Quickstart

This quickstart will guide you through installing Apache Beam and running your first WordCount pipeline. WordCount is a classic example that demonstrates core Beam concepts: reading data, applying transformations, and writing results.

Choose your language

Python
Java
Go

Install Python SDK

Apache Beam requires Python 3.8 or newer. Install the Beam SDK using pip:

pip install apache-beam

For Google Cloud Dataflow support, install with the gcp extra:

pip install apache-beam[gcp]

Create your pipeline

Create a file named wordcount.py with the following code:

import re
import argparse
import apache_beam as beam
from apache_beam.io import ReadFromText, WriteToText
from apache_beam.options.pipeline_options import PipelineOptions

def main(argv=None):
parser = argparse.ArgumentParser()
parser.add_argument(
    '--input',
    dest='input',
    default='gs://dataflow-samples/shakespeare/kinglear.txt',
    help='Input file to process.')
parser.add_argument(
    '--output',
    dest='output',
    default='output.txt',
    help='Output file to write results to.')

known_args, pipeline_args = parser.parse_known_args(argv)
pipeline_options = PipelineOptions(pipeline_args)

with beam.Pipeline(options=pipeline_options) as p:
    # Read the text file into a PCollection
    lines = p | ReadFromText(known_args.input)
    
    # Count the occurrences of each word
    counts = (
        lines
        | 'Split' >> beam.FlatMap(lambda x: re.findall(r'[A-Za-z\']+', x))
        | 'PairWithOne' >> beam.Map(lambda x: (x, 1))
        | 'GroupAndSum' >> beam.CombinePerKey(sum))
    
    # Format the counts into strings
    output = counts | 'Format' >> beam.Map(
        lambda word_count: '%s: %s' % (word_count[0], word_count[1]))
    
    # Write the output
    output | WriteToText(known_args.output)

if __name__ == '__main__':
main()

Run the pipeline

Execute the pipeline locally using the DirectRunner:

python wordcount.py --output output.txt

This will process Shakespeare’s King Lear and write word counts to output.txt-00000-of-00001.

What’s happening?

Read input

ReadFromText reads lines from the input file into a PCollection of strings.

Split into words

FlatMap applies a regex to extract individual words from each line.

Create key-value pairs

Map transforms each word into a tuple of (word, 1).

Count occurrences

CombinePerKey groups by word and sums the counts.

Format output

Another Map formats each word-count pair as a string.

Write results

WriteToText writes the formatted results to output files.

Install Java SDK

Apache Beam requires Java 8 or newer. Add the Beam SDK to your Maven pom.xml:

<dependencies>
<dependency>
    <groupId>org.apache.beam</groupId>
    <artifactId>beam-sdks-java-core</artifactId>
    <version>2.53.0</version>
</dependency>
<dependency>
    <groupId>org.apache.beam</groupId>
    <artifactId>beam-runners-direct-java</artifactId>
    <version>2.53.0</version>
    <scope>runtime</scope>
</dependency>
</dependencies>

Create your pipeline

Create WordCount.java:

package com.example;

import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.options.*;
import org.apache.beam.sdk.transforms.*;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;

import java.util.regex.Pattern;
import java.util.stream.Stream;

public class WordCount {

static class ExtractWordsFn extends DoFn<String, String> {
    private final Pattern pattern = Pattern.compile("[a-zA-Z']+");
    
    @ProcessElement
    public void processElement(@Element String element, 
                               OutputReceiver<String> receiver) {
        Stream<String> stream = pattern.splitAsStream(element);
        stream.forEach(word -> {
            if (!word.isEmpty()) {
                receiver.output(word);
            }
        });
    }
}

static class FormatAsTextFn extends SimpleFunction<KV<String, Long>, String> {
    @Override
    public String apply(KV<String, Long> input) {
        return input.getKey() + ": " + input.getValue();
    }
}

public interface WordCountOptions extends PipelineOptions {
    @Description("Path of the file to read from")
    @Default.String("gs://apache-beam-samples/shakespeare/kinglear.txt")
    String getInputFile();
    void setInputFile(String value);
    
    @Description("Path of the file to write to")
    @Validation.Required
    String getOutput();
    void setOutput(String value);
}

public static void main(String[] args) {
    WordCountOptions options = PipelineOptionsFactory
        .fromArgs(args)
        .withValidation()
        .as(WordCountOptions.class);
    
    Pipeline p = Pipeline.create(options);
    
    p.apply("ReadLines", TextIO.read().from(options.getInputFile()))
     .apply("ExtractWords", ParDo.of(new ExtractWordsFn()))
     .apply("CountWords", Count.perElement())
     .apply("FormatResults", MapElements.via(new FormatAsTextFn()))
     .apply("WriteCounts", TextIO.write().to(options.getOutput()));
    
    p.run().waitUntilFinish();
}
}

Run the pipeline

Compile and run your pipeline:

mvn compile exec:java -Dexec.mainClass=com.example.WordCount \
    -Dexec.args="--output=output.txt"

What’s happening?

Read input

TextIO.read() reads lines from the input file into a PCollection.

Extract words

ParDo applies the ExtractWordsFn DoFn to split lines into words.

Count occurrences

Count.perElement() counts how many times each word appears.

Format output

MapElements formats each KV<String, Long> as a readable string.

Write results

TextIO.write() writes the formatted results to output files.

Install Go SDK

Apache Beam requires Go 1.20 or newer. Install the Beam Go SDK:

go get -u github.com/apache/beam/sdks/v2/go/pkg/beam

Create your pipeline

Create wordcount.go:

package main

import (
"context"
"flag"
"fmt"
"log"
"regexp"
"strings"

"github.com/apache/beam/sdks/v2/go/pkg/beam"
"github.com/apache/beam/sdks/v2/go/pkg/beam/io/textio"
"github.com/apache/beam/sdks/v2/go/pkg/beam/register"
"github.com/apache/beam/sdks/v2/go/pkg/beam/transforms/stats"
"github.com/apache/beam/sdks/v2/go/pkg/beam/x/beamx"
)

var (
input  = flag.String("input", "gs://apache-beam-samples/shakespeare/kinglear.txt", 
                    "Input file")
output = flag.String("output", "", "Output file (required)")
wordRE = regexp.MustCompile(`[a-zA-Z]+('[a-z])?`)
)

func init() {
register.DoFn3x0[context.Context, string, func(string)](&extractFn{})
register.Function2x1(formatFn)
register.Emitter1[string]()
}

type extractFn struct{}

func (f *extractFn) ProcessElement(ctx context.Context, line string, 
                               emit func(string)) {
for _, word := range wordRE.FindAllString(line, -1) {
    emit(word)
}
}

func formatFn(w string, c int) string {
return fmt.Sprintf("%s: %v", w, c)
}

func CountWords(s beam.Scope, lines beam.PCollection) beam.PCollection {
s = s.Scope("CountWords")
col := beam.ParDo(s, &extractFn{}, lines)
return stats.Count(s, col)
}

func main() {
flag.Parse()
beam.Init()

if *output == "" {
    log.Fatal("No output provided")
}

p := beam.NewPipeline()
s := p.Root()

lines := textio.Read(s, *input)
counted := CountWords(s, lines)
formatted := beam.ParDo(s, formatFn, counted)
textio.Write(s, *output, formatted)

if err := beamx.Run(context.Background(), p); err != nil {
    log.Fatalf("Failed to execute job: %v", err)
}
}

Run the pipeline

Execute the pipeline locally:

go run wordcount.go --output=output.txt

What’s happening?

Read input

textio.Read() reads lines from the input file into a PCollection.

Extract words

The extractFn DoFn uses regex to extract words from each line and emits them.

Count occurrences

stats.Count() counts occurrences of each word.

Format output

The formatFn function formats word-count pairs as strings.

Write results

textio.Write() writes the formatted results to output files.

Running on other runners

By default, pipelines run locally using the DirectRunner. To run on a distributed backend:

python wordcount.py \
  --runner=DataflowRunner \
  --project=YOUR_PROJECT_ID \
  --region=us-central1 \
  --temp_location=gs://YOUR_BUCKET/temp \
  --output=gs://YOUR_BUCKET/output

Next steps

Programming guide

Learn about advanced transforms, windowing, and triggers

Pipeline I/O

Connect to databases, message queues, and cloud storage

Testing pipelines

Write unit and integration tests for your pipelines

Tour of Beam

Interactive tutorials covering all Beam concepts

Getting Started

Core Concepts

SDKs

Runners

I/O Connectors

Choose your language

Install Python SDK

Create your pipeline

Run the pipeline

What’s happening?

Install Java SDK

Create your pipeline

Run the pipeline

What’s happening?

Install Go SDK

Create your pipeline

Run the pipeline

What’s happening?

Running on other runners

Next steps

Programming guide

Pipeline I/O

Testing pipelines

Tour of Beam

Build docs developers (and LLMs) love

Getting Started

Core Concepts

SDKs

Runners

I/O Connectors

Documentation Index

​Choose your language

​Install Python SDK

​Create your pipeline

​Run the pipeline

​What’s happening?

​Install Java SDK

​Create your pipeline

​Run the pipeline

​What’s happening?

​Install Go SDK

​Create your pipeline

​Run the pipeline

​What’s happening?

​Running on other runners

​Next steps

Programming guide

Pipeline I/O

Testing pipelines

Tour of Beam

Build docs developers (and LLMs) love

Choose your language

Install Python SDK

Create your pipeline

Run the pipeline

What’s happening?

Install Java SDK

Create your pipeline

Run the pipeline

What’s happening?

Install Go SDK

Create your pipeline

Run the pipeline

What’s happening?

Running on other runners

Next steps