This page gives examples of how to use Dataflow to perform bulk Cloud Firestore operations in an Apache Beam pipeline . Apache Beam supports a connector for Cloud Firestore . You can use this connector to run batch and streaming operations in Dataflow.
We recommend using Dataflow and Apache Beam for large scale data processing workloads.
The Cloud Firestore connector for Apache Beam is available in Java. For more information about the Cloud Firestore connector, see the Apache Beam SDK for Java .
Before you begin
Before you read this page, you should be familiar with the Programming model for Apache Beam .
To run the samples, you must enable the Dataflow API .Example Cloud Firestore pipelines
The examples below demonstrate a pipeline that writes data and one that reads and filters data. You can use these samples as a starting point for your own pipelines.
Running the sample pipelines
The source code for the samples is available in the googleapis/java-firestore GitHub repository . To run these samples, download the source code and see the README .
Example Write
pipeline
The following example creates documents in the cities-beam-sample
collection:
public class ExampleFirestoreBeamWrite { private static final FirestoreOptions FIRESTORE_OPTIONS = FirestoreOptions . getDefaultInstance (); public static void main ( String [] args ) { runWrite ( args , "cities-beam-sample" ); } public static void runWrite ( String [] args , String collectionId ) { // create pipeline options from the passed in arguments PipelineOptions options = PipelineOptionsFactory . fromArgs ( args ). withValidation (). as ( PipelineOptions . class ); Pipeline pipeline = Pipeline . create ( options ); RpcQosOptions rpcQosOptions = RpcQosOptions . newBuilder () . withHintMaxNumWorkers ( options . as ( DataflowPipelineOptions . class ). getMaxNumWorkers ()) . build (); // create some writes Write write1 = Write . newBuilder () . setUpdate ( Document . newBuilder () // resolves to // projects/<projectId>/databases/<databaseId>/documents/<collectionId>/NYC . setName ( createDocumentName ( collectionId , "NYC" )) . putFields ( "name" , Value . newBuilder (). setStringValue ( "New York City" ). build ()) . putFields ( "state" , Value . newBuilder (). setStringValue ( "New York" ). build ()) . putFields ( "country" , Value . newBuilder (). setStringValue ( "USA" ). build ())) . build (); Write write2 = Write . newBuilder () . setUpdate ( Document . newBuilder () // resolves to // projects/<projectId>/databases/<databaseId>/documents/<collectionId>/TOK . setName ( createDocumentName ( collectionId , "TOK" )) . putFields ( "name" , Value . newBuilder (). setStringValue ( "Tokyo" ). build ()) . putFields ( "country" , Value . newBuilder (). setStringValue ( "Japan" ). build ()) . putFields ( "capital" , Value . newBuilder (). setBooleanValue ( true ). build ())) . build (); // batch write the data pipeline . apply ( Create . of ( write1 , write2 )) . apply ( FirestoreIO . v1 (). write (). batchWrite (). withRpcQosOptions ( rpcQosOptions ). build ()); // run the pipeline pipeline . run (). waitUntilFinish (); } private static String createDocumentName ( String collectionId , String cityDocId ) { String documentPath = String . format ( "projects/%s/databases/%s/documents" , FIRESTORE_OPTIONS . getProjectId (), FIRESTORE_OPTIONS . getDatabaseId ()); return documentPath + "/" + collectionId + "/" + cityDocId ; } }
The example uses the following arguments to configure and run a pipeline:
GOOGLE_CLOUD_PROJECT= project-id REGION= region TEMP_LOCATION=gs:// temp-bucket /temp/ NUM_WORKERS= number-workers MAX_NUM_WORKERS= max-number-workers
Example Read
Pipeline
The following example pipeline reads documents from the cities-beam-sample
collection, applies a filter for documents where field country
is set to USA
, and returns the names of the matching documents.
public class ExampleFirestoreBeamRead { public static void main ( String [] args ) { runRead ( args , "cities-beam-sample" ); } public static void runRead ( String [] args , String collectionId ) { FirestoreOptions firestoreOptions = FirestoreOptions . getDefaultInstance (); PipelineOptions options = PipelineOptionsFactory . fromArgs ( args ). withValidation (). as ( PipelineOptions . class ); Pipeline pipeline = Pipeline . create ( options ); RpcQosOptions rpcQosOptions = RpcQosOptions . newBuilder () . withHintMaxNumWorkers ( options . as ( DataflowPipelineOptions . class ). getMaxNumWorkers ()) . build (); pipeline . apply ( Create . of ( collectionId )) . apply ( new FilterDocumentsQuery ( firestoreOptions . getProjectId (), firestoreOptions . getDatabaseId ())) . apply ( FirestoreIO . v1 (). read (). runQuery (). withRpcQosOptions ( rpcQosOptions ). build ()) . apply ( ParDo . of ( // transform each document to its name new DoFn<RunQueryResponse , String > () { @ProcessElement public void processElement ( ProcessContext c ) { c . output ( Objects . requireNonNull ( c . element ()). getDocument (). getName ()); } })) . apply ( ParDo . of ( // print the document name new DoFn<String , Void > () { @ProcessElement public void processElement ( ProcessContext c ) { System . out . println ( c . element ()); } })); pipeline . run (). waitUntilFinish (); } private static final class FilterDocumentsQuery extends PTransform<PCollection<String> , PCollection<RunQueryRequest> > { private final String projectId ; private final String databaseId ; public FilterDocumentsQuery ( String projectId , String databaseId ) { this . projectId = projectId ; this . databaseId = databaseId ; } @Override public PCollection<RunQueryRequest> expand ( PCollection<String> input ) { return input . apply ( ParDo . of ( new DoFn<String , RunQueryRequest > () { @ProcessElement public void processElement ( ProcessContext c ) { // select from collection "cities-collection-<uuid>" StructuredQuery . CollectionSelector collection = StructuredQuery . CollectionSelector . newBuilder () . setCollectionId ( Objects . requireNonNull ( c . element ())) . build (); // filter where country is equal to USA StructuredQuery . Filter countryFilter = StructuredQuery . Filter . newBuilder () . setFieldFilter ( StructuredQuery . FieldFilter . newBuilder () . setField ( StructuredQuery . FieldReference . newBuilder () . setFieldPath ( "country" ) . build ()) . setValue ( Value . newBuilder (). setStringValue ( "USA" ). build ()) . setOp ( StructuredQuery . FieldFilter . Operator . EQUAL )) . buildPartial (); RunQueryRequest runQueryRequest = RunQueryRequest . newBuilder () . setParent ( DocumentRootName . format ( projectId , databaseId )) . setStructuredQuery ( StructuredQuery . newBuilder () . addFrom ( collection ) . setWhere ( countryFilter ) . build ()) . build (); c . output ( runQueryRequest ); } })); } } }
The example uses the following arguments to configure and run a pipeline:
GOOGLE_CLOUD_PROJECT= project-id REGION= region TEMP_LOCATION=gs:// temp-bucket /temp/ NUM_WORKERS= number-workers MAX_NUM_WORKERS= max-number-workers
Pricing
Running a Cloud Firestore workload in Dataflow incurs costs for Cloud Firestore usage and Dataflow usage. Dataflow usage is billed for resources that your jobs use. See the Dataflow pricing page for details. For Cloud Firestore pricing, see the Pricing page .
What's next
- See Using Firestore and Apache Beam for data processing for another pipeline example.
- For more about Dataflow and Apache Beam, see the Dataflow documentation .
