Search data assets with Data Catalog

This document describes how to use Data Catalog to search for data assets.

Note: This document describes search for Data Catalog. For information about Dataplex Catalog search, see Search for data assets in Dataplex Catalog and Dataplex Catalog versus Data Catalog.

You can search for the following data assets:

• Analytics Hub linked datasets
• BigQuery datasets, tables, views, and models
• Bigtable instances, clusters, and tables (including column family details)
• Data Catalog tag templates, entry groups, and custom entries
• Dataplex lakes, zones, tables, and filesets
• Dataproc Metastore services, databases, and tables
• Pub/Sub data streams
• Spanner instances, databases, tables, and views
• Vertex AI Models, Datasets, and Vertex AI Feature Store resources
• Assets in enterprise data silos connected to Data Catalog

Search scope

You might have different search results based on your permissions. Data Catalog search results are scoped according to your role.

You can review the different types of IAM role and permissions available for Data Catalog. For example, if you have BigQuery metadata read access to an object, that object appears in your Data Catalog search results.

The following list describes the minimum permissions required to perform search:

• To search for a table, you need bigquery.tables.get permission for that table.

• To search for a dataset, you need bigquery.datasets.get permission for that dataset.

• To search for metadata for a dataset or a table, you need the roles/bigquery.metadataViewer IAM role.

• To search for all resources within a project or organization, you need datacatalog.catalogs.searchAll permission. It works for all resources independent of the source system.

If you have access to a BigQuery table but not to the dataset containing that table, the table still shows up as expected in the Data Catalog search. The same access logic applies to all supported systems, such as Pub/Sub and Data Catalog itself.

Recall issues in search

Data Catalog search queries don't guarantee full recall. Results that match your query might not be returned, even in subsequent result pages. Additionally, if you repeat search queries, returned (and not returned) results can vary.

If you are experiencing recall issues and you don't have to fetch the results in any specific order, consider setting the orderBy parameter to default when calling the catalog.search method.

Use the admin_search flag

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Using the admin_search flag on the search request ensures full recall. Administrator search requires datacatalog.catalogs.searchAll permission to be set on all projects and organizations in the search scope. When using admin_search, only default orderBy is allowed.

Date-sharded tables

Data Catalog aggregates date-sharded tables into a single logical entry. This entry has the same schema as the table shard with the most recent date, and contains aggregate information about the total number of shards. The entry derives its access level from the dataset it belongs to. Data Catalog search only shows these logical entries if the user has the access to the dataset that contains them. Individual date-sharded tables aren't visible in Data Catalog search, even if they are present in Data Catalog and can be tagged.

Filters

Filters let you narrow down search results. All filters are grouped in sections:

• Scope to limit search to starred items only.
• Systems such as BigQuery, Pub/Sub, Dataplex, Dataproc Metastore, custom systems, Vertex AI, and Data Catalog itself. Data Catalog system contains filesets and custom entries.
• Lakes and zones come from Dataplex.
• Data types such as data streams, datasets, lakes, zones, filesets, models, tables, views, services, databases, and custom types.
• Projects lists all projects available to you.
• Tags lists all tag templates (and their individual fields) available to you.
• Datasets come from BigQuery and Vertex AI.
• Public datasets are publicly available data from BigQuery.

You can combine filters from multiple sections to find assets that match at least one condition from every selected section. Multiple filters selected within a single section are evaluated using the OR logical operator. For example, consider the following filter combination:

Example that shows how filters from multiple sections can be combined.

Data Catalog looks for the following:

• BigQuery datasets tagged with MyTemplate1 template.

• BigQuery datasets tagged with MyTemplate2 template.

• BigQuery tables tagged with MyTemplate1 template.

• BigQuery tables tagged with MyTemplate2 template.

Filter by tag value

The Tags filters let you query for assets tagged using a specific template. You can use the Customize menu to further refine results and filter by specific tag values. The tag value filter conditions depend on that tag field's data type. For example, for datetime and number fields, you can specify a specific date or a range.

Filters visibility

The filters displayed in every section depend on the current query in the Search box. The whole set of search results might include entries that match the current query, but the filters that correspond to those entries might not be shown on the Filters pane.

Note: If you change selected filters, filter sets aren't refreshed. For example, if you select the BigQuery system in the Systems section, Data type filters don't narrow down to the BigQuery- specific data types, such as Table, View, and Dataset.

Search for data assets

Console

Console

1. In the Google Cloud console, go to the Dataplex Search page.

   Go to Dataplex Search

2. For Choose search platform, select Data Catalog as the search mode.

3. In the search field, enter your query or use the Filters pane to refine the search parameters.

   You can manually add the following filters:

   • In Projects, add a project filter. Click Add project, search for and select a specific project, and click Open.
   • In Tags, add a tag template filter. Click the Add more tag templates menu, search for and select a specific template, and click OK.

   To search for data assets publicly available in Google Cloud in addition to the assets available to you, select Include public datasets

Additionally, you can do the following:

• Filter your search by adding a keyword:value to your search terms in the search field:

  Keyword	Description
  name:	Match data asset name
  column:	Match column name or nested column name
  description:	Match table description

• Perform a tag search by adding one of the following tag keyword prefixes to your search terms in the search field:

  Tag	Description
  tag:project-name.tag_template_name	Match tag name
  tag:project-name.tag_template_name.key	Match a tag key
  tag:project-name.tag_template_name.key:value	Match tag key:string value pair

Search expression tips

• Enclose your search expression in quotes ("search terms") if it contains spaces.

• You can precede a keyword with "NOT" (all CAPS required) to match the logical negation of the keyword:term filter. You can also use "AND" and "OR" (all-CAPS required) boolean operators to combine search expressions.

  For example:NOT column:term lists all columns except those that match the specified term. For a list of keywords and other terms you can use in a Data Catalog search expression, see Data Catalog search syntax.

Search example

Consider an example where you want to search for the trips table that you set up in Tag a BigQuery table by using Data Catalog:

1. In the search field, enter trips, and click Search.

2. In the Filters pane, select the following:

   • In the Systems section, select BigQuery to exclude data assets with the same name that belong to other systems.
   • In the Projects section, select your project ID to exclude data assets from other projects. If your project isn't displayed, click Add project and select your project.
   • In the Tags section, select Demo Tag Template to check if a tag that uses this template is attached to the trips table. If this template isn't displayed, click the Add more tag templates, search for and select the tag template, and click OK.

With all the selected filters, the search results contain only one entry—the BigQuery trips table in your project with an attached tag that uses the Demo Tag Template template.

Note: To preview the content of a BigQuery table, in the search result, click the next to the table.

Java

Before trying this sample, follow the Java setup instructions in the Data Catalog quickstart using client libraries. For more information, see the Data Catalog Java API reference documentation.

To authenticate to Data Catalog, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.datacatalog.v1.DataCatalogClient;
import com.google.cloud.datacatalog.v1.DataCatalogClient.SearchCatalogPagedResponse;
import com.google.cloud.datacatalog.v1.SearchCatalogRequest;
import com.google.cloud.datacatalog.v1.SearchCatalogRequest.Scope;
import com.google.cloud.datacatalog.v1.SearchCatalogResult;
import java.io.IOException;

// Sample to search catalog
public class SearchAssets {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "my-project-id";
    String query = "type=dataset";
    searchCatalog(projectId, query);
  }

  public static void searchCatalog(String projectId, String query) throws IOException {
    // Create a scope object setting search boundaries to the given organization.
    // Scope scope = Scope.newBuilder().addIncludeOrgIds(orgId).build();

    // Alternatively, search using project scopes.
    Scope scope = Scope.newBuilder().addIncludeProjectIds(projectId).build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (DataCatalogClient dataCatalogClient = DataCatalogClient.create()) {
      // Search the catalog.
      SearchCatalogRequest searchCatalogRequest =
          SearchCatalogRequest.newBuilder().setScope(scope).setQuery(query).build();
      SearchCatalogPagedResponse response = dataCatalogClient.searchCatalog(searchCatalogRequest);

      System.out.println("Search results:");
      for (SearchCatalogResult result : response.iterateAll()) {
        System.out.println(result);
      }
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Data Catalog quickstart using client libraries. For more information, see the Data Catalog Node.js API reference documentation.

To authenticate to Data Catalog, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

// Import the Google Cloud client library.
const {DataCatalogClient} = require('@google-cloud/datacatalog').v1;
const datacatalog = new DataCatalogClient();

async function searchAssets() {
  // Search data assets.

  /**
   * TODO(developer): Uncomment the following lines before running the sample.
   */
  // const projectId = 'my_project'; // Google Cloud Platform project

  // Set custom query.
  const query = 'type=lake';

  // Create request.
  const scope = {
    includeProjectIds: [projectId],
    // Alternatively, search using Google Cloud Organization scopes.
    // includeOrgIds: [organizationId],
  };

  const request = {
    scope: scope,
    query: query,
  };

  const [result] = await datacatalog.searchCatalog(request);

  console.log(`Found ${result.length} datasets in project ${projectId}.`);
  console.log('Datasets:');
  result.forEach(dataset => {
    console.log(dataset.relativeResourceName);
  });
}
searchAssets();

Python

Before trying this sample, follow the Python setup instructions in the Data Catalog quickstart using client libraries. For more information, see the Data Catalog Python API reference documentation.

To authenticate to Data Catalog, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google.cloud import datacatalog_v1

datacatalog = datacatalog_v1.DataCatalogClient()

# TODO: Set these values before running the sample.
project_id = "project_id"

# Set custom query.
search_string = "type=dataset"
scope = datacatalog_v1.types.SearchCatalogRequest.Scope()
scope.include_project_ids.append(project_id)

# Alternatively, search using organization scopes.
# scope.include_org_ids.append("my_organization_id")

search_results = datacatalog.search_catalog(scope=scope, query=search_string)

print("Results in project:")
for result in search_results:
    print(result)

REST & CMD LINE

REST

If you don't have access to Cloud Client libraries for your language or want to test the API using REST requests, see the following examples and refer to the Data Catalog REST API documentation.

1. Search catalog.

Before using any of the request data, make the following replacements:

• organization-id: GCP organization ID
• project-id: GCP project ID

HTTP method and URL:

POST https://datacatalog.googleapis.com/v1/catalog:search

Request JSON body:

{
  "query":"trips",
  "scope":{
    "includeOrgIds":[
      "organization-id"
    ]
  }
}

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: project-id" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://datacatalog.googleapis.com/v1/catalog:search"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://datacatalog.googleapis.com/v1/catalog:search" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "results":[
    {
      "searchResultType":"ENTRY",
      "searchResultSubtype":"entry.table",
"relativeResourceName":"projects/project-id/locations/US/entryGroups/@bigquery/entries/entry1-id",
      "linkedResource":"//bigquery.googleapis.com/projects/project-id/datasets/demo_dataset/tables/taxi_trips"
    },
    {
      "searchResultType":"ENTRY",
      "searchResultSubtype":"entry.table",
      "relativeResourceName":"projects/project-id/locations/US/entryGroups/@bigquery/entries/entry2-id",
      "linkedResource":"//bigquery.googleapis.com/projects/project-id/datasets/demo_dataset/tables/tlc_yellow_trips_2018"
    }
  ]
}

View table details

Use Data Catalog to view table details.

1. In the Google Cloud console, go to the Dataplex Search page.

   Go to Data Catalog Search

2. For Choose search platform, select Data Catalog as the search mode.

3. In the search box, enter the name of a dataset that has a table.

   For example, if you completed the Tag a BigQuery table by using Data Catalog quickstart, you can search for demo-dataset and select the trips table.

4. Click the table.

   The BigQuery table details page opens.

The table details include the following sections:

• BigQuery table details. Includes information such as the time of creation, time of last modification, time of expiration, resource URLs, and labels.

• Tags. Lists the applied tags.You can edit the tags from this page and view the tag template. Click the Actions icon.

• Schema and column tags. Lists the applied schema and their values.

Star your favorite entries and search for them

If you frequently browse the same data assets, you can include their entries in a personalized list by marking them with stars.

1. In the Google Cloud console, go to the Dataplex Search page.

   Go to Data Catalog Search

2. For Choose search platform, select Data Catalog as the search mode.

3. Find your asset, and then star its entry in one of two ways:

   • Click next to the entry in the search results.
   • Click the entry name to open its details page and click the STAR on the action bar at the top.

You can star up to 200 entries.

Starred entries appear in the Starred Entries list on the search page before you enter a search query in the search bar. This list is visible only to you.

To search for only starred entries, in the Filters pane, in the Scope section, select Starred.

You can also use the corresponding methods of Data Catalog API to star and unstar entries. When searching for assets, use the starredOnly parameter in the scope object. For more information, see the catalog.search method.

What's next

• Understand search syntax for Data Catalog.

• Learn how to search for resources in Dataplex Catalog.