## BigQuery assets package

URL: https://docs.atlan.com/apps/connectors/data-warehouses/google-bigquery/sdk/references/package-reference

> Programmatically crawl and manage Google BigQuery assets in Atlan using the Python SDK (pyatlan). Reference for the BigQuery assets package.

:::warning[Deprecated—use the app reference]
This package is deprecated. Use the [app reference](app-reference) for the new `client.app` builder, or see [Manage apps](https://docs.atlan.com/llms/platform/python/manage-apps/llms.txt).
:::

# BigQuery assets package

The [BigQuery assets package](https://ask.atlan.com/hc/en-us/articles/6326782856081)
crawls Google BigQuery assets and publishes them to Atlan for discovery.

## Service account

:::warning[Will create a new connection]
This should only be used to create the workflow the first time. Each time you
run this method it will create a new connection and new assets within that connection
— which could lead to duplicate assets if you run the workflow this way multiple times with the same settings.

Instead, when you want to re-crawl assets, re-run the existing workflow
(see [Re-run existing workflow](#re-run-existing-workflow) below).
:::
To crawl assets from BigQuery using service account authentication:

### Java

:::warning[Coming soon]
:::

### Python

```python showLineNumbers title="BigQuery assets crawling using service account auth"
from pyatlan.client.atlan import AtlanClient
from pyatlan.model.packages import BigQueryCrawler

client = AtlanClient()

crawler = (
 BigQueryCrawler( # (1)
 client=client, # (2)
 connection_name="production", # (3)
 admin_roles=[client.role_cache.get_id_for_name("$admin")], # (4)
 admin_groups=None,
 admin_users=None,
 row_limit=10000, # (5)
 allow_query=True, # (6)
 allow_query_preview=True, # (7)
 )
 .service_account_auth( # (8)
 project_id="test-project-id",
 service_account_json="test-account-json",
 service_account_email="test@test.com",
 )
 .include(assets={"test-include": ["test-asset-1", "test-asset-2"]}) # (9)
 .exclude(assets=None) # (10)
 .exclude_regex(regex=".*_TEST") # (11)
 .custom_config(config={"ignore-all-case": True}) # (12)
 .to_workflow() # (13)
)
response = client.workflow.run(crawler) # (14)
```

1. Base configuration for a new BigQuery crawler.
2. You must provide a client instance.
3. You must provide a name for the connection that the BigQuery assets will exist within.
4. You must specify at **least one connection admin**, either:

 - everyone in a role (in this example, all `$admin` users).
 - a list of groups (names) that will be connection admins.
 - a list of users (names) that will be connection admins.
5. You can specify a maximum number of rows that can be accessed for any asset in the connection.
6. You can specify whether you want to allow queries to this connection.
(`True`, as in this example) or deny all query access to the connection (`False`).
7. You can specify whether you want to allow data previews on this connection
(`True`, as in this example) or deny all sample data previews to the connection (`False`).
8. When using `service_account_auth()`, you need to provide the following information:

 - project ID of your Google Cloud project.
 - entire service account json.
 - service account email.

9. You can also optionally specify the set of assets to include in crawling.
For BigQuery assets, this should be specified as a dict keyed by project name
with each value being a list of tables to include. (If set to None, all table will be crawled.)
10. You can also optionally specify the list of assets to exclude from crawling.
For BigQuery assets, this should be specified as a dict keyed by project name
with each value being a list of tables to exclude. (If set to None, no table will be excluded.)
11. You can also optionally specify the exclude regex
for crawler ignore tables and views based on a naming convention.
12. You can also optionally specify the custom JSON configuration
controlling experimental feature flags for the crawler,
eg: `{"ignore-all-case": True}` to enable crawling assets
with case-sensitive identifiers.
13. Now, you can convert the package into a `Workflow` object.
14. Run the workflow by invoking the `run()` method on the workflow client, passing the created object.

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt)
for details on how you can check the status and wait until the workflow has been completed.
 :::

### Kotlin

:::warning[Coming soon]
:::

### Raw REST API

:::tip[Create the workflow via UI only]
We recommend creating the workflow only via the UI. To rerun an existing workflow, see the steps below.
:::

## Re-run existing workflow

To re-run an existing workflow for BigQuery assets:

### Java

```java showLineNumbers title="Re-run existing BigQuery workflow"
List existing = WorkflowSearchRequest // (1)
 .findByType(client, BigQueryCrawler.PREFIX, 5); // (2)
// Determine which of the results is the BigQuery workflow you want to re-run...
WorkflowRunResponse response = existing.get(n).rerun(client); // (3)
```

1. You can search for existing workflows through the `WorkflowSearchRequest` class.
2. You can find workflows by their type using the `findByType()` helper method and providing the prefix for one of the packages. In this example, we do so for the `BigQueryCrawler`. (You can also specify the maximum number of resulting workflows you want to retrieve as results.)
3. Once you've found the workflow you want to re-run, you can simply call the `rerun()` helper method on the workflow search result. The `WorkflowRunResponse` is just a subtype of `WorkflowResponse` so has the same helper method to monitor progress of the workflow run. Because this operation will execute work in Atlan, you must [provide it an `AtlanClient`](https://docs.atlan.com/llms/platform/python/set-up-sdk/llms.txt) through which to connect to the tenant.

 - Optionally, you can use the `rerun(true)` method with idempotency
 to avoid re-running a workflow that is already in running or in a pending state.
 This will return details of the already running workflow if found, and by default, it is set to `false`

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt)
for details on how you can check the status and wait until the workflow has been completed.
 :::

### Python

```python showLineNumbers title="Re-run existing BigQuery workflow"
from pyatlan.client.atlan import AtlanClient
from pyatlan.model.enums import WorkflowPackage

client = AtlanClient()

existing = client.workflow.find_by_type( # (1)
 prefix=WorkflowPackage.BIGQUERY, max_results=5
)

# Determine which BigQuery workflow (n)

# from the list of results you want to re-run.

response = client.workflow.rerun(existing[n]) # (2)
```

1. You can find workflows by their type using the workflow client `find_by_type()`
method and providing the **prefix** for one of the packages.
In this example, we do so for the `BigQueryCrawler`. (You can also specify
the **maximum number of resulting workflows** you want to retrieve as results.)
2. Once you've found the workflow you want to re-run,
you can simply call the workflow client `rerun()` method.

 - Optionally, you can use `rerun(idempotent=True)` to avoid re-running a workflow that is already in running or in a pending state.
 This will return details of the already running workflow if found, and by default, it is set to `False`.

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt)
for details on how you can check the status and wait until the workflow has been completed.
 :::

### Kotlin

```kotlin showLineNumbers title="Re-run existing BigQuery workflow"
val existing = WorkflowSearchRequest // (1)
 .findByType(client, BigQueryCrawler.PREFIX, 5) // (2)
// Determine which of the results is the
// BigQuery workflow you want to re-run...
val response = existing.get(n).rerun(client) // (3)
```

1. You can search for existing workflows through the `WorkflowSearchRequest` class.
2. You can find workflows by their type using the `findByType()` helper method and providing the prefix for one of the packages. In this example, we do so for the `BigQueryCrawler`. (You can also specify the maximum number of resulting workflows you want to retrieve as results.)
3. Once you've found the workflow you want to re-run, you can simply call the `rerun()` helper method on the workflow search result. The `WorkflowRunResponse` is just a subtype of `WorkflowResponse` so has the same helper method to monitor progress of the workflow run. Because this operation will execute work in Atlan, you must [provide it an `AtlanClient`](https://docs.atlan.com/llms/platform/python/set-up-sdk/llms.txt) through which to connect to the tenant.

 - Optionally, you can use the `rerun(true)` method with idempotency
 to avoid re-running a workflow that is already in running or in a pending state.
 This will return details of the already running workflow if found, and by default, it is set to `false`

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt)
for details on how you can check the status and wait until the workflow has been completed.
 :::

### Raw REST API

:::warning[Requires multiple steps through the raw REST API]
1. Find the existing workflow.
2. Send through the resulting re-run request.
:::
```json showLineNumbers title="POST /api/service/workflows/indexsearch"
{
 "from": 0,
 "size": 5,
 "query": {
 "bool": {
 "filter": [
 {
 "nested": {
 "path": "metadata",
 "query": {
 "prefix": {
 "metadata.name.keyword": {
 "value": "atlan-bigquery" // (1)
 }
 }
 }
 }
 }
 ]
 }
 },
 "sort": [
 {
 "metadata.creationTimestamp": {
 "nested": {
 "path": "metadata"
 },
 "order": "desc"
 }
 }
 ],
 "track_total_hits": true
}
```

1. Searching by the `atlan-bigquery` prefix will ensure you only find existing BigQuery assets workflows.

 :::tip[Name of the workflow]
The name of the workflow will be nested within the `_source.metadata.name` property of the response object.
(Remember since this is a search, there could be multiple results, so you may want to use the other details
in each result to determine which workflow you really want.)
 :::
```json title="POST /api/service/workflows/submit"
{
 "namespace": "default",
 "resourceKind": "WorkflowTemplate",
 "resourceName": "atlan-bigquery-1684500411" // (1)
}
```

1. Send the name of the workflow as the `resourceName` to rerun it.

---