本頁面提供如何使用Dataflow在 Apache Beam管線中執行批次 Cloud Firestore 作業的範例。 Apache Beam 支援 Cloud Firestore 連接器。您可以使用此連接器在 Dataflow 中執行批次和流程操作。
我們建議使用 Dataflow 和 Apache Beam 來處理大規模資料處理工作負載。
適用於 Apache Beam 的 Cloud Firestore 連接器提供 Java 版本。有關 Cloud Firestore 連接器的更多信息，請參閱適用於 Java 的 Apache Beam SDK 。
在你開始之前
在閱讀本頁之前，您應該熟悉Apache Beam 的程式設計模型。
若要執行範例，您必須啟用 Dataflow API 。Cloud Firestore 管道範例
下面的範例示範了一種寫入資料的管道以及一種讀取和過濾資料的管道。您可以使用這些範例作為您自己的管道的起點。
運行範例管道
範例的原始程式碼可在googleapis/java-firestore GitHub 儲存庫中找到。若要執行這些範例，請下載原始程式碼並查看自述文件。
Write管道範例
以下範例在cities-beam-sample集合中建立文件：
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;
}
}此範例使用以下參數來配置和運行管道：
GOOGLE_CLOUD_PROJECT=project-id REGION=region TEMP_LOCATION=gs://temp-bucket/temp/ NUM_WORKERS=number-workers MAX_NUM_WORKERS=max-number-workers
Read管道範例
以下範例管道從cities-beam-sample集合中讀取文檔，對字段country地區設置為USA文檔套用過濾器，並返回匹配文檔的名稱。
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);
}
}));
}
}
}此範例使用以下參數來配置和運行管道：
GOOGLE_CLOUD_PROJECT=project-id REGION=region TEMP_LOCATION=gs://temp-bucket/temp/ NUM_WORKERS=number-workers MAX_NUM_WORKERS=max-number-workers
價錢
在 Dataflow 中執行 Cloud Firestore 工作負載會產生 Cloud Firestore 使用量和 Dataflow 使用費用。資料流使用量依您的作業所使用的資源計費。有關詳細信息，請參閱Dataflow 定價頁面。有關 Cloud Firestore 定價，請參閱定價頁面。
下一步是什麼
- 有關另一個管道範例，請參閱使用 Firestore 和 Apache Beam 進行資料處理。
- 有關 Dataflow 和 Apache Beam 的更多信息，請參閱Dataflow 文件。