Machinery is an asynchronous task queue/job queue based on distributed message passing.
So called tasks (or jobs if you like) are executed concurrently either by many workers on many servers or multiple worker processes on a single server using Golang's goroutines.
Add the Machinery library to your $GOPATH/src:
$ go get github.com/RichardKnop/machinery
Install dependencies:
$ make deps
First, you will need to define some tasks. Look at sample tasks in _examples/tasks/tasks.go
to see few examples.
Second, you will need to launch a worker process:
$ go run _examples/worker/worker.go
Finally, once you have a worker running and waiting for tasks to consume, send some tasks:
$ go run _examples/send/send.go
You will be able to see the tasks being processed asynchronously by the worker:
Machinery has several configuration options. Configuration is encapsulated by a Config
struct and injected as a dependency to objects that need it.
type Config struct {
Broker string `yaml:"broker"`
ResultBackend string `yaml:"result_backend"`
ResultsExpireIn int `yaml:"results_expire_in"`
Exchange string `yaml:"exchange"`
ExchangeType string `yaml:"exchange_type"`
DefaultQueue string `yaml:"default_queue"`
BindingKey string `yaml:"binding_key"`
}
A message broker. Currently supported brokers are:
- AMQP (use AMQP URL such as
amqp://guest:guest@localhost:5672/
) - Redis (use Redis URL such as
redis://127.0.0.1:6379
, or to use password redis://[email protected]:6379)
Result backend to use for keeping task states and results.
Currently supported backends are:
- Redis (use Redis URL such as
redis://127.0.0.1:6379
, or to use passwordredis://[email protected]:6379
) - Memcache (use Memcache URL such as
memcache://10.0.0.1:11211,10.0.0.2:11211
) - AMQP (use AMQP URL such as
amqp://guest:guest@localhost:5672/
)
Keep in mind AMQP is not recommended as a result backend. See Keeping Results
How long to store task results for in seconds. Defaults to 3600 (1 hour).
Exchange name, e.g. machinery_exchange
. Only required for AMQP.
Exchange type, e.g. direct
. Only required for AMQP.
Default queue name, e.g. machinery_tasks
.
The queue is bind to the exchange with this key, e.g. machinery_task
. Only required for AMQP.
A Machinery library must be instantiated before use. The way this is done is by creating a Server
instance. Server
is a base object which stores Machinery configuration and registered tasks. E.g.:
import (
"github.com/RichardKnop/machinery/v1/config"
machinery "github.com/RichardKnop/machinery/v1"
)
var cnf = config.Config{
Broker: "amqp://guest:guest@localhost:5672/",
ResultBackend: "amqp://guest:guest@localhost:5672/",
Exchange: "machinery_exchange",
ExchangeType: "direct",
DefaultQueue: "machinery_tasks",
BindingKey: "machinery_task",
}
server, err := machinery.NewServer(&cnf)
if err != nil {
// do something with the error
}
In order to consume tasks, you need to have one or more workers running. All you need to run a worker is a Server
instance with registered tasks. E.g.:
worker := server.NewWorker("worker_name")
err := worker.Launch()
if err != nil {
// do something with the error
}
Each worker will only consume registered tasks.
Tasks are a building block of Machinery applications. A task is a function which defines what happens when a worker receives a message. Let's say we want to define tasks for adding and multiplying numbers:
func Add(args ...int64) (int64, error) {
sum := int64(0)
for _, arg := range args {
sum += arg
}
return sum, nil
}
func Multiply(args ...int64) (int64, error) {
sum := int64(1)
for _, arg := range args {
sum *= arg
}
return sum, nil
}
Before your workers can consume a task, you need to register it with the server. This is done by assigning a task a unique name:
server.RegisterTasks(map[string]interface{}{
"add": Add,
"multiply": Multiply,
})
Task can also be registered one by one:
server.RegisterTask("add", Add)
server.RegisterTask("multiply", Multiply)
Simply put, when a worker receives a message like this:
{
"UUID": "48760a1a-8576-4536-973b-da09048c2ac5",
"Name": "add",
"RoutingKey": "",
"GroupUUID": "",
"GroupTaskCount": 0,
"Args": [
{
"Type": "int64",
"Value": 1,
},
{
"Type": "int64",
"Value": 1,
}
],
"Immutable": false,
"OnSuccess": null,
"OnError": null,
"ChordCallback": null
}
It will call Add(1, 1). Each task should return an error as well so we can handle failures.
Ideally, tasks should be idempotent which means there will be no unintended consequences when a task is called multiple times with the same arguments.
A signature wraps calling arguments, execution options (such as immutability) and success/error callbacks of a task so it can be sent across the wire to workers. Task signatures implement a simple interface:
type TaskArg struct {
Type string
Value interface{}
}
type TaskSignature struct {
UUID string
Name string
RoutingKey string
GroupUUID string
GroupTaskCount int
Args []TaskArg
Immutable bool
OnSuccess []*TaskSignature
OnError []*TaskSignature
ChordCallback *TaskSignature
}
UUID
is a unique ID of a task. You can either set it yourself or it will be automatically generated.
Name
is the unique task name by which it is registered against a Server instance.
RoutingKey
is used for routing a task to correct queue. If you leave it empty, the default behaviour will be to set it to the default queue's binding key for direct exchange type and to the default queue name for other exchange types.
GroupUUID
, GroupTaskCount are useful for creating groups of tasks.
Args
is a list of arguments that will be passed to the task when it is executed by a worker.
Immutable
is a flag which defines whether a result of the executed task can be modified or not. This is important with OnSuccess
callbacks. Immutable task will not pass its result to its success callbacks while a mutable task will prepend its result to args sent to callback tasks. Long story short, set Immutable to false if you want to pass result of the first task in a chain to the second task.
OnSuccess
defines tasks which will be called after the task has executed successfully. It is a slice of task signature structs.
OnError
defines tasks which will be called after the task execution fails. The first argument passed to error callbacks will be the error returned from the failed task.
ChordCallback
is used to create a callback to a group of tasks.
Machinery encodes tasks to JSON before sending them to the broker. Task results are also stored in the backend as JSON encoded strings. Therefor only types with native JSON representation can be supported. Currently supported types are:
bool
int
int8
int16
int32
int64
unint
uint8
uint16
uint32
uint64
float32
float64
string
Tasks can be called by passing an instance of TaskSignature
to an Server
instance. E.g:
import "github.com/RichardKnop/machinery/v1/signatures"
task := signatures.TaskSignature{
Name: "add",
Args: []signatures.TaskArg{
signatures.TaskArg{
Type: "int64",
Value: 1,
},
signatures.TaskArg{
Type: "int64",
Value: 1,
},
},
}
asyncResult, err := server.SendTask(&task1)
if err != nil {
// failed to send the task
// do something with the error
}
If you configure a result backend, the task states and results will be persisted. Possible states:
const (
PendingState = "PENDING"
ReceivedState = "RECEIVED"
StartedState = "STARTED"
SuccessState = "SUCCESS"
FailureState = "FAILURE"
)
When using AMQP as a result backend, task states will be persisted in separate queues for each task. Although RabbitMQ can scale up to thousands of queues, it is strongly advised to use a better suited result backend (e.g. Memcache) when you are expecting to run a large number of parallel tasks.
type TaskResult struct {
Type string
Value interface{}
}
type TaskState struct {
TaskUUID string
State string
Result *TaskResult
Error string
}
type GroupMeta struct {
GroupUUID string
TaskUUIDs []string
}
TaskResult
represents a return value of a processed task.
TaskState
struct will be serialised and stored every time a task state changes.
GroupMeta
stores useful metadata about tasks within the same group. E.g. UUIDs of all tasks which are used in order to check if all tasks completed successfully or not and thus whether to trigger chord callback.
AsyncResult
object allows you to check for the state of a task:
taskState := asyncResult.GetState()
fmt.Printf("Current state of %v task is:\n", taskState.TaskUUID)
fmt.Println(taskState.State)
There are couple of convenient me methods to inspect the task status:
asyncResult.GetState().IsCompleted()
asyncResult.GetState().IsSuccess()
asyncResult.GetState().IsFailure()
You can also do a synchronous blocking call to wait for a task result:
result, err := asyncResult.Get()
if err != nil {
// getting result of a task failed
// do something with the error
}
fmt.Println(result.Interface())
Running a single asynchronous task is fine but often you will want to design a workflow of tasks to be executed in an orchestrated way. There are couple of useful functions to help you design workflows.
Group
is a set of tasks which will be executed in parallel, independent of each other. E.g.:
import (
"github.com/RichardKnop/machinery/v1/signatures"
machinery "github.com/RichardKnop/machinery/v1"
)
task1 := signatures.TaskSignature{
Name: "add",
Args: []signatures.TaskArg{
signatures.TaskArg{
Type: "int64",
Value: 1,
},
signatures.TaskArg{
Type: "int64",
Value: 1,
},
},
}
task2 := signatures.TaskSignature{
Name: "add",
Args: []signatures.TaskArg{
signatures.TaskArg{
Type: "int64",
Value: 5,
},
signatures.TaskArg{
Type: "int64",
Value: 5,
},
},
}
group := machinery.NewGroup(&task1, &task2)
asyncResults, err := server.SendGroup(group)
if err != nil {
// failed to send the group
// do something with the error
}
SendGroup
returns a slice of AsyncResult
objects. So you can do a blocking call and wait for the result of groups tasks:
for _, asyncResult := range asyncResults {
result, err := asyncResult.Get()
if err != nil {
// getting result of a task failed
// do something with the error
}
fmt.Println(result.Interface())
}
Chord
allows you to define a callback to be executed after all tasks in a group finished processing, e.g.:
import (
"github.com/RichardKnop/machinery/v1/signatures"
machinery "github.com/RichardKnop/machinery/v1"
)
task1 := signatures.TaskSignature{
Name: "add",
Args: []signatures.TaskArg{
signatures.TaskArg{
Type: "int64",
Value: 1,
},
signatures.TaskArg{
Type: "int64",
Value: 1,
},
},
}
task2 := signatures.TaskSignature{
Name: "add",
Args: []signatures.TaskArg{
signatures.TaskArg{
Type: "int64",
Value: 5,
},
signatures.TaskArg{
Type: "int64",
Value: 5,
},
},
}
task3 := signatures.TaskSignature{
Name: "multiply",
}
group := machinery.NewGroup(&task1, &task2)
chord := machinery.NewChord(group, &task3)
chordAsyncResult, err := server.SendChord(chord)
if err != nil {
// failed to send the chord
// do something with the error
}
The above example execute task1 and task2 in parallel, aggregate their results and pass them to task3. Therefor what would end up happening is:
multiply(add(1, 1), add(5, 5))
More explicitely:
(1 + 1) * (5 + 5) = 2 * 10 = 20
SendChord
returns ChordAsyncResult
which follows AsyncResult's interface. So you can do a blocking call and wait for the result of the callback:
result, err := chordAsyncResult.Get()
if err != nil {
// getting result of a chord failed
// do something with the error
}
fmt.Println(result.Interface())
Chain
is simply a set of tasks which will be executed one by one, each successful task triggering the next task in the chain. E.g.:
import (
"github.com/RichardKnop/machinery/v1/signatures"
machinery "github.com/RichardKnop/machinery/v1"
)
task1 := signatures.TaskSignature{
Name: "add",
Args: []signatures.TaskArg{
signatures.TaskArg{
Type: "int64",
Value: 1,
},
signatures.TaskArg{
Type: "int64",
Value: 1,
},
},
}
task2 := signatures.TaskSignature{
Name: "add",
Args: []signatures.TaskArg{
signatures.TaskArg{
Type: "int64",
Value: 5,
},
signatures.TaskArg{
Type: "int64",
Value: 5,
},
},
}
task3 := signatures.TaskSignature{
Name: "multiply",
Args: []signatures.TaskArg{
signatures.TaskArg{
Type: "int64",
Value: 4,
},
},
}
chain := machinery.NewChain(&task1, &task2, &task3)
chainAsyncResult, err := server.SendChain(chain)
if err != nil {
// failed to send the chain
// do something with the error
}
The above example execute task1, then task2 and then task3, passing result of each task to the next task in the chain. Therefor what would end up happening is:
multiply(add(add(1, 1), 5, 5), 4)
More explicitely:
((1 + 1) + (5 + 5)) * 4 = 12 * 4 = 48
SendChain
returns ChainAsyncResult
which follows AsyncResult's interface. So you can do a blocking call and wait for the result of the whole chain:
result, err := chainAsyncResult.Get()
if err != nil {
// getting result of a chain failed
// do something with the error
}
fmt.Println(result.Interface())
First, there are several requirements:
- RabbitMQ
- Go
- Memcached (optional)
On OS X systems, you can install them using Homebrew:
$ brew install rabbitmq
$ brew install redis
$ brew install memcached
$ brew install go
Then get all Machinery dependencies.
$ make deps
$ make test
In order to enable integration tests, you will need to export few environment variables:
$ export AMQP_URL=amqp://guest:guest@localhost:5672/
$ export MEMCACHE_URL=127.0.0.1:11211
$ export REDIS_URL=127.0.0.1:6379
I recommend to run the integration tests when making changes to the code. Due to Machinery being composed of several parts (worker, client) which run independently of each other, integration tests are important to verify everything works as expected.