beefy server at OVH/Hetzner and play around with it)

gravypod 3 years ago |

As a side note:

> I did all my calculations for this project using Calca (which is great although buggy, laggy and unmaintained. I might switch to Soulver) and I’ll be including all calculations as snippets from my calculation notebook.

I've always wanted an {open source, stable, unit-aware} version of something like this which could be run locally or in the browser (with persistence on a server). I have yet to find one. This would be a massive help to anyone who does systems design.

eatonphil 3 years ago |

This is a great exercise in napkin math, even with constraints you've set for yourself that don't fully approximate Twitter (yet). Thanks!

pengaru 3 years ago |

This post reminds me of an experience I had in ~2005 while @ Hostway Chicago.

Unsolicited story time:

Prior to my joining the company Hostway had transitioned from handling all email in a dispersed fashion across shared hosting Linux boxes with sendmail et al, to a centralized "cluster" having disparate horizontally-scaled slices of edge-SMTP servers, delivery servers, POP3 servers, IMAP servers, and spam scanners. That seemed to be their scaling plan anyways.

In the middle of this cluster sat a refrigerator sized EMC fileserver for storing the Maildirs. I forget the exact model, but it was quite expensive and exotic for the time, especially for an otherwise run of the mill commodity-PC based hosting company. It was a big shiny expensive black box, and everyone involved seemed to assume it would Just Work and they could keep adding more edge-SMTP/POP/IMAP or delivery servers if those respective services became resource constrained.

At some point a pile of additional customers were migrated into this cluster, through an acquisition if memory serves, and things started getting slow/unstable. So they go add more machines to the cluster, and the situation just gets worse.

Eventually it got to where every Monday was known as Monday Morning Mail Madness, because all weekend nobody would read their mail. Then come Monday, there's this big accumulation of new unread messages that now needs to be downloaded and either archived or deleted.

The more servers they added the more NFS clients they added, and this just increased the ops/sec experienced at the EMC. Instead of improving things they were basically DDoSing their overpriced NFS server by trying to shove more iops down its throat at once.

Furthermore, by executing delivery and POP3+IMAP services on separate machines, they were preventing any sharing of buffer caches across these embarrassingly cache-friendly when colocated services. When the delivery servers wrote emails through to the EMC, the emails were also hanging around locally in RAM, and these machines had several gigabytes of RAM - only to never be read from. Then when customers would check their mail, the POP3/IMAP servers always needed to hit the EMC to access new messages, data that was probably sitting uselessly in a delivery server's RAM somewhere.

None of this was under my team's purview at the time, but when the castle is burning down every Monday, it becomes an all hands on deck situation.

When I ran the rough numbers of what was actually being performed in terms of the amount of real data being delivered and retrieved, it was a trivial amount for a moderately beefy PC to handle at the time.

So it seemed like the obvious thing to do was simply colocate the primary services accessing the EMC so they could actually profit from the buffer cache, and shut off most of the cluster. At the time this was POP3 and delivery (smtpd), luckily IMAP hadn't taken off yet.

The main barrier to doing this all with one machine was the amount of RAM required, because all the services were built upon classical UNIX style multi-process implementations (courier-pop and courier-smtp IIRC). So in essence the main reason most of this cluster existed was just to have enough RAM for running multiprocess POP and SMTP sessions.

What followed was a kamikaze-style developed-in-production conversion of courier-pop and courier-smtp to use pthreads instead of processes by yours truly. After a week or so of sleepless nights we had all the cluster's POP3 and delivery running on a single box with a hot spare. Within a month or so IIRC we had powered down most of the cluster, leaving just spam scanning and edge-SMTP stuff for horizontal scaling, since those didn't touch the EMC. Eventually even the EMC was powered down, in favor of drbd+nfs on more commodity linux boxes w/coraid.

According to my old notes it was a Dell 2850 w/8GB RAM we ended up with for the POP3+delivery server and identical hot spare, replacing racks of comparable machines just having less RAM. >300,000 email accounts.

icedchai 3 years ago | |

Sounds reasonable. Back in the 90's, at an early ISP, we had about 6,000 POP3/SMTP accounts on a FreeBSD box with 128 megs of RAM and a decent (for 1996) SCSI disk. I recall we did some kernel tuning (max processes and file handles, maybe?)

BenjiWiebe 3 years ago | |

Thank you for writing this.

spullara 3 years ago |

When I was working there I implemented my patent during a hack week (given a set of follows return the list of matching tweet ids, very similar to his prototype):

https://patents.google.com/patent/US20120136905A1/en (licensed under Innovators Patent Agreement, https://github.com/twitter/innovators-patent-agreement)

I could have definitely served all the chronological timeline requests on a normal server with lower latency that the 1.1 home timeline API. There are a bunch of numbers in the calculations that he is doing that are off but not by an order of magnitude. The big issue is that since I left back then Twitter has added ML ads, ML timeline and other features that make current Twitter much harder to fit on a machine than 2013 Twitter.

steviesands 3 years ago | |

A few thoughts. The first is, are we asking the wrong questions? Should it be, "If I spend 10m on hardware for predicting ads (storage/compute) that generates 25m in revenue, should I buy the hardware?". Sure, we can "minify" twitter, and it's a wonderful thought experiment, but it seems devoid of the context of revenue generation.

The second is, it's interesting to understand social media industry wide infra cost per user. If you look at FB, Snap, etc. they are within all within an order of magnitude in cost per DAU (DAU / Cost of revenue) of each other. This can be verified via 10-ks which show Twitter at $1.4B vs. SNAP 1.7B Cost of Revenue. The major difference between the platforms is revenue per user, with FB being the notable exception.

Also would you summarize the patent/architecture? The link is a bit opaque/hard to read.

Note: Cost of Revenue does also include TAC and revenue sharing (IIRC) and not just Infra costs but in theory they would also be at similar levels.

eg. SNAPs 10-k https://d18rn0p25nwr6d.cloudfront.net/CIK-0001564408/da8288a...

spullara 3 years ago | | |

The basic idea of the system was to scan a reverse chronologically ordered list of "user id, tweet id", filtering out any tweet whose user wasn't in the follow set (or sets in the case of scan sharing) until you retrieved enough tweets for the timeline request. There are a bunch of variants in the patent, but that is the basic idea. At the time, I estimated that Twitter was spending 80% of its CPU time in the DC doing thrift/json/html serialization/deserialization and mused about merging all the separate services into a single process. Lot's of opportunity for optimization.

KaiserPro 3 years ago |

> A friend points out that IBM Z mainframes have a bunch of the resiliency software and hardware infrastructure I mention,

Sure its expensive, and you have to deal with IBM, who are either domain experts or mouth breathers. Sure it'll cost you $2m but!

the opex of running a team of 20 engineers is pretty huge. Especially as most of the hard bits of redundant multi-machine scaling are solved for you by the mainframe. Redundancy comes for free(well not free, because you are paying for it in hardware/software)

Plus, IBM redbooks are the golden standard of documentation. Just look at this: https://www.redbooks.ibm.com/redbooks/pdfs/sg248254.pdf its the redbook for GPFS (scalable multi-machine filesystem, think ZFS but with a bunch more hooks.)

Once you've read that, you'll know enough to look after a cluster of storage.

viraptor 3 years ago |

This is in no way a criticism of the analysis. But what I think is a hidden cost of an idea like this (that hasn't been pointed out) is the ability to extend the features. With a tightly integrated system like that you may want to add a frobnicator as a test - now that whole system would need to change to accommodate that, because all the timeline processing happens more or less in memory. Making things external / network based adds overhead, but makes plugging in / removing an extra feature much easier. If you count the cost of work required for making changes, then burning money on the "unnecessary" horizontal scaling may not be a bad idea. Wanna add new ads analytics? Just plug into this common firehouse/summary endpoint without worrying about the internals. Wanna test a new implementation of some component? Run both in parallel, plugging into same inputs. Etc.

firstSpeaker 3 years ago |

This is one of the most interesting part of the whole post for me:

Through intense digging I found a researcher who left a notebook public including tweet counts from many years of Twitter’s 10% sampled “Decahose” API and discovered the surprising fact that tweet rate today is around the same as or lower than 2013! Tweet rate peaked in 2014 and then declined before reaching new peaks in the pandemic. Elon recently tweeted the same 500M/day number which matches the Decahose notebook and 2013 blog post, so this seems to be true! Twitter’s active users grew the whole time so I think this reflects a shift from a “posting about your life to your friends” platform to an algorithmic content-consumption platform.

So, the number of writes has been the same for a good long while.

swellguy 3 years ago |

You have violated the number one rule in Silicon Valley: If it doesn't take at least "N" "engineers" to "solve" a problem who report directly to moi, then how am I relevant? So I agree this is entirely possible, but no one would build this with any funding.

henning 3 years ago |

No rate limiting, API data, quote tweets, view count, threads, likes, mentions, notifications, ads, video, images, account blocking (permanent or TTL), account muting (permanent or TTL), word filtering (permanent or TTL), moderation/reporting, user profile storage, or the fact that tweets that display show more than just the tweet itself. No mention that tweet activity all occurs concurrently and therefore the loading script is not at all a realistic estimate of real activity.

But sure, go ahead and take this as evidence that 10 people could build Twitter as I'm sure that's what will happen to this post. If that's true, why haven't they already done so? It should only take a couple weeks and one beefy machine, right?

siliconc0w 3 years ago |

Enjoyed the write up, would be curious to see the twitter spend broken down by functionality given all the extra stuff they do. I imagine it's a non-linear relationship where the company has to burn more and more cash with every new feature (and esp things like Advertising which you need once your spend surpasses what a simple subscription can offer), more scale adds more complexity, bureaucracy and overhead (management, hr & recruiting, legal&accounting, etc). While it's likely there is waste (some of which is inevitable, see 'overhead' above) a super bareboens twitter can maybe run within one beefy machine but a 'real' twitter ends up needing millions + lots of people.

systemvoltage 3 years ago |

It's worth noting Stackoverflow's production arch: https://stackexchange.com/performance

mcqueenjordan 3 years ago |

Fun thought experiment! I can't help but be reminded of the Good Will Hunting quote, though:

SEAN: So if I asked you about art you’d probably give me the skinny on every art book ever written. Michelangelo? You know a lot about him. Life’s work, political aspirations, him and the pope, sexual orientation, the whole works, right? But I bet you can’t tell me what it smells like in the Sistine Chapel. You’ve never actually stood there and looked up at that beautiful ceiling. Seen that.

knubie 3 years ago |

> I’m not sure how real Twitter works but I think based on Elon’s whiteboard photo and some tweets I’ve seen by Twitter (ex-)employees it seems to be mostly the first approach using fast custom caches/databases and maybe parallelization to make the merge retrievals fast enough.

I think Twitter does (or at some point did) use a combination of the first and second approach. The vast majority of tweets used the first approach, but tweets from accounts with a certain threshold of followers used the second approach.

fleddr 3 years ago |

"Through intense digging I found a researcher who left a notebook public including tweet counts from many years of Twitter’s 10% sampled “Decahose” API and discovered the surprising fact that tweet rate today is around the same as or lower than 2013! Tweet rate peaked in 2014 and then declined before reaching new peaks in the pandemic. Elon recently tweeted the same 500M/day number which matches the Decahose notebook and 2013 blog post, so this seems to be true! Twitter’s active users grew the whole time so I think this reflects a shift from a “posting about your life to your friends” platform to an algorithmic content-consumption platform."

I know it's not the core premise of the article, but this is very interesting.

I believe that 90% of tweets per day are retweets, which supports the author's conclusion that Twitter is largely about reading and amplifying others.

That would leave 50 million "original" tweets per day, which you should probably separate as main tweets and reply tweets. Then there's bots and hardcore tweeters tweeting many times per day, and you'll end up with a very sobering number of actual unique tweeters writing original tweets.

I'd say that number would be somewhere in the single digit millions of people. Most of these tweets get zero engagement. It's easy to verify this yourself. Just open up a bunch of rando profiles in a thread and you'll notice a pattern. A symmetrical amount of followers and following typically in the range of 20-200. Individual tweets get no likes, no retweets, no replies, nothing. Literally tweeting into the void.

If you'd take away the zero engagement tweets, you'll arrive at what Twitter really is. A cultural network. Not a social network. Not a network of participation. A network of cultural influencers consisting of journalists, politicians, celebrities, companies and a few witty ones that got lucky. That's all it is: some tens of thousands of people tweeting and the rest leeching and responding to it.

You could argue that is true for every social network, but I just think it's nowhere this extreme. Twitter is also the only "social" network that failed to (exponentially) grow in a period that you might as well consider the golden age of social networks. A spectacular failure.

Musk bought garbage for top dollar. The interesting dynamic is that many Twitter top dogs have an inflated status that cannot be replicated elsewhere. They're kind of stuck. They achieved their status with hot take dunks on others, but that tactic doesn't really work on any other social network.

thriftwy 3 years ago |

I remember Stack Overflow running on a single Windows Server box and mocking fellow LAMP developers with their propensity towards having dozens of VMs to same effect.

That was some time ago, though.

Nextgrid 3 years ago | |

I believe that's still the case: https://stackexchange.com/performance

9 web servers to serve the entire network. I wish more developers were aware of just how performant modern (non-cloud) hardware is.

sgt 3 years ago | | |

SO only has two proxy servers, in which one is a failover? I bet a lot of people wouldn't believe that if you casually mentioned it.

VLM 3 years ago |

Interesting optimization idea: If 95% of the users are bots, and your ML algorithms are smart enough to figure who's bot and who's bio, you could save a lot of traffic by not publishing tweets to bots as no one is going to read them anyway. Of course if that traffic included advertisements, you'd also lose 95% of your ad revenue.

The ultimate extension of this "run it all on one machine" meme would be to run the bots on the single machine along with the service.

Hasu 3 years ago | |

> Of course if that traffic included advertisements, you'd also lose 95% of your ad revenue.

Not so, serving an ad to a bot gains you no revenue, because ad networks charge for clicks, not impressions. If a significant percentage of your ad clicks are from bots, you're running a defective advertising platform and won't have customers for long regardless.

toteno 3 years ago | | |

I agree with you overall.

Just a small nitpick: most ad networks optimize for price of impression, so at the end of the day they charge for impressions (just not always directly).

If your ad has low click rate and average price then it just won't be shown, because it's more profitable for an ad network to show ad with better click rate or with better price (i.e. with better price for impression) .

mizzao 3 years ago |

While you might not want to do this with actual Twitter, any sort of high-performance computing workload can run substantially faster on a single optimized machine than on a distributed computing environment.

I learned this the hard way when I was running a medium-sized MapReduce job in grad school that was over 100x faster when run as a local direct computation with some numerical optimizations.

robluxus 3 years ago | |

Obligatory "you don't need Hadoop": https://news.ycombinator.com/item?id=14401399

snotrockets 3 years ago |

I ask candidates I interview to design a certain service. Most ask about scale, to which I like to direct the question back at them: it's going to be huge. As big as Twitter. How big would that be, do you think?

Most then suggest scale that would make the service run comfortable from a not-too powerful machine, and then go to design data-center spanning distributed service.

mattbillenstein 3 years ago |

Interesting abstract system design type problem - I think it becomes difficult if you have to shard the data though because all the assumptions about the hot set being in RAM break all the performance guarantees now I think... Which is I think basically what Twitter's existing backend has to deal with.

z3t4 3 years ago |

A Twitter clone could probably run in a teenagers closet, but not after it has been iterated by 10000 monkeys.

fortran77 3 years ago |

I've thought about this problem, too, and blocklists seem like a hard problem to implement efficiently. I have a few thousand users blocked, and several hundred keywords, phrases and emoji. How are these processed efficiently?

jeffbee 3 years ago |

I like this kind of exercise. One thing I am not seeing is analytics, logs and so forth that as I understand it are significant portions of Twitter's production cost story.

lazyasciiart 3 years ago | |

If it's this cheap to run you don't need analytics because you don't need to monetize it, and if it's this simple you don't need logs because it'll all work correctly the first time!

kevingadd 3 years ago | | |

"You don't need to monetize it" who's going to fund your Twitter-as-a-charity? What happens when the free money goes away? Businesses have to pay the bills eventually one way or another, you need to plan for that in advance

jeffbee 3 years ago | | |

How do you implement trending without logs?

tluyben2 3 years ago | |

Anyone have a complete list of functional blocks that form Twitter? Beyond the obvious and what we see?

Marazan 3 years ago | | |

You need the blocks for the obvious for what we see because it is not necessarily obvious to everyone.

Over the last couple of months I've seen comments that summarise Twitter as a read-only service that doesn't have any real time posting requirements and similarly other comments that treat it as a write-only service with no real time read / fast search requirements.

Without _all_ the blocks even the simple surface level Twitter will have complexity people miss.

betimsl 3 years ago |

I'm just curious as to what kind of motherboard this personal computer is going to have? I'm asking this because of the limit on PCIe bandwidth. 100gbit NIC? How?

betaby 3 years ago | |

I have some servers in production with https://www.nvidia.com/en-us/networking/ethernet/connectx-6/ There is a an AUX card to load PCI lanes properly, like on this picture

https://lenovopress.lenovo.com/assets/images/LP1195/Mellanox...

tpmx 3 years ago | |

Serving Netflix Video Traffic at 800Gb/s and Beyond [pdf]

https://news.ycombinator.com/item?id=32519881

sammy2255 3 years ago |

A bit out of touch to think that the bandwidth alliance will let you push 500TB a month through them for free

surume 3 years ago |

Ops: "One of our instances went down" Everyone else: "Gaaaahhh"

irq 3 years ago |

Excellent article! I wish the font size on mobile was bigger.

truth_seeker 3 years ago |

How to subdue the cost of abstraction. Very well explained !

Halan 3 years ago |

Let’s hope Elon doesn’t read this

castratikron 3 years ago |

Does 4chan fit on one machine?

lightlyused 3 years ago |

It is all fun running on one machine until a capacitor leaks or something else goes south.

wonnage 3 years ago |

This doesn’t seem to support fetching a specific tweet by id?

sgt 3 years ago | |

No need, for those you can display a "Twitter is experiencing issues" or something similar. That will encourage the user to return to the main app and enjoying infinite scroll.

sitkack 3 years ago |

I am both embarrassed and disappointed with the negativity this post has attracted.

A litanny of "gotchas", where someone attempts to best the OP. What about x, y and z? It can't possibly scale. Twitter is so much more than this, etc.

The OP isn't making the assertion that Twitter should replace their current system with a single large machine.

The whole thread paints a picture of HN like it is full of a bunch of half-educated, uncreative negative brats.

To the people that encourage a fun discussion, thank you! Great things are not built by people who only see how something cannot possibly work.

Xeoncross 3 years ago | |

I must admit, as a bystander I'm torn here.

I love that Tristan put out this post and made it so detailed with plenty of assumptions to cover. I also like to hear about possible issues and assumptions which the crowd calls out. Even naysayers can be helpful.

I want both, but I don't want to crowd to go to far and kill the desire to produce this kind of content.

trishume 3 years ago | | |

To me most interesting are factors I didn't consider in features I did cover. Next most interesting are features I didn't cover which are kinda core to Twitter being good, and also pose interesting performance problems, like the person who mentioned spam/abuse detection. After that are non-core features which pose interesting performance problems that are different from problems I already covered.

The comments that I think aren't contributing much are ones that mention features that I didn't cover but make no attempt to argue that they're actually hard to implement efficiently, or that assert that because I didn't implement something it isn't feasible to make as fast as I calculate, without arguing what would actually stop me from implementing something that efficient. Or ones who repeat that this isn't practical, which I say at length in the post.

racingmars 3 years ago | | |

> I want both, but I don't want to crowd to go to far and kill the desire to produce this kind of content.

I think it's easy to have both. It's all about the tone of the responses.

For example, instead of "your assumptions are wrong, this would collapse because X" or "this is dumb because real Twitter does Y which yours doesn't handle," I think responses could be framed as:

"Wow, neat thought experiment! If I were to approach this same problem, I might make an allowance of more than 280 bytes of storage per tweet to allow for additional metadata that is probably needed to make everything work together; I wonder if that can be accommodated with an even beefier big computer?"

Or "What a great writeup of building a simplified Twitter! After the features you've accounted for, the next most important feature of Twitter for me personally is Y. What kinds of things would we have to do to stretch your idea to handle that? [or, I bet with the addition of X we could make that happen in this setup too!]"

I think many criticisms could be turned into constructive positive additions to the original article versus attacks against the idea of the article.

kissgyorgy 3 years ago | |

If he would have called it anything else but Twitter and stated it is theoretical or a hobby project or something for fun, I would be fine with it, but don't mention Twitter anywhere near because it has nothing to do with Twitter.

idealmedtech 3 years ago | | |

A post like this without Twitter in the title isn't nearly as provocative, and would probably have died in /new. It's because of the authors decision to scope out a Twitter clone that we're having this discussion!

bcrosby95 3 years ago | | |

It has nothing to do with Twitter, except if you described the features of the project, the vast majority of people would say "oh, its like Twitter?"

mcenedella 3 years ago | |

Amen!

LastTrain 3 years ago | |

Yours is by far the most negative post in this thread - in tone and content. I’m sorry but this kind of proposal is intentionally controversial and invites picking through it like this. Glad the author has thicker skin than you.

andrewstuart 3 years ago |

Most projects I encounter these days instantly reach for kubernetes, containers and microservices or cloud functions.

I find it much more appealing to just make the whole thing run on one fast machine. When you suggest this tend to people say "but scaling!", without understanding how much capacity there is in vertical.

The thing most appealing about single server configs is the simplicity. The more simple a system easy, likely the more reliable and easy to understand.

The software thing most people are building these days can easily run lock stock and barrel on one machine.

I wrote a prototype for an in-memory message queue in Rust and ran it on the fastest EC2 instance I could and it was able to process nearly 8 million messages a second.

You could be forgiven for believing the only way to write software is is a giant kablooie of containers, microservices, cloud functions and kubernetes, because that's what the cloud vendors want you to do, and it's also because it seems to be the primary approach discussed. Every layer of such stuff add complexity, development, devops, maintenance, support, deployment, testing and (un)reliability. Single server systems can be dramatically mnore simple because you can trim is as close as possible down to just the code and the storage.

britneybitch 3 years ago |

> colo cost + total server cost/(3 year) => $18,471/year

Meanwhile the company I just left was spending more than this for dozens of kubernetes clusters on AWS before signing a single customer. Sometimes I wonder what I'm still doing in this industry.

traceroute66 3 years ago | |

> spending more than this for dozens of kubernetes clusters on AWS before signing a single customer

Yup.

Cloud is 21st century Nickel & Diming.

Sure it sounds cheap, everything is priced in small sounding cents per unit.

But then it very quickly becomes a compounding vicious circle ... a dozen different cloud tools, each charged at cents per unit, those units often being measured in increments of hours....next thing you know is your cloud bill has as many zeros on the end of it as the number of cloud services you are using. ;-)

And that's before we start talking about the data egress costs.

With colo you can start off with two 1/4 rack spaces at two different sites for resilience. You can get quite a lot of bang for your buck in a 1/4 rack with today's kit.

jacobsenscott 3 years ago |

Nice. While there may be some impracticalities to actually doing this for twitter, 99% of the software out there could run on a fraction of a single commodity server. People complain about the carbon burn of crypto, and they are right, but I bet it is dwarfed by the carbon burn of all the shitty over-provisioned and over-architected CRUD apps running interpreted languages. Unfortunately with universities teaching python of all things we'll have (or maybe already do have) a whole generation of developers that actually have no idea how powerful a modern computer is.

I suppose there's a chance AI will get to the point where we can feed it a ruby/python/js/whatever code base and it can emit the functionally equivalent machine code as a single binary (even a microservices mess).

etaioinshrdlu 3 years ago | |

I've been experimenting with GPT3 as a "compiler" and it's always amazing when it works. Extrapolating from here, I think this is right on the nose -- we feed in any high level language and get reasonably efficient assembly out the other end, furthermore, the assembly is more human-like than a compiler would give you.

There's some big problems with this approach today, namely, it's not always right, and it may sometimes be half right (miss edge cases).

But think of where this AI technology is headed -- it stands to reason it will eventually work pretty much perfect.

And then I think we'll see another very strong trend -- large AI models replacing other forms of software. Why write a compiler when GPT3 can compile C to asm? Why write an interpreter when GPT3 can "compile" python to C?

The AI model is hilariously less-efficient than traditional software, but it will be far far cheaper and faster to create than the traditional equivalent.

What other types of software will be replaced by AI models?

jbotdev 3 years ago | |

I think people overestimate how much CPU time a typical CRUD app spends on actual business logic, even with an interpreted language like Ruby or Python. What I’ve seen is the bottleneck is largely memory, such that you can pack a ton of these apps on a machine with a few cores and a lot of RAM.

The stuff that actually is CPU-bound often ends up being written in an appropriate language, or uses C extensions (e.g. ML and data science libraries for Python).

jacobsenscott 3 years ago | | |

Sometimes. But in a rails app, and probably any app using an orm, transforming database rows to activerecord objects makes the cpu go brrr. And thats all ruby code. A port of AR to rust would be amazing, but idk how feasible given all the metaprogramming.

throwmeup123 3 years ago |

The title is highly misleading for some theoretical "exploration".

dang 3 years ago | |

Ok, we've put a question mark up there to make it more explorationy.

trishume 3 years ago | | |

As the author, this sounds good to me! I'll probably even change the actual title to match. I originally was going to make it a question mark and the only reason I didn't is https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline... when I think the answer is probably "could probably be somewhat done" rather than "no".

kierank 3 years ago |

This is as realistic as the moon rocket in my back garden.

twp 3 years ago |

This post solves all of the easy problems (i.e. make simple stuff go fast) and none of the hard problems (i.e. build a system that still works when other stuff breaks).

This post is perfect world thinking. We don't live in a perfect world.

kureikain 3 years ago |

Not to the extreme of fitting everything into one machine but I have explorer the idea of separate stateless workload into its own machine.

However, the stateless workload can still operate in a read-only manner if the stateful component failed.

I run an email forwarding service[1], and one of challenge is how can I ensure the email forwarding still work even if my primary database failed.

And I come up with a design that the app boot up, and load entire routing data from my postgres into its memory data structure, and persisted to local storage. So if postgres datbase failed, as long as I have an instance of those app(which I can run as many as I can), the system continue to work for existing customer.

The app use listen/notify to load new data from postgres into its memory.

Not exactly the same concept as the artcile, but the idea is that we try to design the system in a way where it can operate fully on a single machine. Another cool thing is that it easiser to test this, instead of loading data from Postgres, it can load from config files, so essentially the core biz logic is isolated into a single machine.

---

https://mailwip.com

- They're stupidly popular, so it basically nullifies the setup steps. - Once setup, they by combinding both OS layers and App, they solve more of the problem and are therefore slightly more reliable. - They're self-documenting as long as you understand bash, docker, and don't do weird shit like build an undocumented intermediary layer.