A Golang based high performance, scalable and distributed workflow framework
It allows to programmatically author distributed workflow as Directed Acyclic Graph (DAG) of tasks. GoFlow executes your tasks on an array of workers by uniformly distributing the loads
Install GoFlow
go mod init myflow
go get github.com/s8sg/goflowLibrary to Build Flow
github.com/s8sg/goflow/flow/v1
Make a flow.go file
package main
import (
"fmt"
goflow "github.com/s8sg/goflow/v1"
flow "github.com/s8sg/goflow/flow/v1"
)
// Workload function
func doSomething(data []byte, option map[string][]string) ([]byte, error) {
return []byte(fmt.Sprintf("you said \"%s\"", string(data))), nil
}
// Define provide definition of the workflow
func DefineWorkflow(workflow *flow.Workflow, context *flow.Context) error {
dag := workflow.Dag()
dag.Node("test", doSomething)
return nil
}
func main() {
fs := &goflow.FlowService{
Port: 8080,
RedisURL: "localhost:6379",
OpenTraceUrl: "localhost:5775",
WorkerConcurrency: 5,
}
fs.Register("myflow", DefineWorkflow)
fs.Start()
}
Start()runs a HTTP Server that listen on the provided Port. It also runs a flow worker that handles the workload
Start redis
docker run --name redis -p 6379:6379 -d redisRun the Flow
go build -o goflow
./goflowcurl -d hallo localhost:8080/myflowUsing the goflow client you can request the flow directly. The requests are always async and gets queued for the workers to pick up
fs := &goflow.FlowService{
RedisURL: "localhost:6379",
}
fs.Execute("myflow", &goflow.Request{
Body: []byte("hallo")
})GoFlow scale horizontally, you can distribute the load by just adding more instances
Alternatively you can start your GoFlow in worker mode. As a worker, GoFlow only handles the workload instead of running an HTTP server. If required you can only scale the workers
fs := &goflow.FlowService{
RedisURL: "localhost:6379",
OpenTraceUrl: "localhost:5775",
WorkerConcurrency: 5,
}
fs.Register("myflow", DefineWorkflow)
fs.StartWorker()Register() allows user to bind multiple flows onto single flow service.
This way one instance of server/worker can be used for more than one flows
fs.Register("createUser", DefineCreateUserFlow)
fs.Register("deleteUser", DefineDeleteUserFlow)The initial example is a single vertex DAG. Single vertex DAG are great for synchronous task
Using GoFlow's DAG construct one can achieve more complex compositions with multiple vertexes and connect them using edges.
A multi-vertex flow is always asynchronous in nature where each nodes gets distributed across the workers
Below is an example of a simple multi vertex flow to validate a KYC image of a user and mark the user according to the result.
This is a asynchronous flow with three steps
func DefineWorkflow(f *flow.Workflow, context *flow.Context) error {
dag := f.Dag()
dag.Node("get-kyc-image", getPresignedURLForImage)
dag.Node("face-detect", detectFace)
dag.Node("mark-profile", markProfileBasedOnStatus)
dag.Edge("get-kyc-image", "face-detect")
dag.Edge("face-detect", "mark-profile")
return nil
}Branching are great for parallelizing independent workloads in separate branches
Branching can be achieved with simple vertex and edges. GoFlow provides a special operator Aggregator to aggregate result of multiple branch on a converging node
We are extending our earlier example to include a new requirement to match the face with existing data
and we are performing the operation in parallel to reduce time
func DefineWorkflow(f *flow.Workflow, context *flow.Context) error {
dag := f.Dag()
dag.Node("get-kyc-image", getPresignedURLForImage)
dag.Node("face-detect", detectFace)
dag.Node("face-match", matchFace)
// Here mark-profile depends on the result from face-detect and face-match,
// we are using a aggregator to create unified results
dag.Node("mark-profile", markProfileBasedOnStatus, flow.Aggregator(func(responses map[string][]byte) ([]byte, error) {
status := validateResults(responses["face-detect"], responses["face-match"])
return []byte(status), nil
}))
dag.Edge("get-kyc-image", "face-detect")
dag.Edge("get-kyc-image", "face-match")
dag.Edge("face-detect", "mark-profile")
dag.Edge("face-match", "mark-profile")
return nil
}Subdag allows to reuse existing DAG by embedding it into DAG with wider functionality
SubDag is available as a GoFlow DAG construct which takes a separate DAG as an input and composite it within a vertex, where the vertex completion depends on the embedded DAG's completion
func (currentDag *Dag) SubDag(vertex string, dag *Dag)Say we have a separate flow that needs the same set of steps to validate a user.
With our earlier example we can separate out the validation process into subdag and put it
in a library that can be shared across different flows
func KycImageValidationDag() *flow.Dag {
dag := flow.NewDag()
dag.Node("verify-url", s3DocExists)
dag.Node("face-detect", detectFace)
dag.Node("face-match", matchFace)
dag.Node("generate-result", func(data []byte, option map[string][]string) ([]byte, error) {
return data, nil
},
flow.Aggregator(func(responses map[string][]byte) ([]byte, error) {
status := validateResults(responses["face-detect"], responses["face-match"])
status = "failure"
if status {
status = "success"
}
return []byte(status), nil
}
))
dag.Edge("verify-url", "face-detect")
dag.Edge("verify-url", "face-match")
dag.Edge("face-detect", "generate-result")
dag.Edge("face-match", "generate-result")
return dag
}Our existing flow embeds the KycImageValidation DAG
func DefineWorkflow(f *flow.Workflow, context *flow.Context) error {
dag := f.Dag()
dag.Node("get-image", getPresignedURLForImage)
dag.SubDag("verify-image", common.KycImageValidationDag)
dag.Node("mark-profile", markProfileBasedOnStatus)
dag.Edge("get-image", "verify-image")
dag.Edge("verify-image", "mark-profile")
return nil
}Conditional branching is a great way to choose different execution path dynamically
GoFlow provides a DAG component called ConditionalBranch. ConditionalBranch creates a vertex that composites different conditional branches as an individual subdags, each identified with a unique key resemble the condition
func (currentDag *Dag) ConditionalBranch(vertex string, conditions []string, condition sdk.Condition,
options ...BranchOption) (conditiondags map[string]*Dag)
Condition is a special handler that allows user to dynamically choose one or more execution path based on the result from earlier node and return a set of condition Keys
User gets the condition branches as a response where each branch specific dags are mapped against the specific condition. User can farther define each branch using the DAG constructs
Below is the updated example with a conditional Branch where we are trying to call face-match only when face-detect passes
func KycImageValidationDag() *flow.Dag {
dag := flow.NewDag()
dag.Node("verify-url", s3DocExists)
dag.Node("face-detect", detectFace)
// here face match happen only when face-detect is success
branches = dag.ConditionalBranch("handle-face-detect-response", []string{"pass"}, func(response []byte) []string {
response := ParseFaceDetectResponse(response)
if response[0] == "pass" { return []string{"pass"} }
return []string{}
})
// On the pass branch we are performing the `face-match` . If condition `pass`
// is not matched execution of next node `generate-result` is continued
branches["pass"].Node("face-match", matchFace)
dag.Node("generate-result", generateResult)
dag.Edge("verify-url", "face-detect")
dag.Edge("face-detect", "handle-face-detect-response")
dag.Edge("handle-face-detect-response", "generate-result")
return dag
}You can also have multiple conditional branch in a workflow and different nodes corresponding to each branch
Below is the updated example with two conditional Branches where we are trying to call face-match or create-user based on response from previous node
func KycImageValidationDag() *flow.Dag {
dag := flow.NewDag()
dag.Node("verify-url", s3DocExists)
dag.Node("face-detect", detectFace)
// here face match happen only when face-detect is success
// otherwise create-user is called
branches = dag.ConditionalBranch("handle-face-detect-response", []string{"pass", "fail"},
func(response []byte) []string {
response := ParseFaceDetectResponse(response)
if response.isSuccess() { return []string{"pass"} }
return []string{"fail"}
})
// On the pass branch we are performing the `face-match`
branches["pass"].Node("face-match", matchFace)
// on the fail branch we are performing `create-user`
branches["fail"].Node("create-user", createUser)
dag.Node("generate-result", generateResult)
dag.Edge("verify-url", "face-detect")
dag.Edge("face-detect", "handle-face-detect-response")
dag.Edge("handle-face-detect-response", "generate-result")
return dag
}Foreach branching allows user to iteratively perform a certain set of task for a range of values
GoFlow provides a DAG component called ForEachBranch. ForEachBranch creates a vertex composites of a subdag that defines the flow within the iteration
func (currentDag *Dag) ForEachBranch(vertex string, foreach sdk.ForEach, options ...BranchOption) (dag *Dag)
ForEach is a special handler that allows user to dynamically return a set of key and values. For each of the items in the returned set, the user defined dag will get executed
User gets the foreach branch as a response and can define the flow using the DAG constructs
We are updating our flow to execute over a set of user that has been listed for possible fraud
func DefineWorkflow(f *flow.Workflow, context *flow.Context) error {
dag := f.Dag()
dag.Node("get-users", getListedUsers)
verifyDag = dag.ForEachBranch("for-each-user-verify", func(data []byte) map[string][]byte {
users := ParseUsersList(data)
forEachSet := make(map[string][]byte)
for _, user := range users {
forEachSet[user.id] = []byte(user.GetKycImageUrl())
}
return forEachSet
})
verifyDag.SubDag("verify-image", KycImageValidationDag)
verifyDag.Node("mark-profile", markProfileBasedOnStatus)
verifyDag.Edge("verify-image", "mark-profile")
dag.Edge("get-users", "for-each-user-verify")
return nil
}