BigQuery to Elasticsearch template

The BigQuery to Elasticsearch template is a batch pipeline that ingests data from a BigQuery table into Elasticsearch as documents. The template can either read the entire table or read specific records using a supplied query.

Pipeline requirements

The source BigQuery table must exist.
A Elasticsearch host on a Google Cloud instance or on Elastic Cloud with Elasticsearch version 7.0 or later. Must be accessible from the Dataflow worker machines.

Template parameters

Required parameters

connectionUrl: The Elasticsearch URL in the format https://hostname:[port]. If using Elastic Cloud, specify the CloudID. For example, https://elasticsearch-host:9200.
apiKey: The Base64-encoded API key to use for authentication.
index: The Elasticsearch index that the requests are issued to. For example, my-index.

Optional parameters

inputTableSpec: The BigQuery table to read from. If you specify inputTableSpec, the template reads the data directly from BigQuery storage by using the BigQuery Storage Read API (https://cloud.google.com/bigquery/docs/reference/storage). For information about limitations in the Storage Read API, see https://cloud.google.com/bigquery/docs/reference/storage#limitations. You must specify either inputTableSpec or query. If you set both parameters, the template uses the query parameter. For example, <BIGQUERY_PROJECT>:<DATASET_NAME>.<INPUT_TABLE>.
outputDeadletterTable: The BigQuery table for messages that failed to reach the output table. If a table doesn't exist, it is created during pipeline execution. If not specified, <outputTableSpec>_error_records is used. For example, <PROJECT_ID>:<DATASET_NAME>.<DEADLETTER_TABLE>.
query: The SQL query to use to read data from BigQuery. If the BigQuery dataset is in a different project than the Dataflow job, specify the full dataset name in the SQL query, for example: <PROJECT_ID>.<DATASET_NAME>.<TABLE_NAME>. By default, the query parameter uses GoogleSQL (https://cloud.google.com/bigquery/docs/introduction-sql), unless useLegacySql is true. You must specify either inputTableSpec or query. If you set both parameters, the template uses the query parameter. For example, select * from sampledb.sample_table.
useLegacySql: Set to true to use legacy SQL. This parameter only applies when using the query parameter. Defaults to false.
queryLocation: Needed when reading from an authorized view without underlying table's permission. For example, US.
queryTempDataset: With this option, you can set an existing dataset to create the temporary table to store the results of the query. For example, temp_dataset.
KMSEncryptionKey: If reading from BigQuery using query source, use this Cloud KMS key to encrypt any temporary tables created. For example, projects/your-project/locations/global/keyRings/your-keyring/cryptoKeys/your-key.
elasticsearchUsername: The Elasticsearch username to authenticate with. If specified, the value of apiKey is ignored.
elasticsearchPassword: The Elasticsearch password to authenticate with. If specified, the value of apiKey is ignored.
batchSize: The batch size in number of documents. Defaults to 1000.
batchSizeBytes: The batch size in number of bytes. Defaults to 5242880 (5mb).
maxRetryAttempts: The maximum number of retry attempts. Must be greater than zero. Defaults to no retries.
maxRetryDuration: The maximum retry duration in milliseconds. Must be greater than zero. Defaults to no retries.
propertyAsIndex: The property in the document being indexed whose value specifies _index metadata to include with the document in bulk requests. Takes precedence over an _index UDF. Defaults to none.
javaScriptIndexFnGcsPath: The Cloud Storage path to the JavaScript UDF source for a function that specifies _index metadata to include with the document in bulk requests. Defaults to none.
javaScriptIndexFnName: The name of the UDF JavaScript function that specifies _index metadata to include with the document in bulk requests. Defaults to none.
propertyAsId: A property in the document being indexed whose value specifies _id metadata to include with the document in bulk requests. Takes precedence over an _id UDF. Defaults to none.
javaScriptIdFnGcsPath: The Cloud Storage path to the JavaScript UDF source for the function that specifies _id metadata to include with the document in bulk requests. Defaults to none.
javaScriptIdFnName: The name of the UDF JavaScript function that specifies the _id metadata to include with the document in bulk requests. Defaults to none.
javaScriptTypeFnGcsPath: The Cloud Storage path to the JavaScript UDF source for a function that specifies _type metadata to include with documents in bulk requests. Defaults to none.
javaScriptTypeFnName: The name of the UDF JavaScript function that specifies the _type metadata to include with the document in bulk requests. Defaults to none.
javaScriptIsDeleteFnGcsPath: The Cloud Storage path to the JavaScript UDF source for the function that determines whether to delete the document instead of inserting or updating it. The function returns a string value of true or false. Defaults to none.
javaScriptIsDeleteFnName: The name of the UDF JavaScript function that determines whether to delete the document instead of inserting or updating it. The function returns a string value of true or false. Defaults to none.
usePartialUpdate: Whether to use partial updates (update rather than create or index, allowing partial documents) with Elasticsearch requests. Defaults to false.
bulkInsertMethod: Whether to use INDEX (index, allows upserts) or CREATE (create, errors on duplicate _id) with Elasticsearch bulk requests. Defaults to CREATE.
trustSelfSignedCerts: Whether to trust self-signed certificate or not. An Elasticsearch instance installed might have a self-signed certificate, Enable this to true to by-pass the validation on SSL certificate. (Defaults to: false).
disableCertificateValidation: If true, trust the self-signed SSL certificate. An Elasticsearch instance might have a self-signed certificate. To bypass validation for the certificate, set this parameter to true. Defaults to false.
apiKeyKMSEncryptionKey: The Cloud KMS key to decrypt the API key. This parameter is required if the apiKeySource is set to KMS. If this parameter is provided, pass in an encrypted apiKey string. Encrypt parameters using the KMS API encrypt endpoint. For the key, use the format projects/<PROJECT_ID>/locations/<KEY_REGION>/keyRings/<KEY_RING>/cryptoKeys/<KMS_KEY_NAME>. See: https://cloud.google.com/kms/docs/reference/rest/v1/projects.locations.keyRings.cryptoKeys/encrypt For example, projects/your-project-id/locations/global/keyRings/your-keyring/cryptoKeys/your-key-name.
apiKeySecretId: The Secret Manager secret ID for the apiKey. If the apiKeySource is set to SECRET_MANAGER, provide this parameter. Use the format projects/<PROJECT_ID>/secrets/<SECRET_ID>/versions/<SECRET_VERSION>. For example, projects/your-project-id/secrets/your-secret/versions/your-secret-version`.
apiKeySource: The source of the API key. Allowed values are PLAINTEXT, KMS orand SECRET_MANAGER. This parameter is required when you use Secret Manager or KMS. If apiKeySource is set to KMS, apiKeyKMSEncryptionKey and encrypted apiKey must be provided. If apiKeySource is set to SECRET_MANAGER, apiKeySecretId must be provided. If apiKeySource is set to PLAINTEXT, apiKey must be provided. Defaults to: PLAINTEXT.
socketTimeout: If set, overwrites the default max retry timeout and default socket timeout (30000ms) in the Elastic RestClient.
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 functions

This template supports user-defined functions (UDFs) at several points in the pipeline, described below. For more information, see Create user-defined functions for Dataflow templates.

Index function

Returns the index to which the document belongs.

Template parameters:

javaScriptIndexFnGcsPath: the Cloud Storage URI of the JavaScript file.
javaScriptIndexFnName: the name of the JavaScript function.

Function specification:

Input: the Elasticsearch document, serialized as a JSON string.
Output: the value of the document's _index metadata field.

Document ID function

Returns the document ID.

Template parameters:

javaScriptIdFnGcsPath: the Cloud Storage URI of the JavaScript file.
javaScriptIdFnName: the name of the JavaScript function.

Function specification:

Input: the Elasticsearch document, serialized as a JSON string.
Output: the value of the document's _id metadata field.

Document deletion function

Specifies whether to delete a document. To use this function, set the bulk insert mode to INDEX and provide a document ID function.

Template parameters:

javaScriptIsDeleteFnGcsPath: the Cloud Storage URI of the JavaScript file.
javaScriptIsDeleteFnName: the name of the JavaScript function.

Function specification:

Input: the Elasticsearch document, serialized as a JSON string.
Output: return the string "true" to delete the document, or "false" to upsert the document.

Mapping type function

Returns the document's mapping type.

Template parameters:

javaScriptTypeFnGcsPath: the Cloud Storage URI of the JavaScript file.
javaScriptTypeFnName: the name of the JavaScript function.

Function specification:

Input: the Elasticsearch document, serialized as a JSON string.
Output: the value of the document's _type metadata field.

Run the template

Console

Go to the Dataflow Create job from template page.

Go to Create job from template

In the Job name field, enter a unique job name.
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.
From the Dataflow template drop-down menu, select the BigQuery to Elasticsearch template.
In the provided parameter fields, enter your parameter values.
Click Run job.

gcloud

In your shell or terminal, run the template:

gcloud dataflow flex-template run JOB_NAME \
    --project=PROJECT_ID \
    --region=REGION_NAME \
    --template-file-gcs-location=gs://dataflow-templates-REGION_NAME/VERSION/flex/BigQuery_to_Elasticsearch \
    --parameters \
inputTableSpec=INPUT_TABLE_SPEC,\
connectionUrl=CONNECTION_URL,\
apiKey=APIKEY,\
index=INDEX

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
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.
INPUT_TABLE_SPEC: your BigQuery table name.
CONNECTION_URL: your Elasticsearch URL.
APIKEY: your base64 encoded API key for authentication.
INDEX: your Elasticsearch index.

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/flexTemplates:launch
{
   "launch_parameter": {
      "jobName": "JOB_NAME",
      "parameters": {
          "inputTableSpec": "INPUT_TABLE_SPEC",
          "connectionUrl": "CONNECTION_URL",
          "apiKey": "APIKEY",
          "index": "INDEX"
      },
      "containerSpecGcsPath": "gs://dataflow-templates-LOCATION/VERSION/flex/BigQuery_to_Elasticsearch",
   }
}

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.
INPUT_TABLE_SPEC: your BigQuery table name.
CONNECTION_URL: your Elasticsearch URL.
APIKEY: your base64 encoded API key for authentication.
INDEX: your Elasticsearch index.

Template source code

Java

/*
 * Copyright (C) 2021 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.v2.elasticsearch.templates;

import com.google.cloud.teleport.metadata.MultiTemplate;
import com.google.cloud.teleport.metadata.Template;
import com.google.cloud.teleport.metadata.TemplateCategory;
import com.google.cloud.teleport.v2.common.UncaughtExceptionLogger;
import com.google.cloud.teleport.v2.elasticsearch.options.BigQueryToElasticsearchOptions;
import com.google.cloud.teleport.v2.elasticsearch.transforms.WriteToElasticsearch;
import com.google.cloud.teleport.v2.transforms.BigQueryConverters.ReadBigQueryTableRows;
import com.google.cloud.teleport.v2.transforms.BigQueryConverters.TableRowToJsonFn;
import com.google.cloud.teleport.v2.transforms.JavascriptTextTransformer.TransformTextViaJavascript;
import com.google.cloud.teleport.v2.transforms.PythonExternalTextTransformer;
import com.google.common.base.Strings;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.values.PCollection;

/**
 * The {@link BigQueryToElasticsearch} pipeline exports data from a BigQuery table to Elasticsearch.
 *
 * <p>Check out <a
 * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v2/googlecloud-to-elasticsearch/README_BigQuery_to_Elasticsearch.md">README</a>
 * for instructions on how to use or modify this template.
 */
@MultiTemplate({
  @Template(
      name = "BigQuery_to_Elasticsearch",
      category = TemplateCategory.BATCH,
      displayName = "BigQuery to Elasticsearch",
      description =
          "The BigQuery to Elasticsearch template is a batch pipeline that ingests data from a BigQuery table into Elasticsearch as documents. "
              + "The template can either read the entire table or read specific records using a supplied query.",
      optionsClass = BigQueryToElasticsearchOptions.class,
      skipOptions = {
        "javascriptTextTransformReloadIntervalMinutes",
        "pythonExternalTextTransformGcsPath",
        "pythonExternalTextTransformFunctionName"
      },
      flexContainerName = "bigquery-to-elasticsearch",
      documentation =
          "https://cloud.google.com/dataflow/docs/guides/templates/provided/bigquery-to-elasticsearch",
      contactInformation = "https://cloud.google.com/support",
      preview = true,
      requirements = {
        "The source BigQuery table must exist.",
        "A Elasticsearch host on a Google Cloud instance or on Elastic Cloud with Elasticsearch version 7.0 or above and should be accessible from the Dataflow worker machines.",
      }),
  @Template(
      name = "BigQuery_to_Elasticsearch_Xlang",
      category = TemplateCategory.BATCH,
      displayName = "BigQuery to Elasticsearch with Python UDFs",
      type = Template.TemplateType.XLANG,
      description =
          "The BigQuery to Elasticsearch template is a batch pipeline that ingests data from a BigQuery table into Elasticsearch as documents. "
              + "The template can either read the entire table or read specific records using a supplied query.",
      optionsClass = BigQueryToElasticsearchOptions.class,
      skipOptions = {
        "javascriptTextTransformReloadIntervalMinutes",
        "javascriptTextTransformGcsPath",
        "javascriptTextTransformFunctionName"
      },
      flexContainerName = "bigquery-to-elasticsearch-xlang",
      documentation =
          "https://cloud.google.com/dataflow/docs/guides/templates/provided/bigquery-to-elasticsearch",
      contactInformation = "https://cloud.google.com/support",
      preview = true,
      requirements = {
        "The source BigQuery table must exist.",
        "A Elasticsearch host on a Google Cloud instance or on Elastic Cloud with Elasticsearch version 7.0 or above and should be accessible from the Dataflow worker machines.",
      })
})
public class BigQueryToElasticsearch {
  /**
   * Main entry point for pipeline execution.
   *
   * @param args Command line arguments to the pipeline.
   */
  public static void main(String[] args) {
    UncaughtExceptionLogger.register();

    BigQueryToElasticsearchOptions options =
        PipelineOptionsFactory.fromArgs(args)
            .withValidation()
            .as(BigQueryToElasticsearchOptions.class);

    run(options);
  }

  /**
   * Runs the pipeline with the supplied options.
   *
   * @param options The execution parameters to the pipeline.
   * @return The result of the pipeline execution.
   */
  private static PipelineResult run(BigQueryToElasticsearchOptions options) {

    // Create the pipeline.
    Pipeline pipeline = Pipeline.create(options);
    /*
     * Steps: 1) Read records from BigQuery via BigQueryIO.
     *        2) Create json string from Table Row.
     *        3) Write records to Elasticsearch.
     */

    boolean useJavascriptUdf = !Strings.isNullOrEmpty(options.getJavascriptTextTransformGcsPath());
    boolean usePythonUdf = !Strings.isNullOrEmpty(options.getPythonExternalTextTransformGcsPath());
    if (useJavascriptUdf && usePythonUdf) {
      throw new IllegalArgumentException(
          "Either javascript or Python gcs path must be provided, but not both.");
    }

    /*
     * Step #1: Read from BigQuery. If a query is provided then it is used to get the TableRows.
     */
    PCollection<String> readJsonDocuments =
        pipeline
            .apply(
                "ReadFromBigQuery",
                ReadBigQueryTableRows.newBuilder()
                    .setOptions(options.as(BigQueryToElasticsearchOptions.class))
                    .build())

            /*
             * Step #2: Convert table rows to JSON documents.
             */
            .apply("TableRowsToJsonDocument", ParDo.of(new TableRowToJsonFn()));

    /*
     * Step #3: Apply UDF functions (if specified)
     */
    PCollection<String> udfOut;
    if (usePythonUdf) {
      udfOut =
          readJsonDocuments
              .apply(
                  "MapToRecord",
                  PythonExternalTextTransformer.FailsafeRowPythonExternalUdf
                      .stringMappingFunction())
              .setRowSchema(PythonExternalTextTransformer.FailsafeRowPythonExternalUdf.ROW_SCHEMA)
              .apply(
                  "InvokeUDF",
                  PythonExternalTextTransformer.FailsafePythonExternalUdf.newBuilder()
                      .setFileSystemPath(options.getPythonExternalTextTransformGcsPath())
                      .setFunctionName(options.getPythonExternalTextTransformFunctionName())
                      .build())
              .apply(
                  "MapToStringElements",
                  ParDo.of(new PythonExternalTextTransformer.RowToStringElementFn()));
    } else {
      udfOut =
          readJsonDocuments.apply(
              TransformTextViaJavascript.newBuilder()
                  .setFileSystemPath(options.getJavascriptTextTransformGcsPath())
                  .setFunctionName(options.getJavascriptTextTransformFunctionName())
                  .build());
    }

    /*
     * Step #4: Write converted records to Elasticsearch
     */
    udfOut.apply(
        "WriteToElasticsearch",
        WriteToElasticsearch.newBuilder()
            .setUserAgent("dataflow-bigquery-to-elasticsearch-template/v2")
            .setOptions(options.as(BigQueryToElasticsearchOptions.class))
            .build());

    return pipeline.run();
  }
}

What's next

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