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Produce working product first, validate the idea, stabilize the business, start generating profit, and then you can start optimizing your costs.
In fact optimization is by far the easiest part of the process because there are many system programming experts on this HN thread who consider these optimizations to be trivial.
Looks like they're missing the obvious optimisation of putting the record data right after the CacheEntry members instead of allocating memory separately though. But that might just be me as a C-programmer talking and not be all that easy in Rust.
[1] https://doc.rust-lang.org/reference/dynamically-sized-types....
[2] https://doc.rust-lang.org/nomicon/exotic-sizes.html
I assumed they couldn't do that because they're using it with some kind of generic HashMap<K, V>. In that situation, can "V" be dynamically sized?
A dynamically sized "V" would mean you can't have an array of them, which might preclude some hash map implementations.
, which HashMap does not do, i.e. the keys and values have to have a statically known size.
Cloudflare started to pick Zig recently, for projects, that have memory constraints.
If you previous had three distinct Vec objects, then Rust would guarantee that you can't index out of bounds. If you now put all those objects into a single Vec and rely on offsets, then you now open the door to indexing out of range of these sub-slices without any panics.
It's a minor point, and it doesn't really invalidate the optimization, but I'm surprised the article didn't mention it.
For example in the Vec case, you could theoretically build an alternative which encodes the “three sections” property internally, and ensures correctness at construction time for the pointers. Not as completely safe as a Vec, but you can still get similar benefits for the “business logic”.
But I agree, just having a custom structure that does not provide a safe wrapper around this would be sacrificing standard guarantees.
Thank being said in this case it should be impossible to index out of bounds so maybe a panic is warented.
Which isn’t to say this optimization is a bad idea, just to say it’s sort of a straw man to imply coding in Rust to take advantage of safety guarantees is “serving Rust”
When I was using one malloc() per entry, a large blacklist took up 237 megabytes of memory. The same blacklist, once optimized to be loaded with a single malloc() call, only took up 9.5 megabytes of memory.
https://samboy.github.io/blog/entries/MaraDNS.html#BlogEntry...
Relevant support page, though light in details: https://support.mozilla.org/en-US/kb/captive-portal
Edit: ah, yes, DNS can be hijacked too (requires intercepting outgoing traffic on port 53 therefore incompatible with DoH), that may require fewer computing resources. Still need http otherwise the server cannot use the correct cert chain.
Edit 2: Wikipedia says both methods are used: https://en.wikipedia.org/wiki/Captive_portal and also mentions RFC 8910. I suspected something like that existed, hence my initial disclaimer.
My point was: that domain is not treated any differently from other domains.
> Big Pineapple uses jemalloc, an allocator designed for multithreaded, allocation-heavy workloads.
jemalloc multithreaded performance is actually poor(ish) compared to other modern allocators, which makes it a weird choice. But even weirder is why they're even using an allocator in the first place compared to a va MAP_ANON | MAP_NORESERVE arena carveout approach? You can also do punning that way too, which I'm not even certain if Rust supports?
What's the speed of service/response time relative to the data source?
At that point it might be enough to replace your multiple caches with fewer in-RAM databases?
It's an interesting problem.
Not really, TTLs are often short, but IPs might not change for years.
You can probably generate your own TTL, at scale, and avoid many DNS requests.
Or alternatively, if you don't tamper why would I want to use a service that serves stale data?
The data source is authoritative name servers operated by third parties, some of which are slow on their own, some of which are behind slow or lossy networks. Origin response times vary between probably 1 ms and 2 seconds +/- origins that never respond.
DNS is designed to distribute query load to the edge as much as possible, and that's enabled by caching. It just so happens that "the edge" is now becoming concentrated among a small set of providers because they wanted to make a business out of it.[1] They knew that this would be expensive going in, though.
[1] Nobody has to use 8.8.8.8 or 1.1.1.1. Most people can use their ISP's cache or a local cache instead without any noticeable difference in behavior.
Interestingly this is exactly how netlink works-ish: https://manpages.ubuntu.com/manpages/focal/man3/netlink.3.ht...
You start, get the type & length, and then that is how many bytes you read.
Some issues with that when you deserialize, from a raw stream in to `[u8; 4096]` buffer, the alignment is only guaranteed to be on 1 byte, not 4 bytes.
In practice it is 4 bytes, but if you run those tests with Miri, you'll get yelled at. So the fix there is to declare the buffer with a type that mandates the alignment of the largest type that you're going to be deserializing.
So then you start your buffer as follows: `[u32; 1024]`, and with `slice::from_raw_parts` you get to turn that into `[u8; 4096]` with the expected alignment.
As an exercise I wrote a streaming parser for netlink, the current existing package serializes everything, all at once.
Were there no design discussions/reviews when the system was setup to catch trivial things like this?
Rule 1. You can't tell where a program is going to spend its time. Bottlenecks occur in surprising places, so don't try to second guess and put in a speed hack until you've proven that's where the bottleneck is.
Rule 2. Measure. Don't tune for speed until you've measured, and even then don't unless one part of the code overwhelms the rest.
Rule 3. Fancy algorithms are slow when n is small, and n is usually small. Fancy algorithms have big constants. Until you know that n is frequently going to be big, don't get fancy. (Even if n does get big, use Rule 2 first.)
Rule 4. Fancy algorithms are buggier than simple ones, and they're much harder to implement. Use simple algorithms as well as simple data structures.
Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
https://web.archive.org/web/20260314210910/https://users.ece...
So you agree that they should've designed the system to use the appropriate data structure from the beginning?
At the point someone queries the 100TB of RAM, then maybe it is worth revisiting but even that has risks. You have to design the migration path, have fallback mechanisms etc.
So how would you decide which path to take in situations like this?
if you spend cycles on nitty gritty opinions like this time to market goes out further and further out. some napkin math, 130 gen13 servers cost "only" ~$2.6M. relative to the importance of the 1.1.1.1 and the market at the time. that is nothing to cloudflare.
this is not to say good system design does not matter. it very much does, but making that call at that time would've butchered the prodcut very much similar to google+, youtube etc.
Another interesting thing that happens is you don't necessarily know what form your actual optimizations will need to take. Later when your systems grow you discover the suboptimal parts you hadn't optimized for.
Very early on at Cloudflare I worked on part of the DNS infrastructure that took DNS records from the UI and got them in a state for actual authoritative serving. The system had been constructed anticipating Cloudflare having millions of customers with unique domains, but it had not been constructed for a single customer with a single domain with millions of records. This caused a periodic slow down in DNS record updating while the system churned on that one customer.
In a different job I worked on a piece of optimization software that needed to keep track of "node" A is reachable from node "B". This had been implemented as a matrix (literally a malloced NxN matrix of ints storing 0 or 1) which worked really well for small systems. But you'd be out of memory really fast on a large project. I replaced the matrix with a hash table and all was good because the matrix was actually really sparse.