Faas-flow - Function Composition for OpenFaaS
- Pure FaaS with OpenFaaS
- Fast Built with
Go
- Secured With
HMAC
- Stateless By design
- Tracing With
open-tracing
- Available As
faas-flow
template
Faas-flow tower visualizes and monitors flow functions.
Overview
Faas-flow allows you to realize OpenFaaS function composition with ease. By
defining a simple pipeline, you can orchestrate multiple functions without
having to worry about the internals.
func Define(flow *faasflow.Workflow, context *faasflow.Context) (err error) {
flow.SyncNode().Apply("Func1").Apply("Func2")
return nil
}
After building and deploying, it will give you an OpenFaaS function that
orchestrates calling Func2
with the output of Func1
.
Use Cases
Faas-flow as a function composure provides the back-bone for building complex
solutions and promote automation.
Data Processing Pipeline
Faas-flow can orchestrate a pipeline with long and short running function
performing ETL jobs without having to orchestrate them manually or maintaining a
separate application. Faas-flow ensures the execution order of several functions
running in parallel or dynamically and provides rich construct to aggregate
results while maintaining the intermediate data.
Application Orchestration Workflow
Functions are great for isolating certain functionalities of an application.
Although one still need to call the functions, write workflow logic, handle
parallel processing and retries on failures. Using Faas-flow you can combine
multiple OpenFaaS functions with little codes while your workflow will scale
up/down automatically to handle the load.
Function Reusability
Fass-flow allows you to write function only focused on solving one problem
without having to worry about the next. It makes function loosely coupled from
the business logic promoting reusability. You can write the stateless function
and use it across multiple applications, where Faas-flow maintains the execution
state for individual workflow per requests.
Pipeline Definition
By supplying a number of pipeline operators, the complex composition can be
achieved with little work:
The above pipelines can be achieved with little, but powerful code:
Sync chain
func Define(flow *faasflow.Workflow, context *faasflow.Context) (err error) {
flow.SyncNode()
.Apply("func1")
.Apply("func2")
.Modify(func(data []byte) ([]byte, error) {
// do something
return data, nil
})
return nil
}
Async chain
func Define(flow *faasflow.Workflow, context *faasflow.Context) (err error) {
dag := flow.Dag()
dag.Node("n1").Apply("func1")
dag.Node("n2")
.Apply("func2")
.Modify(func(data []byte) ([]byte, error) {
// do something
return data, nil
})
dag.Node("n3").Apply("func4")
dag.Edge("n1", "n2")
dag.Edge("n2", "n3")
return nil
}
Parallel branching
func Define(flow *faasflow.Workflow, context *faasflow.Context) (err error) {
dag := flow.Dag()
dag.Node("n1").Modify(func(data []byte) ([]byte, error) {
// do something
return data, nil
})
dag.Node("n2").Apply("func1")
dag.Node("n3").Apply("func2").Modify(func(data []byte) ([]byte, error) {
// do something
return data, nil
})
dag.Node("n4", faasflow.Aggregator(func(data map[string][]byte) ([]byte, error) {
// aggregate branch result data["n2"] and data["n3"]
return []byte(""), nil
}))
dag.Edge("n1", "n2")
dag.Edge("n1", "n3")
dag.Edge("n2", "n4")
dag.Edge("n3", "n4")
return nil
}
Dynamic branching
func Define(flow *faasflow.Workflow, context *faasflow.Context) (err error) {
dag := flow.Dag()
dag.Node("n1").Modify(func(data []byte) ([]byte, error) {
// do something
return data, nil
})
conditionalDags := dag.ConditionalBranch(
"C",
[]string{"c1", "c2"}, // possible conditions
func(response []byte) []string {
// for each returned condition the corresponding branch will execute
// this function executes in the runtime of condition C
return []string{"c1", "c2"}
},
faasflow.Aggregator(func(data map[string][]byte) ([]byte, error) {
// aggregate all dynamic branches results
return []byte(""), nil
}),
)
conditionalDags["c2"].Node("n1").Apply("func1").Modify(func(data []byte) ([]byte, error) {
// do something
return data, nil
})
foreachDag := conditionalDags["c1"].ForEachBranch(
"F",
func(data []byte) map[string][]byte {
// for each returned key in the hashmap a new branch will be executed
// this function executes in the runtime of foreach F
return map[string][]byte{"f1": data, "f2": data}
},
faasflow.Aggregator(func(data map[string][]byte) ([]byte, error) {
// aggregate all dynamic branches results
return []byte(""), nil
}),
)
foreachDag.Node("n1").Modify(func(data []byte) ([]byte, error) {
// do something
return data, nil
})
dag.Node("n2")
dag.Edge("n1", "C")
dag.Edge("C", "n2")
return nil
}
Full implementation of the above examples are available
here.
Faas-flow Design
The current design consideration is made based on the below goals:
- Leverage the OpenFaaS platform
- Not to violate the notions of function
- Provide flexibility, scalability, and adaptability
Just as function as any other
Faas-flow is deployed and provisioned just like any other OpenFaaS function. It
allows Faas-flow to take advantage of rich functionalities available on
OpenFaaS. Faas-flow provide an OpenFaaS template (faas-flow
) and just like any
other OpenFaaS function it can be deployed with faas-cli
.
Adapter pattern for zero instrumentation in code
Faas-flow function follows the adapter pattern. Here the adaptee is the
functions and the adapter is the flow. For each node execution, Faas-flow handle
the calls to the functions. Once the execution is over, it forwards an event to
itself. This way the arrangement logic is separated from the functions and is
implemented in the adapter. Compositions need no code instrumentations, making
functions completely independent of the details of the compositions.
Aggregate pattern as chaining
Aggregation of separate function calls is done as chaining. Multiple functions
can be called from a single node with order maintained as per the chain. This
way one execution node can be implemented as an aggregator function that invokes
multiple functions collects the results, optionally applies business logic, and
returns a consolidated response to the client or forward to next nodes.
Faas-flow fuses the adapter pattern and aggregate pattern to support more
complex use cases.
Event driven iteration
OpenFaaS uses Nats for event delivery and Faas-flow leverages
OpenFaaS platform. Node execution in Faas-flow starts by a completion event of
one or more previous nodes. A completion event denotes that all the previous
dependent nodes have completed. The event carries the execution state and
identifies the next node to execute. With events Faas-flow asynchronously
carry-on execution of nodes by iterating itself over and over till all nodes are
executed.
3rd party KV store for coordination
When executing branches, one node is dependent on more than one predecessor
nodes. In that scenario, the event for completion is generated by coordination
of earlier nodes. Like any distributed system the coordination is achieved via a
centralized service. Faas-flow keeps the logic of the coordination controller
inside of Faas-flow implementation and lets the user use any external
synchronous KV store by implementing
StateStore
.
Results from function execution and intermediate data can be handled by the user
manually. Faas-flow provides data-store for intermediate result storage. It
automatically initializes, store, retrieve and remove data between nodes. This
fits great for data processing applications. Faas-flow keeps the logic of
storage controller inside of Faas-flow implementation and lets the user use any
external object storage by implementing
DataStore
.
Faas-flow design is not fixed and like any good design, it is evolving. Please
contribute to make it better.
Getting Started
Deploy OpenFaaS
FaasFlow requires the OpenFaaS to be deployed and the OpenFaaS Cli to be installed. You
can either have your OpenFaaS deployed in Kubernets or
in Swarm.
To deploy OpenFaaS and to
install the OpenFaaS cli client follow this guide:
https://docs.openfaas.com/deployment/.
Deploy Faas-flow Components with Faas-flow Infra
Faas-Flow infra provides the kubernetes and swarm deployment resources for faas-flow dependencies. Follow the README to deploy Faas-Flow Infra
in Kubernets or in Swarm
Deploy Faas-flow Tower
Faas-Flow tower provides the dashboard to visualise and monitor your flow. Follow the README to deploy Faas-Flow tower on OpenFaaS
Writing Flow
This example implements a very simple flow to Greet
Get template
Pull faas-flow
template with the faas-cli
faas template pull https://github.com/s8sg/faas-flow
Create new flow function
Create a new function using faas-flow
template
faas new greet --lang faas-flow
Edit stack.yml
Edit function stack file greet.yml
greet:
lang: faas-flow
handler: ./greet
image: greet:latest
labels:
faas-flow: 1
annotations:
faas-flow-desc: "test flow to greet"
environment_file:
- flow.yml
secrets:
- s3-secret-key
- s3-access-key
Add configuration
Add a separate configuration file flow.yml
with faas-flow related configuration.
environment:
gateway: "gateway.openfaas:8080" # The address of OpenFaaS gateway
enable_tracing: true # tracing allows to monitor requests
trace_server: "jaeger-agent.faasflow:5775" # The address of jaeger tracing agent
consul_url: "consul.faasflow:8500" # The address of consul
s3_url: "minio.faasflow:9000" # The address of minio
Edit flow definition
Edit greet/handler.go
and Update Define()
func Define(flow *faasflow.Workflow, context *faasflow.Context) (err error) {
flow.SyncNode().Modify(func(data []byte) ([]byte, error) {
result := "Hello " + string(data)
return []byte(result), nil
})
return nil
}
Build and Deploy
Build and deploy
faas build -f greet.yml
faas deploy -f greet.yml
This function will generate one Synchronous node
Modify("name") -> Hello name
All calls will be performed in one single execution of the flow function and
result will be returned to the callee.
Note: For flow that has more than one nodes, Faas-flow doesn't return any
response. External storage or callback can be used to retrieve an async result.
Invoke
echo "Adam" | faas invoke greet
Request Tracking by ID
For each new request, faas-flow generates a unique Request Id
for the flow.
The same Id is used when logging.
2018/08/13 07:51:59 [Request `bdojh7oi7u6bl8te4r0g`] Created
2018/08/13 07:52:03 [Request `bdojh7oi7u6bl8te4r0g`] Received
The assigned request Id is set on the response header X-Faas-Flow-Reqid
One may provide custom request Id by setting X-Faas-Flow-Reqid
in the request
header.
FaasFlow Tower enables the real time monitoring
for each requests. Request traces are visible when enable_tracing
is enabled. FaaSFlow is
the best way to monitor flows and execution status of each node for each request.
Below is an example of tracing page for a request of
faas-flow-example.
Use of Callback
To receive a result of long running FaaSFlow request, you can specify the
X-Faas-Flow-Callback-Url
. FaaSFlow will invoked the callback URL with the
final result and with the request ID set as X-Faas-Flow-Reqid
in request
Header.
Note: X-Callback-Url
from OpenFaaS is not supported in FaaSFlow.
Pause, Resume or Stop Request
A request in faas-flow has three states:
- Running
- Paused
- Stopped
Faas-flow doesn't keep the state of a finished request
To pause a running request:
faas invoke <workflow_name> --query pause-flow=<request_id>
To resume a paused request
faas invoke <workflow_name> --query resume-flow=<request_id>
To stop an active (paused/running) request
faas invoke <workflow_name> --query stop-flow=<request_id>
Use of context
Context can be used inside definition for different use cases. Context provide
various information such as:
- HttpQuery to retrieve original request queries
- State to get flow state
- Node to get current node
along with that it wraps the DataStore to store data
Store data in context with DataStore
Context uses DataStore
to store/retrieve data. User can do the same by calling
Get()
, Set()
, and Del()
from context
:
flow.SyncNode().
Modify(func(data []byte) {
// parse data and set to be used later
// json.Unmarshal(&req, data)
context.Set("commitsha", req.Sha)
})
.Apply("myfunc")
.Modify(func(data []byte) {
// retrieve the data that was set in the context
commitsha, _ = context.GetString("commitsha")
// use the query
})
Getting Http Query to Workflow
Http Query to flow can be used retrieved from context using context.Query
flow.SyncNode()
.Apply("myfunc", Query("auth-token", context.Query.Get("token"))) // pass as a function query
.Modify(func(data []byte) {
token = context.Query.Get("token") // get query inside modifier
})
Use of request context
Node, requestId, State is provided by the context
currentNode := context.GetNode()
requestId := context.GetRequestId()
state := context.State
for more details check Faas-flow
GoDoc.
External StateStore
for coordination controller
Faas-flow implements coordination controller and store the intermediate request
with StateStore. By default Faas-flow uses
consul as default
state-store, although user can define custom state-store with StateStore
interface and use any external Synchronous KV store as backend.
type StateStore interface {
// Configure the StateStore with flow name and request ID
Configure(flowName string, requestId string)
// Initialize the StateStore (called only once in a request span)
Init() error
// Set a value (override existing, or create one)
Set(key string, value string) error
// Get a value
Get(key string) (string, error)
// Compare and Update a value
Update(key string, oldValue string, newValue string) error
// Cleanup all the resorces in StateStore (called only once in a request span)
Cleanup() error
}
The custom StateStore
can be set with OverrideStateStore()
at
function/handler.go
:
// OverrideStateStore provides the override of the default StateStore
func OverrideStateStore() (faasflow.StateStore, error) {
myss, err := myStateStore.Init()
return myss, err
}
StateStore
is mandatory for a FaaSFlow to operate.
Official state-stores
External DataStore
for storage controller
Faas-flow uses the DataStore
to store partially completed data between nodes
and request context data. By default Faas-flow uses
minio as default data-store,
although user can define custom data-store with DataStore
interface and use
any external storage as backend.
type DataStore interface {
// Configure the DaraStore with flow name and request ID
Configure(flowName string, requestId string)
// Initialize the DataStore (called only once in a request span)
Init() error
// Set store a value for key, in failure returns error
Set(key string, value string) error
// Get retrives a value by key, if failure returns error
Get(key string) (string, error)
// Del delets a value by a key
Del(key string) error
// Cleanup all the resorces in DataStore
Cleanup() error
}
Data Store can be implemented and set by user at the OverrideDataStore()
at
function/handler.go
:
// OverrideDataStore provides the override of the default DataStore
func OverrideDataStore() (faasflow.DataStore, error) {
myds, err := myDs.Init()
return myds, err
}
DataStore
is mandatory for a FaaSFlow to operate.
Available data-stores
- MinioDataStore:
allows to store data in amazon s3 or local minio DB (default).
Cleanup with Finally()
Finally provides an efficient way to perform post-execution steps of the flow.
If specified Finally()
invokes in case of both failure and success of the
flow. A Finally method can be set as:
func Define(flow *faasflow.Workflow, context *faasflow.Context) (err error) {
// Define flow
flow.SyncNode().Modify(func(data []byte) {
// parse data and set to be used later
// json.Unmarshal(&req, data)
context.Set("commitsha", req.Sha)
}).
Apply("myfunc").Modify(func(data []byte) {
// retrieve the data in different node from context
commitsha, _ = context.GetString("commitsha")
})
flow.OnFailure(func(err error) {
// failure handler
})
flow.Finally(func() {
// delete the state resource
context.Del("commitsha")
})
}
Contribute
Join Faasflow Slack
for more.