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Map UDF

Map in a Map vertex takes an input and returns 0, 1, or more outputs (also known as flat-map operation). Map is an element wise operator.

Builtin UDF

There are some Built-in Functions that can be used directly.

Build Your Own UDF

You can build your own UDF in multiple languages.

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

Streaming Mode

In cases the map function generates more than one output (e.g., flat map), the UDF can be configured to run in a streaming mode instead of batching, which is the default mode. In streaming mode, the messages will be pushed to the downstream vertices once generated instead of in a batch at the end. The streaming mode can be enabled by setting the annotation numaflow.numaproj.io/map-stream to true in the vertex spec.

Note that to maintain data orderliness, we restrict the read batch size to be 1.

spec:
  vertices:
    - name: my-vertex
      metadata:
        annotations:
          numaflow.numaproj.io/map-stream: "true"
      limits:
        # mapstreaming won't work if readBatchSize is != 1      
        readBatchSize: 1

Check the links below to see the UDF examples in streaming mode for different languages.

Available Environment Variables

Some environment variables are available in the user defined function container, they might be useful in your 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

Configuration data can be provided to the UDF container at runtime multiple ways.