My £4 a month server can handle 4.2M requests a day
mark.mcnally.je
mark.mcnally.je
I'm surprised by HN sometimes.
His solution was a clever hack because he worked around the law without apparently breaking it directly - clever.
That said gambling systems are specifically one class of software I refuse to work on.
Incidentally both gambling AND fintech (including crypto) are on the list of industries I refuse to do work with. So I guess BTC gambling would have been off the table for two reasons.
I mean, I have mad respect for the hustle with the former MP etc. I agree with what you say in that you did not actually break the law - because you found a loophole (made a loophole? hustled it? again, I'm impressed). You ran a gambling site from IoM though :-)
It added a nice little feature too, which was that every spin and deck could be stored on a separate server that would show them all at the end of the day. This was a little before "proven randomness" took off in btc casinos, but I made the RNG reports available daily for analysis (without explaining the whole infrastructure, obviously).
[edit] I just want to say that yes, you're obviously right, and yeah, I ran a casino from the IoM... without anyone knowing if that was okay or not... and it was just a moment in my life. of which I'm proud, I guess. I was living illegally in a small apartment in Alhama de Granada after violating my EU visa. hah. It was a great, great piece of software and I don't know if I'll ever write anything that good again. But it didn't really change my life or anything.
What a story.
"where the random chance takes place" could easily be interpreted as where the random number is _used_ and not where it has been created. Creating random numbers is not "chance" per se (in this context). Using random numbers to e.g. determining a winner would be the chance in my opinion.
>>> where the random number is _used_
This was my main concern, and it was exactly what I needed a lawyer to sign off on before I set up a rig there. I was told that the gambling laws applied to where the chance took place, not where the money is distributed... after all, the whole thing with the IoM and the reason it's allowed to be a tax haven is that lots of people need to move money around without a lot of questions. But they defined gambling in this specific way and if only the money moved but the dice roll didn't take place on their shores, then it wasn't gambling under their jurisdiction. What you bring up was the conversation I had before locating there.
HN has a silly but effective piece of anti-flamewar UX which is that it hides the reply link in certain cases (some function of thread depth + amount of comments by you i think). However you can still reply by opening the comment on question (click on the timestamp, ie the "1 hour ago" link). Maybe you hit that.
Around here (and probably elsewhere) bars aren't allowed to make wine stronger by adding spirit.
So if you mix a drink from wine (or similar) and spirit in that order you might lose your license.
Put the spirit in the glass first and all is ok.
I guess at this point it is just a shibboleth that inspectors use to see if the bar has read the rules at all, kind of like the no brown m&ms.
Point is though: rules matter, you can lose your license over it.
This isn't a court of law. I can understand trying to avoid language that makes it sound like you may have been in violation of the law to a judge or jury, but you literally described that what you did was intended to keep operating a service that had been banned by using an absurd technicality in the definition of the ban.
I'm honestly surprised it worked (though having a former MP of the tiny nation you were operating in as a lawyer might have helped) considering that your service still facilitated online gambling directly and was advertised as such, despite the randomness source being on a remote server rather than local.
In other words, using a non-local randomness source (like a remote server you cURL into or a webcam pointed at a bunch of lava lamps) is functionally indistinguishable from a local dice roll or other source of entropy. This "hack" is so flimsy it likely wouldn't hold up in court in a nation that is actually interested in pursuing such violations that has a population larger than a small city.
When I did it, the only thing I was really afraid of was getting arrested if/when I stepped back on American soil. There was redundancy so I could run the whole thing in Costa Rica if I had to cold shutdown the IoM servers. And the coin was in private wallets, mostly on my laptop. But I was very concerned about breaking any laws, anywhere. I was the only one to implement ID verification and fully block American players.
Call it a hack or whatever, they wanted my business and I needed their servers, and I split up my code so it would be legal according to their laws. Not too different from what a lot of companies do.
I'm not arguing that you violated any laws. You made it very clear that you went to great lengths to avoid doing anything that could have resulted in consequences to yourself.
EDIT: Since HN's rate limiting won't let me reply for a few hours, I'll just address the replies inline:
I'm not jealous. I'm sure noduerme made a sizable chunk of money with the whole operation at the time but their profile says they're now working as a taxi driver and sold all their bitcoin before the peak. They probably have a lot of other interesting stories to tell and that's nice. But dismissing any hostility or criticism as jealousy is thought terminating and frankly below even HN's standards.
Based on their backstory in the replies, I can see where their attitude comes from, but they severely underestimate how big of a problem gambling addiction is and how much of the profit of the gambling industry relies on it.
It's nice if the casino their parents worked at turned away obvious addicts but the word "obvious" is doing a lot of work here and there are also clear business reasons you don't want obvious addicts in your establishment the same way bars will be happy to have repeat customers buying drinks for five hours every day but will turn them away if they get blackout drunk or unsightly. "Not doing it for the money" may give you a clean conscience but it doesn't change the consequences of your actions.
It's also important to point out that online gambling is by its nature functionally anonymous for the gambler (even if you record IDs for legal reasons). The online casino isn't going to turn away the addict until they can no longer pay or have to resort to fraud to keep up the habit. And even if the casino implements limits, the proliferation of online casinos makes it considerably easier to go hopping than if you have to physically drive somewhere.
Gambling addiction not only ruins the lives of the addict but also impacts their friends and family, not just financially. It's true that not every person who gambles is an addict but the line between an expensive hobby and a managed addiction is hard to draw until you undeniably cross it.
But if you need a comment on HN to explain to you why gambling and especially online gambling is bad, a comment on HN isn't going to be enough to convince you.
Total profit from 2 years running the site? About $50k. It was a hobby. I never quit my job. I also turned away 95% of the hits because they were coming from America.
[edit] I should add that I strongly advised other BTC site owners, especially casino owners, to follow certain guidelines, and watched one of them who I had told to be careful launch, make about $1M with one game on a crappy website, and get jailed within a year. That wasn't the trajectory I was interested in.
Hah! This made me laugh. Ok so not FOMO..(trust me, wasn't worth it except for the thrills).. why hostile? I was born in a Vegas family. My uncles all worked as blackjack dealers and pit bosses. When I was 7 they used to leave me in a corner of the casino for hours and tell me to stand there while my parents went and gambled. I taught myself to code there on a TRS-80 Model 100 in basic and practically the first thing I wrote was a slot machine. My view is that adults want to go gamble and that's their decision. I never took a dime from anyone I saw with a gambling problem... I would ban them from my site if they seemed addicted. I like to gamble myself. I count cards. Like everyone on my site... because my decks were single shuffle. So don't be so judgmental. I didn't do it for the money. I did it because I love the games.
The same jealousy that's called "opinionated" in software development, but really means "Doing things differently than how I do them threatens me, because my sense of superiority is rooted in how I do things."
I'm equally hostile to corporations doing that. I don't recall the HN comment threats about Google doing the kind of things you describe being full of replies congratulating their ingenuity.
Big corporation doing bad thing is bad doesn't mean much smaller corporation doing bad thing is okay, it means we should work on preventing that bad thing and if we seemingly can't we should reconsider the underlying systemic conditions that enable it.
No, not really. It shows that you went to great lengths to find ways to exploit a loophole where, even though you are clearly breaking the spirit of the law, you argue that it doesn't break the letter of the law.
I get it that you have a vested interest in keeping up the plausible deniability thing, but you know it and everyone knows it that you went through great lengths to put up a tech infrastructure which meets absolutely no requirement other than exploiting a loophole.
I mean, you explicitly expressed your personal concerns in this very discussion regarding what you personally chose to describe as testing "legally grey areas". Who do you expect to fool?
Come on. Cut the bullshit.
> Which when I began coding it, seemed completely legal and possible in Costa Rica. As the laws started to change - and even before Bitcoin came on the scene - I looked for how to do it without running afoul of anything. So it's not like I set out with a plan to exploit all the legal loopholes in the world, I just adapted my code and split it apart as necessary.
You are clearly and unequivocally stating that you set to exploit all the legal loopholes once your "grey area" was made black and white in Costa Rica.
Please, spare the thread from all that nonsense. You're not fooling anyone.
I didn't exploit anything. I worked within the legal options that were available. In any case, I don't understand the accusation.
My only regret is that I didn't have the capital to buy a full license in the IoM or Malta outright. But the truth is, I wrote the whole thing from scratch and I was determined to launch it. You're free to your opinions, but you ought to avoid judging people's intentions while misreading their words.
There is no "The law", just a bunch of different jurisdictions with different laws. He didn't break Switzerland law. Does Switzerland's law not matter for some reason?
Morally dubious? For sure.
Just because OP isn't in jail doesn't mean they didn't break the law. Laws are broken all the time.
Generous to assume that all that break laws go to jail.
Obviously he did not break the law, finding holes like that is the number one job of a tax-lawyer.
First, he did comply with all applicable law. No laws were broken.
Second, he did not break the spirit of the law. The law clearly allows gambling from the Isle of Man.
Third, he did not conflate the law with morality. What is the morality of a 400,000 GBP 'licensing' fee? Laws around licensing are weird. Another poster mentioned that pouring wine than liquor into a glass is illegal, but liquor then wine is fine. Not much moral sense in that reg.
I looked for investment, put together thick books of plans every few months, built 24 games... No one would get behind it, and I couldn't just shelf it, so this was how I launched it without breaking any laws.
A few months in, one existing online casino network offered me $100k to just hand it over to them and then come work for them, but I considered the offer extremely insulting.
I would read a book about this, fwiw.
So I went down a rabbit hole of trying to license the software for in-room gaming on cruise ships and Vegas hotel casinos. But Bally and Caesars pretty much dominated that space... if you even want to get a new game certified by the Nevada Gcb you have to put $100k down, non-refundable, for them to review the software (per game) and then they might start a review in a year or two. Bally gets to cut the line. I also had trouble trying to patent my original games. And of course, the world was coming closer to a consensus that Bitcoin would have to be regulated. So one day, I refunded all my players their balances and turned off the lights.
Does it really matter if you get your random number from /dev/urandom or a server in Switzerland?
What I mean is that this:
> The IoM server would call the Swiss server every time a hand was dealt
might seem like a clever loophole around the laws in IoM, but in reality it sounds to me like the kind of technicalities that wouldn't really pass the reasoning of a human judge, who in their duty of interpreting the law and its intended spirit, would probably consider this an invalid trick and thus that the RNG of the system still resided in IoM, even if technically it didn't.
But of course, none of this matters if the casino never had any legal battle to fight where this idea could be tested in court, which is the equivalent of not being "caught".
[1] https://erskinemay.parliament.uk/section/5023/corruption-or-...
I’ve had an MP literally solicit this from me - he emailed a bunch of local businesses basically stating his price list. He’s no longer an MP, or in politics - but you see this stuff everywhere, all the time. Any member’s bill you see has almost certainly been sponsored.
It's like saying a politician in the UK is a "senator" and expecting people think you are an authority on the subject.
Yes, only big companies can successfully "hack" the law based on its letter, see e.g. tax evasion.
Back in my last startup, I was doing a crypto market intelligence website that subscribed to full trade & order book feeds from the top 10 exchanges. It handled about 3K incoming messages/second (~260M per day), including all of the message parsing, order book update, processing, streaming to websocket connections on any connected client, and archival to PostGres for historical processing. Total hardware required was 1 m4.large + 1 r5.large AWS instances, for a bit under $200/month, and the boxes would regularly run at about 50% CPU.
Codebase was pure server-side Kotlin running on the JVM. Jackson for JSON parsing, when the exchange didn't provide their own client library (I used the native client libraries when they did). Think I used Undertow for exchange websockets, and Jetty for webserving & client websockets. Postgres for DB.
The threading model was actually the biggest bottleneck, and took a few tries to get right. I did JSON parsing and conversion to a common representation on the incoming IO thread. Then everything would get dumped into a big producer/consumer queue, and picked up by a per-CPU threadpool. Main thread handled price normalization (many crypto assets don't trade in USD, so you have to convert through BTC/ETH/USDT to get dollar prices), order book update, volume computations, opportunity detection, and other business logic. It also compared timestamps on incoming messages, and each new second, it'd aggregate the messages for that second (I only cared about historical data on a 1s basis) and hand them off to a separate DB thread. DB would do a big bulk insert every second; this is how I kept database writes below Postgres's QPS limit. Client websocket connections were handled internally within Jetty, which I think uses a threadpool and NIO.
Key architectural principles were 1) do everything in RAM - the RDS machine was the only one that touched disk, and writes to it were strictly throttled 2) throw away data as soon as you're done with it - I had a bunch of OOM issues by trying to put unparsed messages in the main producer/consumer queue rather than parsing and discarding them 3) aggregate & compute early - keep final requirements in mind and don't save raw data you don't need 4) separate blocking and non-blocking activities on different threads, preferring non-blocking whenever possible and 5) limit threads to only those activities that are actively doing work.
For that particular use-case (or related financial ones) I'd consider Rust, which was a bit too immature when I was working on this but would give you some extra speed. HFT is winner-take-all and the bar has risen significantly even in the last couple years, so if I were putting my own money at risk now I'd want the absolute fastest processing possible.
The real-time data was visualized on a website. Here is an example. https://algot.io/
This reminds me of back in 2003, a friend of mine worked for an online casino vendor; basically, if you wanted to run an online casino, you'd buy the software from a company and customize it to fit your theme.
They were often written in Java, ASP.NET, and so on. They were extremely heavyweight. They'd need 8-10 servers for 10k users. They hogged huge amounts of RAM.
My friend wrote the one this company was selling in C. Not even C++, mind you, just C. The game modules were chosen at compile time, so unwanted games didn't exist. The entire binary (as in, 100% of the code) compiled to just over 3 MB when stripped. He could handle 10k concurrent users on one single-core server.
I'm never gonna stop writing things in Python, but it still amazes me what can happen when you get down close to the metal.
The trick is basically that you have to eschew the last 15 years of "productivity" enhancements. Pretty much any dynamic language is out; if you must use the JVM or .NET, store as much as possible in flat buffers of primitive types. I ended up converting order books from the obvious representation (hashtable mapping prices to a list of Order structs) to a pair of SortedMaps from FastUtils, which provides an unboxed float representation with no pointers. That change ended up reducing memory usage by about 4x.
You can fit a lot of ints and floats in today's 100G+ machines, way more than needed to represent the entire cryptocurrency market. You just can't do that when you're chasing 3 pointers, each with their associated object headers, to store 4 bytes.
Does the $4K include the cost of the RAM? Where can I find these servers? Thanks!
To answer your question, you can't find these servers because they don't exist. A server with 4T of RAM will cost you at a minimum $20,000 and that will be for some really crappy low-grade RAM. Realistically for an actual server that one would use in an actual semi-production setting, you're looking at a minimum of $35,000 for 4TB of RAM and that's just for the RAM alone, although to be fair that 35k ends up dominating the cost of the entire system.
HN doesn't look exactly like SlashDot, but it's absolutely just like SlashDot.
Intelligent discourse by knowledgeable persons.
The omission of GNAA and "frosty" posts are a massive boon.
Which is a far cry from the claimed 4TB, but still, damn.
I don't think they're typically making things up. It's what I prefer to call Reddit knowledge. They saw someone else claim it somewhere, they believed it, and so they're repeating it so they can be part of the conversation (people like to belong, like to be part of, like to join). It's an extremely common process on Reddit and most forums, and HN isn't immune to it. Most people don't read much and don't acquire most of their knowledge from high quality sources, their (thought to be correct) wider knowledge - on diverse topics they have no specialization on - is frequently acquired from what other people say and that they believe. So they flip around on Reddit or Twitter for a bit, digest a few nuggets of questionable 'knowledge' and then regurgitate it at some later point, in a process of wanting to participate and belong socially. It's how political talking points function for example, passed down to mimic distributors that spread the gospel to other mimic followers (usually without questioning). It's how religion functions. And it's how most teachers / teaching functions, the teachers are distribution mimics (mimics with a bullhorn, granted authority by other mimics to keep the system going, to clone).
It's because some very high percentage of all of humans are mimics. It's not something Reddit caused of course, it's biology, it's a behavior that has always been part of humanity. It's an increased odds of success method of optimizing for survival of the species, successful outcomes, meets the Internet age. It's why most people are inherent followers, and can never be (nor desire to be) leaders. It's why few people create anything original or even attempt to across a lifetime. It's why such a small fraction of the population are very artistic, particularly drawn to that level of creative expression. If you're a mimic biologically it's very difficult to be the opposite. This seems to be viewed by most people as an insult (understandably, as mimics are the vast majority of the population and control the vote), however it's not, it's simply how most living things function, system wise, by mimicry (or even more direct forms of copying). Humans aren't that special, we're not entirely distinct from all the other systems of animal behavior.
That saying, safety in numbers? That's what that is all about. Mimicry. Don't stand out.
The reason most Wall Street money managers can't beat the S&P 500? It's because they're particularly aggressive mimics, they intentionally copy eachother toward safe, very prosperous, gentle mediocrity. They play a game of follow, with popular trends (each decade or era on Wall Street has popular trends/fads). Don't drift too far below the other mimics and it's a golden ticket.
Nobody got fired for buying IBM? Same thing. Mimic what has worked well for many others is biologically typically a high success outcome pattern (although amusingly not always, it can also in rare occasions lead off a cliff).
The Taliban? The Soviet Union? Nazism? Genocide? Multi generational patterns of mistake repetition passed down from parental units? That's how you get that. People mimic (perhaps especially parental units; biology very much in action), even in cases where it's an unsuccessful/negative pattern. All bad (and good) ideologies have mimic distributors and mimic followers, the followers do what they're told and implement as they're told. And usually there are only a very small number of originators, which is where the mimic distributors get their material.
The concept of positive role models? It's about mimicry toward successful outcomes.
https://www.broadberry.com/dual-amd-epyc-rackmount-servers/a...
Looking on ebay I can find some pretty decent R820 with 512GB each for right around $1500 a piece. Not counting any storage, even if they come used with some spinning hard drives, would end up replacing with SSDs. So more like three servers, 1.5TB of RAM, for $4500.
My data sets are far too big to fit into memory/cache. Disk pressure can be alleviated by optimizing queries but it's a game of whack-a-mole.
I have exhausted EBS i/o and been forced to resort to dirty tricks. With RDS you can just pay more but that only scales to a point – normally the budget.
OTOH a service loading the single core with the main thread is a frequent sight :( Interpreted languages like Python can easily spend 30% of time just on the deserialization overhead, converting the data from a DB into a result set, and then into ORM instances.
We had a system management backend at my last company. Loading the users list was unbearably slow; 10+ seconds on a warm cache. Not too terrible, except that most user management tasks required a page reload, so it was just wildly infuriating.
Eventually I took a look at the code for the page, which queried LDAP for user data and the database for permissions data. It did:
get list of users
foreach user:
get list of all permissions
filter down to the ones assigned directly to the user
foreach user:
get list of all groups
foreach group:
get list of all permissions
filter down to the ones assigned to the group
filter down to the ones the user has
I replaced it with
get list of all users
get list of all groups
get list of all permissions
<filter accordingly>
Also worth noting was that fetching the user list required shelling out to a command (a python script) which shelled out to a command (ldapsearch), and the whole system was a nightmare. There were also dozens of pages where almost no processing was done in the view, but a bunch of objects with lazy-loaded properties were passed into the template and always used, so when benchmarking you'd get 0.01 seconds for the entire function and then 233 seconds for "return render(...)' because for every single row in the database (dozens or hundreds) the template would access a property that would trigger another SQL call to the backend, rather than just doing one giant "SELECT ALL THE THINGS" and hammering it out that way.
Note that we also weren't using Django's foreign keys support, so we couldn't even tell Django to "fetch everything non-lazily" because it had no idea.
If that app were written right it could have run on a Raspberry Pi 2, but instead there was no amount of cores that could have sped it up.
For SQL you can also do a stored procedure. Sometimes that works well if you are good at your DBMS's procedure language and the schema is good.
With either technique you are still pulling all the data you need from the DB but with multiple queries instead of a stored procedure you are usually pulling more data than you need with each query and then dropping any rows or fields you’re not interested in. Together with multiple calls over the network to the DB server and (often) multiple SQL connection setups this is much worse for performance on both the web and database servers
Lots of people seem to not realize that db roundtrips are expensive, and should be avoided whenever possible.
One of the best illustrations of this I've found is in Transaction Processing book by Jim Gray and Andreas Reuters where they illustrate the relative cost of getting data from CPU vs CPU cache vs RAM vs cross host query.
[0]: https://stackoverflow.com/questions/97197/what-is-the-n1-sel...
In the case of groups and permissions there's probably only a few of each, so fetching all of them is probably fine. But depending on your data -- say you're fetching comments written by a subset of users, you can tweak the above to use IN filtering, something like this Python-ish code:
users = select('SELECT id, name FROM users WHERE id IN $1', user_ids)
comments = select('SELECT user_id, text FROM comments WHERE user_id IN $1', user_ids)
comments_by_user_id = defaultdict(list)
for c in comments:
comments_by_user_id[c.user_id].append(c)
for u in users:
u.comments = comments_by_user_id[u.id]
For development, we had a ?queries=1 query parameter you could add to the URL to show the number of SQL queries and their total time at the bottom of the page. Very helpful when trying to optimize this stuff. "Why is this page doing 350 queries totalling 5 seconds? Oops, I must have an N+1 query issue!"
users = User.objects.all()
for u in user:
print(u.name, len(u.comments))
users = select('SELECT id, name FROM users WHERE id IN $1', user_ids)
for u in user:
num_comments = get('SELECT COUNT(*) FROM comments WHERE user_id = $1', u.id)
print(u.name, num_comments)
The other thing I don't like about (most) ORMs is they fetch all the columns by default, even if the code only uses one or two of them. I know most ORMs provide a way to explicitly specify the columns you want, but the easy/simple default is to fetch them all.
I get the value ORMs provide: save a lot of boilerplate and give you nice classes with methods for your tables. I wonder if there's a middle ground where you couldn't do obviously bad things with the ORM without explicitly opting into them. Or even just a heuristic mode for development where it yelled loudly if it detected what looked like an N+1 issue or other query inside a loop.
https://github.com/django-query-profiler/django-query-profil... has a neat option for detecting likely N+1 queries. I usually use the Django Debug Toolbar for this.
Django's ".only()" method lets you specify just the columns you want to retrieve - with the downside that any additional property access can trigger another SQL query. I thought I'd seen code somewhere that can turn those into errors but I'm failing to dig it up again now.
I've used the assertNumQueries() assertion in tests to guard against future changes that accidentally increase the number of queries being made without me intending that.
The points you raise are valid, but there are various levels of mitigations for them. Always room for improvement though!
users = User.objects.all()
for u in user:
print(u.name, len(u.comments))
Django ORM has a succinct way of doing the "SELECT COUNT(*)" pattern:
users = User.objects.all()
for u in user:
print(u.name, u.comments.count())
users = User.objects.annotate(n_comments=Count("comments"))
for u in user:
print(u.name, u.n_comments)And this is how you end up with the problems the parent is describing. During testing and when you setup the system you always have a small dataset so it appears to work fine. But when it’s real work the system collapses.
> SELECT user_id, COUNT(*) FROM comments WHERE user_id IN $1 GROUP BY user_id
Fwiw, ActiveRecord with the Bullet gem does exactly that. I'd guess there's an equivalent for Django.
It's one of those things that in the long run would have been much more time effective to write, but the debugging never quite took long enough each time to make me take the time.
SqlAlchemy has this as part of the ORM, it should really be part of Django IMO.
I'm so glad Symfony (PHP framework) has a built-in profiler and analysis tooling there...
It turned out that the dashboard had been built on top of Wordpress. The way that it checked if the user had permission to access the dashboard was to query all users, join the meta table which held the permission as a serialized object, run a full text search to check which users had permission to access this page, and return the list of all users with permission to access the page. Then, it checked if the current user was in that list.
I switched it to only check permissions for the current user, and the page loaded instantaneously.
Everything else are network hops and what I call "distributed clutter", including authorizing via a third party like Auth0 multiple times for machine-to-machine token (because "zero trust"!), multiple parameter store calls, hitting a dcache, if interacting with a serverless function, cold starts, API gateway latency, etc...
So for the meat of a 20-40 ms call, we get about a 400ms-2s backend response time.
Then if you are loading a front end SPA with javascript...fugetaboutit it
But DevOps will say "but my managed services and infinite scalability!"
- it's memory efficient
- it's atomic
- it's faster
Also doesn't LDAP support filtering in query?
In that case, was there a reason joins couldn't be used? As it still seems pretty wasteful (and less performant) to load all of this data in memory and post-process; whereas a well-indexed database could possibly do it faster and with less-memory usage.
for each user in (get_freeipa_users | grep_attribute uid):
email = (get_freeipa_users | client_side_find user | grep_attribute email)
last_change = (get_freeipa_users | client_side_find user | grep_attribute krblastpwdchange)
expiration = (get_freeipa_users | client_side_find user | grep_attribute krbpasswordexpiration)
# Some slightly incorrect date math...
send_email
The mistake of course was not thinking about why this approach is faster in a database query and that it doesn't work that way when you already need to get all the data out of LDAP to do anything with it.
I'm guessing if you put all this data into Kinesis or message queues it would end up costing quite a bit more.
They're rent seeking in other ways though, no worries.
If you do it individually, there are public developer docs for each exchange that explain how their API works. It's generally free as long as you're not making a large number of active trades.
I'm working and company which process "real" exchanges, like NASDAQ, LSE, and, especially, OPRA feed.
We've added 20+ crypto exchanges in our portfolio this year, and all of them are processed on one old server which is unable to process NASDAQ Total View in real-time anymore.
On the other hand, whole OPRA feed (more than 5Gbit/s or 65B/day, yes, it is billions, messages of very optimized binary protocol, not this crappy JSON) is processed by our code on one modern server. Nothing special, two sockets of Intel Xeons (not even Platinums).
https://msgpack.org/index.html
Good protobuf vs msgpack comparison: https://medium.com/@hugovs/the-need-for-speed-experimenting-...
Most big USA exchanges uses custom fixed-layout protocols, where each message is described in documentation, but not in machine-readable way. European ones still use FAST.
I didn't seen FIX in the wild for data feeds, but it is used for brokers, to submit orders to exchange (our company didn't do this part, we only consume feeds).
I don't know why, but all Crypto Exchanges use JSON, not protobufs or something like this, and didn't publish any formal schemes.
Fun fact: one crypto exchange put GZIP'ed and base64'ed JSON data into JSON which pushed to websocket, to save bandwidth. IMHO, it is peak of bad design.
FAST is not particularly common in Europe
the large European venues use fixed-width binary encoding (LSE group, Euronext, CBOE Europe)
https://uploads-ssl.webflow.com/5ba40927ac854d8c97bc92d7/5bf...
If you're optimizing for latency JSON is pretty terrible, but most people who use it are optimizing for interoperability and ease of development. It works just fine for that, and you can recover decent bandwidth just by compressing it.
JSON is very inefficient both in bytes (32 bit price is 4 bytes in binary and could be 7+ bytes as string, think "1299.99" for example) and CPU: to parse "1299.99" you need burn a lot of cycles, and if it is number of cents stored as native 4-byte number you need 3 shifts and 4 binary ors at most, if you need to change endianness, and in most cases it is simple memory copy of 4 bytes, 1-2 CPU cycle.
When you have binary protocol, you could skip fields which you are not interested in as simple as "offset = offset + <filed-size>" (where <filed-size> is compile-time constant!) and in JSON you need to parse whole thing anyway.
Difference between converting binary packet to internal data structure and parsing JSON with same data to same structure could be ten-fold easily, and you need to be very creative to parse JSON without additional memory allocations (it is possible, but code becomes very dirty and fragile), and memory allocation and/or deallocation costs a lot, both in GC languages and languages with manual memory management.
This is typical (from NASDAQ http://www.nasdaqtrader.com/content/technicalsupport/specifi... ):
Prices are integer fields. When converted to a decimal format, prices are in fixed point format with 6 whole number places followed by 4 decimal digits. The maximum price in OUCH 4.2 is $199,999.9900 (decimal, 7735939C hex). When entering market orders for a cross, use the special price of $214,748.3647 (decimal, 7FFFFFFF hex).
For NASDAQ it seems to have been something around 430k / share... Buffett's BRK shares threatened to hit that limit a couple months ago: https://news.ycombinator.com/item?id=27044044
X-Stream feeds do for example
One, that he’s surprised by how small crypto markets are.
Two, that this one server (or very few server processing) thing scales quite well to billions of messages a day.
I didn’t find any element of smugness here, but maybe I misread the tone.
I mean I'm still going to use it for client/server communication and the like because I don't have serious performance constraints enough to warrant something that will be more difficult to develop for etc, but still.
The thing is - when two engineers get smug, oftentimes lots of fairly interesting technical details get exchanged, so such discussions aren't really useless to bystanders.
I'm more than a little annoyed that so much data engineering is still done in Scala Spark or PySpark. Both suffer from pretty high memory overhead, which leads to suboptimal resource utilization. I've worked with a few different systems that compile their queries into C/C++ (which is transparent to the developer). Those tend to be significantly faster or can use fewer nodes to process.
I get that quick & dirty scripts for exploration don't need to be super optimized, and that throwing more hardware at the problem _can_ be cheaper than engineering time, but in my experience, the latter ends up costing my org tens of millions of dollars annually -- just write some code and allocate a ton of resources to make it work in a reasonable amount of time.
I'm hopeful that Ballista[1], for example, will see uptake and improve this.
[0] https://en.wikipedia.org/wiki/Andy_and_Bill%27s_law
[1] https://github.com/apache/arrow-datafusion/tree/master/balli...
What I've seen is that you need people who deeply understand the system (e.g. Spark) to be able to tune for these edge cases (e.g. see [1] for examples of some of the tradeoffs between different processing schemes). Those people are expensive (think $500k+ annual salaries) and are really only cost effective when your compute spend is in the tens of millions or higher annually. Everyone else is using open source and throwing more compute at the problem or relying on their data scientists/data engineers to figure out what magic knob to turn.
That being said, Spark is literally the only (relatively) easy way to run distributed ML that's open source. The competitors are GPU's (if you have a GPU friendly problem) and running multiple Python processes across the network.
(I'm really hoping that people will now school me, and I'll discover a much better way in the comments).
I was thinking about how they must have a routine that’s constantly taking mouse input, buffering history, and running some algorithm to determine when user input is a mouse “shake”.
And how many features like this add up to eat up a nontrivial amount of resources.
In some places there's no room left for unnecessary abstractions, I can imagine most of the code touching mouse / cursor handling is in that category.
Truncating the impulse response after five time constants wouldn't really change its output noticeably, and even if you truncated it after two or three time constants it would still be inferior to the box filter for this application, though less bad. So in that sense the problem isn't that it's infinite.
Likewise, you could certainly design a direct-form IIR filter that did a perfectly adequate job of approximating a box filter for this sort of application, and that might actually be a reasonable thing to do if you wanted to do something like this with a bunch of op-amps or microwave passives instead of code.
So the fact that the impulse response is infinite is neither necessary nor sufficient for the problem.
The problem with the simple single-pole filter is that by putting so much weight on very recent samples, you sort of throw away some information about samples that aren't quite so recent and become more vulnerable to false triggering from a single rapid mouse movement, so you have to set the threshold higher to compensate.
It turns out that pretty much any time you have code that interacts with the world outside computers, you end up doing DSP. Graphics processing algorithms are DSP; software-defined radio is DSP; music synthesis is DSP; Kalman filters for position estimation is DSP; PID controllers for thermostats or motor control are DSP; converting sonar echoes into images is DSP; electrocardiogram analysis is DSP; high-frequency trading is DSP (though most of the linear theory is not useful there). So if you're interested in programming and also interested in graphics, sound, communication, or other things outside of computers, you will appreciate having studied DSP.
int m = abs(dx) + abs(dy); // Manhattan distance
c -= c >> 5; // exponential decay without a multiply (not actually faster on most modern CPUs)
c += m;
s += m; // update running sum
size_t j = (i + 1) % n; // calculate index in prefix sum table to overwrite
int d = s - t[j]; // calculate sum of last n mouse movement Manhattan distances
t[j] = s;
i = j;
int s = t[i] + m;
Once you've computed your smoothed mouse velocity in c or d, you compare it against some kind of predetermined threshold, or maybe apply a smoothstep to it to get the mouse pointer size.
Roughly I think WanderPanda's approach is about 12 RISCish CPU instructions, and nostrademons's approach is about 18 but works a lot better. Either way you're probably looking at about 4-8 clock cycles on one core per mouse movement, considerably less than actually drawing the mouse pointer (if you're doing it on the CPU, anyway).
Does that help?
Looks like it's measuring something like number of direction changes in relation to distance traveled; ignoring the y axis completely.
Possible but unlikely. Well-written desktop software never constantly taking input, it's sleeping on OS kernel primitives like poll/epoll/IOCP/etc waiting for these inputs.
Operating systems don't generate mouse events at 1kHz unless you actually move the mouse.
Other possibility, do you have a gaming mouse with 1000Hz polling rate configured?
Source: https://retrocomputing.stackexchange.com/questions/11533/why...
[0] https://github.com/MicrosoftDocs/azure-docs/blob/master/arti...
[1] https://wiki.hpccsystems.com/pages/viewpage.action?pageId=28...
If it had an SQL layer, I'd spend time evangelising it, but it's not worth learning another query language for.
There exists a world where it got open-sourced before Hadoop was built, and in that world it's probably everywhere.
To my amusement, my little SQLite prototype smoked the “enterprise” database. Turns out that a MacBook Pro SSD performs better than the SAN, and the query planner needs more tlc. We ended up running the queries off my laptop for a few days while the DBAs did their thing.
(I'm totally speculating, but your story seems so true that it inspired me :-)
I have now seen this anti pattern in multiple places now
This is interesting. Can you elaborate a bit?
Furthermore, pyspark is by far the most popular and used spark, and it’s also got the absolute world-worst atrocious mechanical sympathy. Why?
Developer velocity trumps compute velocity any day?
(I want the niceness of python and the performance of eg firebolt. Why must I pick?)
(There is a general thing to get spark “off heap” and use generic query compute on the spark sql space, but it is miles behind those who start off there)
If you’re anywhere in the US, let me know.
Of course, a lot of it depends on what your app does for each request but most apps are simple enough and can live with being a monolith / single fat binary running on a single instance.
The problem with today's DevOps culture is that they present K8's as answers for everything. Instead of defining a clear line on when to use them and when not to.
It served up to 70K of subscribers, call center with 30-40 employees, payment systems integration, everything.
Next was 8 socket Intel server. We were never able to saturate it's CPUs - 300 Mhz (or was it 400 ?) bus was a stopper. It served 350-400K of subscribers.
And next: we changed architecture and used 2 servers with 2 socket Intel CPUs again but that was time when Ghz frequencies appeared on market. We dreamed about 4xAMD server. We came to ~1 mln of active subscribers.
Nowadays: every phone has more power than it was those servers. Typical react application consumes more resources than billing system. Gigabyte here, gigabyte there - nobody counts them.
/grumpy oldster mode
I guess that the post was written as an answer to the mangadex post [1]. Mangadex was handling 3k req/sec involving dB queries. It was not just a cached Html page.
50req/sec for a Html file is super low which shows that a $4 month server cant do much actually. So yes this is enough for a blog, but a lot of websites are not blogs
There's too much competition involved in writing normal apps, which often attract significant investment that bootstrapped startups struggle to compete with.
It's interesting to see what kind of performance is possible for next to no money, when you throw out basic assumptions like using a database, and then start thinking about what you could build out of it.
Another example of clever use of resources is the https://haveibeenpwned.com/ website. Using a bloom filter (I think) to turn what could have been a back-end lookup into a "front end lookup" by requesting a small file from the server based on the password hash.
The only issue I have with the OP is his assumption that you'd get a nice smooth 60 request/second throughout the day! Most likely will be lumpy, and in the top of the lumpy periods (where most of your visitors visit) performance will be bad.
<?php echo("this is a benchmark") ?>Use apache to serve Django + wsgi? Just use Django asgi and nginx and you will get a higher number.
Did you read the post?
I did, and all I see is someone spinning some numbers idly, like, hey, if I can lay 1 brick every second, then with 20000 people we can build a house in one second! So good!
a) entirely and totally lacking in experience running a heavy load website.
b) 50 requests a minute is so atrociously bad, it’s not even worth talking about.
c) there isnt any db load going on here, this is a full page single table query. See https://docs.djangoproject.com/en/3.2/ref/contrib/flatpages/
Sure maybe a db exists, but it’s not relevant when you compare this to the complexity of doing write operations.
Ie. this is some hiiiigh level arm chair commentary right here.
Sure, they’re just talking about their website, but anyone going “oh yeah, look at this, those mangadex guys should learn a thing or two and run it on django”. …has no idea what they’re talking about.
This is shifting the goal posts. Your initial comment claimed that there are no database queries being made.
In any case, you've amended your argument, I have no further comments.
That was a typo, the worst performance they tested was 54 reqs / second.
> Please don't comment on whether someone read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that."
I'd guess a response to the mangadex thread? https://news.ycombinator.com/item?id=28440742
I tried a bunch of different stuff and ended up using Haskell - all of its popular web libraries are fast as hell. Go was fast but its standard library leaked sockets or I was not cleaning up connections properly or something, and it would tank whenever something went viral. All the popular interpreted language backend I tried were absurdly slow, like tens of RPS.
Source for my current thing is at http://yager.io/Server.hs. It also does all my RSS stuff, image processing for my photo gallery, etc.
Then with my second update, he told me that the app must be broken or that the script must be dying. There is no way it could complete this fast.
What was the issue? We processed terabytes of data. Each and every single line processed created a new connection to the database and left it hanging. A try catch was added when the connections failed and restarted the process. Removing the connection from the for loop and properly handling it reduced the time drastically.
And... why would you loop through millions of records when you can use batches? Also this was a phperlbashton* script. I turned it into a single PHP script and called it a day.
As a consequence, backup time was reduced to 2 hours as opposed to 12 hours (no one was allowed on the website until the back up was done).
Modern machines are incredibly fast.
* PHP/Perl/Bash/Python
I'm assuming this was an internal website and backups were scheduled for evenings/weekends?
It's the same story as yours, but with human effort. I was about to cut the human out entirely.
It's the same story as yours, but with human effort. I was about to cut the human out entirely, and fix a ton of errors in the process.
This was for a genomics project and they ran it on a supercomputer. When I looked into it, they were reading the entire input into a giant array before doing one pass and dumping the result out to disk. I made a tiny change (it was a Perl script) to make it stream the I/O instead.
This is the most extreme example I've come across of people using computing power just because it's there. Nobody questioned why the script took so long to run because the data really was in the TBs and other stuff also took that long to run. Waiting a day for the results was considered normal. I see the same thing on desktop apps etc., on a much smaller scale, of course. When I run an electron app it takes several hundred milleseconds to do anything at all. But nobody questions whether it should because everything takes several hundred milliseconds.
Until I worked out during some minor maintenance task that every request was logged to a flat file. Appended. Every request. The file was probably 100gb by the time I found it and every request log would lock the logging file. The server had been running for a couple of years by that time.
Of course I screwed up more than I fixed. :D
https://en.wikipedia.org/wiki/C10k_problem
"By the early 2010s millions of connections on a single commodity 1U rackmount server became possible: over 2 million connections (WhatsApp, 24 cores, using Erlang on FreeBSD),[6][7] 10–12 million connections (MigratoryData, 12 cores, using Java on Linux).[5][8]"
Although I do understand the boxes listed above have more resources then the VPS you are using. I am also not criticizing your write up, or results, bench-marking is in general interesting to do. I just wanted to provide some additional information.
Wouldn't you run out of TCP sockets?
What am I missing?
I'm not sure exactly what you mean by "run out of TCP sockets", but theoretically speaking, the only limitation is how much memory is available to store the necessary info about the socket (like address/protocol info and process info).
In practice, OS's do have a "max socket" or "max FD" limit, but that's usually configurable and (with enough RAM) could easily be set to "millions".
Probably the 65k port limit since each connection will get assigned a remote port, which can be solved by binding to multiple local ports and using a load balancer in front or using multiple network interfaces.
As you said in your post, adding caching to your site increased your throughput by ~20% (or +10/req/sec). What you and other sites seem to lack is a more distributed caching, a la CloudFlare, S3 CloudFront, Azure CDN, etc. Those last two only really work well for a static site, however as mentioned in your post that's essentially what you're serving.
While I'm all for having a free-as-in-freedom hosting solution and keeping things lean, the internet is a fickle beast, and nothing looks worse for a company who posts on HN when their technology-oriented site can't handle a few thousand requests per minute. (Or in this case, when a blog claims to handle 4.2M requests a day -- 2.9k req/min)
I haven't read recently, but they were only doing 200 rps per server.
Not all the above apply to a hobby-blog style site, but I wasn't referring only to OP's site in my original comment. I understand that not everyone needs to feed into "fucking Internet gatekeeper"s as you described, but the fact that they provide valuable services is undeniable. They make a complex operation -- one that could mean the difference between a company being able to sell their product or not -- simple.
For OP, I'd also be interested to see the benchmarks between this £4 server, and a £8 or £10 one, same stack.
Google Cloud CDN: Give us time, and we'll do to HTTP what we did to SMTP
they already did it - it's called chrome.
(Obviously the sales thing doesn't apply to OP)
- Make sites that are fast by default: Small bytes sent over the wire, beyond just initial page load, too. Yes, that does mean that your giant Google Tag Manager/Analytics/3rd party script is bloated. Reach out to 3rd parties about reducing their payload size, it's saved me several MB over the years. Also, not writing efficient CSS is a huge killer when it comes to byte site. Devs shouldn't "leave it just in case" when it comes to code, you have version control for a reason. And when a new feature comes out, clear out the old cruft.
- Avoid unnecessary DB calls: Obviously you need to get the data onto the page somehow, but if you can server-side render, then cache that result, you're reducing the overall calls to the DB. Also, optimizing queries to return only-what-you-need responses helps reducing total bytes over the wire
- Balance between Server and Client side: Not only are servers getting more powerful, so are client devices. Some logic can be offloaded to clients in most cases, but there needs to be a balance. Business-critical logic should probably be done server side, but things like pagination & sorting -- so long as they client will likely see or use all the data -- is fine in my book. Having 2000 rows of JSON in memory is totally OK, but rendering 2000 at once might cause some issues. Again, balance
- Hopping on the latest-and-greatest bandwagon isn't the best: Devs hate re-writing the site every 6 months, and really the newest framework might not be the best for your use case. Keep up to date with new technology, but saying "not for me" is fine.
- Don't let (non technical) managers make technology decision: See above. More often than not, C-levels want to use shiny new things they read an article about on LinkedIn once, no matter if it fits the needs of the company or not. Thankfully I've only been at one place that was like that, but while I was there it was hell. Current VP was an original developer on the site back in the early 00's, so he knows how to deflect BS for us. That VP also knows that he's outdated in his knowledge by now, so he trust the Devs to make technical decisions that are best for the company.
Barely over a second here. Much better than vast majority of "webscale" services.
Whether that means actually using static sites for stuff that can be static or just properly caching expensive things. Even dynamic content doesn't have to be slow, but many CMS are seriously inefficient without a cache. I'm not really blaming the CMSes entirely here, part of that is because they need to be extremely flexible, but once you need dozens of DB queries per page it'll fall over quickly on small hardware.
Next has a thing called Incremental Static Regeneration[0] which allows us to grab the top ~100 pages from the CMS at build time, generate the pages, then cache them for however long we want. The rest of the pages are grabbed when requested, then are cached for the same amount of time. After the time, they're re-grabbed from the DB, then re-cached. Overall I think we're down to around 5-10% of the way things were done before, which was -- you guessed it -- hit the DB on every page load _just in case_.
Sit the Next.js site behind CloudFlare, and then we also don't really pay data transfer costs. Our servers are just low-tier GKE nodes, and we run around 3k/visitors at any given time, sometimes spiking up to 8k concurrent.
[0] https://nextjs.org/docs/basic-features/data-fetching#increme...
But static content is of course still much faster and also much simpler.
Timing info from Firefox: Blocked: 0ms DNS resolution: 8ms Connecting: 9ms TLS setup: 12ms Sending: 0ms Waiting: 30ms
The very last resource (favicon.ico) loaded after 466ms and that's mostly because of the other files being requested only after the CSS has come in (after about 195ms). All in all the entire site (without the Matomo tracking JS) loaded in half a second.
Maybe the website has switched hosts in the last ten minutes, I guess, but I doubt it. I think this is more likely to be a problem related to distance to origin and saturation of the underlying connection.
I just figured I'd start some conversation on the post, since there wasn't any comments when I initially looked. For better or for worse it seems like I got people talking.
> Parts of the blog posts are cached using memcached for 10 mins
That means Django needs to accept the request, route it, pull the data from memcached, render the template.
For such a site I'd just set the `Cache-Control` headers and stick Varnish in-front of it acting as a reverse proxy. That'd likely increase the page load times significantly and make the backend simpler not worrying about manually caching in memcached and just setting the correct `Cache-Control` http header.
As it's budget hosting i'd probably not even bother with Varnish and outsource that to Cloudflares generous free tier, it's cheating as your server (Origin) isn't doing 4.2m requests but the practicality is really convenient.
If you want to see where the theoretical limits lie, check out some of the fringe work around the LMAX Disruptor and .NET/C#:
https://medium.com/@ocoanet/improving-net-disruptor-performa...
You will find the upper bound of serialized processing to be somewhere around 500 million events per second.
Personally, I have not pushed much beyond 7 million per second, but I also use reference types, non-ideal allocation strategies, etc.
For making this a web-friendly thing: The trick I have found is to establish a websocket with your clients, and then pipe all of their events down with DOM updates coming up the other way. These 2 streams are entirely decoupled by way of the ringbuffer and a novel update/event strategy. This is how you can chew through insane numbers of events per unit time. All client events get thrown into a gigantic bucket which gets dumped into the CPU furnace in perfectly-sized chunks. The latency added by this approach is measured in hundreds of microseconds to maybe a millisecond. The more complex the client interactions (i.e. more events per unit time), the better this works. Blazor was the original inspiration for this. I may share my implementation at some point in the near future.
Could you detail this, please? I don't get it. What is the flow?
1. Browser is sending events to web server via web socket, instantly as the event is occurring (?)
2. ? (what exactly does the server do?)
Upon receiving an event from the client socket, it is immediately inserted into the LMAX ring buffer for processing.
Updates to the client are triggered by events+state determining when a redraw is required and issuing a special "ClientRedraw" event into the same queue. These events are grouped by client so that we can aggregate multiple potential updates in a single actual redraw. These result in view updates being pushed back down to the relevant clients. One performance trick here is that the client redraw is dispatched asynchronously from the server, so there is no blocking on processing the subsequent batches each time.
You can think of an E2E client view update as always requiring 2 events - the client event that triggered the change to domain state, and the actual redraw event(s) that result. For applications where the client should update at a fixed interval (e.g. game), a high performance timer implementation injects periodic redraw events. Because the upper bound of the ring buffer latency is around a millisecond, this allows for incredibly low jitter on real time events. Scheduling client draws as simple domain events is feasible.
Sometimes we see people fetishizing bigger and faster, then gatekeeping when people want to do the same work with modest means, whether it a four quid a month hosting service or a first generation Raspberry Pi. Not everyone has the money or desire for bigger & faster, and it's nice to see that here.
If you are the sole developer working on your own site - be it a side project/hobby/labour of love or your source of income - you have complete control up and down the stack and have the leeway to tweak performance wherever needed - whether that's indexing and optimizing queries in the backend, reducing the size of your static assets, caching, whatever. You can even yank whole features if you feel their inherent complexity and load outweighs their usefulness.
In anything including and above a medium sized company, a single developer will rarely have the leeway to do anything beyond tinker with their small slice of the stack. They might spend some hours carefully optimizing a query, but it's for naught because the frontend team have screwed up the webpack settings and the JS load runs into many MB. Or you have both done your jobs but the PM wants a ton of analytics on every page. And the CEO's pet feature is a maintenance and performance nightmare but nobody has the clout to have it removed or even simplified. Nobody wants to waste sprints on paying down tech debt in a feature factory, so it becomes progressively harder to fix performance issues.
At that point, the cheaper and politically easier option is to just fire the money cannon at expensive cloud services and hope the extra spend squeezes out some performance gains.
That blog post got hugged by HN but it didn't even raise the CPU above 10% on a single core.
And a Raspberry Pi 3B+ is dog slow. And severely limited by bandwidth, unlike the Raspberry Pi 4B+. (But it uses less power so that's why I use a 3B+).
However I have another point to make. Professional rack-mount servers from HP and Dell can be had second hand for dirt cheap and you get a ton of CPU (20+ cores) and an ocean of RAM for next to nothing.
For many applications, an old Gen8 or similar Dell server will perform more than adequately. Even more so if you have a little bit more to spend on Gen9.
They are so cheap that you can like buy four to eight, sprinkle them across two different datacenters and even if one breaks, you won't be in any hurry.
[1]: https://louwrentius.com/this-blog-is-now-running-on-solar-po...
- Too many WSGI connections if the timeouts aren’t tweaked
- Too many database connections, especially without caching and tuning
- on the Apache side if MaxRequestWorkers isn’t set there will be memory issues with 1GB RAM
- the disk could easily hit IOPS limits, especially if there is a noisy neighbor
It’s not likely all or any of these things will hit IRL, but that all depends on traffic and usage patterns. It matters not, if you were getting 4.2 M requests each day you’d be in the Alexa Top 1000 and could probably shell out for the $8 server :)
However, relying on people themselves is often not the best stable solution. I am wondering if all these N^2 mistakes people made can be prevented by innovative means like language features, framework improvements, tooling and etc. And I'm talking about prevention, not the post mortem perf measure and fix kind
Parliamentary enquiry:(PDF) https://www.aph.gov.au/DocumentStore.ashx?id=0a7f6bd5-8716-4...
https://www.zdnet.com/article/census-2016-among-worst-it-deb...
https://www.theguardian.com/australia-news/2016/aug/10/compu...
Visitors don't come neatly one after the other. You might only have 1M requests a day but get random spikes with 100 requests at the same time.
I really suspect the website would fall long before it hits anything close to 4.2 million requests (which the author also seems to except).
That all said - long live tiny web servers!
1. Systems Operations is first and foremost about understanding systems, in all of their complexity, which means understanding the internals of your OS primarily.
2. Performance and networking, in particular, are super important areas to focus on understanding when it comes to learning the topic to help with software development.
3. A lot of it is about understanding concepts in abstract and being able to extrapolate to other situations and apply these concepts, so there's actually quite a lot of useful information that can be learned on one OS and still applied to another OS (or on one game engine and applied to another, et al).
Here's a few books I think are worth reading, not in any particular order of prevalence, but loosely categorized
Databases:
High Performance MySQL: https://www.amazon.com/gp/product/1449314287/
SQL Queries for Mere Mortals: https://www.amazon.com/gp/product/0321992474/
The Art of SQL: https://www.amazon.com/gp/product/0596008945/
Networking:
TCP/IP Illustrated: https://www.amazon.com/exec/obidos/ISBN=0201633469/wrichards... (updates on author's site at http://www.kohala.com/start/tcpipiv1.html)
The TCP/IP Guide: https://www.amazon.com/TCP-Guide-Comprehensive-Illustrated-P...
UNIX Network Programming: https://www.amazon.com/dp/0131411551
Beej's Guide to Network Programming: http://beej.us/guide/bgnet/
Operating Systems:
Operating Systems Concepts: https://www.amazon.com/Operating-System-Concepts-Abraham-Sil... (various editions, I have the 7th edition... I recommend you find the latest)
Modern Operating Systems: https://www.amazon.com/Modern-Operating-Systems-Andrew-Tanen... (the "Tanenbaum Book")
Operating Systems Design and Implementation: https://www.amazon.com/Operating-Systems-Design-Implementat-... (the other one, the "MINIX Book")
Windows Internals:
Part 1: https://www.amazon.com/Windows-Internals-Part-architecture-m...
Part 2: https://www.amazon.com/Windows-Internals-Part-2-7th/dp/01354... (I had the pleasure of being taught from this book by Mark Russinovich and David Solomon at a previous employer, was an amazing class and these books are incredible resources even applied outside of Windows, we used 5th edition, I linked 7th, which has the 2nd part pending publication).
MacOS Internals:
Part 1: https://www.amazon.com/MacOS-iOS-Internals-User-Mode/dp/0991...
Part 2: https://www.amazon.com/MacOS-iOS-Internals-II-Kernel/dp/0991...
Part 3: https://www.amazon.com/MacOS-iOS-Internals-III-Insecurity/dp...
Linux Kernel Programming:
Part 1: https://www.amazon.com/Linux-Kernel-Development-Cookbook-pro...
Part 2: https://www.amazon.com/Linux-Kernel-Programming-Part-Synchro...
The Linux Programming Interface: https://www.amazon.com/Linux-Programming-Interface-System-Ha...
General Systems Administration:
Essential Systems Administration: https://www.amazon.com/gp/product/0596003439/
UNIX and Linux Systems Administration Handbook: https://www.amazon.com/UNIX-Linux-System-Administration-Hand...
The Linux Command Line and Shell Scripting Bible: https://www.amazon.com/Linux-Command-Shell-Scripting-Bible/d...
UNIX Shell Programming: https://www.amazon.com/Unix-Shell-Programming-Stephen-Kochan...
BASH Hackers Wiki: https://wiki.bash-hackers.org/
TLDP Advanced BASH Scripting Guide: https://tldp.org/LDP/abs/html/
The Debian Administrator's Handbook: https://debian-handbook.info/browse/stable/
TLDP Linux System Administrator's Guide: https://tldp.org/LDP/sag/html/index.html
Performance & Benchmarking:
Systems Performance: https://www.amazon.com/Systems-Performance-Brendan-Gregg-dp-... (this is Brendan Gregg's book where you learn about the magic of dtrace)
BPF Performance Tools: https://www.amazon.com/Performance-Tools-Addison-Wesley-Prof... (the newer Brendan Gregg book about BPF, stellar)
The Art of Computer Systems Performance Analysis: https://www.cse.wustl.edu/~jain/books/perfbook.htm (no longer available from Amazon, but is available direct from publisher. This is basically the one book you should read about creating and structuring benchmarks or performance tests)
I guess that's a "reading list", but this is just a small part of what you need to know to excel in systems operations.
I would say for the typical software developer writing web applications, the most important thing to know is how databases work and how networking works, since these are going to be the primary items affecting your application performance. But there's obviously topics not included in this list that are also worth understanding, such as browser/DOM internals, how caching and CDNs work, and web-specific optimizations that can be achievable with HTTP/2 or QUIC.
For the average software developer writing desktop applications, I'd say make sure you /really/ understand OS internals... at the base everything you do on a computer system is based on what the OS provides to you. Even though you are abstracted (possibly many layers) away from this, being able to peel back the layers and understand what's /really/ happening is essential to writing high-quality application code that is performant and secure, as well as making you a champ at debugging issues.
If you're trying to get into systems operations as a field, this is just a brush over the top surface and there's a lot deeper diving required.
Perhaps you can suggest a book or roadmap to learn it?
The raw queries themselves are fast enough, but for some reason running them in a framework, transforming them in to a Resource and dumping it as json takes so long that I'm scared to find out what this super popular framework is even doing under the hood.
Once I learn enough Python I'd like to compare its performance to something like FastAPI. But even that probably won't come near what these recent posts are describing.
(Disclaimer - it's just a side project and I haven't really looked in to making it faster)
A well written mobile app doesn't really have any need to be sluggish at all, including smooth animations and fast scrolling lists, it was doable 10 years ago, it's doable now. (*I don't know about games).
But unlike on the server side, the accepted wisdom in most places I've worked at is that the answer to the performance problems is: a new framework.
(I feel like this is a lie that developers tell the business side, and maybe themselves. It avoids having to explain that software is hard, sometimes you don't get it right the first time, and if you don't spend time and effort tending to it, it can turn into an ungodly and expensive mess - and that's got nothing to do with the hardware or the framework)
His site, https://peepopoll.com/, took about 10s to load for me. It’s also good to chart other metrics like response times while you benchmark. Requests per second isn’t the same as a low response time
I think you mean a second, but yeah, old tech is fast.
I find it funny when I read “raw html” emphatically, as if it was akin to writing assembly.
The database is also the part that doesn’t easily scale, unless you pick a highly scalable database from the outset, and those have their own complexity and tradeoffs as well.
That’s why I believe every project should start with a bulletproof model of how the database will work first, then fill in the other details from there.
It’s not always as easy as picking Postgres and calling it a day, unfortunately.
I'm really more surprised that static serving is so slow at 180 rps. This should be able to easily saturate the network, statically serving files is very, very fast. From what I see in the blog I doubt that the files are very large, so there is probably some other bottleneck or I'm missing something here.
The reason this is cheaper in a sense is because Workers deploys globally and needs zero devops. Per our estimates, this setup (for our workload) gets expensive once the request range goes beyond 1.5 billion a month after which deploying to colos worldwide becomes cheaper even with associated cost of devops.
We served 500GB of data the first month.
I imagine that the hosting company lost money on us (but they never called to complain).
anyone remember Cobalt server?
https://en.wikipedia.org/wiki/Cobalt_Networks#/media/File:Co...
Only static websites are the one which handle large amount of requests at low cost. Web hosting providers don't make money out of those clients, so they run shared plans.
Really we need to compare apples to apples (how many watt)!
Most of you have an external IP address, open port 80 and put it to good use before they put you behind a shared IP!
$6 VPS can handle 500,000 requests daily
On this server, I have PHP-fpm workers, nginx and MariaDB
The average CPU usage about 30%, load average is about 0.5
Not really. Real world traffic won't be uniform over one entire day. 50 QPS would be more accurate.
> Service Unavailable
> The server is temporarily unable to service your request due to maintenance downtime or capacity problems. Please try again later.
Why am I the only one not impressed by this.
Sure, that's true - but to try to progress the conversation: how would you measure the complexity of serving web requests, in order to perform more advanced cost comparisons?
(bandwidth wouldn't be quite right.. or at least not sufficient - maybe something like I/O, memory and compute resource used?)
#1 Minimalism. You don't need 400 KB of JS to display some mostly text content to your users with some interactivity sprinkled in.
You don't need to reinvent office software, or very rich text editors in browsers, stop using the web as a universal delivery platform/mechanism, because that's not what it was meant for. When browsers will ship integrated dependencies so that even CDNs don't need to be hit (like versions of jQuery, Bootstrap and numerous JS frameworks as well as WASM code like Blazor which contains a .NET runtime), then you'll be able to do that, but arguably that will never happen.
Use the web as a platform for displaying primarily text content with the occasional images, forms and a little bit of interactivity sprinkled in. Most sites out there simply aren't and shouldn't be like this (that said, when you have exceptional reasons for throwing aside that suggestion, do so): https://geargenerator.com
#2 Static content. You don't need to use Wordpress, Drupal, Joomla or many of the other CMSes out there, since they can get really heavyweight with numerous plugins and are not only a security challenge, but are also problematic from a performance perspective.
Consider using static site generators instead. When reading an article of yours, the DB shouldn't even be hit, since most of the article contents are unlikely to change often, so you should be able to pre-render each of the article versions as a set of static HTML and use the common JS/CSS that you already have for the rest of the articles. Furthermore, it's easy to just jump into CMSes and introduce ungodly amounts of complexity, all of which cause your back end to process bunches of code for each request. Static files don't have that drawback.
#3 Caching. Know when and what to cache, and how. Images, JS files, CSS files and even entire HTML pages should be cache friendly. Know which ones aren't, make exceptions for those and cache everything else.
Not only is it not necessary to hit the DB for many of the pages in your site at all, but also sometimes you shouldn't even hit the back end either. The most popular pages of your site should just live in a cache somewhere, be it within your web servers or a separate solution, so that they can be returned instantly. HTML is good for this, use it.
Furthermore, know what cache policies to use. Sometimes even the cache resources shouldn't be redownloaded, if the user already has these resources loaded from a different page. Use bundle splitting responsibly, extract common functionality in easily cacheable bundles and set the appropriate headers.
And yet, i've seen a surprising amount of ignorance in regards to caching, static site generation and even how large webpages have gotten: https://idlewords.com/talks/website_obesity.htm
I don't claim to know it all, but working towards the goal of efficiently using pages should definitely be viewed as an important one: be it because you want to pay less for your infrastructure, or care about the environment, or even just want to manage fewer nodes.
Instead, nowadays far too many orgs just try to be the first to market and ignore the engineering based approach to ensuring that the solutions are not only functional but also sustainable. That saddens me.
HTTPS and certificates? i have no clue how to setup that, i use dns from cloudflare and they have it all automatic for free
if your employees are asking you to pay ton of money for your services, hire someone else
GH/GL-pages still respond to HTTP/HTTPS requests, in my dictionary that's a webserver, but I guess for the millennials (I say with snark) it means "I don't have to think about what happens, it just serves my content for me."
congrats?
I don't mind reading about politics but I come to HN to read about tech. We can go elsewhere to get whatever politics we desire.
~10k rps (it was concurrent connections but close enough) was state of the art in 1999. Now 22 years later ~50 rps is somehow impressive.
I assume (based on 180 req/s for static page) that he is using mpm_prefork, where each Apache child handles a single connection. If he switched to mpm_event, which uses event loop like nginx, ~10k rps should easily be achievable, but I don't think WSGI would work with that.
Yes there are wildly unnecessary abstractions that are used for small sites/apps, but I would contend they are artifacts of someone who it’s trying to learn something new, and/or get promoted. I have no problem with the former.
I honestly don't understand how that can be true. I'm not suggesting you're lying of course, but when you put it this way it's almost like people are actively trying to slow their programs down. I have a few ideas on why that might be the case (switch to slow interpreted languages, switch to bigger web frameworks, bigger payload) but even that wouldn't explain all of it. Do you have any idea why things are this way?
It's something anyone who has done this for any length of time knows, that HN is impressed by this is confusing to some of us. If you were trying to get as little out of your server as possible you'd serve cached content using this framework in this language.
Is this stuff not being learned?
> Is this stuff not being learned?
I don't know if it is. I recently finished my studies, and most people had no curiosity at all. As in, they learned a framework early, used it everywhere, and got a job using it. I do remember reading a few times on tutorial that you should put a Nginx as reverse proxy in front of your Django/Flask/Express server to server static files, so I think most people know/do that but I'm not sure.
On the other hand, having the wisdom of knowing what can be static in the first place? I don't think that it's something teached. In fact this kind of wisdom can be hard to find outside of reading lots of sources frequently in hope of finding little nuggets like that. I don't think I was ever taught explicitely "You should first try to find out if the work you're trying to do is necessary in the first place". In a way it's encoded in YAGNI, but YAGNI isn't universal, and is usually understood at the code level and not the tooling level.
I think the trick is realising that reaching for a "programming language" is just one of the tools we have to solve a certain problem, and probably the last one we should reach for! For a stable system, you want less moving parts. A good programmer fights for it.
Can you solve a problem just by storing a JSON file somewhere? Can you solve a problem without a backend? Can you solve a frontend problem with just CSS or just HTML? Can you solve a problem without Javascript? Can you solve a data storage problem with just a database instead of database+Redis? Do you really need a full-fledged web framework where a micro-framework would suffice? Do you need micro services, Kubernetes, containers and whatnot for your site before it gets its first visitor?
I find that a lot of people go for the "more powerful" tool just to cover their asses. They don't want surprises in the future, so they just go for something that will cover all bases. But what you actually want is the things with the least power [1].
Another issue is that intelligent people have an anti-superpower called "rationalisation". They can justify every single decision they make, as misguided as it is. So it doesn't matter if a website could be done with a single HTML file: it is always possible to find a reasonable explanation for why it needed k8s, micro-services and four languages.
[1] Using the least powerful tool also has other advantages, see Tim Berners-Lee "Principle of Least Power" for example - https://blog.codinghorror.com/the-principle-of-least-power/
All traffic is static by definition. You are not modifying bytes when they are in transit to user. And you don't have to serve different bytes each microsecond just because users want to be "up-to-date". The network latency is usually around 40ms or so. If your website serves 1000s of requests per second, you should be able to cache each response for 10ms, and no one will ever notice (today this is called "micro-caching").
Of course, most webpages can't be cached as whole — they have multiple "dynamic" parts and have to be put together before serving to user. But you can cache each of those parts! This is even simpler if you do client-side rendering (which is why MangaDex abysmal performance is pathetic).
Then there are ETags — arbitrary strings, that can be used as keys for HTTP caching. By encoding information about each "part" into a substring of ETag you can perform server-side rendering and still cache 100% of your site within static web-server, such as Nginx. The backend can be written in absolute hogwash of language such as Js or Python, but the site will run fast because most requests will hit Nginx instead of slow backend. ETags are very powerful, — there is literally no webpage, that can't be handled by a well-made ETag.
Even pages, that need to be tailored to user's IP can be cached. It is tricky, but possible with Nginx alone.
Instead of "static" you are better off thinking in terms of "content-addressable".
Sometimes there's also a misunderstanding of metrics that leads devs to not think about performance tuning. Like, "4.2M requests a day" is clearly incorrect at this 50rps benchmark. Traffic is not linear, it's bursty. You will never serve 50rps of human traffic steadily for 24 hours. If you're serving 4.2M requests per day, 90% of it will be in a 12 hour window, peaking at whenever people have lunch or get off work, with a short steep climb leading to a longer tail. So to not crash your site at peak visitorship, you realistically need to handle 300+ rps in order to achieve 4.2M requests per day. (But also that's requests per second... if under load it takes 5 seconds to load your site, you can still serve a larger amount of traffic, it's just slower... so a different benchmark is also "how many requests before the server literally falls over")
Let's do web development. Since you mentioned payloads: today they're bigger, and often come with redundant fields, or sometimes they're not even paginated! This slows down the database I/O, requires more cache space, slows down the serialisation, slows down compression, requires more memory and bandwidth...
And then you also have the number of requests per page. Ten years ago you'd make one request that would serve you all the data in one go, but today each page calls a bunch of endpoints. Each endpoint has to potentially authenticate/authorise, go to the cache, go to the database, and each payload is probably wasteful too, as in the previous paragraph.
About authentication and authorisation: One specific product I worked on had to perform about 20 database queries for each request just for checking the permissions of the user. We changed the authentication to use a JWT-like token and moved the authorisation part to inside each query (adding "where creator_id = ?" to objects). We no longer needed 20 database queries before the real work.
About 15 years ago I would have done "the optimised way" simply because it was much easier. I would have used SQL Views for complex queries. With ORMs it gets a bit harder, and it takes time to convince the team that SQL views are not just a stupid relic of the past.
Libraries are often an issue that goes unnoticed too. I mentioned serialisation above: this was a bottleneck in a Rails app I worked. Some responses were taking 600ms or more to serialise. We changed to fast_jsonapi and it went to sub-20ms times for the same payload that was 600ms. This app already had responses tailored to each request, but imagine if we were dumping the entire records in the payload...
Another common one is also related to SQL: when I was a beginner dev, our on-premises product was very slow in one customer: some things on the interface were taking upwards of 30 seconds. That wasn't happening in tests or in smaller customers. A veteran sat down by my side and explained query plans, and we brought that number down to milliseconds after improving indexing and removing useless joins.
A few weeks ago an intern tried to put a javascript .sort() inside a .filter() and I caught it. Accidentally quadratic (actually it was more like O(n^4)). He tried to defend himself with a "benchmark" and show it wasn't a problem. A co-worker then ran anonymised production data into it and it choked immediately. Now imagine this happening on hundreds of libraries maintained by voluntaries on Github: https://accidentallyquadratic.tumblr.com
All those things are very simple, and you certainly know all of them. They're the bread and butter of our profession, but honestly somewhere along the way it became difficult to measure and change those things. Why that happened is left as an exercise.
> All those things are very simple, and you certainly know all of them.
I wonder about that. Most of the people I graduated with didn't know about complexity. Some had never touched a relational database, most probably didn't know about views. I doubt most of them knew what serialization mean.
(Sorry in advance for the rant)
I also remember piercing together my first programs from other people’s code. Whenever I needed an internet forum I’d build one. Actually, all my internet friends, even the ones who didn’t go into programming, were doing web forums and blogs from scratch!
Today people consider that a heresy. “How dare you not use Wordpress”.
My generation just didn’t care, we built everything from scratch because it was a badge of honor to have something made by us. We didn’t care about money, but we ended up with abilities that pay a lot of cash. People who started programming post the 2000s just didn’t do it...
I think it is visible that I sorta resent the folks (both the younger, and the older who arrived late at the scene) constantly telling me I shouldn’t bother “re-inventing the wheel”. Well, guess what: programming is my passion, fuck the people telling me to use Unity to make my game, or Wordpress to do my blog.
django is on that list, near the bottom, at 15k. Clearly his request response is more complicated than a simple fortune.
There are a few factors I've experience here. Interpreted languages have encouraged development patterns that are slow. Ease of allocating memory has tended to promote its overuse. Coding emphasis has been heavily weighted on developer productivity and correctness of code over lean and fast.
I find that poor web server configurations are pretty common. Smaller shops tend to use off the shelf frameworks rather than roll their own systems. Various framework "production" setups often don't include any caching at all. Static files are compressed and then sent on every request instead of preserving a pool of pre-compressed common pages/assets/responses. It's like the framework creators just assume there's going to be a CDN in front of the system, and so they don't even try to make the system fast.
The latest crop of web devs have very little experience setting up production systems correctly. Companies seem more interested in AWS skills than profiling. Then you have architectural pits like microservices. Since there is so little emphasis on individual system performance it seems that it has become or is becoming a lost skill.
Then there is so much money being thrown at successful SaaS that it just doesn't matter that their infrastructure costs are potentially 50x what they actually need. It seems that the only people squeezing performance out of software are the poor blokes who are scrimping by on shoestrings with no VC money in sight.
If you have any tips on how to get ~10k rps (or even a more reasonable improvement) on a £4 a month server, I at least would be very interested in hearing about them.
I was talking with an architect at a bank whose team was having trouble getting under a 2-second maximum for page views. They blamed it on having to make TCP requests to other services, and said something like "at a couple hundred milliseconds per request, it adds up quickly!" My head nearly exploded at that. I spun up some quick tests in AWS to show exactly how many requests one could make in 2000 ms. I don't have the numbers handy, but the number is very large.
This junky slice of a server handling full page requests in 20 ms is a fine example to counter thinking that's endemic in enterprise spaces.
Also, that "awful" 1MB memcpy is likely all in L3 cache these days. But even if it weren't in cache, we're talking about an operation that takes 50 microseconds (1MB read + 1MB written == 25microseconds + 25 microseconds).
Given that modern CPUs have like 16+ MBs of L3 cache (and more), and some mainstream desktop CPUs have 1MB of L2 cache... its very possible that this memcpy is far faster in practice than you can imagine.
1MB is big, its a million bytes. But CPUs are on the scale of billions, so 1MB is actually rather small by modern standards. Its surprisingly difficult to get intuition correct these days...
My point isn't that it's impressive in some ultra-tuned performance sense. It's that doing pretty mundane things on pretty basic servers is still very fast compare with a) the past, or b) what a lot of developers are used to professionally. That's why it is interesting to the crowd here.
My site updates some data every 10 minutes or so, I don't think that would work with GitHub Pages. Maybe you could do something with Cloudflare Pages combined with Workers, but Workers have a limited free tier. The normal Cloudflare CDN scales to infinity for free.
My £0 a month server can handle 4.2M requests a day [1]
[1] https://ahamlett.com/blog/post/My-%C2%A30-a-month-server-can...