The Performance Cost of Node.js and GraphQL

https://www.softwareatscale.dev/p/the-hidden-performance-cost-of-nodejs

by todsacerdoti • 3 years ago

41 37 3 years ago

We could write a one-shot resolver that implements the entire query. [...] This brings up the meta-question about why use GraphQL in the first place [...].

This seems like a reasonable approach to me. Give downstream-developers the opportunity to work unobstructed, then observe which queries are slow / heavily used, and implement optimised resolvers for just these cases.

In the worst case, you might not save any work on your end, but at least teams that depend on your api are never blocked from iterating.

throwaway2990• 3 years ago

Or just stop separating front end and backend. The worst companies to work for are the ones who separate these 2 then try to patch it with junk like graphql.

SebastianKra2• 3 years ago

Well, personally I have a bigger aversion to companies who hire dogmatic developers that discard an entire technology as junk, without even considering the scenarios in which you might prefer a horizontal separation of teams.

29athrowaway• 3 years ago

"2x-3x latency increase"

With respect to what?

bastawhiz2• 3 years ago

Instead of multiple batches of promises, we fetch the user, items, and details in one shot. This brings up the meta-question about why use GraphQL in the first place, but that’s a larger conversation for a separate time.

At my current job we have a TON of methods like the one just above this paragraph, and they're terrible to work with. It's hard to write (correctly), it's hard to test (correctly), and it is simply tedious to make work. I could write four lines of declarative GQL (that fetches exactly what I ask for from the DB) or a real mess of a method.

In practice, it's rarely the case that you're returning 10k of anything. To look at the author's benchmark, the difference between 20ms and 60ms for a far nicer and more maintainable dev experience (for 1000 returned items) is not a compelling argument.

Hell, if I look at our stack, the biggest cost is creating parameterized queries with many hundreds of IDs to select to prevent N+1 queries. I've spent countless hours optimizing the slowest code (that uses the pattern the author suggests) to use JOINs instead. Removing the round trip and the cost of shipping the list of IDs back to the DB consumed far more time than you might expect.

If you want to go down an even deeper rabbit hole, look at the cost of serializing your data to JSON and deserializing it on the client. When you're shipping tens of thousands of records to the client, that's likely hundreds of kilobytes or even megabytes of data. Serializing that is expensive!

tango129• 3 years ago

Totally feel the pain. Ironically, most DBs have started supported really good json aggregation functions.

Combined with lateral joins, it’s possible to even push this ser/de to the DB directly and have minimal unpacking and packing in sending that data back to the client!

ElectricalUnion2• 3 years ago

To me those advanced json aggregation functions are to handle the fact that most DBs accept de-normalized JSON fields.

Direct DB ser/de looks like a bad idea, it's often much easier to scale the application server layer that the DB layer on most cases.

And in most situations you want to "expand" the data as close as possible to the final location.

If you really care about having very low overhead ser/de you would probably go for protobuf/Cap'n Proto almost everywhere instead.

DanielHB2• 3 years ago

Most applications don't even need to optimise for N+1 queries and the ones that do could get by with just optimising it in a few places. Measure first then optimise as you see the need.

GraphQL batching together what would be multiple waterfall REST requests is such a perf boost for most cases that N+1 is really only an issue if your DB is bottlenecked.

tango129• 3 years ago

I think there are 2 subtleties here on where you optimize perf:

1. Frontend makes n+1 requests to the api. Either use GraphQL or build a custom endpoint that shapes a perfect response.

2. Build an api server that fetches data from the db efficiently without n+1 hitting the DB.

The whole tension introduced with resolver/data-loader ways of building GraphQL, is that while it makes 1 easy, it makes 2 harder. Compared to bespoke REST-ish endpoints.

bastawhiz2• 3 years ago

I'd normally agree, but this is dangerous advice. Why? Because by the time you notice, it's not slow, it's down. N+1 queries don't scale badly as a function of users, it scales badly as a function of cumulative user behavior. One unexpectedly active user can make you self-DoS your database. Even a DB proxy like pgbouncer won't really help because parameterized queries are likely implemented with stored procedures, which require an exclusive DB connection, layering bottlenecks on bottlenecks.

ttfkam• 3 years ago

All this pain when most of the time folks could just fire up Postgraphile or Hasura, point them at their Postgres database, and go sip mai tais by the pool. I honestly don't understand why folks insist on writing their resolvers by hand in 2023.

"But I don't want folks having direct knowledge of my database schema," I hear as a retort.

1. Most of the time for most projects, the GraphQL schema or REST API is a direct analog of the database.

2. You can always make a new Postgres schema with views to expose just the data features you want with annotations to show where the foreign key relationships should be. Still worlds easier than writing and maintaining resolver and data loader logic by hand.

brycelarkin• 3 years ago

Postgraphile just released their v5 update (still in beta) and it’s even more awesome than v4. No more crazy queries to address the n+1 problem.

Exuma• 3 years ago

Postgraphile looks awesome. Thx for pointing it out

ShadowBanThis013• 3 years ago

I looked at GraphQL and couldn't get past the fact that it doesn't provide a way to express joins. Isn't that a problem?

alex_lav3• 3 years ago

A join is a SQL-ism. GraphQL allows you to express _relationships_, which the underlying database would fulfill via a join.

gedy• 3 years ago

Hasura.io was mentioned above. I'd recommend you take a look at their schema extensions to do this.

However, please keep in mind graphql is an API convention, much as REST is used. REST doesn't provide a way for joins either. Both are not direct DB access.

wdb2• 3 years ago

Yeah, Hasura is really nice. Saves a lot of time

stephen• 3 years ago

One of Bun's assertions is that their AsyncLocalStorage is significantly faster than Node's, b/c it purposefully only implements the subset of async_hooks that can be implemented w/o affecting performance:

https://github.com/oven-sh/bun/pull/3089

I haven't had a chance to test it out yet, but perhaps the OP could add Bun to the mix? To compare it's promise overhead vs. Node's promise overhead.

Also there is an issue in Node about performing the same optimization:

https://github.com/nodejs/node/issues/46265

tango129• 3 years ago

IMHO: The way we often approach GraphQL execution is a little dated. It’s perhaps legacy thinking from times when we had data layers that couldn’t be expected to scale even for the majority of use cases. But that has completely changed over the last 5-10 years - DBs can scale pretty massively, for specific workloads with very little work required at the app server layer. So if we used DBs to actually model as close to our domain entities as possible (and yes, we’ll need multiple DBs sooner or later), then a huge part of our work is already technically done.

So - here’s another way to approach building GraphQL:

1. Build a “read graph” so that you get a highly composable API that can almost entirely be pushed down into a data layer (eg: Postgres). This would involve techniques similar to building a compiler. Which is hard, but the great news is that it’s easy to make reusable across people who share the same data layer (Eg: Postgres).

There are only 2 business logic concerns at layer, both of which can be pushed down, which helps simplify the problem. 1) modelling/transformation: which can be done in the language of the data layer and pushed down (like views) 2) authz: which can also be done in the language of the data layer and pushed down (like rls)

2. The “command graph”: a set of controllers written in whatever language you please that makes changes to the data layer (or delegates to an orchestration system) and returns a reference to the read graph after having made changes.

This is kind of how we approach the problem at Hasura - a connector SDK that makes it easy to build reusable query compilers for different DBs or API services.

And it’s been great to see similar ideas in the ecosystem - let’s make it easy to execute a tree of functions of create a query plan, instead of executing a tree of data fetch functions (like the OP). Grafast by the postgraphile folks comes to mind.

wdb2• 3 years ago

I only received upset emails when a (businesS) consumable API was GraphQL only.

The Performance Cost of Node.js and GraphQL

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