> From what I hear, the main draw is separating what you want from how you get it, so your calling code can just focus on what it needs. But you can use regular functions to do that. What libraries like Pathom do is leave it open to the caller what shape of data they need.
hmmm... it would be interesting to try an approach where you make heavy use of memoization and then write your functions to take the the minimal set of inputs (e.g. just the primary key for a record). I'm not sure if that's exactly what you had in mind, but here's a strawman example:
;; instead of having a resolver with this input
{:input [:person/age
:person/name
{:person/pet [:pet/species
:pet/n-legs]}]}
;; you could have this plain function which calls regular functions to get its
;; input, each of which only need a single entity ID for their input
(defn get-person-stuff [db person-id]
(let [age (get-person-age db person-id)
name (get-person-name db person-id)
pet-id (get-person-pet db person-id)
pet-species (get-pet-species db pet-id)
pet-n-legs (get-pet-n-legs db pet-id)]
...))
And you know, I think that would be workable, even though it feels more boilerplatey to me. It would still get you the main benefit of not having to keep track of all the data shapes that are needed by the functions you're calling etc. Some off-the-cuff thoughts:
- with this approach you have a single function for each attribute, so you don't have the situation with pathom/biff.graph where there are multiple resolvers that could be called to get a particular attribute. However note that you could always put an assertion in your codebase that ensures no two resolvers share the same output key, which would then also give you the ability to know exactly what resolvers are being called.
- my example above doesn't include optional inputs, so that's logic you'd also need to write into all your functions: don't fetch the pet data if the pet ID is nil, don't return anything if the person name is nil, etc.
- if you do all that with regular code instead of dependency injection, that does mean you have more code to test, and you have to either supply a test DB (and populate it with everything the functions you're calling need) or mock out the functions. With the dependency injection approach you get plain-old-pure-functions which helps keep your unit tests nice and dumb.
- I like the readability of being able to look at the input / output queries and know exactly what shape of data I'm dealing with.
- There might be performance issues with the memoized functions approach. Pathom and biff.graph both support batch resolvers for example, and I'm not sure if you could do the equivalent as cleanly with the functions approach. And Pathom of course has its additional query planning step which does... stuff.
Going back to your comment, some thoughts:
> But I think letting the caller do subtle query changes that can completely change which resolvers are triggered and how something is fetched is kinda leaky.
This is an area where you might like biff.graph more than Pathom. Since there's no query planning step, the way that biff.graph executes your queries should be fairly predictable. It's basically just doing a depth-first traversal of your query.
(My first bullet point above is relevant too--you can always restrict yourself to having only one resolver per attribute so there's no question of what resolver is getting used.)
> How do you write the perfect resolver for all situations? How do you keep them from accidentally exploding their fetches?
Typically you write resolvers with only one level of joins/nesting and then let the query engine do the rest. so e.g. instead of writing a resolver that returns something like `{:person/pet {:pet/id 1, :pet/toys [{:toy/id 2, ...}, ...]}}`, you would have one resolver that returns `{:person/pet {:pet/id 1}}` and then another resolver that takes a pet ID and returns `{:pet/toys [{:toy/id 2}, ...]}` etc.
So there is a trade-off here in that e.g. you may end up running multiple database queries even though you could've stuffed everything you need into a single database query. That is mitigated by batch resolvers at least so you don't get N+1 query problems.
I've never needed to do this myself yet, but if you do run into any places where the performance isn't good enough, you can always write those bits the regular way (e.g. have a resolver that does a more complex query and returns nested data and/or don't even use pathom/biff.graph for this one bit). i.e. optimize where needed but stick with the default in most places.
> Is it not better to have things be explicit through function calls instead of chasing down disjointed call graphs?
There are pros and cons I think. Sometimes you want to know how an input is being computed and sometimes you want to be able to understand some logic in isolation. In practice I've acclimated quite a bit to the graph structure; I feel like it does a nice job of helping you split your code into the right "chunks".
> Typically you write resolvers with only one level of joins/nesting and then let the query engine do the rest. so e.g. instead of writing a resolver that returns something like `{:person/pet {:pet/id 1, :pet/toys [{:toy/id 2, ...}, ...]}}`, you would have one resolver that returns `{:person/pet {:pet/id 1}}` and then another resolver that takes a pet ID and returns `{:pet/toys [{:toy/id 2}, ...]}` etc.
> So there is a trade-off here in that e.g. you may end up running multiple database queries even though you could've stuffed everything you need into a single database query. That is mitigated by batch resolvers at least so you don't get N+1 query problems
This is the crux of my issue, and batch resolvers don't solve all of it. Batch resolvers solve cases where you need multiple iterations of the same query with different inputs. But in your example, that's two different resolvers that were broken down into atomic units. From what I understand, batch resolvers don't help with that. You need to write a third resolver that can get the outputs of both.
And in that case, it would be nice to have a query planner that can, at the very least, see that a single query could be done with 1 resolver and not two.
yep, so if it's important for the application you're working on that you always run the minimum number of database queries possible, biff.graph isn't a good fit. Pathom's query planner might work as you've described; I'm not sure.
As for function memoization, I previously tried this workflow and after scratching my head about it, I think it's just not possible to make it scale properly (in the sense of making a library of resolvers/functions where you don't know how they'll be used exactly). The memoized function has no way to know how often it's called. It can be called 2 times, or 2000 times. So it's unclear how large its cache should be and there isn't a clear mechanism for when to flush the cache. I couldn't find a good mechanism to safely use it. In the Pathom model .. as far as I understand you just don't need to worry about that since the outputs are "cached" in the context of a query (or an inner input) and discarded when you're "out of context".
Since often you have many similar requests it can make sense to add a layer of memoization a the top level to remember the last request (cache of size 1) but otherwise it should scale okay. Though I'm sure it's not difficult to create pathological cases where it probably doesn't work and you end up recomputing stuff.
You could always introduce an explicit caching context by doing something like `(binding [cache (atom {})] ...)` whenever you start using some functions like this. If you were trying to use this approach inside a library then you could wrap the public functions with that. Not sure if that would work for the way you were trying to do it.
> It's very cool you managed to make a mini Pathom - esp in so few lines of code :))
Thanks! The possibility of doing this had been on my mind for a while... and then I finally got around to trying it since all I had to do to get started was say "try making something like pathom but without [...]". I actually have all the prompts and feedback for the initial POC over here[1] since at the time I was using github issues/comments for my LLM-driven-development workflow.
Over the past few weeks as prep for release I went over all the code manually (especially since the whole point of this thing is for the implementation to be easy to understand) and basically rewrote the whole thing, or at least that's what it felt like.
> But the end result looks almost identical? Resolver declarations are a bit reorganized and look a bit cleaner - though you could do that with a wrapper around Pathom. Why not fork Pathom and just make some QOL adjustments?
The main thing I was going for was just to reduce the implementation size; the tweaks I made to e.g. `defresolver` were really just a side thing. To give some more background on the motivations, an issue I've had sometimes with Pathom is figuring out what's going wrong when my queries don't give me the results I'd expect. A few times as part of that I've gone spelunking through the Pathom codebase but still had never built up a complete understanding of how the query planning and execution works, which has meant that my debugging has always been more trial-and-error / black-box than I'd prefer. So I wanted to see "what is the least complex way that I could take an EQL query and figure out the results, even if the way I do it is dumber than the way Pathom does it?"
i.e. I'm trying to minimize the amount of time it takes for someone to read the code and understand exactly what's going on under the hood. Hence layering more code on top of Pathom would only hinder that goal.
> the tweaks I made to e.g. `defresolver` were really just a side thing
If it weren't for those, would Pathom be a drop-in replacement? Or is there different logic?
I'm a bit of a beginner with this all myself, so yeah, I get how it's a bit of a black box :)) Think it's very cool you re-implemented it.
> To give some more background on the motivations, an issue I've had sometimes with Pathom is figuring out what's going wrong when my queries don't give me the results I'd expect
I'm curious in what scenario PathomViz is not giving enough info. I had a lot of trouble getting it working tbh (never got the nested query working) but from the docs it seems like it should give you all the information you'd need to reason back to why you get a particular output. Reimplement all this debugging stuff seems potentially a lot of work - but maybe I'm wrong. More tools around the diagnostic output https://pathom3.wsscode.com/docs/debugging/ is something I hope to explore eventually.
> If it weren't for those, would Pathom be a drop-in replacement? Or is there different logic?
I could've written biff.graph to work with actual Pathom resolvers. In fact it wouldn't be hard to write a shim that takes Pathom resolvers and returns biff.graph resolvers. Although not all resolvers would work since biff.graph doesn't support everything in EQL (e.g. union queries, attribute parameters).
The query results aren't strictly guaranteed to be the same, so even with a shim I wouldn't recommend dropping biff.graph into a large project that's already using Pathom. And then that's not even getting into all the Pathom features that biff.graph doesn't support at all (lenient mode, plugins, async mode, the graphql adapter...).
But as for the core concepts, yeah I'd say they're pretty close.
> I'm curious in what scenario PathomViz is not giving enough info. I had a lot of trouble getting it working tbh
I had that trouble too heh heh--I tried running it I know at least once but didn't succeed. I don't remember exactly what the issue was... but I probably should figure that out.
Even if I got better at debugging Pathom though, for Biff I would still prefer to have an implementation that's easier for users to understand so that ideally they don't even need extra tools to aid with debugging.
FWIW there is an example here[1] of what the biff.graph error looks like when a nested required attribute can't be resolved. That file also has examples of some additional validation logic I've thrown in, e.g. biff.graph will complain if one resolver declares an attribute as a join and another resolver declares it as a scalar. Sometime for our codebase at work I'll probably write some assertions to do those kinds of checks on our Pathom resolvers.
Oh sorry, I kind of meant it the other way around. You start with biff.graph and you'd swap in Pathom if the featureset or performance wasn't adequate. It makes sense that since you support a subset that it doesn't work the other way around! It might make sense to reinvent the wheel if there is a clear gain - but if there isn't any big innovation going on in the library interface, it's generally nice to keep the same interface if it's easy enough to do - but that's just my opinion haha
And yeah, now that I have a larger application with Pathom.. I should retry Viz too :))
And that's very cool you're taking error seriously. Sorry, I missed it when I looked at the rep the first time! Thanks for your help with Pathom a few months back (kxygk on Github)
ah got it. yeah, in that case you can write a biff -> pathom resolver shim that works for everything. Again though the main thing is just the fact that they have two completely different query engines and aren't guaranteed to give the same results. e.g. off the top of my head I can think of a contrived scenario where biff.graph might not be able to resolve something but Pathom can since it can "look ahead" in the query planning step.
Maybe that kind of situation is fine and the question is really just if there are queries that biff.graph can handle which Pathom can't. If your resolvers are written correctly maybe not? But there have definitely been times with Pathom where I did something wrong that threw off the query planner in ways I didn't expect.
In any case, if I end up wanting to support migrating easily between the two as a core feature, I'd definitely want to e.g. do a bunch of generative tests to find out what kinds of queries end up with different results. Until then, a downside of supporting Pathom resolvers without a shim is that it might give people the false impression that biff.graph is a drop-in replacement for Pathom or vice-versa.
So far though the main target audience I have for biff.graph is people (biff users) who have never even heard of Pathom before, so interchangeability hasn't been a top concern. Though if many people start using biff 2 and then eventually some of the start wanting to migrate to pathom, I'd be down to explore that area.
And haha yeah nice to bump into you again--I think I remembered your username from reddit, assuming it's geokon there.
Yeah, it's a big eye-opener. I'd like to see if I can figure out an ergonomic way to do it in Python since I do a fair amount of work in that, and passing ORM objects around isn't great.
I have an old repo that explores the concept at bmritz/datajet. I’ve also toyed around with the idea of using type hints and type aliases in python as the “data key” (equivalent to :user/id). Would love to have something equivalent in python.
AI has been working out well for me writing Clojure, both in personal projects and at work. Documentation, not so much... I write all that by hand.
For Biff I've been using AI to generate a rough draft of all the code and then I take a manual pass over things before releasing. Seems to be a good middle ground.
As the author of a different project also named Biff, I do have to warn you that half the comments on your HN posts will be people quoting back to the future--though I haven't decided yet if that's annoying or an engagement hack!
I've been working on this kind of thing over the past several years (for a while full time as an attempted entrepreneur, now on the side for the past couple years). The latest iteration is https://yakread.com -- hit "take a look around" and you can see the "home page"/a list of recommendations without signing up. The recommendations are personalized, i.e. the probability you'll see any particular post depends on your individual interactions with past posts, if you've signed up. (it does collaborative filtering with spark mllib). So that may be a bit different from what you had in mind, since your comment sounds more like an unpersonalized system, but with some extra exploration thrown in. However in practice I suspect the biggest thing the collaborative filtering is doing at Yakread's current scale (not much) is learning which items are good/bad in general.
I also do have some methods baked in for doing exploration. "Epsilon greedy" is a common simple approach where x% of the recommendations are purely random. I do a bit more of a linear thing where I rank all the posts by how many times they've been recommended, then I pick a percentage 0 - 100, then I throw out the top x% most popular (previously recommended) items. that also gives you some flexibility to try out different distributions for the x% variable.
Thank you so much! "Epsilon greedy" sounds like a great approach for the general idea I had in mind — I only glanced it but will read it more deeply.
I'll definitely try out your product, but I have to say — an enter your email box is surprisingly high-friction and if you weren't a considerate person I'd met on Hacker News I'd probably close the tab when I saw that. I'll try it out and see if there's a particular reason why you need to capture an email address so early on, but I'd bet if you simplified it you'd get more traffic!
Thanks for the feedback. I've structured Yakread (and its predecessors) as a daily email newsletter because it increases user retention tremendously. It's much less work for users if Yakread can show up in a place they already check regularly (their email inbox) rather than trying to get users right away to build a habit of visiting a new website regularly. The most common approach to this problem for consumer products is to make a mobile app so you can send push notifications; I like email a lot more since it's a bit more decentralized and is/can be less pushy (no pun intended).
But yeah, I wouldn't be opposed to trying out an alternate landing page that shows you article recommendations up front with a signup box somewhere. Could be interesting to see how both approaches perform in an A/B test. Especially if I ever made a concerted effort to get traffic from HN; then structuring the site a bit more like HN would probably be great. Maybe even aggregate comments from bluesky/mastodon? Once I get through the mountain of other TODO items that's been piling up :).
You can do that, it's just slow if there are a lot of results.
Agreed you want to keep data in your main database normalized since it's easier to reason about and avoid bugs/inconsistencies in the data. The inherent trade-off is just that it's more computationally expensive to get the denormalized data.
The idea of materialized views is to get the best of both worlds: your main database stays normalized, and you have a secondary data store (or certain tables/whatever inside your main database, depends on the implementation) that get automatically precomputed from your normalized data. So you can get fast queries without needing to introduce a bunch of logic for maintaining the denormalized data.
The hard part is how do you actually keep those materialized views up to date. e.g. if you're ok with stale data, you can do a daily batch job to update your views. If you want to the materialized views to be always up-to-date then things get harder; the solution described in the article is one attempt at addressing that problem.
