Import historical user events

This page describes how to import user event data from past events in bulk into Vertex AI Search for retail. User event data is required for training models. After you've set up real-time event recording, it can take a considerable amount of time to record sufficient user event data to train your models. Importing historical data can accelerate the process.

The import procedures on this page apply to both recommendations and search. After you import data, both services are able to use those events, so you don't need to import the same data twice if you use both services.

You can import events from:

General tips to start

Import events from Cloud Storage tutorial

This tutorial shows how to import user events from Cloud Storage.

To follow step-by-step guidance for this task directly in the Cloud Shell Editor, click Guide me:

Guide me

Import events from BigQuery tutorial

This tutorial shows how to import user events from BigQuery.

To follow step-by-step guidance for this task directly in the Cloud Shell Editor, click Guide me:

Guide me

Import events inline tutorial

This tutorial shows how to import user events data inline.

To follow step-by-step guidance for this task directly in the Cloud Shell Editor, click Guide me:

Guide me

Before you begin

To avoid import errors and ensure that there is sufficient data to generate good results, review the following information before importing your user events.

Event import considerations

This section describes the methods that can be used for batch importing of your historical user events, when you might use each method, and some of their limitations.

Cloud Storage Description Import data in a JSON format from files loaded in a Cloud Storage bucket. Each file must be 2 GB or smaller, and up to 100 files at a time can be imported. The import can be done using the Google Cloud console or cURL. Uses the Product JSON data format, which allows custom attributes.
When to use If you need higher volumes of data to be loaded in a single step.
Limitations If your data is in Google Analytics or Merchant Center, that data can only be exported to BigQuery and requires the extra step of then importing it to Cloud Storage.
BigQuery Description Import data from a previously loaded BigQuery table that uses the Vertex AI Search for retail schema. Can be performed using Google Cloud console or cURL.
When to use If you are also using analytics or preprocessing event data before importing it.
Limitations Requires the extra step of creating a BigQuery table that maps to the Vertex AI Search for retail schema. If you have a high volume of user events, also consider that BigQuery is a higher cost resource than Cloud Storage.
BigQuery with Analytics 360 Description Import pre-existing data from Analytics 360 into Vertex AI Search for retail.
When to use If you have Analytics 360 and track conversions for recommendations or searches. No additional schema mapping is required.
Limitations Only a subset of attributes is available, so some advanced Vertex AI Search for retail features cannot be used. Tracking impressions in Google Analytics is required if you plan to use search.
BigQuery with Google Analytics 4 Description Import pre-existing data from Google Analytics 4 into Vertex AI Search for retail.
When to use If you have Google Analytics 4 and track conversions for recommendations or searches. No additional schema mapping is required.
Limitations Only a subset of attributes is available, so some advanced Vertex AI Search for retail features cannot be used. If you plan to use search, you need to set up event parameter key-value pairs for tracking; the recommended key is search_query.
Inline import Description Import using a call to the userEvents.import method.
When to use If you want to have the increased privacy of having all authentication occur on the backend and are capable of performing a backend import.
Limitations Usually more complicated than a web import.

Import user events from Cloud Storage

Import user events from Cloud Storage using the Google Cloud console or the userEvents.import method.

Console

  1. Go to the Data> page in the Search for Retail console.

    Go to the Data page
  2. Click Import to open the Import Data panel.
  3. Choose User events.
  4. Select Google Cloud Storage as the data source.
  5. Choose Retail User Events Schema as the schema.
  6. Enter the Cloud Storage location of your data.
  7. Click Import.

cURL

Use the userEvents.import method to import your user events.

  1. Create a data file for the input parameters for the import. Use the GcsSource object to point to your Cloud Storage bucket.

    You can provide multiple files, or just one.

    • INPUT_FILE: A file or files in Cloud Storage containing your user event data. See About user events for examples of each user event type format. Make sure each user event is on its own single line, with no line breaks.
    • ERROR_DIRECTORY: A Cloud Storage directory for error information about the import.

    The input file fields must be in the format gs://<bucket>/<path-to-file>/. The error directory must be in the format gs://<bucket>/<folder>/. If the error directory does not exist, Vertex AI Search for retail creates it. The bucket must already exist.

    {
"inputConfig":{
 "gcsSource": {
   "inputUris": ["INPUT_FILE_1", "INPUT_FILE_2"],
  },
  "errorsConfig":{"gcsPrefix":"ERROR_DIRECTORY"}
}

  2. Import your catalog information by making a POST request to the userEvents:import REST method, providing the name of the data file.

    export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json

curl -X POST \
     -v \
     -H "Content-Type: application/json; charset=utf-8" \
     -H "Authorization: Bearer "$(gcloud auth application-default print-access-token)"" \
     --data @./DATA_FILE.json \
  "https://retail.googleapis.com/v2/projects/PROJECT_ID/locations/global/catalogs/default_catalog/userEvents:import"
  }
}'

Import user events from BigQuery

Import user events from BigQuery using the Google Cloud console or the userEvents.import method.

Set up BigQuery access

Follow the instructions in Setting up access to your BigQuery dataset to give your Vertex AI Search for retail service account a BigQuery Data Owner role for your BigQuery dataset.

Import your user events from BigQuery

You can import 360 events using the Search for Retail console or the userEvents.import method.

Console

  1. Go to the Data> page in the Search for Retail console.

    Go to the Data page
  2. Click Import to open the Import Data panel.
  3. Choose User events.
  4. Select BigQuery as the data source.

  5. Select the data schema.

  6. Enter the BigQuery table where your data is located.
  7. Optional: Enter the location of a Cloud Storage bucket in your project as a temporary location for your data.
    If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
  8. Optional: Under Show advanced options, enter the location of a Cloud Storage bucket in your project as a temporary location for your data.

    If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
  9. Click Import.

curl

Import your user events by including the data for the events in your call to the userEvents.import method. See the userEvents.import API reference.

The value you specify for dataSchema depends on what you're importing:

export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json

curl \
-v \
-X POST \
-H "Content-Type: application/json; charset=utf-8" \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token)"" \
"https://retail.googleapis.com/v2/projects/PROJECT_ID/locations/global/catalogs/default_catalog/userEvents:import" \
--data '{
  "inputConfig": {
    "bigQuerySource": {
      "datasetId": "DATASET_ID",
      "tableId": "TABLE_ID",
      "dataSchema": "SCHEMA_TYPE"
  }
}
}'

Import Analytics 360 user events with BigQuery

You can import Analytics 360 user events if you have integrated Analytics 360 with BigQuery and use Enhanced Ecommerce.

The following procedures assume you are familiar with using BigQuery and Analytics 360.

Before you begin

Before you begin the next steps, make sure:

Check your data source

  1. Make sure that the user event data that you will import is correctly formatted in a BigQuery table you have access to.

    Make sure that the table is named project_id:ga360_export_dataset.ga_sessions_YYYYMMDD.

    See the Google Analytics documentation for more about the table format and naming.

  2. In the BigQuery Google Cloud console, select the table from the Explorer panel to preview the table.

    Check that:

    1. The clientId column has a valid value—for example, 123456789.123456789.

      Note that this value is different from the full _ga cookie value (which has a format such as GA1.3.123456789.123456789).

    2. The hits.transaction.currencyCode column has a valid currency code.

    3. If you plan to import search events, check that either a hits.page.searchKeyword or hits.customVariable.searchQuery column is present.

      While Vertex AI Search for retail requires both searchQuery and productDetails to return a list of search results, Analytics 360 doesn't store both search queries and product impressions in one event. For Vertex AI Search for retail to work, you need to create a tag at the data layer or a JavaScript Pixel to be able to import both types of user events from Google Analytics sources:

      • searchQuery, which is read from the search_term parameter, or from view_search_results events, is derived from either hits.page.searchKeyword, or from hits.customVariables.customVarValue if hits.customVariables.customVarName is searchQuery.
      • productDetails, the product impression which is read from the items parameter of the view_item_list event, is taken from hits.product if hits.product.isImpressions is TRUE.

  3. Check the consistency of item IDs between the uploaded catalog and the Analytics 360 user event table.

    Using any product ID from the hits.product.productSKU column in the BigQuery table preview, use the product.get method to make sure the same product is in your uploaded catalog.

    export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json

   curl \
     -v \
     -X GET \
     -H "Content-Type: application/json; charset=utf-8" \
     -H "Authorization: Bearer "$(gcloud auth application-default print-access-token)"" \
     "https://retail.googleapis.com/v2/projects/PROJECT_NUMBER/locations/global/catalogs/default_catalog/branches/default_branch/products/PRODUCT_ID"

Import your Analytics 360 events

You can import Google Analytics 360 events using the Search for Retail console or the userEvents.import method.

Console

  1. Go to the Data> page in the Search for Retail console.

    Go to the Data page
  2. Click Import to open the Import Data panel.
  3. Choose User events.
  4. Select BigQuery as the data source.

  5. Select the data schema.

  6. Enter the BigQuery table where your data is located.
  7. Optional: Enter the location of a Cloud Storage bucket in your project as a temporary location for your data.
    If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
  8. Optional: Under Show advanced options, enter the location of a Cloud Storage bucket in your project as a temporary location for your data.

    If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
  9. Click Import.

REST

Import your user events by including the data for the events in your call to the userEvents.import method.

For dataSchema, use the value user_event_ga360.

export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json
curl \\
  -v \\
  -X POST \\
  -H "Content-Type: application/json; charset=utf-8" \\
  -H "Authorization: Bearer "$(gcloud auth application-default print-access-token)"" \\
  "https://retail.googleapis.com/v2/projects/PROJECT_ID/locations/global/catalogs/default_catalog/userEvents:import" \\
  --data '{
    "inputConfig": {
      "bigQuerySource": {
        "datasetId": "some_ga360_export_dataset",
        "tableId": "ga_sessions_YYYYMMDD",
        "dataSchema": "user_event_ga360"
    }
  }
}'

Java

public static String importUserEventsFromBigQuerySource()
    throws IOException, InterruptedException, ExecutionException {
  UserEventServiceClient userEventsClient = getUserEventServiceClient();

  BigQuerySource bigQuerySource = BigQuerySource.newBuilder()
      .setProjectId(PROJECT_ID)
      .setDatasetId(DATASET_ID)
      .setTableId(TABLE_ID)
      .setDataSchema("user_event")
      .build();

  UserEventInputConfig inputConfig = UserEventInputConfig.newBuilder()
      .setBigQuerySource(bigQuerySource)
      .build();

  ImportUserEventsRequest importRequest = ImportUserEventsRequest.newBuilder()
      .setParent(DEFAULT_CATALOG_NAME)
      .setInputConfig(inputConfig)
      .build();

  String operationName = userEventsClient
      .importUserEventsAsync(importRequest).getName();

  userEventsClient.shutdownNow();
  userEventsClient.awaitTermination(2, TimeUnit.SECONDS);

  return operationName;
}

Import your Analytics 360 home-page-views with BigQuery

In Analytics 360, home-page-view events are not distinguished from other page-view events. This means that home-page-view events are not imported as events with the other event types (such as detail-page-view) in Import your Analytics 360 events.

The following procedure explains how you can extract home-page-view events from your Analytics 360 data and import them into Vertex AI Search for retail. In short, this is done by extracting users' views of the home page (identified by the home-page path) into a new BigQuery table and then importing data from that new table into Vertex AI Search for retail.

To import home-page-view events from Analytics 360 into Vertex AI Search for retail:

  1. Create a BigQuery dataset or make sure that you have a BigQuery dataset available that you can add a table to.

    This dataset can be in your Vertex AI Search for retail project or in the project where you have your Analytics 360 data. It is the target dataset into which you'll copy the Analytics 360 home-page-view events.

  2. Create a BigQuery table in the dataset as follows:

    1. Replace the variables in the following SQL code as follows.

      • target_project_id: The project where the dataset from step 1 is located.

      • target_dataset: The dataset name from step 1.

      CREATE TABLE TARGET_PROJECT_ID.TARGET_DATASET.ga_homepage (
 eventType STRING NOT NULL,
 visitorId STRING NOT NULL,
 userId STRING,
 eventTime STRING NOT NULL
);

    2. Copy the SQL code sample.

    3. Open the BigQuery page in the Google Cloud console.

      Go to the BigQuery page

    4. If it's not already selected, select the target project.

    5. In the Editor pane, paste the SQL code sample.

    6. Click Run and wait for the query to finish running.

    Running this code creates a table in the format target_project_id:target_dataset.ga_homepage_YYYYMMDD—for example, my-project:view_events.ga_homepage_20230115.

  3. Copy the Analytics 360 home-page-view events from your Analytics 360 data table into the table created in the preceding step 2.

    1. Replace the variables in the following SQL example code as follows:

      • source_project_id: The ID of the project that contains the Analytics 360 data in a BigQuery table.

      • source_dataset: The dataset in the source project that contains the Analytics 360 data in a BigQuery table.

      • source_table: The table in the source project that contains the Analytics 360 data.

      • target_project_id: The same target project ID as in the preceding step 2.

      • target_dataset: The same target dataset as in the preceding step 2.

      • path: This is the path to the home page. Usually this is /—for example, if the home page is example.com/. However, if the home page is like examplepetstore.com/index.html, the path is /index.html.

      INSERT INTO `TARGET_PROJECT_ID.TARGET_DATASET.ga_homepage(eventType,visitorId,userID,eventTime)`

SELECT
  "home-page-view" as eventType,
  clientId as visitorId,
  userId,
  CAST(FORMAT_TIMESTAMP("%Y-%m-%dT%H:%M:%SZ",TIMESTAMP_SECONDS(visitStartTime)) as STRING) AS eventTime

FROM
  `SOURCE_PROJECT_ID.SOURCE_DATASET.SOURCE_TABLE`, UNNEST(hits) as hits

WHERE hits.page.pagePath = "PATH" AND visitorId is NOT NULL;

    2. Copy the SQL code sample.

    3. Open the BigQuery page in the Google Cloud console.

      Go to the BigQuery page

    4. If it's not already selected, select the target project.

    5. In the Editor pane, paste the SQL code sample.

    6. Click Run and wait for the query to finish running.

  4. Follow the instructions in Import user events from BigQuery to import the home-page-view events from the target table. During schema selection, if you import using console, select Retail User Events Schema; if you import using userEvents.import, specify user_event for the dataSchema value.

  5. Delete the table and dataset that you created in steps 1 and 2.

Import Google Analytics 4 user events with BigQuery

You can import Google Analytics 4 user events if you have integrated Google Analytics 4 with BigQuery and use Google Analytics Ecommerce.

The following procedures assume you are familiar with using BigQuery and Google Analytics 4.

Before you begin

Before you begin the next steps, make sure:

Check your data source

To make sure that your user event data is prepared for importing, follow these steps.

For a table of Google Analytics 4 fields that Vertex AI Search for retail uses and which Vertex AI Search for retail fields they map to, see Google Analytics 4 user event fields.

For all Google Analytics event parameters, see the Google Analytics Events reference documentation.

  1. Make sure that the user event data that you will import is correctly formatted in a BigQuery table you have access to.

    • The dataset should be named analytics_PROPERTY_ID.
    • The table should be named events_YYYYMMDD.

    For information about the table names and format, see the Google Analytics documentation.

  2. In the BigQuery Google Cloud console, select the dataset from the Explorer panel and find the table of user events that you plan to import.

    Check that:

    1. The event_params.key column has a currency key and that its associated string value is a valid currency code.

    2. If you plan to import search events, check that the event.event_params.key column has a search_term key and an associated value.

      While Vertex AI Search for retail requires both searchQuery and productDetails to return a list of search results, Google Analytics 4 doesn't store both search queries and product impressions in one event. For Vertex AI Search for retail to work, you need to create a tag at the data layer or from a JavaScript Pixel to be able to import both types of user events from Google Analytics sources:

      • searchQuery, which is read from the search_term parameter, or from view_search_results events.
      • productDetails, the product impression which is read from the items parameter of the view_item_list event.

      For information about search in Google Analytics 4, see search in the Google Analytics documentation.

  3. Check the consistency of item IDs between the uploaded catalog and the Google Analytics 4 user event table.

    To make sure that a product in the Google Analytics 4 user table is also in your uploaded catalog, copy a product ID from the event.items.item_id column in the BigQuery table preview and use the product.get method to check if that product ID is in your uploaded catalog.

    export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json

   curl \
     -v \
     -X GET \
     -H "Content-Type: application/json; charset=utf-8" \
     -H "Authorization: Bearer "$(gcloud auth application-default print-access-token)"" \
     "https://retail.googleapis.com/v2/projects/PROJECT_NUMBER/locations/global/catalogs/default_catalog/branches/default_branch/products/PRODUCT_ID"

Set up BigQuery access

Follow the instructions in Setting up access to your BigQuery dataset to give your Vertex AI Search for retail service account a BigQuery Data Owner role for your BigQuery dataset.

Import your Google Analytics 4 events

You can import Google Analytics 4 events using the Search for Retail console or the userEvents.import method.

Import Google Analytics 4 events using the console

  1. Go to the Data> page in the Search for Retail console.

    Go to the Data page
  2. Click Import to open the Import Data panel.
  3. Choose User events.
  4. Select BigQuery as the data source.

  5. Select the data schema.

  6. Enter the BigQuery table where your data is located.
  7. Optional: Enter the location of a Cloud Storage bucket in your project as a temporary location for your data.
    If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
  8. Optional: Under Show advanced options, enter the location of a Cloud Storage bucket in your project as a temporary location for your data.

    If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
  9. Click Import.

Import Google Analytics 4 events using the API

Import your user events by including the data for the events in your call to the userEvents.import method. See the userEvents.import API reference.

For dataSchema, use the value user_event_ga4.

export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json
curl \\
  -v \\
  -X POST \\
  -H "Content-Type: application/json; charset=utf-8" \\
  -H "Authorization: Bearer "$(gcloud auth application-default print-access-token)"" \\
  "https://retail.googleapis.com/v2/projects/PROJECT_ID/locations/global/catalogs/default_catalog/userEvents:import" \\
  --data '{
    "inputConfig": {
      "bigQuerySource": {
        "projectId": "PROJECT_ID",
        "datasetId": "DATASET_ID",
        "tableId": "TABLE_ID",
        "dataSchema": "user_event_ga4"
    }
  }
}'

Import user events inline

You can import user events inline by including the data for the events in your call to the userEvents.import method.

The easiest way to do this is to put your user event data into a JSON file and provide the file to cURL.

For the formats of the user event types, see About user events.

curl

  1. Create the JSON file:

    {
  "inputConfig": {
    "userEventInlineSource": {
      "userEvents": [
        {
          <userEvent1>>
        },
        {
          <userEvent2>
        },
        ....
      ]
    }
  }
}

  2. Call the POST method:

    curl -X POST \
     -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     --data @./data.json \
  "https://retail.googleapis.com/v2/projects/PROJECT_ID/locations/global/catalogs/default_catalog/userEvents:import"

Java

public static String importUserEventsFromInlineSource(
    List<UserEvent> userEventsToImport)
    throws IOException, InterruptedException, ExecutionException {
  UserEventServiceClient userEventsClient = getUserEventServiceClient();

  UserEventInlineSource inlineSource = UserEventInlineSource.newBuilder()
      .addAllUserEvents(userEventsToImport)
      .build();

  UserEventInputConfig inputConfig = UserEventInputConfig.newBuilder()
      .setUserEventInlineSource(inlineSource)
      .build();

  ImportUserEventsRequest importRequest = ImportUserEventsRequest.newBuilder()
      .setParent(DEFAULT_CATALOG_NAME)
      .setInputConfig(inputConfig)
      .build();

  String operationName = userEventsClient
      .importUserEventsAsync(importRequest).getName();

  userEventsClient.shutdownNow();
  userEventsClient.awaitTermination(2, TimeUnit.SECONDS);

  return operationName;
}

Historical catalog data

You can also import the historical catalog data that appears in your historical user events. This historical catalog data can be helpful because past product information can be used to enrich the user events, which can improve model accuracy.

For more details, see Import historical catalog data.

View imported events

View event integration metrics in the Events tab on the Search for Retail console Data page. This page shows all events written or imported in last year. Metrics can take up to 24 hours to appear after successful data ingestion.

Go to the Data page

What's next