| tag | 1f6a48c3fbfb08d00e427999d06354fc174da140 | |
|---|---|---|
| tagger | Kenneth Knowles <kenn@apache.org> | Tue Oct 24 20:23:05 2017 -0700 |
| object | 0f05e56cb04600b3851ffee5c93b22dd01fe7e86 |
v0.3.20150210
| commit | 0f05e56cb04600b3851ffee5c93b22dd01fe7e86 | [log] [tgz] |
|---|---|---|
| author | relax <relax@google.com> | Tue Feb 10 15:47:40 2015 -0800 |
| committer | Davor Bonaci <davorbonaci@users.noreply.github.com> | Tue Feb 10 16:13:32 2015 -0800 |
| tree | 7a1882e1e08cc4fa97242c51ffb57c27aa09650f | |
| parent | 1294029bd8b2311f4a2dacda8d74aa024f0ba0fb [diff] |
Allows users of PubsubIO to specify which pubsub labels are used to propagate record timestamps and record ids. [] ------------- Created by MOE: http://code.google.com/p/moe-java MOE_MIGRATED_REVID=86030744
Google Cloud Dataflow provides a simple, powerful programming model for building both batch and streaming parallel data processing pipelines.
The Cloud Dataflow SDK is used to access the Google Cloud Dataflow service, which is currently in Alpha and restricted to whitelisted users.
The SDK is publicly available and can be used for local execution by anyone. Note, however, that the SDK is also an Alpha release and may change significantly over time. The SDK is built to be extensible and support additional execution environments (“runners”) beyond local execution and the Google Cloud Dataflow service. As the product matures, we look forward to working with you to improve Cloud Dataflow.
The key concepts in this programming model are:
PCollection: represents a collection of data, which could be bounded or unbounded in size.PTransform: represents a computation that transforms input PCollections into output PCollections.Pipeline: manages a directed acyclic graph of PTransforms and PCollections, which is ready for execution.PipelineRunner: specifies where and how the pipeline should execute.We provide three PipelineRunners:
DirectPipelineRunner runs the pipeline on your local machine.DataflowPipelineRunner submits the pipeline to the Dataflow Service, where it runs using managed resources in the Google Cloud Platform (GCP).BlockingDataflowPipelineRunner submits the pipeline to the Dataflow Service via the DataflowPipelineRunner and then prints messages about the job status until the execution is complete.The Dataflow Service is currently in the Alpha phase of development and access is limited to whitelisted users.
Additionally, in partnership with Cloudera, you can run Dataflow pipelines on an Apache Spark backend. The relevant runner code is hosted in this repository.
This repository consists of two modules:
SDK module provides a set of basic Java APIs to program against.Examples module provides a few samples to get started. We recommend starting with the WordCount example.The following command will build both modules and install them in your local Maven repository:
mvn clean install
You can speed up the build and install process by using the following options:
To skip execution of the unit tests, run:
mvn install -DskipTests
While iterating on a specific module, use the following command to compile and reinstall it. For example, to reinstall the examples module, run:
mvn install -pl examples
Be careful, however, as this command will use the most recently installed SDK from the local repository (or Maven Central) even if you have changed it locally.
To run Maven using multiple threads, run:
mvn -T 4 install
After building and installing, you can execute the WordCount and other example pipelines using the DirectPipelineRunner on your local machine:
mvn exec:java -pl examples \ -Dexec.mainClass=com.google.cloud.dataflow.examples.WordCount \ -Dexec.args="--input=<INPUT FILE PATTERN> --output=<OUTPUT FILE>"
If you have been whitelisted for Alpha access to the Dataflow Service and followed the developer setup steps, you can use the BlockingDataflowPipelineRunner to execute the WordCount example in the GCP. In this case, you specify your project name, pipeline runner, and the staging location in Google Cloud Storage (GCS), as follows:
mvn exec:java -pl examples \ -Dexec.mainClass=com.google.cloud.dataflow.examples.WordCount \ -Dexec.args="--project=<YOUR GCP PROJECT NAME> --stagingLocation=<YOUR GCS LOCATION> --runner=BlockingDataflowPipelineRunner"
GCS location should be entered in the form of gs://bucket/path/to/staging/directory. GCP project refers to its name (not number), which has been whitelisted for Cloud Dataflow. Refer to Google Cloud Platform for general instructions on getting started with GCP.
Alternatively, you may choose to bundle all dependencies into a single JAR and execute it outside of the Maven environment. For example, after building and installing as usual, you can execute the following commands to create the bundled JAR of the Examples module and execute it both locally and in GCP:
mvn bundle:bundle -pl examples java -cp examples/target/google-cloud-dataflow-java-examples-all-bundled-manual_build.jar \ com.google.cloud.dataflow.examples.WordCount \ --input=<INPUT FILE PATTERN> --output=<OUTPUT FILE> java -cp examples/target/google-cloud-dataflow-java-examples-all-bundled-manual_build.jar \ com.google.cloud.dataflow.examples.WordCount \ --project=<YOUR GCP PROJECT NAME> --stagingLocation=<YOUR GCS LOCATION> --runner=BlockingDataflowPipelineRunner
Other examples can be run similarly by replacing the WordCount class name with BigQueryTornadoes, DatastoreWordCount, TfIdf, TopWikipediaSessions, etc. 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.
We welcome all usage-related questions on Stack Overflow tagged with google-cloud-dataflow.
Please use issue tracker on GitHub to report any bugs, comments or questions regarding SDK development.