These Dataflow templates are an effort to solve simple, but large, in-Cloud data tasks, including data import/export/backup/restore and bulk API operations, without a development environment. The technology under the hood which makes these operations possible is the Google Cloud Dataflow service combined with a set of Apache Beam SDK templated pipelines.
Google is providing this collection of pre-implemented Dataflow templates as a reference and to provide easy customization for developers wanting to extend their functionality.
As of November 18, 2021, our default branch is now named "main". This does not affect forks. If you would like your fork and its local clone to reflect these changes you can follow GitHub's branch renaming guide.
Maven commands should be run on the unified-templates.xml
aggregator POM. An example would be:
mvn clean install -f unified-templates.xml -pl v2/pubsub-binary-to-bigquery -am
- BigQuery to Bigtable
- BigQuery to Datastore
- BigQuery to TFRecords
- Bigtable to GCS Avro
- Bulk Compressor
- Bulk Decompressor
- Datastore Bulk Delete *
- Datastore to BigQuery
- Datastore to GCS Text *
- Datastore to Pub/Sub *
- Datastore Unique Schema Count
- DLP Text to BigQuery (Streaming)
- GCS Avro to Bigtable
- GCS Avro to Spanner
- GCS Text to Spanner
- GCS Text to BigQuery *
- GCS Text to Datastore
- GCS Text to Pub/Sub (Batch)
- GCS Text to Pub/Sub (Streaming)
- Jdbc to BigQuery
- Pub/Sub to BigQuery *
- Pub/Sub to Datastore *
- Pub/Sub to GCS Avro
- Pub/Sub to GCS Text
- Pub/Sub to Pub/Sub
- Pub/Sub to Splunk *
- Spanner to GCS Avro
- Spanner to GCS Text
- Word Count
* Supports user-defined functions (UDFs).
For documentation on each template's usage and parameters, please see the official docs.
- Java 11
- Maven 3
Build the entire project using the maven compile command.
mvn clean compile
IntelliJ, by default, will often skip necessary Maven goals, leading to build failures. You can fix these in the Maven view by going to Module_Name > Plugins > Plugin_Name where Module_Name and Plugin_Name are the names of the respective module and plugin with the rule. From there, right-click the rule and select "Execute Before Build".
The list of known rules that require this are:
- common > Plugins > protobuf > protobuf:compile
- common > Plugins > protobuf > protobuf:test-compile
From either the root directory or v2/ directory, run:
mvn spotless:apply
This will format the code and add a license header. To verify that the code is formatted correctly, run:
mvn spotless:check
The directory to run the commands from is based on whether the changes are under v2/ or not.
Dataflow templates can be created using a maven command which builds the project and stages the template file on Google Cloud Storage. Any parameters passed at template build time will not be able to be overwritten at execution time.
mvn compile exec:java \
-Dexec.mainClass=com.google.cloud.teleport.templates.<template-class> \
-Dexec.cleanupDaemonThreads=false \
-Dexec.args=" \
--project=<project-id> \
--stagingLocation=gs://<bucket-name>/staging \
--tempLocation=gs://<bucket-name>/temp \
--templateLocation=gs://<bucket-name>/templates/<template-name>.json \
--runner=DataflowRunner"
Once the template is staged on Google Cloud Storage, it can then be
executed using the
gcloud CLI
tool. The runtime parameters required by the template can be passed in the
parameters field via comma-separated list of paramName=Value
.
gcloud dataflow jobs run <job-name> \
--gcs-location=<template-location> \
--zone=<zone> \
--parameters <parameters>
User-defined functions (UDFs) allow you to customize a template's functionality by providing a short JavaScript function without having to maintain the entire codebase. This is useful in situations which you'd like to rename fields, filter values, or even transform data formats before output to the destination. All UDFs are executed by providing the payload of the element as a string to the JavaScript function. You can then use JavaScript's in-built JSON parser or other system functions to transform the data prior to the pipeline's output. The return statement of a UDF specifies the payload to pass forward in the pipeline. This should always return a string value. If no value is returned or the function returns undefined, the incoming record will be filtered from the output.
Template | UDF Input Type | Input Description | UDF Output Type | Output Description |
---|---|---|---|---|
Datastore Bulk Delete | String | A JSON string of the entity | String | A JSON string of the entity to delete; filter entities by returning undefined |
Datastore to Pub/Sub | String | A JSON string of the entity | String | The payload to publish to Pub/Sub |
Datastore to GCS Text | String | A JSON string of the entity | String | A single-line within the output file |
GCS Text to BigQuery | String | A single-line within the input file | String | A JSON string which matches the destination table's schema |
Pub/Sub to BigQuery | String | A string representation of the incoming payload | String | A JSON string which matches the destination table's schema |
Pub/Sub to Datastore | String | A string representation of the incoming payload | String | A JSON string of the entity to write to Datastore |
Pub/Sub to Splunk | String | A string representation of the incoming payload | String | The event data to be sent to Splunk HEC events endpoint. Must be a string or a stringified JSON object |
/**
* A transform which adds a field to the incoming data.
* @param {string} inJson
* @return {string} outJson
*/
function transform(inJson) {
var obj = JSON.parse(inJson);
obj.dataFeed = "Real-time Transactions";
obj.dataSource = "POS";
return JSON.stringify(obj);
}
/**
* A transform function which only accepts 42 as the answer to life.
* @param {string} inJson
* @return {string} outJson
*/
function transform(inJson) {
var obj = JSON.parse(inJson);
// only output objects which have an answer to life of 42.
if (obj.hasOwnProperty('answerToLife') && obj.answerToLife === 42) {
return JSON.stringify(obj);
}
}