A Pipeline
consists of multiple vertices, Source
, Sink
and UDF(User Defined Functions)
.
UDF runs as a sidecar container in a Vertex Pod, processes the received data. The communication between the main container (platform code) and the sidecar container (user code) is through gRPC over Unix Domain Socket.
Data processing in the UDF is supposed to be idempotent.
There are some Built-in Functions that can be used directly.
You can build your own UDF in multiple languages. A User Defined Function could be as simple as below in Golang.
package main
import (
"context"
functionsdk "github.com/numaproj/numaflow-go/pkg/function"
"github.com/numaproj/numaflow-go/pkg/function/server"
)
func mapHandle(_ context.Context, key string, d functionsdk.Datum) functionsdk.Messages {
// Directly forward the input to the output
return functionsdk.MessagesBuilder().Append(functionsdk.MessageToAll(d.Value()))
}
func main() {
server.New().RegisterMapper(functionsdk.MapFunc(mapHandle)).Start(context.Background())
}
Check the links below to see the UDF examples for different languages.
After building a docker image for the written UDF, specify the image as below in the vertex spec.
spec:
vertices:
- name: my-vertex
udf:
container:
image: my-python-udf-example:latest
Some environment variables are available in the user defined function Pods, they might be useful in you own UDF implementation.
NUMAFLOW_NAMESPACE
- Namespace.NUMAFLOW_POD
- Pod name.NUMAFLOW_REPLICA
- Replica index.NUMAFLOW_PIPELINE_NAME
- Name of the pipeline.NUMAFLOW_VERTEX_NAME
- Name of the vertex.
Configuration data can be provided to the UDF container at runtime multiple ways.
environment variables
args
command
volumes
init containers