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# Python Streaming Pipelines

Python streaming pipeline execution is experimentally available (with some
[limitations](#unsupported-features)) starting with Beam SDK version 2.5.0.


## Why use streaming execution?

Beam creates an unbounded PCollection if your pipeline reads from a streaming or
continously-updating data source (such as Cloud Pub/Sub). A runner must
process an unbounded PCollection using a streaming job that runs continuously,
as the entire collection is never available for processing at any one time.
[Size and boundedness]({{ site.baseurl }}/documentation/programming-guide/#size-and-boundedness)
has more information about bounded and unbounded collections.


## Modifying a pipeline to use stream processing

To modify a batch pipeline to support streaming, you must make the following
code changes:

* Use an I/O connector that supports reading from an unbounded source.
* Use an I/O connector that supports writing to an unbounded source.
* Choose a [windowing strategy]({{ site.baseurl }}/documentation/programming-guide/index.html#windowing).

The Beam SDK for Python includes two I/O connectors that support unbounded
PCollections: Google Cloud Pub/Sub (reading and writing) and Google BigQuery
(writing).

The following snippets show the necessary code changes to modify the batch
WordCount example to support streaming:

These batch WordCount snippets are from
[wordcount.py](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/wordcount.py).
This code uses the TextIO I/O connector to read from and write to a bounded
collection.

```
  lines = p | 'read' >> ReadFromText(known_args.input)
  ...

  counts = (lines
            | 'split' >> (beam.ParDo(WordExtractingDoFn())
                          .with_output_types(six.text_type))
            | 'pair_with_one' >> beam.Map(lambda x: (x, 1))
            | 'group' >> beam.GroupByKey()
            | 'count' >> beam.Map(count_ones))
  ...

  output = counts | 'format' >> beam.Map(format_result)

  # Write the output using a "Write" transform that has side effects.
  output | 'write' >> WriteToText(known_args.output)
```

These streaming WordCount snippets are from
[streaming_wordcount.py](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/streaming_wordcount.py).
This code uses an I/O connector that reads from and writes to an unbounded
source (Cloud Pub/Sub) and specifies a fixed windowing strategy.

```
  lines = p | beam.io.ReadFromPubSub(topic=known_args.input_topic)
  ...

  counts = (lines
            | 'split' >> (beam.ParDo(WordExtractingDoFn())
                          .with_output_types(six.text_type))
            | 'pair_with_one' >> beam.Map(lambda x: (x, 1))
            | beam.WindowInto(window.FixedWindows(15, 0))
            | 'group' >> beam.GroupByKey()
            | 'count' >> beam.Map(count_ones))

  ...

  output = counts | 'format' >> beam.Map(format_result)

  # Write to Pub/Sub
  output | beam.io.WriteStringsToPubSub(known_args.output_topic)
```

## Running a streaming pipeline

To run the example streaming WordCount pipeline, you must have a Cloud Pub/Sub
input topic and output topic. To create, subscribe to, and pull from a topic for
testing purposes, you can use the commands in the [Cloud Pub/Sub quickstart](https://cloud.google.com/pubsub/docs/quickstart-cli).

The following simple bash script feeds lines of an input text file to your input
topic:

```
cat <YOUR_LOCAL_TEXT_FILE> | while read line; do gcloud pubsub topics publish <YOUR_INPUT_TOPIC_NAME> “ $line”; done
```

Alternately, you can read from a publicly available Cloud Pub/Sub stream, such
as `projects/pubsub-public-data/topics/taxirides-realtime`. However, you must
create your own output topic to test writes.

The following commands run the
[streaming_wordcount.py](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/streaming_wordcount.py)
example streaming pipeline. Specify your Cloud Pub/Sub project and input topic
(`--input_topic`), output Cloud Pub/Sub project and topic (`--output_topic`).

{:.runner-direct}
```
# DirectRunner requires the --streaming option
python -m apache_beam.examples.streaming_wordcount \
  --input_topic "projects/YOUR_PUBSUB_PROJECT_NAME/topics/YOUR_INPUT_TOPIC" \
  --output_topic "projects/YOUR_PUBSUB_PROJECT_NAME/topics/YOUR_OUTPUT_TOPIC" \
  --streaming
```

{:.runner-apex}
```
This runner is not yet available for the Python SDK.
```

{:.runner-flink-local}
```
This runner is not yet available for the Python SDK.
```

{:.runner-flink-cluster}
```
This runner is not yet available for the Python SDK.
```

{:.runner-spark}
```
This runner is not yet available for the Python SDK.
```

{:.runner-dataflow}
```
# As part of the initial setup, install Google Cloud Platform specific extra components.
pip install apache-beam[gcp]

# DataflowRunner requires the --streaming option
python -m apache_beam.examples.streaming_wordcount \
  --runner DataflowRunner \
  --project YOUR_GCP_PROJECT \
  --temp_location gs://YOUR_GCS_BUCKET/tmp/ \
  --input_topic "projects/YOUR_PUBSUB_PROJECT_NAME/topics/YOUR_INPUT_TOPIC" \
  --output_topic "projects/YOUR_PUBSUB_PROJECT_NAME/topics/YOUR_OUTPUT_TOPIC" \
  --streaming
```

Check your runner's documentation for any additional runner-specific information
about executing streaming pipelines:

- [DirectRunner streaming execution]({{ site.baseurl }}/documentation/runners/direct/#streaming-execution)
- [DataflowRunner streaming execution]({{ site.baseurl }}/documentation/runners/dataflow/#streaming-execution)


## Unsupported features

Python streaming execution does not currently support the following features.

### General Beam features

These unsupported Beam features apply to all runners.

- `State` and `Timers` APIs
- Custom source API
- Splittable `DoFn` API
- Handling of late data
- User-defined custom `WindowFn`

### DataflowRunner specific features

Additionally, `DataflowRunner` does not currently support the following Cloud
Dataflow specific features with Python streaming execution.

- Streaming autoscaling
- Updating existing pipelines
- Cloud Dataflow Templates
- Some monitoring features, such as msec counters, display data, metrics, and
  element counts for transforms. However, logging, watermarks, and element
  counts for sources are supported.


