Firestore to Cloud Storage Text template

The Firestore to Cloud Storage Text template is a batch pipeline that reads Firestore entities and writes them to Cloud Storage as text files. You can provide a function to process each entity as a JSON string. If you don't provide such a function, every line in the output file will be a JSON-serialized entity.

Pipeline requirements

Firestore must be set up in the project before running the pipeline.

Template parameters

Required parameters

• firestoreReadGqlQuery : A GQL (https://cloud.google.com/datastore/docs/reference/gql_reference) query that specifies which entities to grab. For example, SELECT * FROM MyKind.
• firestoreReadProjectId : The ID of the Google Cloud project that contains the Firestore instance that you want to read data from.
• textWritePrefix : The Cloud Storage path prefix that specifies where the data is written. (Example: gs://mybucket/somefolder/).

Optional parameters

• firestoreReadNamespace : The namespace of the requested entities. To use the default namespace, leave this parameter blank.
• javascriptTextTransformGcsPath : The Cloud Storage URI of the .js file that defines the JavaScript user-defined function (UDF) to use. For example, gs://my-bucket/my-udfs/my_file.js.
• javascriptTextTransformFunctionName : The name of the JavaScript user-defined function (UDF) to use. For example, if your JavaScript function code is myTransform(inJson) { /*...do stuff...*/ }, then the function name is myTransform. For sample JavaScript UDFs, see UDF Examples (https://github.com/GoogleCloudPlatform/DataflowTemplates#udf-examples).

User-defined function

Optionally, you can extend this template by writing a user-defined function (UDF). The template calls the UDF for each input element. Element payloads are serialized as JSON strings. For more information, see Create user-defined functions for Dataflow templates.

Function specification

The UDF has the following specification:

• Input: a Firestore entity, serialized as a JSON string.
• Output: the string value to write to Cloud Storage.

Run the template

Console

1. Go to the Dataflow Create job from template page.
Go to Create job from template
2. In the Job name field, enter a unique job name.
3. Optional: For Regional endpoint, select a value from the drop-down menu. The default region is us-central1.

   For a list of regions where you can run a Dataflow job, see Dataflow locations.

4. From the Dataflow template drop-down menu, select the Firestore to Text Files on Cloud Storage template.
5. In the provided parameter fields, enter your parameter values.
6. Click Run job.

gcloud

Note: To use the Google Cloud CLI to run classic templates, you must have Google Cloud CLI version 138.0.0 or later.

In your shell or terminal, run the template:

gcloud dataflow jobs run JOB_NAME \
    --gcs-location gs://dataflow-templates-REGION_NAME/VERSION/Firestore_to_GCS_Text \
    --region REGION_NAME \
    --parameters \
firestoreReadGqlQuery="SELECT * FROM FIRESTORE_KIND",\
firestoreReadProjectId=FIRESTORE_PROJECT_ID,\
firestoreReadNamespace=FIRESTORE_NAMESPACE,\
javascriptTextTransformGcsPath=PATH_TO_JAVASCRIPT_UDF_FILE,\
javascriptTextTransformFunctionName=JAVASCRIPT_FUNCTION,\
textWritePrefix=gs://BUCKET_NAME/output/

Replace the following:

• JOB_NAME: a unique job name of your choice
• REGION_NAME: the region where you want to deploy your Dataflow job—for example, us-central1
• VERSION: the version of the template that you want to use

  You can use the following values:

  • latest to use the latest version of the template, which is available in the non-dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/latest/
  • the version name, like 2023-09-12-00_RC00, to use a specific version of the template, which can be found nested in the respective dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/
  Caution: The latest version of templates might update with breaking changes. Your production environments should use templates kept in the most recent dated parent folder to prevent these breaking changes from affecting your production workflows.
• BUCKET_NAME: the name of your Cloud Storage bucket
• FIRESTORE_PROJECT_ID: the Google Cloud project ID where the Firestore instance exists
• FIRESTORE_KIND: the type of your Firestore entities
• FIRESTORE_NAMESPACE: the namespace of your Firestore entities
• JAVASCRIPT_FUNCTION: the name of the JavaScript user-defined function (UDF) that you want to use

  For example, if your JavaScript function code is myTransform(inJson) { /*...do stuff...*/ }, then the function name is myTransform. For sample JavaScript UDFs, see UDF Examples.

• PATH_TO_JAVASCRIPT_UDF_FILE: the Cloud Storage URI of the .js file that defines the JavaScript user-defined function (UDF) you want to use—for example, gs://my-bucket/my-udfs/my_file.js

API

To run the template using the REST API, send an HTTP POST request. For more information on the API and its authorization scopes, see projects.templates.launch.

POST https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/templates:launch?gcsPath=gs://dataflow-templates-LOCATION/VERSION/Firestore_to_GCS_Text
{
   "jobName": "JOB_NAME",
   "parameters": {
       "firestoreReadGqlQuery": "SELECT * FROM FIRESTORE_KIND"
       "firestoreReadProjectId": "FIRESTORE_PROJECT_ID",
       "firestoreReadNamespace": "FIRESTORE_NAMESPACE",
       "javascriptTextTransformGcsPath": "PATH_TO_JAVASCRIPT_UDF_FILE",
       "javascriptTextTransformFunctionName": "JAVASCRIPT_FUNCTION",
       "textWritePrefix": "gs://BUCKET_NAME/output/"
   },
   "environment": { "zone": "us-central1-f" }
}

Replace the following:

• PROJECT_ID: the Google Cloud project ID where you want to run the Dataflow job
• JOB_NAME: a unique job name of your choice
• LOCATION: the region where you want to deploy your Dataflow job—for example, us-central1
• VERSION: the version of the template that you want to use

  You can use the following values:

  • latest to use the latest version of the template, which is available in the non-dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/latest/
  • the version name, like 2023-09-12-00_RC00, to use a specific version of the template, which can be found nested in the respective dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/
  Caution: The latest version of templates might update with breaking changes. Your production environments should use templates kept in the most recent dated parent folder to prevent these breaking changes from affecting your production workflows.
• BUCKET_NAME: the name of your Cloud Storage bucket
• FIRESTORE_PROJECT_ID: the Google Cloud project ID where the Firestore instance exists
• FIRESTORE_KIND: the type of your Firestore entities
• FIRESTORE_NAMESPACE: the namespace of your Firestore entities
• JAVASCRIPT_FUNCTION: the name of the JavaScript user-defined function (UDF) that you want to use

  For example, if your JavaScript function code is myTransform(inJson) { /*...do stuff...*/ }, then the function name is myTransform. For sample JavaScript UDFs, see UDF Examples.

• PATH_TO_JAVASCRIPT_UDF_FILE: the Cloud Storage URI of the .js file that defines the JavaScript user-defined function (UDF) you want to use—for example, gs://my-bucket/my-udfs/my_file.js

Template source code

Java

/*
 * Copyright (C) 2018 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License"); you may not
 * use this file except in compliance with the License. You may obtain a copy of
 * the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 * License for the specific language governing permissions and limitations under
 * the License.
 */
package com.google.cloud.teleport.templates;

import com.google.cloud.teleport.metadata.Template;
import com.google.cloud.teleport.metadata.TemplateCategory;
import com.google.cloud.teleport.templates.DatastoreToText.DatastoreToTextOptions;
import com.google.cloud.teleport.templates.common.DatastoreConverters.DatastoreReadOptions;
import com.google.cloud.teleport.templates.common.DatastoreConverters.ReadJsonEntities;
import com.google.cloud.teleport.templates.common.FirestoreNestedValueProvider;
import com.google.cloud.teleport.templates.common.JavascriptTextTransformer.JavascriptTextTransformerOptions;
import com.google.cloud.teleport.templates.common.JavascriptTextTransformer.TransformTextViaJavascript;
import com.google.cloud.teleport.templates.common.TextConverters.FilesystemWriteOptions;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.ValueProvider;

/**
 * Dataflow template which copies Datastore Entities to a Text sink. Text is encoded using JSON
 * encoded entity in the v1/Entity rest format: <a
 * href="https://cloud.google.com/datastore/docs/reference/rest/v1/Entity">Entity</a>
 *
 * <p>Check out <a
 * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v1/README_Datastore_to_GCS_Text.md">README_Datastore_to_GCS_Text</a>
 * or <a
 * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v1/README_Firestore_to_GCS_Text.md">README_Firestore_to_GCS_Text</a>
 * for instructions on how to use or change this template.
 */
@Template(
    name = "Datastore_to_GCS_Text",
    category = TemplateCategory.LEGACY,
    displayName = "Datastore to Text Files on Cloud Storage [Deprecated]",
    description =
        "The Datastore to Cloud Storage Text template is a batch pipeline that reads Datastore entities and writes them "
            + "to Cloud Storage as text files. You can provide a function to process each entity as a JSON string. "
            + "If you don't provide such a function, every line in the output file will be a JSON-serialized entity.",
    optionsClass = DatastoreToTextOptions.class,
    skipOptions = {
      "firestoreReadNamespace",
      "firestoreReadGqlQuery",
      "firestoreReadProjectId",
      "javascriptTextTransformReloadIntervalMinutes"
    },
    documentation =
        "https://cloud.google.com/dataflow/docs/guides/templates/provided/datastore-to-cloud-storage",
    contactInformation = "https://cloud.google.com/support",
    requirements = {"Datastore must be set up in the project before running the pipeline."})
@Template(
    name = "Firestore_to_GCS_Text",
    category = TemplateCategory.BATCH,
    displayName = "Firestore (Datastore mode) to Text Files on Cloud Storage",
    description =
        "The Firestore to Cloud Storage Text template is a batch pipeline that reads Firestore entities and writes them "
            + "to Cloud Storage as text files. You can provide a function to process each entity as a JSON string. "
            + "If you don't provide such a function, every line in the output file will be a JSON-serialized entity.",
    optionsClass = DatastoreToTextOptions.class,
    skipOptions = {
      "datastoreReadNamespace",
      "datastoreReadGqlQuery",
      "datastoreReadProjectId",
      "javascriptTextTransformReloadIntervalMinutes"
    },
    documentation =
        "https://cloud.google.com/dataflow/docs/guides/templates/provided/firestore-to-cloud-storage",
    contactInformation = "https://cloud.google.com/support",
    requirements = {"Firestore must be set up in the project before running the pipeline."})
public class DatastoreToText {

  public static ValueProvider<String> selectProvidedInput(
      ValueProvider<String> datastoreInput, ValueProvider<String> firestoreInput) {
    return new FirestoreNestedValueProvider(datastoreInput, firestoreInput);
  }

  /** Custom PipelineOptions. */
  public interface DatastoreToTextOptions
      extends PipelineOptions,
          DatastoreReadOptions,
          JavascriptTextTransformerOptions,
          FilesystemWriteOptions {}

  /**
   * Runs a pipeline which reads in Entities from Datastore, passes in the JSON encoded Entities to
   * a Javascript UDF, and writes the JSON to TextIO sink.
   *
   * @param args arguments to the pipeline
   */
  public static void main(String[] args) {
    DatastoreToTextOptions options =
        PipelineOptionsFactory.fromArgs(args).withValidation().as(DatastoreToTextOptions.class);

    Pipeline pipeline = Pipeline.create(options);

    pipeline
        .apply(
            ReadJsonEntities.newBuilder()
                .setGqlQuery(
                    selectProvidedInput(
                        options.getDatastoreReadGqlQuery(), options.getFirestoreReadGqlQuery()))
                .setProjectId(
                    selectProvidedInput(
                        options.getDatastoreReadProjectId(), options.getFirestoreReadProjectId()))
                .setNamespace(
                    selectProvidedInput(
                        options.getDatastoreReadNamespace(), options.getFirestoreReadNamespace()))
                .build())
        .apply(
            TransformTextViaJavascript.newBuilder()
                .setFileSystemPath(options.getJavascriptTextTransformGcsPath())
                .setFunctionName(options.getJavascriptTextTransformFunctionName())
                .build())
        .apply(TextIO.write().to(options.getTextWritePrefix()).withSuffix(".json"));

    pipeline.run();
  }
}

What's next

• Learn about Dataflow templates.
• See the list of Google-provided templates.