dataloadgen is an implementation of a pattern popularized by Facebook's Dataloader.
It works as follows:
- A Loader object is created per graphql request.
- Each of many concurrently executing graphql resolver functions call Load() on the Loader object with different keys. Let's say K1, K2, K3
- Each call to Load() with a new key is delayed slightly (a few milliseconds) so that the Loader can load them together.
- The customizable fetch function of the loader takes a list of keys and loads data for all of them in a single batched request to the data storage layer. It might send
[K1,K2,K3]and get back[V1,V2,V3]. - The Loader takes case of sending the right resight to the right caller and the result is cached for the duration of the graphql request.
Usage:
go get github.com/vikstrous/dataloadgenSee the usage example in the documentation:
package main
import (
"context"
"fmt"
"strconv"
"time"
"github.com/vikstrous/dataloadgen"
)
// fetchFn is shown as a function here, but it might work better as a method
func fetchFn(keys []string) (ret []int, errs []error) {
for _, key := range keys {
num, err := strconv.ParseInt(key, 10, 32)
ret = append(ret, int(num))
errs = append(errs, err)
}
return
}
func main() {
ctx := context.Background()
// Per-request setup code:
loader := dataloadgen.NewLoader(fetchFn)
// In every graphql resolver:
result, err := loader.Load(ctx, "1")
if err != nil {
panic(err)
}
fmt.Println(result)
}dataloaden uses code generation and has similar performance dataloader does not use code generation but has much worse performance and is more difficult to use
Benchmarks show that this package is faster than both of the above and I find it easier to use.
BenchmarkDataloader/caches-8 4152324 270.3 ns/op 168 B/op 5 allocs/op
BenchmarkDataloader/random_spread-8 1000000 1281 ns/op 626 B/op 11 allocs/op
BenchmarkDataloader/concurently-8 33159 55575 ns/op 32649 B/op 160 allocs/op
BenchmarkDataloader/all_in_one_request-8 10000 7556166 ns/op 2574411 B/op 60032 allocs/op
BenchmarkDataloaden/caches-8 17960090 67.73 ns/op 24 B/op 1 allocs/op
BenchmarkDataloaden/random_spread-8 1223949 955.0 ns/op 279 B/op 5 allocs/op
BenchmarkDataloaden/concurently-8 27093 43594 ns/op 2867 B/op 76 allocs/op
BenchmarkDataloaden/all_in_one_request-8 10000 1410499 ns/op 487876 B/op 10007 allocs/op
BenchmarkDataloadgen/caches-8 22032517 53.61 ns/op 8 B/op 0 allocs/op
BenchmarkDataloadgen/random_spread-8 2558128 483.7 ns/op 287 B/op 4 allocs/op
BenchmarkDataloadgen/concurently-8 31900 34903 ns/op 2906 B/op 71 allocs/op
BenchmarkDataloadgen/all_in_one_request-8 10000 1032841 ns/op 573619 B/op 7 allocs/op