The examples included in this module serve to demonstrate the basic functionality of Google Cloud Dataflow, and act as starting points for the development of more complex pipelines.
A good starting point for new users is our set of word count examples, which computes word frequencies. This series of four successively more detailed pipelines is described in detail in the accompanying walkthrough.
MinimalWordCount is the simplest word count pipeline and introduces basic concepts like Pipelines, PCollections, ParDo, and reading and writing data from external storage.
WordCount introduces Dataflow best practices like PipelineOptions and custom PTransforms.
DebuggingWordCount shows how to view live aggregators in the Dataflow Monitoring Interface, get the most out of Cloud Logging integration, and start writing good tests.
WindowedWordCount shows how to run the same pipeline over either unbounded PCollections in streaming mode or bounded PCollections in batch mode.
The examples in this repository can be built and executed from the root directory by running:
mvn compile exec:java -pl examples \ -Dexec.mainClass=<MAIN CLASS> \ -Dexec.args="<EXAMPLE-SPECIFIC ARGUMENTS>"
For example, you can execute the WordCount pipeline on your local machine as follows:
mvn compile exec:java -pl examples \ -Dexec.mainClass=com.google.cloud.dataflow.examples.WordCount \ -Dexec.args="--inputFile=<LOCAL INPUT FILE> --output=<LOCAL OUTPUT FILE>"
Once you have followed the general Cloud Dataflow Getting Started instructions, you can execute the same pipeline on fully managed resources in Google Cloud Platform:
mvn compile exec:java -pl examples \ -Dexec.mainClass=com.google.cloud.dataflow.examples.WordCount \ -Dexec.args="--project=<YOUR CLOUD PLATFORM PROJECT ID> \ --stagingLocation=<YOUR CLOUD STORAGE LOCATION> \ --runner=BlockingDataflowPipelineRunner"
Make sure to use your project id, not the project number or the descriptive name. The Cloud Storage location should be entered in the form of gs://bucket/path/to/staging/directory.
Alternatively, you may choose to bundle all dependencies into a single JAR and execute it outside of the Maven environment. For example, you can execute the following commands to create the bundled JAR of the examples and execute it both locally and in Cloud Platform:
mvn package java -cp examples/target/google-cloud-dataflow-java-examples-all-bundled-<VERSION>.jar \ com.google.cloud.dataflow.examples.WordCount \ --inputFile=<INPUT FILE PATTERN> --output=<OUTPUT FILE> java -cp examples/target/google-cloud-dataflow-java-examples-all-bundled-<VERSION>.jar \ com.google.cloud.dataflow.examples.WordCount \ --project=<YOUR CLOUD PLATFORM PROJECT ID> \ --stagingLocation=<YOUR CLOUD STORAGE LOCATION> \ --runner=BlockingDataflowPipelineRunner
Other examples can be run similarly by replacing the WordCount class path with the example classpath, e.g. com.google.cloud.dataflow.examples.cookbook.BigQueryTornadoes, and adjusting runtime options under the Dexec.args parameter, as specified in the example itself.
Note that when running Maven on Microsoft Windows platform, backslashes (\) under the Dexec.args parameter should be escaped with another backslash. For example, input file pattern of c:\*.txt should be entered as c:\\*.txt.
After you've finished running your first few word count pipelines, take a look at the cookbook directory for some common and useful patterns like joining, filtering, and combining.
The complete directory contains a few realistic end-to-end pipelines.
See the Java 8 examples as well. This directory includes a Java 8 version of the MinimalWordCount example, as well as series of examples in a simple ‘mobile gaming’ domain. This series introduces some advanced concepts and provides additional examples of using Java 8 syntax. Other than usage of Java 8 lambda expressions, the concepts that are used apply equally well in Java 7.