databricks-labs-pytester 0.3.0

Python Testing for Databricks

Python Testing for Databricks

• Python Testing for Databricks
  • Ecosystem
  • PyTest Fixtures
    • Logging
    • Installation
    • debug_env_name fixture
    • debug_env fixture
    • env_or_skip fixture
    • ws fixture
    • make_random fixture
    • make_instance_pool fixture
    • make_instance_pool_permissions fixture
    • make_job fixture
    • make_job_permissions fixture
    • make_cluster fixture
    • make_cluster_permissions fixture
    • make_cluster_policy fixture
    • make_cluster_policy_permissions fixture
    • make_pipeline fixture
    • make_warehouse fixture
    • make_group fixture
    • make_user fixture
    • make_pipeline_permissions fixture
    • make_notebook fixture
    • make_notebook_permissions fixture
    • make_directory fixture
    • make_directory_permissions fixture
    • make_repo fixture
    • make_repo_permissions fixture
    • make_workspace_file_permissions fixture
    • make_workspace_file_path_permissions fixture
    • make_secret_scope fixture
    • make_secret_scope_acl fixture
    • make_authorization_permissions fixture
    • make_udf fixture
    • make_catalog fixture
    • make_schema fixture
    • make_table fixture
    • make_storage_credential fixture
    • product_info fixture
    • sql_backend fixture
    • sql_exec fixture
    • sql_fetch_all fixture
    • make_model fixture
    • make_experiment fixture
    • make_experiment_permissions fixture
    • make_warehouse_permissions fixture
    • make_lakeview_dashboard_permissions fixture
    • workspace_library fixture
    • log_workspace_link fixture
    • make_dashboard_permissions fixture
    • make_alert_permissions fixture
    • make_query fixture
    • make_query_permissions fixture
    • make_registered_model_permissions fixture
    • make_serving_endpoint fixture
    • make_serving_endpoint_permissions fixture
    • make_feature_table_permissions fixture
• Project Support

Installation

Add a databricks-labs-pytester dependency to your pyproject.toml file (or legacy requirements.txt file). You can also install it directly from the command line:

pip install databricks-labs-pytester

If you use hatch as a build system, make sure to add databricks-labs-pytester as a test-time dependency and not as a compile-time dependency, otherwise your wheels will transitively depend on pytest, which is not usually something you need.

[project]
name = "name-of-your-project"
# ...
dependencies = [
  "databricks-sdk~=0.30",
  # ... dependencies required for your code to execute
]

[tool.hatch.envs.default]
dependencies = [
  # ... dependencies required to test/validate/format your code:
    "black~=24.3.0",
    "coverage[toml]~=7.4.4",
    "mypy~=1.9.0",
    "pylint~=3.2.2",
    "pylint-pytest==2.0.0a0",
    "databricks-labs-pylint~=0.4.0",
    "databricks-labs-pytester~=0.2", # <= this library 
    "pytest~=8.3.3",
    "pytest-cov~=4.1.0",
    "pytest-mock~=3.14.0",
    "pytest-timeout~=2.3.1",
    "pytest-xdist~=3.5.0",
    "python-lsp-server>=1.9.0",
    "ruff~=0.3.4",
    "types-PyYAML~=6.0.12",
    "types-requests~=2.31.0",
]

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Ecosystem

Built on top of Databricks SDK for Python, this library is part of the Databricks Labs Python ecosystem, which includes the following projects:

• PyLint Plugin for Databricks for static code analysis and early bug detection.
• Blueprint for Python-native pathlib.Path-like interfaces, Managing Python App installations within Databricks Workspaces, Application Migrations, and Building Wheels.
• LSQL for lightweight SQL handling and dashboards-as-code.
• UCX for automated migrations into Unity Catalog and LSP plugin for static code analysis for UC compatibility.

See this video for a quick overview of the Databricks Labs Python ecosystem.

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PyTest Fixtures

PyTest Fixtures are a powerful way to manage test setup and teardown in Python. This library provides a set of fixtures to help you write integration tests for Databricks. These fixtures were incubated within the Unity Catalog Automated Migrations project for more than a year and are now available for other projects to simplify integration testing with Databricks.

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Logging

This library is built on years of debugging integration tests for Databricks and its ecosystem.

That's why it comes with a built-in logger that traces creation and deletion of dummy entities through links in the Databricks Workspace UI. If you run the following code:

def test_new_user(make_user, ws):
    new_user = make_user()
    home_dir = ws.workspace.get_status(f"/Users/{new_user.user_name}")
    assert home_dir.object_type == ObjectType.DIRECTORY

You will see the following output, where the first line is clickable and will take you to the user's profile in the Databricks Workspace UI:

12:30:53  INFO [d.l.p.fixtures.baseline] Created [e-mail]: https://.....azuredatabricks.net/#settings/workspace/identity-and-access/users/735...
12:30:53 DEBUG [d.l.p.fixtures.baseline] added workspace user fixture: User(active=True, display_name='[e-mail]', ...)
12:30:58 DEBUG [d.l.p.fixtures.baseline] clearing 1 workspace user fixtures
12:30:58 DEBUG [d.l.p.fixtures.baseline] removing workspace user fixture: User(active=True, display_name='[e-mail]', ...)

You may need to add the following to your conftest.py file to enable this:

import logging

from databricks.labs.blueprint.logger import install_logger

install_logger()

logging.getLogger('databricks.labs.pytester').setLevel(logging.DEBUG)

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debug_env_name fixture

Specify the name of the debug environment. By default, it is set to .env, which will try to find a file named .env in any of the parent directories of the current working directory and load the environment variables from it via the debug_env fixture.

Alternatively, if you are concerned of the risk of .env files getting checked into version control, we recommend using the ~/.databricks/debug-env.json file to store different sets of environment variables. The file cannot be checked into version control by design, because it is stored in the user's home directory.

This file is used for local debugging and integration tests in IDEs like PyCharm, VSCode, and IntelliJ IDEA while developing Databricks Platform Automation Stack, which includes Databricks SDKs for Python, Go, and Java, as well as Databricks Terraform Provider and Databricks CLI. This file enables multi-environment and multi-cloud testing with a single set of integration tests.

The file is typically structured as follows:

$ cat ~/.databricks/debug-env.json
{
   "ws": {
     "CLOUD_ENV": "azure",
     "DATABRICKS_HOST": "....azuredatabricks.net",
     "DATABRICKS_CLUSTER_ID": "0708-200540-...",
     "DATABRICKS_WAREHOUSE_ID": "33aef...",
        ...
   },
   "acc": {
     "CLOUD_ENV": "aws",
     "DATABRICKS_HOST": "accounts.cloud.databricks.net",
     "DATABRICKS_CLIENT_ID": "....",
     "DATABRICKS_CLIENT_SECRET": "....",
     ...
   }
}

And you can load it in your conftest.py file as follows:

@pytest.fixture
def debug_env_name():
    return "ws"

This will load the ws environment from the ~/.databricks/debug-env.json file.

If any of the environment variables are not found, env_or_skip fixture will gracefully skip the execution of tests.

See also debug_env.

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debug_env fixture

Loads environment variables specified in debug_env_name fixture from a file for local debugging in IDEs, otherwise allowing the tests to run with the default environment variables specified in the CI/CD pipeline.

See also acc, env_or_skip, ws, debug_env_name, is_in_debug.

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env_or_skip fixture

Fixture to get environment variables or skip tests.

It is extremely useful to skip tests if the required environment variables are not set.

In the following example, test_something would only run if the environment variable SOME_EXTERNAL_SERVICE_TOKEN is set:

def test_something(env_or_skip):
    token = env_or_skip("SOME_EXTERNAL_SERVICE_TOKEN")
    assert token is not None

See also acc, make_udf, sql_backend, debug_env, is_in_debug.

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ws fixture

Create and provide a Databricks WorkspaceClient object.

This fixture initializes a Databricks WorkspaceClient object, which can be used to interact with the Databricks workspace API. The created instance of WorkspaceClient is shared across all test functions within the test session.

See detailed documentation for the list of environment variables that can be used to authenticate the WorkspaceClient.

In your test functions, include this fixture as an argument to use the WorkspaceClient:

def test_workspace_operations(ws):
    clusters = ws.clusters.list_clusters()
    assert len(clusters) >= 0

See also log_workspace_link, make_alert_permissions, make_authorization_permissions, make_catalog, make_cluster, make_cluster_permissions, make_cluster_policy, make_cluster_policy_permissions, make_dashboard_permissions, make_directory, make_directory_permissions, make_experiment, make_experiment_permissions, make_feature_table, make_feature_table_permissions, make_group, make_instance_pool, make_instance_pool_permissions, make_job, make_job_permissions, make_lakeview_dashboard_permissions, make_model, make_notebook, make_notebook_permissions, make_pipeline, make_pipeline_permissions, make_query, make_query_permissions, make_registered_model_permissions, make_repo, make_repo_permissions, make_secret_scope, make_secret_scope_acl, make_serving_endpoint, make_serving_endpoint_permissions, make_storage_credential, make_udf, make_user, make_warehouse, make_warehouse_permissions, make_workspace_file, make_workspace_file_path_permissions, make_workspace_file_permissions, spark, sql_backend, debug_env, product_info.

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acc fixture

Create and provide a Databricks AccountClient object.

This fixture initializes a Databricks AccountClient object, which can be used to interact with the Databricks account API. The created instance of AccountClient is shared across all test functions within the test session.

Requires DATABRICKS_ACCOUNT_ID environment variable to be set. If DATABRICKS_HOST points to a workspace host, the fixture would automatically determine the account host from it.

See detailed documentation for the list of environment variables that can be used to authenticate the AccountClient.

In your test functions, include this fixture as an argument to use the AccountClient:

def test_listing_workspaces(acc):
    workspaces = acc.workspaces.list()
    assert len(workspaces) >= 1

See also make_acc_group, debug_env, product_info, env_or_skip.

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spark fixture

Get Databricks Connect Spark session. Requires databricks-connect package to be installed.

Usage:

def test_databricks_connect(spark):
    rows = spark.sql("SELECT 1").collect()
    assert rows[0][0] == 1

See also ws.

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sql_backend fixture

Create and provide a SQL backend for executing statements.

Requires the environment variable DATABRICKS_WAREHOUSE_ID to be set.

See also make_schema, make_table, make_udf, sql_exec, sql_fetch_all, ws, env_or_skip.

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sql_exec fixture

Execute SQL statement and don't return any results.

See also sql_backend.

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sql_fetch_all fixture

Fetch all rows from a SQL statement.

See also sql_backend.

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make_random fixture

Fixture to generate random strings.

This fixture provides a function to generate random strings of a specified length. The generated strings are created using a character set consisting of uppercase letters, lowercase letters, and digits.

To generate a random string with default length of 16 characters:

random_string = make_random()
assert len(random_string) == 16

To generate a random string with a specified length:

random_string = make_random(k=8)
assert len(random_string) == 8

See also make_acc_group, make_catalog, make_cluster, make_cluster_policy, make_directory, make_experiment, make_feature_table, make_group, make_instance_pool, make_job, make_model, make_notebook, make_pipeline, make_query, make_repo, make_schema, make_secret_scope, make_serving_endpoint, make_table, make_udf, make_user, make_warehouse, make_workspace_file.

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make_instance_pool fixture

Create a Databricks instance pool and clean it up after the test. Returns a function to create instance pools. Use instance_pool_id attribute from the returned object to get an ID of the pool.

Keyword Arguments:

• instance_pool_name (str, optional): The name of the instance pool. If not provided, a random name will be generated.
• node_type_id (str, optional): The node type ID of the instance pool. If not provided, a node type with local disk and 16GB memory will be used.
• other arguments are passed to WorkspaceClient.instance_pools.create method.

Usage:

def test_instance_pool(make_instance_pool):
    logger.info(f"created {make_instance_pool()}")

See also ws, make_random, watchdog_remove_after.

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make_instance_pool_permissions fixture

No description yet.

See also ws.

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make_job fixture

Create a Databricks job and clean it up after the test. Returns a function to create jobs, that returns a Job instance.

Keyword Arguments:

• name (str, optional): The name of the job. If not provided, a random name will be generated.
• path (str, optional): The path to the notebook or file used in the job. If not provided, a random notebook or file will be created
• [DEPRECATED: Use path instead] notebook_path (str, optional): The path to the notebook. If not provided, a random notebook will be created.
• content (str | bytes, optional): The content of the notebook or file used in the job. If not provided, default content of make_notebook will be used.
• task_type (type[NotebookTask] | type[SparkPythonTask], optional): The type of task. If not provides, type[NotebookTask] will be used.
• instance_pool_id (str, optional): The instance pool id to add to the job cluster. If not provided, no instance pool will be used.
• spark_conf (dict, optional): The Spark configuration of the job. If not provided, Spark configuration is not explicitly set.
• libraries (list, optional): The list of libraries to install on the job.
• tags (list[str], optional): A list of job tags. If not provided, no additional tags will be set on the job.
• tasks (list[Task], optional): A list of job tags. If not provided, a single task with a notebook task will be created, along with a disposable notebook. Latest Spark version and a single worker clusters will be used to run this ephemeral job.

Usage:

def test_job(make_job):
    logger.info(f"created {make_job()}")

See also ws, make_random, make_notebook, make_workspace_file, watchdog_remove_after.

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make_job_permissions fixture

No description yet.

See also ws.

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make_cluster fixture

Create a Databricks cluster, waits for it to start, and clean it up after the test. Returns a function to create clusters. You can get cluster_id attribute from the returned object.

Keyword Arguments:

• single_node (bool, optional): Whether to create a single-node cluster. Defaults to False.
• cluster_name (str, optional): The name of the cluster. If not provided, a random name will be generated.
• spark_version (str, optional): The Spark version of the cluster. If not provided, the latest version will be used.
• autotermination_minutes (int, optional): The number of minutes before the cluster is automatically terminated. Defaults to 10.

Usage:

def test_cluster(make_cluster):
    logger.info(f"created {make_cluster(single_node=True)}")

See also ws, make_random, watchdog_remove_after.

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make_cluster_permissions fixture

No description yet.

See also ws.

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make_cluster_policy fixture

Create a Databricks cluster policy and clean it up after the test. Returns a function to create cluster policies, which returns CreatePolicyResponse instance.

Keyword Arguments:

• name (str, optional): The name of the cluster policy. If not provided, a random name will be generated.

Usage:

def test_cluster_policy(make_cluster_policy):
    logger.info(f"created {make_cluster_policy()}")

See also ws, make_random, watchdog_purge_suffix.

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make_cluster_policy_permissions fixture

No description yet.

See also ws.

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make_pipeline fixture

Create Delta Live Table Pipeline and clean it up after the test. Returns a function to create pipelines. Results in a CreatePipelineResponse instance.

Keyword Arguments:

• name (str, optional): The name of the pipeline. If not provided, a random name will be generated.
• libraries (list, optional): The list of libraries to install on the pipeline. If not provided, a random disposable notebook will be created.
• clusters (list, optional): The list of clusters to use for the pipeline. If not provided, a single node cluster will be created with 16GB memory and local disk.

Usage:

def test_pipeline(make_pipeline, make_pipeline_permissions, make_group):
    group = make_group()
    pipeline = make_pipeline()
    make_pipeline_permissions(
        object_id=pipeline.pipeline_id,
        permission_level=PermissionLevel.CAN_MANAGE,
        group_name=group.display_name,
    )

See also ws, make_random, make_notebook, watchdog_remove_after, watchdog_purge_suffix.

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make_warehouse fixture

Create a Databricks warehouse and clean it up after the test. Returns a function to create warehouses.

Keyword Arguments:

• warehouse_name (str, optional): The name of the warehouse. If not provided, a random name will be generated.
• warehouse_type (CreateWarehouseRequestWarehouseType, optional): The type of the warehouse. Defaults to PRO.
• cluster_size (str, optional): The size of the cluster. Defaults to 2X-Small.

Usage:

def test_warehouse_has_remove_after_tag(ws, make_warehouse):
    new_warehouse = make_warehouse()
    created_warehouse = ws.warehouses.get(new_warehouse.response.id)
    warehouse_tags = created_warehouse.tags.as_dict()
    assert warehouse_tags["custom_tags"][0]["key"] == "RemoveAfter"

See also ws, make_random, watchdog_remove_after.

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make_group fixture

This fixture provides a function to manage Databricks workspace groups. Groups can be created with specified members and roles, and they will be deleted after the test is complete. Deals with eventual consistency issues by retrying the creation process for 30 seconds and then waiting for up to 3 minutes for the group to be provisioned. Returns an instance of Group.

Keyword arguments:

• members (list of strings): A list of user IDs to add to the group.
• roles (list of strings): A list of roles to assign to the group.
• display_name (str): The display name of the group.
• entitlements (list of strings): A list of entitlements to assign to the group.

The following example creates a group with a single member and independently verifies that the group was created:

def test_new_group(make_group, make_user, ws):
    user = make_user()
    group = make_group(members=[user.id])
    loaded = ws.groups.get(group.id)
    assert group.display_name == loaded.display_name
    assert group.members == loaded.members

See also ws, make_random, watchdog_purge_suffix.

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make_acc_group fixture

This fixture provides a function to manage Databricks account groups. Groups can be created with specified members and roles, and they will be deleted after the test is complete.

Has the same arguments and behavior as make_group fixture but uses the account client instead of the workspace client.

Example usage:

def test_new_account_group(make_acc_group, acc):
    group = make_acc_group()
    loaded = acc.groups.get(group.id)
    assert group.display_name == loaded.display_name

See also acc, make_random, watchdog_purge_suffix.

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make_user fixture

This fixture returns a function that creates a Databricks workspace user and removes it after the test is complete. In case of random naming conflicts, the fixture will retry the creation process for 30 seconds. Returns an instance of User. Usage:

def test_new_user(make_user, ws):
    new_user = make_user()
    home_dir = ws.workspace.get_status(f"/Users/{new_user.user_name}")
    assert home_dir.object_type == ObjectType.DIRECTORY

See also ws, make_random, watchdog_purge_suffix.

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make_pipeline_permissions fixture

No description yet.

See also ws.

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make_notebook fixture

Returns a function to create Databricks Notebooks and clean them up after the test. The function returns os.PathLike object.

Keyword arguments:

• path (str, optional): The path of the notebook. Defaults to dummy-* notebook in current user's home folder.
• content (str | bytes | io.BinaryIO, optional): The content of the notebook. Defaults to print(1) for Python and SELECT 1 for SQL.
• language (Language, optional): The language of the notebook. Defaults to Language.PYTHON.
• encoding (str, optional): The file encoding. Defaults to sys.getdefaultencoding().
• format (ImportFormat, optional): The format of the notebook. Defaults to ImportFormat.SOURCE.
• overwrite (bool, optional): Whether to overwrite the notebook if it already exists. Defaults to False.

This example creates a notebook and verifies that print(1) is in the content:

def test_creates_some_notebook(make_notebook):
    notebook = make_notebook()
    assert "print(1)" in notebook.read_text()

See also make_job, make_pipeline, ws, make_random, watchdog_purge_suffix.

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make_notebook_permissions fixture

No description yet.

See also ws.

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make_workspace_file fixture

Returns a function to create Databricks workspace file and clean up after the test. The function returns os.PathLike object.

Keyword arguments:

• path (str, optional): The path of the file. Defaults to dummy-* notebook in current user's home folder.
• content (str | bytes, optional): The content of the file. Defaults to print(1) for Python and SELECT 1 for SQL.
• language (Language, optional): The language of the notebook. Defaults to Language.PYTHON.
• encoding (str, optional): The file encoding. Defaults to sys.getdefaultencoding().

This example creates a notebook and verifies that the workspace path is an existing file with contents print(1):

def test_create_file(make_workspace_file):
    workspace_file = make_workspace_file()
    assert workspace_file.is_file()
    assert "print(1)" in workspace_file.read_text()

TODO: Merge functionality with make_notebook if WorkspacePath supports creating notebooks.

See also make_job, ws, make_random, watchdog_purge_suffix.

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make_directory fixture

Returns a function to create Databricks Workspace Folders and clean them up after the test. The function returns os.PathLike object.

Keyword arguments:

• path (str, optional): The path of the notebook. Defaults to dummy-* folder in current user's home folder.

This example creates a folder and verifies that it contains a notebook:

def test_creates_some_folder_with_a_notebook(make_directory, make_notebook):
    folder = make_directory()
    notebook = make_notebook(path=folder / 'foo.py')
    files = [_.name for _ in folder.iterdir()]
    assert ['foo.py'] == files
    assert notebook.parent == folder

See also make_experiment, ws, make_random, watchdog_purge_suffix.

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make_directory_permissions fixture

No description yet.

See also ws.

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make_repo fixture

Returns a function to create Databricks Repos and clean them up after the test. The function returns a RepoInfo object.

Keyword arguments:

• url (str, optional): The URL of the repository.
• provider (str, optional): The provider of the repository.
• path (str, optional): The path of the repository. Defaults to /Repos/{current_user}/sdk-{random}-{purge_suffix}.

Usage:

def test_repo(make_repo):
    logger.info(f"created {make_repo()}")

See also ws, make_random, watchdog_purge_suffix.

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make_repo_permissions fixture

No description yet.

See also ws.

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make_workspace_file_permissions fixture

No description yet.

See also ws.

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make_workspace_file_path_permissions fixture

No description yet.

See also ws.

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make_secret_scope fixture

This fixture provides a function to create secret scopes. The created secret scope will be deleted after the test is complete. Returns the name of the secret scope.

To create a secret scope and use it within a test function:

def test_secret_scope_creation(make_secret_scope):
    secret_scope_name = make_secret_scope()
    assert secret_scope_name.startswith("dummy-")

See also ws, make_random.

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make_secret_scope_acl fixture

This fixture provides a function to manage access control lists (ACLs) for secret scopes. ACLs define permissions for principals (users or groups) on specific secret scopes.

Arguments:

• scope: The name of the secret scope.
• principal: The name of the principal (user or group).
• permission: The permission level for the principal on the secret scope.

Returns a tuple containing the secret scope name and the principal name.

To manage secret scope ACLs using the make_secret_scope_acl fixture:

from databricks.sdk.service.workspace import AclPermission

def test_secret_scope_acl_management(make_user, make_secret_scope, make_secret_scope_acl):
    scope_name = make_secret_scope()
    principal_name = make_user().display_name
    permission = AclPermission.READ

    acl_info = make_secret_scope_acl(
        scope=scope_name,
        principal=principal_name,
        permission=permission,
    )
    assert acl_info == (scope_name, principal_name)

See also ws.

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make_authorization_permissions fixture

No description yet.

See also ws.

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make_udf fixture

Create a UDF and return its info. Remove it after the test. Returns instance of FunctionInfo.

Keyword Arguments:

• catalog_name (str): The name of the catalog where the UDF will be created. Default is hive_metastore.
• schema_name (str): The name of the schema where the UDF will be created. Default is a random string.
• name (str): The name of the UDF. Default is a random string.
• hive_udf (bool): If True, the UDF will be created as a Hive UDF. Default is False.

Usage:

def test_make_some_udfs(make_schema, make_udf):
    schema_a = make_schema(catalog_name="hive_metastore")
    make_udf(schema_name=schema_a.name)
    make_udf(schema_name=schema_a.name, hive_udf=True)

See also ws, env_or_skip, sql_backend, make_schema, make_random.

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make_catalog fixture

Create a catalog and return its info. Remove it after the test. Returns instance of CatalogInfo.

Keyword Arguments:

• name (str): The name of the catalog. Default is a random string.

Usage:

def test_catalog_fixture(make_catalog, make_schema, make_table):
    from_catalog = make_catalog()
    from_schema = make_schema(catalog_name=from_catalog.name)
    from_table_1 = make_table(catalog_name=from_catalog.name, schema_name=from_schema.name)
    logger.info(f"Created new schema: {from_table_1}")

See also ws, make_random, watchdog_remove_after.

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make_schema fixture

Create a schema and return its info. Remove it after the test. Returns instance of SchemaInfo.

Keyword Arguments:

• catalog_name (str): The name of the catalog where the schema will be created. Default is hive_metastore.
• name (str): The name of the schema. Default is a random string.

Usage:

def test_catalog_fixture(make_catalog, make_schema, make_table):
    from_catalog = make_catalog()
    from_schema = make_schema(catalog_name=from_catalog.name)
    from_table_1 = make_table(catalog_name=from_catalog.name, schema_name=from_schema.name)
    logger.info(f"Created new schema: {from_table_1}")

See also make_table, make_udf, sql_backend, make_random, watchdog_remove_after.

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make_table fixture

Create a table and return its info. Remove it after the test. Returns instance of TableInfo.

Keyword Arguments:

• catalog_name (str): The name of the catalog where the table will be created. Default is hive_metastore.
• schema_name (str): The name of the schema where the table will be created. Default is a random string.
• name (str): The name of the table. Default is a random string.
• ctas (str): The CTAS statement to create the table. Default is None.
• non_delta (bool): If True, the table will be created as a non-delta table. Default is False.
• external (bool): If True, the table will be created as an external table. Default is False.
• external_csv (str): The location of the external CSV table. Default is None.
• external_delta (str): The location of the external Delta table. Default is None.
• view (bool): If True, the table will be created as a view. Default is False.
• tbl_properties (dict): The table properties. Default is None.
• hiveserde_ddl (str): The DDL statement to create the table. Default is None.
• storage_override (str): The storage location override. Default is None.
• columns (list): The list of columns. Default is None.

Usage:

def test_catalog_fixture(make_catalog, make_schema, make_table):
    from_catalog = make_catalog()
    from_schema = make_schema(catalog_name=from_catalog.name)
    from_table_1 = make_table(catalog_name=from_catalog.name, schema_name=from_schema.name)
    logger.info(f"Created new schema: {from_table_1}")

See also make_query, sql_backend, make_schema, make_random, watchdog_remove_after.

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make_storage_credential fixture

Create a storage credential and return its info. Remove it after the test. Returns instance of StorageCredentialInfo.

Keyword Arguments:

• credential_name (str): The name of the storage credential. Default is a random string.
• application_id (str): The application ID for the Azure service principal. Default is an empty string.
• client_secret (str): The client secret for the Azure service principal. Default is an empty string.
• directory_id (str): The directory ID for the Azure service principal. Default is an empty string.
• aws_iam_role_arn (str): The ARN of the AWS IAM role. Default is an empty string.
• read_only (bool): If True, the storage credential will be read-only. Default is False.

Usage:

def test_storage_credential(env_or_skip, make_storage_credential, make_random):
    random = make_random(6).lower()
    credential_name = f"dummy-{random}"
    make_storage_credential(
        credential_name=credential_name,
        aws_iam_role_arn=env_or_skip("TEST_UBER_ROLE_ID"),
    )

See also ws, watchdog_remove_after.

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product_info fixture

No description yet.

See also acc, ws.

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make_model fixture

Returns a function to create Databricks Models and clean them up after the test. The function returns a GetModelResponse object.

Keyword arguments:

• model_name (str, optional): The name of the model. Defaults to dummy-*.

Usage:

from databricks.sdk.service.iam import PermissionLevel

def test_models(make_group, make_model, make_registered_model_permissions):
    group = make_group()
    model = make_model()
    make_registered_model_permissions(
        object_id=model.id,
        permission_level=PermissionLevel.CAN_MANAGE,
        group_name=group.display_name,
    )

See also make_serving_endpoint, ws, make_random, watchdog_remove_after.

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make_experiment fixture

Returns a function to create Databricks Experiments and clean them up after the test. The function returns a CreateExperimentResponse object.

Keyword arguments:

• path (str, optional): The path of the experiment. Defaults to dummy-* experiment in current user's home folder.
• experiment_name (str, optional): The name of the experiment. Defaults to dummy-*.

Usage:

from databricks.sdk.service.iam import PermissionLevel

def test_experiments(make_group, make_experiment, make_experiment_permissions):
    group = make_group()
    experiment = make_experiment()
    make_experiment_permissions(
        object_id=experiment.experiment_id,
        permission_level=PermissionLevel.CAN_MANAGE,
        group_name=group.display_name,
    )

See also ws, make_random, make_directory, watchdog_purge_suffix.

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make_experiment_permissions fixture

No description yet.

See also ws.

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make_warehouse_permissions fixture

No description yet.

See also ws.

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make_lakeview_dashboard_permissions fixture

No description yet.

See also ws.

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log_workspace_link fixture

rns a function to log a workspace link.

See also ws.

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make_dashboard_permissions fixture

No description yet.

See also ws.

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make_alert_permissions fixture

No description yet.

See also ws.

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make_query fixture

Create a query and remove it after the test is done. Returns the LegacyQuery object.

Keyword Arguments:

• sql_query: The query to be stored. Default is SELECT * FROM <newly created random table>.

Usage:

from databricks.sdk.service.sql import PermissionLevel

def test_permissions_for_redash(
    make_user,
    make_query,
    make_query_permissions,
):
    user = make_user()
    query = make_query()
    make_query_permissions(
        object_id=query.id,
        permission_level=PermissionLevel.CAN_EDIT,
        user_name=user.display_name,
    )

See also ws, make_table, make_random, watchdog_remove_after.

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make_query_permissions fixture

No description yet.

See also ws.

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make_registered_model_permissions fixture

No description yet.

See also ws.

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make_serving_endpoint fixture

Returns a function to create Databricks Serving Endpoints and clean them up after the test. The function returns a ServingEndpointDetailed object.

Under the covers, this fixture also creates a model to serve on a small workload size.

Usage:

def test_endpoints(make_group, make_serving_endpoint, make_serving_endpoint_permissions):
    group = make_group()
    endpoint = make_serving_endpoint()
    make_serving_endpoint_permissions(
        object_id=endpoint.response.id,
        permission_level=PermissionLevel.CAN_QUERY,
        group_name=group.display_name,
    )

See also ws, make_random, make_model, watchdog_remove_after.

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make_serving_endpoint_permissions fixture

No description yet.

See also ws.

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make_feature_table fixture

No description yet.

See also ws, make_random.

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make_feature_table_permissions fixture

No description yet.

See also ws.

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watchdog_remove_after fixture

Purge time for test objects, representing the (UTC-based) hour from which objects may be purged.

See also make_catalog, make_cluster, make_instance_pool, make_job, make_model, make_pipeline, make_query, make_schema, make_serving_endpoint, make_storage_credential, make_table, make_warehouse, watchdog_purge_suffix.

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watchdog_purge_suffix fixture

HEX-encoded purge time suffix for test objects.

See also make_acc_group, make_cluster_policy, make_directory, make_experiment, make_group, make_notebook, make_pipeline, make_repo, make_user, make_workspace_file, watchdog_remove_after.

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is_in_debug fixture

Returns true if the test is running from a debugger in IDE, otherwise false.

The following IDE are supported: IntelliJ IDEA (including Community Edition), PyCharm (including Community Edition), and Visual Studio Code.

See also debug_env, env_or_skip.

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Project Support

Please note that this project is provided for your exploration only and is not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS, and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of this project.

Any issues discovered through the use of this project should be filed as GitHub Issues on this repository. They will be reviewed as time permits, but no formal SLAs for support exist.