databricks

README

Databricks SDK for Go

Beta: This SDK is supported for production use cases, but we do expect future releases to have some interface changes; see Interface stability. We are keen to hear feedback from you on these SDKs. Please file issues, and we will address them | See documentation at Go Packages | See also the Terraform Provider | See also the SDK for Python | See also the SDK for Java

The Databricks SDK for Go includes functionality to accelerate development with Go for the Databricks Lakehouse. It covers all public Databricks REST API operations. The SDK's internal HTTP client is robust and handles failures on different levels by performing intelligent retries.

Contents

• Getting started
• Authentication
• Code examples
• Long running operations
• Paginated responses
• GetByName utility methods
• Node type and Databricks Runtime selectors
• Integration with io interfaces for DBFS
• User Agent Request Attribution
• Error Handling
• Logging
• Testing
• Interface stability

Getting started

1. On your local development machine with Go already installed and a Go code project active, create a go.mod file to track your Go code's dependencies by running the go mod init command, for example:

   go mod init sample
   

2. Take a dependency on the Databricks SDK for Go package by running the go mod edit -require command:

   go mod edit -require github.com/databricks/databricks-sdk-go@latest
   

   Your go.mod file should now look like this:

   module sample
   
   go 1.18
   
   require github.com/databricks/databricks-sdk-go v0.9.0
   
   // Indirect dependencies will go here.
   

3. Within your project, create a Go code file that imports the Databricks SDK for Go. The following example, in a file named main.go with the following contents, simply lists all the clusters in your Databricks workspace:

   package main
   
   import (
     "context"
   
     "github.com/databricks/databricks-sdk-go"
     "github.com/databricks/databricks-sdk-go/service/compute"
   )
   
   func main() {
     w := databricks.Must(databricks.NewWorkspaceClient())
     all, err := w.Clusters.ListAll(context.Background(), compute.ListClustersRequest{})
     if err != nil {
       panic(err)
     }
     for _, c := range all {
       println(c.ClusterName)
     }
   }
   

4. Add any misssing module dependencies by running the go mod tidy command:

   go mod tidy
   

   Note: If you get the error go: warning: "all" matched no packages, you forgot to add the preceding Go code file that imports the Databricks SDK for Go.

5. Grab copies of all packages needed to support builds and tests of packages in your main module, by running the go mod vendor command:

   go mod vendor
   

6. Set up Databricks authentication on your local development machine by running databricks configure command, if you have not done so already. For details, see the next section, Authentication.

7. Run your Go code file, assuming a file named main.go, by running the go run command:

   go run main.go
   

   Assuming the preceding example code is run, the output is:

   [TRACE] Loading config via environment
   [TRACE] Loading config via config-file
   ...
   [TRACE] Attempting to configure auth: pat
   [TRACE] Attempting to configure auth: basic
   [TRACE] Attempting to configure auth: azure-client-secret
   ...
   

Authentication

If you use Databricks configuration profiles or Databricks-specific environment variables for Databricks authentication, the only code required to start working with a Databricks workspace is the following code snippet, which instructs the Databricks SDK for Go to use its default authentication flow:

w := databricks.Must(databricks.NewWorkspaceClient())
w./*press TAB for autocompletion*/

The conventional name for the variable that holds the workspace-level client of the Databricks SDK for Go is w, which is shorthand for workspace.

In this section

• Default authentication flow
• Databricks native authentication
• Azure native authentication
• Google Cloud Platform native authentication
• Overriding .databrickscfg
• Additional authentication configuration options
• Custom credentials provider

Default authentication flow

If you run the Databricks Terraform Provider, the Databricks CLI, or applications that target the Databricks SDKs for other langauges, most likely they will all interoperate nicely together. By default, the Databricks SDK for Go tries the following authentication methods, in the following order, until it succeeds:

1. Databricks native authentication
2. Azure native authentication
3. Google Cloud Platform native authentication
4. If the SDK is unsuccessful at this point, it returns an authentication error and stops running.

You can instruct the Databricks SDK for Go to use a specific authentication method by setting the AuthType field in *databricks.Config as described in the following sections.

For each authentication method, the SDK searches for compatible authentication credentials in the following locations, in the following order. Once the SDK finds a compatible set of credentials that it can use, it stops searching:

1. Credentials that hard-coded into *databricks.Config.

   Caution: Databricks does not recommend hard-coding credentials into *databricks.Config, as they can be exposed in plain text in version control systems. Use environment variables or configuration profiles instead.

2. Credentials in Databricks-specific environment variables.

3. For Databricks native authentication, credentials in the .databrickscfg file's DEFAULT configuration profile from its default file location (~ for Linux or macOS, and %USERPROFILE% for Windows).

4. For Azure or Google Cloud Platform native authentication, the SDK searches for credentials through the Azure CLI or Google Cloud CLI as needed.

Depending on the Databricks authentication method, the SDK uses the following information. Presented are the *databricks.Config arguments, their descriptions, any corresponding environment variables, and any corresponding .databrickscfg file fields, respectively.

Databricks native authentication

By default, the Databricks SDK for Go initially tries Databricks token authentication (AuthType: "pat" in *databricks.Config). If the SDK is unsuccessful, it then tries Databricks basic (username/password) authentication (AuthType: "basic" in *databricks.Config).

• For Databricks token authentication, you must provide Host and Token; or their environment variable or .databrickscfg file field equivalents.
• For Databricks basic authentication, you must provide Host, Username, and Password (for AWS workspace-level operations); or Host, AccountID, Username, and Password (for AWS, Azure, or GCP account-level operations); or their environment variable or .databrickscfg file field equivalents.

*databricks.Config argument	Description	Environment variable / .databrickscfg file field
Host	(String) The Databricks host URL for either the Databricks workspace endpoint or the Databricks accounts endpoint.	DATABRICKS_HOST / host
AccountID	(String) The Databricks account ID for the Databricks accounts endpoint. Only has effect when Host is either https://accounts.cloud.databricks.com/ (AWS), https://accounts.azuredatabricks.net/ (Azure), or https://accounts.gcp.databricks.com/ (GCP).	DATABRICKS_ACCOUNT_ID / account_id
Token	(String) The Databricks personal access token (PAT) (AWS, Azure, and GCP) or Azure Active Directory (Azure AD) token (Azure).	DATABRICKS_TOKEN / token
Username	(String) The Databricks username part of basic authentication. Only possible when Host is *.cloud.databricks.com (AWS).	DATABRICKS_USERNAME / username
Password	(String) The Databricks password part of basic authentication. Only possible when Host is *.cloud.databricks.com (AWS).	DATABRICKS_PASSWORD / password

For example, to use Databricks token authentication:

package main

import (
	"bufio"
	"context"
	"fmt"
	"os"
	"strings"

	"github.com/databricks/databricks-sdk-go"
	"github.com/databricks/databricks-sdk-go/config"
)

func main() {
	// Perform Databricks token authentication for a Databricks workspace.
	w, err := databricks.NewWorkspaceClient(&databricks.Config{
		Host:        askFor("Host:"),                  // workspace url
		Token:       askFor("Personal Access Token:"), // PAT
		Credentials: config.PatCredentials{},          // enforce PAT auth
	})
	if err != nil {
		panic(err)
	}
	me, err := w.CurrentUser.Me(context.Background())
	if err != nil {
		panic(err)
	}
	fmt.Printf("Hello, my name is %s!\n", me.DisplayName)
}

func askFor(prompt string) string {
	var s string
	r := bufio.NewReader(os.Stdin)
	for {
		fmt.Fprint(os.Stdout, prompt+" ")
		s, _ = r.ReadString('\n')
		s = strings.TrimSpace(s)
		if s != "" {
			break
		}
	}
	return s
}

Azure native authentication

By default, the Databricks SDK for Go first tries Azure client secret authentication (AuthType: "azure-client-secret" in *databricks.Config). If the SDK is unsuccessful, it then tries Azure CLI authentication (AuthType: "azure-cli" in *databricks.Config). See Manage service principals.

The Databricks SDK for Go picks up an Azure CLI token, if you've previously authenticated as an Azure user by running az login on your machine. See Get Azure AD tokens for users by using the Azure CLI.

To authenticate as an Azure Active Directory (Azure AD) service principal, you must provide one of the following. See also Add a service principal to your Azure Databricks account:

• AzureResourceID, AzureClientSecret, AzureClientID, and AzureTenantID; or their environment variable or .databrickscfg file field equivalents.
• AzureResourceID and AzureUseMSI; or their environment variable or .databrickscfg file field equivalents.

*databricks.Config argument	Description	Environment variable / .databrickscfg file field
AzureResourceID	(String) The Azure Resource Manager ID for the Azure Databricks workspace, which is exchanged for a Databricks host URL.	DATABRICKS_AZURE_RESOURCE_ID / azure_workspace_resource_id
AzureUseMSI	(Boolean) true to use Azure Managed Service Identity passwordless authentication flow for service principals. Requires AzureResourceID to be set.	ARM_USE_MSI / azure_use_msi
AzureClientSecret	(String) The Azure AD service principal's client secret.	ARM_CLIENT_SECRET / azure_client_secret
AzureClientID	(String) The Azure AD service principal's application ID.	ARM_CLIENT_ID / azure_client_id
AzureTenantID	(String) The Azure AD service principal's tenant ID.	ARM_TENANT_ID / azure_tenant_id
AzureEnvironment	(String) The Azure environment type (such as Public, UsGov, China, and Germany) for a specific set of API endpoints. Defaults to PUBLIC.	ARM_ENVIRONMENT / azure_environment

For example, to use Azure client secret authentication:

w, err := databricks.NewWorkspaceClient(&databricks.Config{
  Host:              askFor("Host:"),
  AzureResourceID:   askFor("Azure Resource ID:"),
  AzureTenantID:     askFor("AAD Tenant ID:"),
  AzureClientID:     askFor("AAD Client ID:"),
  AzureClientSecret: askFor("AAD Client Secret:"),
  Credentials:       config.AzureClientSecretCredentials{},
})

Google Cloud Platform native authentication

By default, the Databricks SDK for Go first tries GCP credentials authentication (AuthType: "google-credentials" in *databricks.Config). If the SDK is unsuccessful, it then tries Google Cloud Platform (GCP) ID authentication (AuthType: "google-id" in *databricks.Config).

The Databricks SDK for Go picks up an OAuth token in the scope of the Google Default Application Credentials (DAC) flow. This means that if you have run gcloud auth application-default login on your development machine, or launch the application on the compute, that is allowed to impersonate the Google Cloud service account specified in GoogleServiceAccount. Authentication should then work out of the box. See Creating and managing service accounts.

To authenticate as a Google Cloud service account, you must provide one of the following:

• Host and GoogleCredentials; or their environment variable or .databrickscfg file field equivalents.
• Host and GoogleServiceAccount; or their environment variable or .databrickscfg file field equivalents.

*databricks.Config argument	Description	Environment variable / .databrickscfg file field
GoogleCredentials	(String) GCP Service Account Credentials JSON or the location of these credentials on the local filesystem.	GOOGLE_CREDENTIALS / google_credentials
GoogleServiceAccount	(String) The Google Cloud Platform (GCP) service account e-mail used for impersonation in the Default Application Credentials Flow that does not require a password.	DATABRICKS_GOOGLE_SERVICE_ACCOUNT / google_service_account

For example, to use Google ID authentication:

w, err := databricks.NewWorkspaceClient(&databricks.Config{
  Host:                 askFor("Host:"),
  GoogleServiceAccount: askFor("Google Service Account:"),
  Credentials:          config.GoogleDefaultCredentials{},
})

Overriding .databrickscfg

For Databricks native authentication, you can override the default behavior in *databricks.Config for using .databrickscfg as follows:

*databricks.Config argument	Description	Environment variable
Profile	(String) A connection profile specified within .databrickscfg to use instead of DEFAULT.	DATABRICKS_CONFIG_PROFILE
ConfigFile	(String) A non-default location of the Databricks CLI credentials file.	DATABRICKS_CONFIG_FILE

For example, to use a profile named MYPROFILE instead of DEFAULT:

w := databricks.Must(databricks.NewWorkspaceClient(&databricks.Config{
  Profile:  "MYPROFILE",
}))
// Now call the Databricks workspace APIs as desired...

Additional authentication configuration options

For all authentication methods, you can override the default behavior in *databricks.Config as follows:

*databricks.Config argument	Description	Environment variable
AuthType	(String) When multiple auth attributes are available in the environment, use the auth type specified by this argument. This argument also holds the currently selected auth.	(None)
HTTPTimeoutSeconds	(Integer) Number of seconds for HTTP timeout. Default is 60.	(None)
RetryTimeoutSeconds	(Integer) Number of seconds to keep retrying HTTP requests. Default is 300 (5 minutes).	(None)
DebugTruncateBytes	(Integer) Truncate JSON fields in debug logs above this limit. Default is 96.	DATABRICKS_DEBUG_TRUNCATE_BYTES
DebugHeaders	(Boolean) true to debug HTTP headers of requests made by the application. Default is false, as headers contain sensitive data, such as access tokens.	DATABRICKS_DEBUG_HEADERS
RateLimit	(Integer) Maximum number of requests per second made to Databricks REST API.	DATABRICKS_RATE_LIMIT

For example, to turn on debug HTTP headers:

w := databricks.Must(databricks.NewWorkspaceClient(&databricks.Config{
  DebugHeaders: true,
}))
// Now call the Databricks workspace APIs as desired...

Custom credentials provider

In some cases, you may want to have deeper control over authentication to Databricks. This can be achieved by creating your own credentials provider that returns an HTTP request visitor:

type CustomCredentials struct {}

func (c *CustomCredentials) Name() string {
	return "custom"
}

func (c *CustomCredentials) Configure(ctx context.Context, cfg *config.Config) (func(*http.Request) error, error) {
	return func(r *http.Request) error {
		token := "..."
		r.Header.Set("Authorization", fmt.Sprintf("Bearer %s", token))
		return nil
	}, nil
}

func main() {
	w := databricks.Must(databricks.NewWorkspaceClient(&databricks.Config{
		Credentials: &CustomCredentials{},
	}))
    // ..
}

Code examples

To find code examples that demonstrate how to call the Databricks SDK for Go, see the top-level examples folder within this repository

Long-running operations

More than 20 methods across different Databricks APIs are long-running operations for managing things like clusters, command execution, jobs, libraries, Delta Live Tables pipelines, and Databricks SQL warehouses. For example, in the Clusters API, once you create a cluster, you receive a cluster ID, and the cluster is in the PENDING state while Databricks takes care of provisioning virtual machines from the cloud provider in the background. But the cluster is only usable in the RUNNING state. Another example is the API for running a job or repairing the run: right after the run starts, the run is in the PENDING state, though the job is considered to be finished only when it is in the TERMINATED or SKIPPED states. And of course you. would want to know the error message when the long-running operation times out or why things fail. And sometimes you want to configure a custom timeout other than the default of 20 minutes.

To hide all of the integration-specific complexity from the end user, Databricks SDK for Go provides a high-level API for triggering the long-running operations and waiting for the releated entities to reach the right state or return back the error message about the problem in case of failure. All long-running operations have the XxxAndWait name pattern, where Xxx is the operation name. All these generated methods return information about the relevant entity once the operation is finished. It is possible to configure a custom timeout to XxxAndWait by providing a functional option argument constructed by retries.Timeout[Zzz](time.Duration) function, where Zzz is the result type of XxxAndWait.

In the following example, CreateAndWait returns ClusterInfo only once the cluster is in the RUNNING state, otherwise it will timeout in 10 minutes:

clusterInfo, err = w.Clusters.CreateAndWait(ctx, clusters.CreateCluster{
    ClusterName:            "Created cluster",
    SparkVersion:           latestLTS,
    NodeTypeId:             smallestWithDisk,
    AutoterminationMinutes: 10,
    NumWorkers:             1,
}, retries.Timeout[clusters.ClusterInfo](10*time.Minute))

In this section

• Command execution on clusters
• Cluster library management
• Advanced usage

Command execution on clusters

You can run Python, Scala, R, or SQL code on running interactive Databricks clusters and get the results back. All supplied code gets leading whitespace removed, so that you could easily embed Python code into Go applications. This high-level wrapper comes from the Databricks Terraform provider, where it was tested for over 2 years for use cases such as DBFS mounts and SQL permissions. This interface hides the intricate complexity of all internal APIs involved to simplify the unit-testing experience for command execution. Databricks does not recommending that you use lower-level interfaces for command execution. The execution timeout is 20 minutes and cannot be overriden for the sake of interface simplicity, meaning that you should only use this API if you have some relatively complex executions to perform. Please use jobs in case your commands must run longer than 20 minutes. Or use the Databricks SQL Driver for Go in case your workload type is purely for business intelligence.

res := w.CommandExecutor.Execute(ctx, clusterId, "python", "print(1)")
if res.Failed() {
    return fmt.Errorf("command failed: %w", res.Err())
}
println(res.Text())
// Out: 1

Cluster library management

You can install or uninstall libraries on running Databricks clusters. UpdateAndWait follows all conventions of long-running operations and wraps Install and Uninstall operations, followed by checking for the installation status of the cluster, exposing error messages back in a simplified way. This high-level wrapper came from the Databricks Terraform provider, where it was tested for over 2 years in the databricks_cluster and databricks_library resources. Databricks recommends that you use UpdateAndWait as the only API for cluster library management.

err = w.Libraries.UpdateAndWait(ctx, libraries.Update{
    ClusterId: clusterId,
    Install: []libraries.Library{
        {
            Pypi: &libraries.PythonPyPiLibrary{
                Package: "dbl-tempo",
            },
        },
    },
})

Advanced usage

You can track the intermediate state of a long-running operation while waiting to reach the correct state by supplying the func(i *retries.Info[Zzz]) functional option, where Zzz is the return type of the XxxAndWait method:

clusterInfo, err = w.Clusters.CreateAndWait(ctx, clusters.CreateCluster{
    // ...
}, func(i *retries.Info[clusters.ClusterInfo]) {
    updateIntermediateState(i.Info.StateMessage)
})

Paginated responses

On the platform side, some Databricks APIs have result pagination, and some of them do not. Some APIs follow the offset-plus-limit pagination, some start their offsets from 0 and some from 1, some use the cursor-based iteration, and others just return all results in a single response. The Databricks SDK for Go hides this intricate complexity and generates a more high-level interface for retrieving all results of a certain entity type. The naming pattern is XxxAll, where Xxx is the name of the method to retrieve a single page of results.

all, err := w.Repos.ListAll(ctx, repos.List{})
if err != nil {
    return fmt.Errorf("list repos: %w", err)
}
for _, repo := range all {
    println(repo.Path)
}

GetByName utility methods

On the platform side, most of the Databricks APIs could be retrieved primarily by their identifiers. In some common workflows, it's easier to reason about workspace objects by their names. To simplify development experience and speed-up proof-of-concepts, the Databricks SDK for Go generates code for GetByName client-side utilities. Please keep in mind, that some Databricks APIs don't enforce unique names on objects and these generated helpers return an error whenever duplicate name is detected.

repo, err := w.Repos.GetByPath(ctx, path)
if err != nil {
    return err
}
return w.Repos.Update(ctx, repos.UpdateRepo{
    RepoId: repo.Id,
    Branch: tag,
})

Node type and Databricks Runtime selectors

The Databricks SDK for Go provides selector methods that make developing multi-cloud applications easier and just rely on characteristics of the virtual machine, such as the number of cores or availability of local disks or always picking up the latest Databricks Runtime for the interactive cluster or per-job cluster.

// Fetch the list of spark runtime versions.
sparkVersions, err := w.Clusters.SparkVersions(ctx)
if err != nil {
    return err
}

// Select the latest LTS version.
latestLTS, err := sparkVersions.Select(clusters.SparkVersionRequest{
    Latest:          true,
    LongTermSupport: true,
})
if err != nil {
    return err
}

// Fetch the list of available node types.
nodeTypes, err := w.Clusters.ListNodeTypes(ctx)
if err != nil {
    return err
}

// Select the smallest node type ID.
smallestWithDisk, err := nodeTypes.Smallest(clusters.NodeTypeRequest{
    LocalDisk: true,
})
if err != nil {
    return err
}

// Create the cluster and wait for it to start properly.
runningCluster, err := w.Clusters.CreateAndWait(ctx, clusters.CreateCluster{
    ClusterName:            clusterName,
    SparkVersion:           latestLTS,
    NodeTypeId:             smallestWithDisk,
    AutoterminationMinutes: 15,
    NumWorkers:             1,
})

Integration with io interfaces for DBFS

You can open a file on DBFS for reading or writing with w.Dbfs.Open. This function returns a dbfs.Handle that is compatible with a subset of io interfaces for reading, writing, and closing.

Uploading a file from an io.Reader:

upload, _ := os.Open("/path/to/local/file.ext")
remote, _ := w.Dbfs.Open(ctx, "/path/to/remote/file", dbfs.FileModeWrite|dbfs.FileModeOverwrite)
_, _ = io.Copy(remote, upload)
_ = remote.Close()

Downloading a file to an io.Writer:

download, _ := os.Create("/path/to/local")
remote, _ := w.Dbfs.Open(ctx, "/path/to/remote/file", dbfs.FileModeRead)
_, _ = io.Copy(download, remote)

Reading into and writing from buffers

You can read from or write to a DBFS file directly from a byte slice through the convenience functions w.Dbfs.ReadFile and w.Dbfs.WriteFile.

Uploading a file from a byte slice:

err := w.Dbfs.WriteFile(ctx, "/path/to/remote/file", []byte("Hello world!"))

Downloading a file into a byte slice:

buf, err := w.Dbfs.ReadFile(ctx, "/path/to/remote/file")

pflag.Value for enums

Databricks SDK for Go loosely integrates with spf13/pflag by implementing pflag.Value for all enum types.

User Agent Request Attribution

The Databricks SDK for Go uses the User-Agent header to include request metadata along with each request. By default, this includes the version of the Go SDK, the version of the Go language used by your application, and the underlying operating system. To statically add additional metadata, you can use the WithPartner() and WithProduct() functions in the useragent package. WithPartner() can be used by partners to indicate that code using the Databricks SDK for Go should be attributed to a specific partner. Multiple partners can be registered at once. Partner names can contain any number, digit, ., -, _ or +.

useragent.WithPartner("partner-abc")
useragent.WithPartner("partner-xyz")

WithProduct() can be used to define the name and version of the product that is built with the Databricks SDK for Go. The product name has the same restrictions as the partner name above, and the product version must be a valid SemVer. Subsequent calls to WithProduct() replace the original product with the new user-specified one.

useragent.WithProduct("databricks-example-product", "1.2.0")

If both the DATABRICKS_SDK_UPSTREAM and DATABRICKS_SDK_UPSTREAM_VERSION environment variables are defined, these will also be included in the User-Agent header.

If additional metadata needs to be specified that isn't already supported by the above interfaces, you can use the WithUserAgentExtra() function to register arbitrary key-value pairs to include in the user agent. Multiple values associated with the same key are allowed. Keys have the same restrictions as the partner name above. Values must be either as described above or SemVer strings.

Error handling

The Databricks SDK for Go converts error responses from the Databricks API into the apierr.APIError type. This allows you to inspect the error code, message, and details by asserting the error as apierr.APIError:

_, err := w.Clusters.Create(ctx, compute.CreateCluster{...})
if e, ok := err.(*apierr.APIError); ok {
    fmt.Printf("Error code: %s\n", e.ErrorCode)
    fmt.Printf("Error message: %s\n", e.Message)
    fmt.Printf("Status code: %s\n", e.StatusCode)
    fmt.Printf("Error details: %v\n", e.Details)
}

The SDK also provides predefined errors that correspond to errors returned from the Databricks API. These predefined errors enable you to check for specific error conditions in your code. The SDK handles inconsistencies in error responses between different services, ensuring that errors.Is() and errors.As() function as expected.

For instance, to determine if w.Clusters.GetByClusterId() failed due to a non-existent cluster, use databricks.ErrResourceDoesNotExist:

c, err := w.Clusters.GetByClusterId(ctx, "12345")
if errors.Is(err, databricks.ErrResourceDoesNotExist) {... }

See all predefined errors in the documentation.

Logging

By default, Databricks SDK for Go uses logger.SimpleLogger, which is a levelled proxy to log.Printf, printing to os.Stderr. You can disable logging completely by adding log.SetOutput(io.Discard) to your init() function. You are encouraged to override logging.DefaultLogger with your own implementation that follows the logger.Logger interface.

Since v0.10.0, default logger prints only INFO level messages. To replicate more verbose behavior from the previous versions, set the DEBUG level in SimpleLogger:

import "github.com/databricks/databricks-sdk-go/logger"

func init() {
	logger.DefaultLogger = &logger.SimpleLogger{
		Level: logger.LevelDebug,
	}
}

Current Logger interface will evolve in the future versions of Databricks SDK for Go.

Testing

The Databricks SDK for Go makes it easy to write unit tests for your code that uses the SDK. The SDK provides a mockery-based mock implementation of the SDK's interfaces. You can use this mock implementation to write unit tests for your code that uses the SDK. For example:

package my_test

import (
	"context"
	"testing"

	"github.com/databricks/databricks-sdk-go/experimental/mocks"
	"github.com/databricks/databricks-sdk-go/listing"
	"github.com/databricks/databricks-sdk-go/qa/poll"
	"github.com/databricks/databricks-sdk-go/service/compute"
	"github.com/databricks/databricks-sdk-go/service/iam"
	"github.com/databricks/databricks-sdk-go/service/sql"
	"github.com/stretchr/testify/mock"
)

func TestDatabricksSDK(t *testing.T) {
	ctx := context.Background()
	w := mocks.NewMockWorkspaceClient(t)
	w.GetMockClustersAPI().EXPECT().ListAll(
		ctx,
		mock.AnythingOfType("compute.ListClustersRequest"),
	).Return(
		[]compute.ClusterDetails{
			{ClusterName: "test-cluster-1"},
			{ClusterName: "test-cluster-2"},
		}, nil)

	// You can also mock the AccountClient as follows.
	a := mocks.NewMockAccountClient(t)
	a.GetMockAccountUsersAPI().EXPECT().ListAll(
		ctx,
		mock.AnythingOfType("iam.ListAccountUsersRequest"),
	).Return(
		[]iam.User{
			{DisplayName: "test-user-1"},
			{DisplayName: "test-user-2"},
		}, nil)
}

The SDK also provides several testing utilities to simplify mocking test results.

• The *listing.SliceIterator type simplifies mocking the results of a listing operation. You can specify the items to be iterated over as a slice.
• The qa/poll.Simple() method constructs a poller function to mock the results of polling for a long-running operation.

For example:

func TestDatabricksSDK_helpers(t *testing.T) {
	// To mock iterators, you can provide the items to iterate over with
	// *listing.SliceIterator.
	iterator := listing.SliceIterator[iam.User]([]iam.User{
		{DisplayName: "test-user-1"},
		{DisplayName: "test-user-2"},
	})
	a.GetMockAccountUsersAPI().EXPECT().List(
		ctx,
		mock.AnythingOfType("iam.ListAccountUsersRequest"),
	).Return(&iterator)

	// To mock Wait* structures, you can stub out the Poll field.
	getResponse := sql.GetWarehouseResponse{
		Id: "abc",
	}
	wait := sql.WaitGetWarehouseRunning[struct{}]{
		Poll: poll.Simple(getResponse),
	}
	w.GetMockWarehousesAPI().EXPECT().Edit(mock.Anything, sql.EditWarehouseRequest{}).Return(&wait, nil)
}

Interface stability

During the Beta period, Databricks is actively working on stabilizing the Databricks SDK for Go's interfaces. API clients for all services are generated from specification files that are synchronized from the main platform. You are highly encouraged to pin the exact version in the go.mod file and read the changelog where Databricks documents the changes. Some types of interfaces are more stable than others. For those interfaces that are not yet nightly tested, Databricks may have minor documented backward-incompatible changes, such as fixing mapping correctness from int to int64 or renaming some type names to bring more consistency.

Documentation

Index

• Variables
• func Must[T any](c T, err error) T
• func Version() string
• func WithProduct(name, version string)
• type AccountClient
  • func NewAccountClient(c ...*Config) (*AccountClient, error)
  • func (c *AccountClient) GetWorkspaceClient(ws provisioning.Workspace) (*WorkspaceClient, error)
• type Config
• type WorkspaceClient
  • func NewWorkspaceClient(c ...*Config) (*WorkspaceClient, error)
  • func (w *WorkspaceClient) CurrentWorkspaceID(ctx context.Context) (int64, error)

Variables

var (
	// the request is invalid
	ErrBadRequest = apierr.ErrBadRequest
	// the request does not have valid authentication (AuthN) credentials for the operation
	ErrUnauthenticated = apierr.ErrUnauthenticated
	// the caller does not have permission to execute the specified operation
	ErrPermissionDenied = apierr.ErrPermissionDenied
	// the operation was performed on a resource that does not exist
	ErrNotFound = apierr.ErrNotFound
	// maps to all HTTP 409 (Conflict) responses
	ErrResourceConflict = apierr.ErrResourceConflict
	// maps to HTTP code: 429 Too Many Requests
	ErrTooManyRequests = apierr.ErrTooManyRequests
	// the operation was explicitly canceled by the caller
	ErrCancelled = apierr.ErrCancelled
	// some invariants expected by the underlying system have been broken
	ErrInternalError = apierr.ErrInternalError
	// the operation is not implemented or is not supported/enabled in this service
	ErrNotImplemented = apierr.ErrNotImplemented
	// the service is currently unavailable
	ErrTemporarilyUnavailable = apierr.ErrTemporarilyUnavailable
	// the deadline expired before the operation could complete
	ErrDeadlineExceeded = apierr.ErrDeadlineExceeded
	// unexpected state
	ErrInvalidState = apierr.ErrInvalidState
	// supplied value for a parameter was invalid
	ErrInvalidParameterValue = apierr.ErrInvalidParameterValue
	// operation was performed on a resource that does not exist
	ErrResourceDoesNotExist = apierr.ErrResourceDoesNotExist
	// the operation was aborted, typically due to a concurrency issue such as a sequencer check failure
	ErrAborted = apierr.ErrAborted
	// operation was rejected due a conflict with an existing resource
	ErrAlreadyExists = apierr.ErrAlreadyExists
	// operation was rejected due a conflict with an existing resource
	ErrResourceAlreadyExists = apierr.ErrResourceAlreadyExists
	// operation is rejected due to per-user rate limiting
	ErrResourceExhausted = apierr.ErrResourceExhausted
	// cluster request was rejected because it would exceed a resource limit
	ErrRequestLimitExceeded = apierr.ErrRequestLimitExceeded
	// this error is used as a fallback if the platform-side mapping is missing some reason
	ErrUnknown = apierr.ErrUnknown
	// unrecoverable data loss or corruption
	ErrDataLoss = apierr.ErrDataLoss
)

var ErrNotAccountClient = errors.New("invalid Databricks Account configuration")

var ErrNotWorkspaceClient = errors.New("invalid Databricks Workspace configuration")

Functions

func Must

func Must[T any](c T, err error) T

Must panics if error is not nil. It's intended to be used with databricks.NewWorkspaceClient and databricks.NewAccountClient.

func Version

func Version() string

Version of this SDK

func WithProduct

func WithProduct(name, version string)

WithProduct is expected to be set by developers to differentiate their app from others.

Example setting is:

func init() {
	databricks.WithProduct("your-product", "0.0.1")
}

Types

type AccountClient

type AccountClient struct {
	Config *config.Config

	// These APIs manage access rules on resources in an account. Currently,
	// only grant rules are supported. A grant rule specifies a role assigned to
	// a set of principals. A list of rules attached to a resource is called a
	// rule set.
	AccessControl iam.AccountAccessControlInterface

	// This API allows you to download billable usage logs for the specified
	// account and date range. This feature works with all account types.
	BillableUsage billing.BillableUsageInterface

	// These APIs manage credential configurations for this workspace.
	// Databricks needs access to a cross-account service IAM role in your AWS
	// account so that Databricks can deploy clusters in the appropriate VPC for
	// the new workspace. A credential configuration encapsulates this role
	// information, and its ID is used when creating a new workspace.
	Credentials provisioning.CredentialsInterface

	// These APIs enable administrators to manage custom OAuth app integrations,
	// which is required for adding/using Custom OAuth App Integration like
	// Tableau Cloud for Databricks in AWS cloud.
	CustomAppIntegration oauth2.CustomAppIntegrationInterface

	// These APIs manage encryption key configurations for this workspace
	// (optional). A key configuration encapsulates the AWS KMS key information
	// and some information about how the key configuration can be used. There
	// are two possible uses for key configurations:
	//
	// * Managed services: A key configuration can be used to encrypt a
	// workspace's notebook and secret data in the control plane, as well as
	// Databricks SQL queries and query history. * Storage: A key configuration
	// can be used to encrypt a workspace's DBFS and EBS data in the data plane.
	//
	// In both of these cases, the key configuration's ID is used when creating
	// a new workspace. This Preview feature is available if your account is on
	// the E2 version of the platform. Updating a running workspace with
	// workspace storage encryption requires that the workspace is on the E2
	// version of the platform. If you have an older workspace, it might not be
	// on the E2 version of the platform. If you are not sure, contact your
	// Databricks representative.
	EncryptionKeys provisioning.EncryptionKeysInterface

	// Groups simplify identity management, making it easier to assign access to
	// Databricks account, data, and other securable objects.
	//
	// It is best practice to assign access to workspaces and access-control
	// policies in Unity Catalog to groups, instead of to users individually.
	// All Databricks account identities can be assigned as members of groups,
	// and members inherit permissions that are assigned to their group.
	Groups iam.AccountGroupsInterface

	// The Accounts IP Access List API enables account admins to configure IP
	// access lists for access to the account console.
	//
	// Account IP Access Lists affect web application access and REST API access
	// to the account console and account APIs. If the feature is disabled for
	// the account, all access is allowed for this account. There is support for
	// allow lists (inclusion) and block lists (exclusion).
	//
	// When a connection is attempted: 1. **First, all block lists are
	// checked.** If the connection IP address matches any block list, the
	// connection is rejected. 2. **If the connection was not rejected by block
	// lists**, the IP address is compared with the allow lists.
	//
	// If there is at least one allow list for the account, the connection is
	// allowed only if the IP address matches an allow list. If there are no
	// allow lists for the account, all IP addresses are allowed.
	//
	// For all allow lists and block lists combined, the account supports a
	// maximum of 1000 IP/CIDR values, where one CIDR counts as a single value.
	//
	// After changes to the account-level IP access lists, it can take a few
	// minutes for changes to take effect.
	IpAccessLists settings.AccountIpAccessListsInterface

	// These APIs manage log delivery configurations for this account. The two
	// supported log types for this API are _billable usage logs_ and _audit
	// logs_. This feature is in Public Preview. This feature works with all
	// account ID types.
	//
	// Log delivery works with all account types. However, if your account is on
	// the E2 version of the platform or on a select custom plan that allows
	// multiple workspaces per account, you can optionally configure different
	// storage destinations for each workspace. Log delivery status is also
	// provided to know the latest status of log delivery attempts. The
	// high-level flow of billable usage delivery:
	//
	// 1. **Create storage**: In AWS, [create a new AWS S3 bucket] with a
	// specific bucket policy. Using Databricks APIs, call the Account API to
	// create a [storage configuration object](:method:Storage/Create) that uses
	// the bucket name. 2. **Create credentials**: In AWS, create the
	// appropriate AWS IAM role. For full details, including the required IAM
	// role policies and trust relationship, see [Billable usage log delivery].
	// Using Databricks APIs, call the Account API to create a [credential
	// configuration object](:method:Credentials/Create) that uses the IAM
	// role"s ARN. 3. **Create log delivery configuration**: Using Databricks
	// APIs, call the Account API to [create a log delivery
	// configuration](:method:LogDelivery/Create) that uses the credential and
	// storage configuration objects from previous steps. You can specify if the
	// logs should include all events of that log type in your account (_Account
	// level_ delivery) or only events for a specific set of workspaces
	// (_workspace level_ delivery). Account level log delivery applies to all
	// current and future workspaces plus account level logs, while workspace
	// level log delivery solely delivers logs related to the specified
	// workspaces. You can create multiple types of delivery configurations per
	// account.
	//
	// For billable usage delivery: * For more information about billable usage
	// logs, see [Billable usage log delivery]. For the CSV schema, see the
	// [Usage page]. * The delivery location is
	// `<bucket-name>/<prefix>/billable-usage/csv/`, where `<prefix>` is the
	// name of the optional delivery path prefix you set up during log delivery
	// configuration. Files are named
	// `workspaceId=<workspace-id>-usageMonth=<month>.csv`. * All billable usage
	// logs apply to specific workspaces (_workspace level_ logs). You can
	// aggregate usage for your entire account by creating an _account level_
	// delivery configuration that delivers logs for all current and future
	// workspaces in your account. * The files are delivered daily by
	// overwriting the month's CSV file for each workspace.
	//
	// For audit log delivery: * For more information about about audit log
	// delivery, see [Audit log delivery], which includes information about the
	// used JSON schema. * The delivery location is
	// `<bucket-name>/<delivery-path-prefix>/workspaceId=<workspaceId>/date=<yyyy-mm-dd>/auditlogs_<internal-id>.json`.
	// Files may get overwritten with the same content multiple times to achieve
	// exactly-once delivery. * If the audit log delivery configuration included
	// specific workspace IDs, only _workspace-level_ audit logs for those
	// workspaces are delivered. If the log delivery configuration applies to
	// the entire account (_account level_ delivery configuration), the audit
	// log delivery includes workspace-level audit logs for all workspaces in
	// the account as well as account-level audit logs. See [Audit log delivery]
	// for details. * Auditable events are typically available in logs within 15
	// minutes.
	//
	// [Audit log delivery]: https://docs.databricks.com/administration-guide/account-settings/audit-logs.html
	// [Billable usage log delivery]: https://docs.databricks.com/administration-guide/account-settings/billable-usage-delivery.html
	// [Usage page]: https://docs.databricks.com/administration-guide/account-settings/usage.html
	// [create a new AWS S3 bucket]: https://docs.databricks.com/administration-guide/account-api/aws-storage.html
	LogDelivery billing.LogDeliveryInterface

	// These APIs manage metastore assignments to a workspace.
	MetastoreAssignments catalog.AccountMetastoreAssignmentsInterface

	// These APIs manage Unity Catalog metastores for an account. A metastore
	// contains catalogs that can be associated with workspaces
	Metastores catalog.AccountMetastoresInterface

	// These APIs provide configurations for the network connectivity of your
	// workspaces for serverless compute resources.
	NetworkConnectivity settings.NetworkConnectivityInterface

	// These APIs manage network configurations for customer-managed VPCs
	// (optional). Its ID is used when creating a new workspace if you use
	// customer-managed VPCs.
	Networks provisioning.NetworksInterface

	// These APIs enable administrators to view all the available published
	// OAuth applications in Databricks. Administrators can add the published
	// OAuth applications to their account through the OAuth Published App
	// Integration APIs.
	OAuthPublishedApps oauth2.OAuthPublishedAppsInterface

	// These APIs manage private access settings for this account.
	PrivateAccess provisioning.PrivateAccessInterface

	// These APIs enable administrators to manage published OAuth app
	// integrations, which is required for adding/using Published OAuth App
	// Integration like Tableau Desktop for Databricks in AWS cloud.
	PublishedAppIntegration oauth2.PublishedAppIntegrationInterface

	// These APIs enable administrators to manage service principal secrets.
	//
	// You can use the generated secrets to obtain OAuth access tokens for a
	// service principal, which can then be used to access Databricks Accounts
	// and Workspace APIs. For more information, see [Authentication using OAuth
	// tokens for service principals],
	//
	// In addition, the generated secrets can be used to configure the
	// Databricks Terraform Provider to authenticate with the service principal.
	// For more information, see [Databricks Terraform Provider].
	//
	// [Authentication using OAuth tokens for service principals]: https://docs.databricks.com/dev-tools/authentication-oauth.html
	// [Databricks Terraform Provider]: https://github.com/databricks/terraform-provider-databricks/blob/master/docs/index.md#authenticating-with-service-principal
	ServicePrincipalSecrets oauth2.ServicePrincipalSecretsInterface

	// Identities for use with jobs, automated tools, and systems such as
	// scripts, apps, and CI/CD platforms. Databricks recommends creating
	// service principals to run production jobs or modify production data. If
	// all processes that act on production data run with service principals,
	// interactive users do not need any write, delete, or modify privileges in
	// production. This eliminates the risk of a user overwriting production
	// data by accident.
	ServicePrincipals iam.AccountServicePrincipalsInterface

	// Accounts Settings API allows users to manage settings at the account
	// level.
	Settings settings.AccountSettingsInterface

	// These APIs manage storage configurations for this workspace. A root
	// storage S3 bucket in your account is required to store objects like
	// cluster logs, notebook revisions, and job results. You can also use the
	// root storage S3 bucket for storage of non-production DBFS data. A storage
	// configuration encapsulates this bucket information, and its ID is used
	// when creating a new workspace.
	Storage provisioning.StorageInterface

	// These APIs manage storage credentials for a particular metastore.
	StorageCredentials catalog.AccountStorageCredentialsInterface

	// These APIs manage usage dashboards for this account. Usage dashboards
	// enable you to gain insights into your usage with pre-built dashboards:
	// visualize breakdowns, analyze tag attributions, and identify cost
	// drivers.
	UsageDashboards billing.UsageDashboardsInterface

	// User identities recognized by Databricks and represented by email
	// addresses.
	//
	// Databricks recommends using SCIM provisioning to sync users and groups
	// automatically from your identity provider to your Databricks account.
	// SCIM streamlines onboarding a new employee or team by using your identity
	// provider to create users and groups in Databricks account and give them
	// the proper level of access. When a user leaves your organization or no
	// longer needs access to Databricks account, admins can terminate the user
	// in your identity provider and that user’s account will also be removed
	// from Databricks account. This ensures a consistent offboarding process
	// and prevents unauthorized users from accessing sensitive data.
	Users iam.AccountUsersInterface

	// These APIs manage VPC endpoint configurations for this account.
	VpcEndpoints provisioning.VpcEndpointsInterface

	// The Workspace Permission Assignment API allows you to manage workspace
	// permissions for principals in your account.
	WorkspaceAssignment iam.WorkspaceAssignmentInterface

	// These APIs manage workspaces for this account. A Databricks workspace is
	// an environment for accessing all of your Databricks assets. The workspace
	// organizes objects (notebooks, libraries, and experiments) into folders,
	// and provides access to data and computational resources such as clusters
	// and jobs.
	//
	// These endpoints are available if your account is on the E2 version of the
	// platform or on a select custom plan that allows multiple workspaces per
	// account.
	Workspaces provisioning.WorkspacesInterface

	// These APIs manage budget configurations for this account. Budgets enable
	// you to monitor usage across your account. You can set up budgets to
	// either track account-wide spending, or apply filters to track the
	// spending of specific teams, projects, or workspaces.
	Budgets billing.BudgetsInterface
}

func NewAccountClient

func NewAccountClient(c ...*Config) (*AccountClient, error)

NewAccountClient creates new Databricks SDK client for Accounts or returns error in case configuration is wrong

func (*AccountClient) GetWorkspaceClient

func (c *AccountClient) GetWorkspaceClient(ws provisioning.Workspace) (*WorkspaceClient, error)

GetWorkspaceClient returns a WorkspaceClient for the given workspace. The workspace can be fetched by calling w.Workspaces.Get() or w.Workspaces.List().

The config used for the workspace is identical to that used for the account, except that the host is set to the workspace host, and the account ID is not set.

Example:

a, err := databricks.NewAccountClient()
if err != nil {
	panic(err)
}
ctx := context.Background()
workspaces, err := a.Workspaces.List(ctx)
if err != nil {
	panic(err)
}
w, err := a.GetWorkspaceClient(workspaces[0])
if err != nil {
	panic(err)
}
me, err := w.CurrentUser.Me(ctx)

type Config

type Config config.Config

type WorkspaceClient

type WorkspaceClient struct {
	Config *config.Config

	// These APIs manage access rules on resources in an account. Currently,
	// only grant rules are supported. A grant rule specifies a role assigned to
	// a set of principals. A list of rules attached to a resource is called a
	// rule set. A workspace must belong to an account for these APIs to work.
	AccountAccessControlProxy iam.AccountAccessControlProxyInterface

	// The alerts API can be used to perform CRUD operations on alerts. An alert
	// is a Databricks SQL object that periodically runs a query, evaluates a
	// condition of its result, and notifies one or more users and/or
	// notification destinations if the condition was met. Alerts can be
	// scheduled using the `sql_task` type of the Jobs API, e.g.
	// :method:jobs/create.
	Alerts sql.AlertsInterface

	// The alerts API can be used to perform CRUD operations on alerts. An alert
	// is a Databricks SQL object that periodically runs a query, evaluates a
	// condition of its result, and notifies one or more users and/or
	// notification destinations if the condition was met. Alerts can be
	// scheduled using the `sql_task` type of the Jobs API, e.g.
	// :method:jobs/create.
	//
	// **Note**: A new version of the Databricks SQL API is now available.
	// Please see the latest version. [Learn more]
	//
	// [Learn more]: https://docs.databricks.com/en/sql/dbsql-api-latest.html
	AlertsLegacy sql.AlertsLegacyInterface

	// Apps run directly on a customer’s Databricks instance, integrate with
	// their data, use and extend Databricks services, and enable users to
	// interact through single sign-on.
	Apps apps.AppsInterface

	// In Databricks Runtime 13.3 and above, you can add libraries and init
	// scripts to the `allowlist` in UC so that users can leverage these
	// artifacts on compute configured with shared access mode.
	ArtifactAllowlists catalog.ArtifactAllowlistsInterface

	// A catalog is the first layer of Unity Catalog’s three-level namespace.
	// It’s used to organize your data assets. Users can see all catalogs on
	// which they have been assigned the USE_CATALOG data permission.
	//
	// In Unity Catalog, admins and data stewards manage users and their access
	// to data centrally across all of the workspaces in a Databricks account.
	// Users in different workspaces can share access to the same data,
	// depending on privileges granted centrally in Unity Catalog.
	Catalogs catalog.CatalogsInterface

	// A clean room is a secure, privacy-protecting environment where two or
	// more parties can share sensitive enterprise data, including customer
	// data, for measurements, insights, activation and other use cases.
	//
	// To create clean rooms, you must be a metastore admin or a user with the
	// **CREATE_CLEAN_ROOM** privilege.
	CleanRooms sharing.CleanRoomsInterface

	// You can use cluster policies to control users' ability to configure
	// clusters based on a set of rules. These rules specify which attributes or
	// attribute values can be used during cluster creation. Cluster policies
	// have ACLs that limit their use to specific users and groups.
	//
	// With cluster policies, you can: - Auto-install cluster libraries on the
	// next restart by listing them in the policy's "libraries" field (Public
	// Preview). - Limit users to creating clusters with the prescribed
	// settings. - Simplify the user interface, enabling more users to create
	// clusters, by fixing and hiding some fields. - Manage costs by setting
	// limits on attributes that impact the hourly rate.
	//
	// Cluster policy permissions limit which policies a user can select in the
	// Policy drop-down when the user creates a cluster: - A user who has
	// unrestricted cluster create permission can select the Unrestricted policy
	// and create fully-configurable clusters. - A user who has both
	// unrestricted cluster create permission and access to cluster policies can
	// select the Unrestricted policy and policies they have access to. - A user
	// that has access to only cluster policies, can select the policies they
	// have access to.
	//
	// If no policies exist in the workspace, the Policy drop-down doesn't
	// appear. Only admin users can create, edit, and delete policies. Admin
	// users also have access to all policies.
	ClusterPolicies compute.ClusterPoliciesInterface

	// The Clusters API allows you to create, start, edit, list, terminate, and
	// delete clusters.
	//
	// Databricks maps cluster node instance types to compute units known as
	// DBUs. See the instance type pricing page for a list of the supported
	// instance types and their corresponding DBUs.
	//
	// A Databricks cluster is a set of computation resources and configurations
	// on which you run data engineering, data science, and data analytics
	// workloads, such as production ETL pipelines, streaming analytics, ad-hoc
	// analytics, and machine learning.
	//
	// You run these workloads as a set of commands in a notebook or as an
	// automated job. Databricks makes a distinction between all-purpose
	// clusters and job clusters. You use all-purpose clusters to analyze data
	// collaboratively using interactive notebooks. You use job clusters to run
	// fast and robust automated jobs.
	//
	// You can create an all-purpose cluster using the UI, CLI, or REST API. You
	// can manually terminate and restart an all-purpose cluster. Multiple users
	// can share such clusters to do collaborative interactive analysis.
	//
	// IMPORTANT: Databricks retains cluster configuration information for
	// terminated clusters for 30 days. To keep an all-purpose cluster
	// configuration even after it has been terminated for more than 30 days, an
	// administrator can pin a cluster to the cluster list.
	Clusters compute.ClustersInterface

	// This API allows execution of Python, Scala, SQL, or R commands on running
	// Databricks Clusters. This API only supports (classic) all-purpose
	// clusters. Serverless compute is not supported.
	CommandExecution compute.CommandExecutionInterface

	// Connections allow for creating a connection to an external data source.
	//
	// A connection is an abstraction of an external data source that can be
	// connected from Databricks Compute. Creating a connection object is the
	// first step to managing external data sources within Unity Catalog, with
	// the second step being creating a data object (catalog, schema, or table)
	// using the connection. Data objects derived from a connection can be
	// written to or read from similar to other Unity Catalog data objects based
	// on cloud storage. Users may create different types of connections with
	// each connection having a unique set of configuration options to support
	// credential management and other settings.
	Connections catalog.ConnectionsInterface

	// Fulfillments are entities that allow consumers to preview installations.
	ConsumerFulfillments marketplace.ConsumerFulfillmentsInterface

	// Installations are entities that allow consumers to interact with
	// Databricks Marketplace listings.
	ConsumerInstallations marketplace.ConsumerInstallationsInterface

	// Listings are the core entities in the Marketplace. They represent the
	// products that are available for consumption.
	ConsumerListings marketplace.ConsumerListingsInterface

	// Personalization Requests allow customers to interact with the
	// individualized Marketplace listing flow.
	ConsumerPersonalizationRequests marketplace.ConsumerPersonalizationRequestsInterface

	// Providers are the entities that publish listings to the Marketplace.
	ConsumerProviders marketplace.ConsumerProvidersInterface

	// Credentials manager interacts with with Identity Providers to to perform
	// token exchanges using stored credentials and refresh tokens.
	CredentialsManager settings.CredentialsManagerInterface

	// This API allows retrieving information about currently authenticated user
	// or service principal.
	CurrentUser iam.CurrentUserInterface

	// This is an evolving API that facilitates the addition and removal of
	// widgets from existing dashboards within the Databricks Workspace. Data
	// structures may change over time.
	DashboardWidgets sql.DashboardWidgetsInterface

	// In general, there is little need to modify dashboards using the API.
	// However, it can be useful to use dashboard objects to look-up a
	// collection of related query IDs. The API can also be used to duplicate
	// multiple dashboards at once since you can get a dashboard definition with
	// a GET request and then POST it to create a new one. Dashboards can be
	// scheduled using the `sql_task` type of the Jobs API, e.g.
	// :method:jobs/create.
	Dashboards sql.DashboardsInterface

	// This API is provided to assist you in making new query objects. When
	// creating a query object, you may optionally specify a `data_source_id`
	// for the SQL warehouse against which it will run. If you don't already
	// know the `data_source_id` for your desired SQL warehouse, this API will
	// help you find it.
	//
	// This API does not support searches. It returns the full list of SQL
	// warehouses in your workspace. We advise you to use any text editor, REST
	// client, or `grep` to search the response from this API for the name of
	// your SQL warehouse as it appears in Databricks SQL.
	//
	// **Note**: A new version of the Databricks SQL API is now available.
	// [Learn more]
	//
	// [Learn more]: https://docs.databricks.com/en/sql/dbsql-api-latest.html
	DataSources sql.DataSourcesInterface

	// DBFS API makes it simple to interact with various data sources without
	// having to include a users credentials every time to read a file.
	Dbfs files.DbfsInterface

	// The SQL Permissions API is similar to the endpoints of the
	// :method:permissions/set. However, this exposes only one endpoint, which
	// gets the Access Control List for a given object. You cannot modify any
	// permissions using this API.
	//
	// There are three levels of permission:
	//
	// - `CAN_VIEW`: Allows read-only access
	//
	// - `CAN_RUN`: Allows read access and run access (superset of `CAN_VIEW`)
	//
	// - `CAN_MANAGE`: Allows all actions: read, run, edit, delete, modify
	// permissions (superset of `CAN_RUN`)
	//
	// **Note**: A new version of the Databricks SQL API is now available.
	// [Learn more]
	//
	// [Learn more]: https://docs.databricks.com/en/sql/dbsql-api-latest.html
	DbsqlPermissions sql.DbsqlPermissionsInterface

	// Experiments are the primary unit of organization in MLflow; all MLflow
	// runs belong to an experiment. Each experiment lets you visualize, search,
	// and compare runs, as well as download run artifacts or metadata for
	// analysis in other tools. Experiments are maintained in a Databricks
	// hosted MLflow tracking server.
	//
	// Experiments are located in the workspace file tree. You manage
	// experiments using the same tools you use to manage other workspace
	// objects such as folders, notebooks, and libraries.
	Experiments ml.ExperimentsInterface

	// An external location is an object that combines a cloud storage path with
	// a storage credential that authorizes access to the cloud storage path.
	// Each external location is subject to Unity Catalog access-control
	// policies that control which users and groups can access the credential.
	// If a user does not have access to an external location in Unity Catalog,
	// the request fails and Unity Catalog does not attempt to authenticate to
	// your cloud tenant on the user’s behalf.
	//
	// Databricks recommends using external locations rather than using storage
	// credentials directly.
	//
	// To create external locations, you must be a metastore admin or a user
	// with the **CREATE_EXTERNAL_LOCATION** privilege.
	ExternalLocations catalog.ExternalLocationsInterface

	// The Files API is a standard HTTP API that allows you to read, write,
	// list, and delete files and directories by referring to their URI. The API
	// makes working with file content as raw bytes easier and more efficient.
	//
	// The API supports [Unity Catalog volumes], where files and directories to
	// operate on are specified using their volume URI path, which follows the
	// format
	// /Volumes/<catalog_name>/<schema_name>/<volume_name>/<path_to_file>.
	//
	// The Files API has two distinct endpoints, one for working with files
	// (`/fs/files`) and another one for working with directories
	// (`/fs/directories`). Both endpoints, use the standard HTTP methods GET,
	// HEAD, PUT, and DELETE to manage files and directories specified using
	// their URI path. The path is always absolute.
	//
	// [Unity Catalog volumes]: https://docs.databricks.com/en/connect/unity-catalog/volumes.html
	Files files.FilesInterface

	// Functions implement User-Defined Functions (UDFs) in Unity Catalog.
	//
	// The function implementation can be any SQL expression or Query, and it
	// can be invoked wherever a table reference is allowed in a query. In Unity
	// Catalog, a function resides at the same level as a table, so it can be
	// referenced with the form
	// __catalog_name__.__schema_name__.__function_name__.
	Functions catalog.FunctionsInterface

	// Genie provides a no-code experience for business users, powered by AI/BI.
	// Analysts set up spaces that business users can use to ask questions using
	// natural language. Genie uses data registered to Unity Catalog and
	// requires at least CAN USE permission on a Pro or Serverless SQL
	// warehouse. Also, Databricks Assistant must be enabled.
	Genie dashboards.GenieInterface

	// Registers personal access token for Databricks to do operations on behalf
	// of the user.
	//
	// See [more info].
	//
	// [more info]: https://docs.databricks.com/repos/get-access-tokens-from-git-provider.html
	GitCredentials workspace.GitCredentialsInterface

	// The Global Init Scripts API enables Workspace administrators to configure
	// global initialization scripts for their workspace. These scripts run on
	// every node in every cluster in the workspace.
	//
	// **Important:** Existing clusters must be restarted to pick up any changes
	// made to global init scripts. Global init scripts are run in order. If the
	// init script returns with a bad exit code, the Apache Spark container
	// fails to launch and init scripts with later position are skipped. If
	// enough containers fail, the entire cluster fails with a
	// `GLOBAL_INIT_SCRIPT_FAILURE` error code.
	GlobalInitScripts compute.GlobalInitScriptsInterface

	// In Unity Catalog, data is secure by default. Initially, users have no
	// access to data in a metastore. Access can be granted by either a
	// metastore admin, the owner of an object, or the owner of the catalog or
	// schema that contains the object. Securable objects in Unity Catalog are
	// hierarchical and privileges are inherited downward.
	//
	// Securable objects in Unity Catalog are hierarchical and privileges are
	// inherited downward. This means that granting a privilege on the catalog
	// automatically grants the privilege to all current and future objects
	// within the catalog. Similarly, privileges granted on a schema are
	// inherited by all current and future objects within that schema.
	Grants catalog.GrantsInterface

	// Groups simplify identity management, making it easier to assign access to
	// Databricks workspace, data, and other securable objects.
	//
	// It is best practice to assign access to workspaces and access-control
	// policies in Unity Catalog to groups, instead of to users individually.
	// All Databricks workspace identities can be assigned as members of groups,
	// and members inherit permissions that are assigned to their group.
	Groups iam.GroupsInterface

	// Instance Pools API are used to create, edit, delete and list instance
	// pools by using ready-to-use cloud instances which reduces a cluster start
	// and auto-scaling times.
	//
	// Databricks pools reduce cluster start and auto-scaling times by
	// maintaining a set of idle, ready-to-use instances. When a cluster is
	// attached to a pool, cluster nodes are created using the pool’s idle
	// instances. If the pool has no idle instances, the pool expands by
	// allocating a new instance from the instance provider in order to
	// accommodate the cluster’s request. When a cluster releases an instance,
	// it returns to the pool and is free for another cluster to use. Only
	// clusters attached to a pool can use that pool’s idle instances.
	//
	// You can specify a different pool for the driver node and worker nodes, or
	// use the same pool for both.
	//
	// Databricks does not charge DBUs while instances are idle in the pool.
	// Instance provider billing does apply. See pricing.
	InstancePools compute.InstancePoolsInterface

	// The Instance Profiles API allows admins to add, list, and remove instance
	// profiles that users can launch clusters with. Regular users can list the
	// instance profiles available to them. See [Secure access to S3 buckets]
	// using instance profiles for more information.
	//
	// [Secure access to S3 buckets]: https://docs.databricks.com/administration-guide/cloud-configurations/aws/instance-profiles.html
	InstanceProfiles compute.InstanceProfilesInterface

	// IP Access List enables admins to configure IP access lists.
	//
	// IP access lists affect web application access and REST API access to this
	// workspace only. If the feature is disabled for a workspace, all access is
	// allowed for this workspace. There is support for allow lists (inclusion)
	// and block lists (exclusion).
	//
	// When a connection is attempted: 1. **First, all block lists are
	// checked.** If the connection IP address matches any block list, the
	// connection is rejected. 2. **If the connection was not rejected by block
	// lists**, the IP address is compared with the allow lists.
	//
	// If there is at least one allow list for the workspace, the connection is
	// allowed only if the IP address matches an allow list. If there are no
	// allow lists for the workspace, all IP addresses are allowed.
	//
	// For all allow lists and block lists combined, the workspace supports a
	// maximum of 1000 IP/CIDR values, where one CIDR counts as a single value.
	//
	// After changes to the IP access list feature, it can take a few minutes
	// for changes to take effect.
	IpAccessLists settings.IpAccessListsInterface

	// The Jobs API allows you to create, edit, and delete jobs.
	//
	// You can use a Databricks job to run a data processing or data analysis
	// task in a Databricks cluster with scalable resources. Your job can
	// consist of a single task or can be a large, multi-task workflow with
	// complex dependencies. Databricks manages the task orchestration, cluster
	// management, monitoring, and error reporting for all of your jobs. You can
	// run your jobs immediately or periodically through an easy-to-use
	// scheduling system. You can implement job tasks using notebooks, JARS,
	// Delta Live Tables pipelines, or Python, Scala, Spark submit, and Java
	// applications.
	//
	// You should never hard code secrets or store them in plain text. Use the
	// [Secrets CLI] to manage secrets in the [Databricks CLI]. Use the [Secrets
	// utility] to reference secrets in notebooks and jobs.
	//
	// [Databricks CLI]: https://docs.databricks.com/dev-tools/cli/index.html
	// [Secrets CLI]: https://docs.databricks.com/dev-tools/cli/secrets-cli.html
	// [Secrets utility]: https://docs.databricks.com/dev-tools/databricks-utils.html#dbutils-secrets
	Jobs jobs.JobsInterface

	// These APIs provide specific management operations for Lakeview
	// dashboards. Generic resource management can be done with Workspace API
	// (import, export, get-status, list, delete).
	Lakeview dashboards.LakeviewInterface

	// The Libraries API allows you to install and uninstall libraries and get
	// the status of libraries on a cluster.
	//
	// To make third-party or custom code available to notebooks and jobs
	// running on your clusters, you can install a library. Libraries can be
	// written in Python, Java, Scala, and R. You can upload Python, Java, Scala
	// and R libraries and point to external packages in PyPI, Maven, and CRAN
	// repositories.
	//
	// Cluster libraries can be used by all notebooks running on a cluster. You
	// can install a cluster library directly from a public repository such as
	// PyPI or Maven, using a previously installed workspace library, or using
	// an init script.
	//
	// When you uninstall a library from a cluster, the library is removed only
	// when you restart the cluster. Until you restart the cluster, the status
	// of the uninstalled library appears as Uninstall pending restart.
	Libraries compute.LibrariesInterface

	// A metastore is the top-level container of objects in Unity Catalog. It
	// stores data assets (tables and views) and the permissions that govern
	// access to them. Databricks account admins can create metastores and
	// assign them to Databricks workspaces to control which workloads use each
	// metastore. For a workspace to use Unity Catalog, it must have a Unity
	// Catalog metastore attached.
	//
	// Each metastore is configured with a root storage location in a cloud
	// storage account. This storage location is used for metadata and managed
	// tables data.
	//
	// NOTE: This metastore is distinct from the metastore included in
	// Databricks workspaces created before Unity Catalog was released. If your
	// workspace includes a legacy Hive metastore, the data in that metastore is
	// available in a catalog named hive_metastore.
	Metastores catalog.MetastoresInterface

	// Note: This API reference documents APIs for the Workspace Model Registry.
	// Databricks recommends using [Models in Unity
	// Catalog](/api/workspace/registeredmodels) instead. Models in Unity
	// Catalog provides centralized model governance, cross-workspace access,
	// lineage, and deployment. Workspace Model Registry will be deprecated in
	// the future.
	//
	// The Workspace Model Registry is a centralized model repository and a UI
	// and set of APIs that enable you to manage the full lifecycle of MLflow
	// Models.
	ModelRegistry ml.ModelRegistryInterface

	// Databricks provides a hosted version of MLflow Model Registry in Unity
	// Catalog. Models in Unity Catalog provide centralized access control,
	// auditing, lineage, and discovery of ML models across Databricks
	// workspaces.
	//
	// This API reference documents the REST endpoints for managing model
	// versions in Unity Catalog. For more details, see the [registered models
	// API docs](/api/workspace/registeredmodels).
	ModelVersions catalog.ModelVersionsInterface

	// The notification destinations API lets you programmatically manage a
	// workspace's notification destinations. Notification destinations are used
	// to send notifications for query alerts and jobs to destinations outside
	// of Databricks. Only workspace admins can create, update, and delete
	// notification destinations.
	NotificationDestinations settings.NotificationDestinationsInterface

	// Online tables provide lower latency and higher QPS access to data from
	// Delta tables.
	OnlineTables catalog.OnlineTablesInterface

	// APIs for migrating acl permissions, used only by the ucx tool:
	// https://github.com/databrickslabs/ucx
	PermissionMigration iam.PermissionMigrationInterface

	// Permissions API are used to create read, write, edit, update and manage
	// access for various users on different objects and endpoints.
	//
	// * **[Apps permissions](:service:apps)** — Manage which users can manage
	// or use apps.
	//
	// * **[Cluster permissions](:service:clusters)** — Manage which users can
	// manage, restart, or attach to clusters.
	//
	// * **[Cluster policy permissions](:service:clusterpolicies)** — Manage
	// which users can use cluster policies.
	//
	// * **[Delta Live Tables pipeline permissions](:service:pipelines)** —
	// Manage which users can view, manage, run, cancel, or own a Delta Live
	// Tables pipeline.
	//
	// * **[Job permissions](:service:jobs)** — Manage which users can view,
	// manage, trigger, cancel, or own a job.
	//
	// * **[MLflow experiment permissions](:service:experiments)** — Manage
	// which users can read, edit, or manage MLflow experiments.
	//
	// * **[MLflow registered model permissions](:service:modelregistry)** —
	// Manage which users can read, edit, or manage MLflow registered models.
	//
	// * **[Password permissions](:service:users)** — Manage which users can
	// use password login when SSO is enabled.
	//
	// * **[Instance Pool permissions](:service:instancepools)** — Manage
	// which users can manage or attach to pools.
	//
	// * **[Repo permissions](repos)** — Manage which users can read, run,
	// edit, or manage a repo.
	//
	// * **[Serving endpoint permissions](:service:servingendpoints)** —
	// Manage which users can view, query, or manage a serving endpoint.
	//
	// * **[SQL warehouse permissions](:service:warehouses)** — Manage which
	// users can use or manage SQL warehouses.
	//
	// * **[Token permissions](:service:tokenmanagement)** — Manage which
	// users can create or use tokens.
	//
	// * **[Workspace object permissions](:service:workspace)** — Manage which
	// users can read, run, edit, or manage alerts, dbsql-dashboards,
	// directories, files, notebooks and queries.
	//
	// For the mapping of the required permissions for specific actions or
	// abilities and other important information, see [Access Control].
	//
	// Note that to manage access control on service principals, use **[Account
	// Access Control Proxy](:service:accountaccesscontrolproxy)**.
	//
	// [Access Control]: https://docs.databricks.com/security/auth-authz/access-control/index.html
	Permissions iam.PermissionsInterface

	// The Delta Live Tables API allows you to create, edit, delete, start, and
	// view details about pipelines.
	//
	// Delta Live Tables is a framework for building reliable, maintainable, and
	// testable data processing pipelines. You define the transformations to
	// perform on your data, and Delta Live Tables manages task orchestration,
	// cluster management, monitoring, data quality, and error handling.
	//
	// Instead of defining your data pipelines using a series of separate Apache
	// Spark tasks, Delta Live Tables manages how your data is transformed based
	// on a target schema you define for each processing step. You can also
	// enforce data quality with Delta Live Tables expectations. Expectations
	// allow you to define expected data quality and specify how to handle
	// records that fail those expectations.
	Pipelines pipelines.PipelinesInterface

	// The policy compliance APIs allow you to view and manage the policy
	// compliance status of clusters in your workspace.
	//
	// A cluster is compliant with its policy if its configuration satisfies all
	// its policy rules. Clusters could be out of compliance if their policy was
	// updated after the cluster was last edited.
	//
	// The get and list compliance APIs allow you to view the policy compliance
	// status of a cluster. The enforce compliance API allows you to update a
	// cluster to be compliant with the current version of its policy.
	PolicyComplianceForClusters compute.PolicyComplianceForClustersInterface

	// The compliance APIs allow you to view and manage the policy compliance
	// status of jobs in your workspace. This API currently only supports
	// compliance controls for cluster policies.
	//
	// A job is in compliance if its cluster configurations satisfy the rules of
	// all their respective cluster policies. A job could be out of compliance
	// if a cluster policy it uses was updated after the job was last edited.
	// The job is considered out of compliance if any of its clusters no longer
	// comply with their updated policies.
	//
	// The get and list compliance APIs allow you to view the policy compliance
	// status of a job. The enforce compliance API allows you to update a job so
	// that it becomes compliant with all of its policies.
	PolicyComplianceForJobs jobs.PolicyComplianceForJobsInterface

	// View available policy families. A policy family contains a policy
	// definition providing best practices for configuring clusters for a
	// particular use case.
	//
	// Databricks manages and provides policy families for several common
	// cluster use cases. You cannot create, edit, or delete policy families.
	//
	// Policy families cannot be used directly to create clusters. Instead, you
	// create cluster policies using a policy family. Cluster policies created
	// using a policy family inherit the policy family's policy definition.
	PolicyFamilies compute.PolicyFamiliesInterface

	// Marketplace exchanges filters curate which groups can access an exchange.
	ProviderExchangeFilters marketplace.ProviderExchangeFiltersInterface

	// Marketplace exchanges allow providers to share their listings with a
	// curated set of customers.
	ProviderExchanges marketplace.ProviderExchangesInterface

	// Marketplace offers a set of file APIs for various purposes such as
	// preview notebooks and provider icons.
	ProviderFiles marketplace.ProviderFilesInterface

	// Listings are the core entities in the Marketplace. They represent the
	// products that are available for consumption.
	ProviderListings marketplace.ProviderListingsInterface

	// Personalization requests are an alternate to instantly available
	// listings. Control the lifecycle of personalized solutions.
	ProviderPersonalizationRequests marketplace.ProviderPersonalizationRequestsInterface

	// Manage templated analytics solution for providers.
	ProviderProviderAnalyticsDashboards marketplace.ProviderProviderAnalyticsDashboardsInterface

	// Providers are entities that manage assets in Marketplace.
	ProviderProviders marketplace.ProviderProvidersInterface

	// A data provider is an object representing the organization in the real
	// world who shares the data. A provider contains shares which further
	// contain the shared data.
	Providers sharing.ProvidersInterface

	// A monitor computes and monitors data or model quality metrics for a table
	// over time. It generates metrics tables and a dashboard that you can use
	// to monitor table health and set alerts.
	//
	// Most write operations require the user to be the owner of the table (or
	// its parent schema or parent catalog). Viewing the dashboard, computed
	// metrics, or monitor configuration only requires the user to have
	// **SELECT** privileges on the table (along with **USE_SCHEMA** and
	// **USE_CATALOG**).
	QualityMonitors catalog.QualityMonitorsInterface

	// The queries API can be used to perform CRUD operations on queries. A
	// query is a Databricks SQL object that includes the target SQL warehouse,
	// query text, name, description, tags, and parameters. Queries can be
	// scheduled using the `sql_task` type of the Jobs API, e.g.
	// :method:jobs/create.
	Queries sql.QueriesInterface

	// These endpoints are used for CRUD operations on query definitions. Query
	// definitions include the target SQL warehouse, query text, name,
	// description, tags, parameters, and visualizations. Queries can be
	// scheduled using the `sql_task` type of the Jobs API, e.g.
	// :method:jobs/create.
	//
	// **Note**: A new version of the Databricks SQL API is now available.
	// Please see the latest version. [Learn more]
	//
	// [Learn more]: https://docs.databricks.com/en/sql/dbsql-api-latest.html
	QueriesLegacy sql.QueriesLegacyInterface

	// A service responsible for storing and retrieving the list of queries run
	// against SQL endpoints and serverless compute.
	QueryHistory sql.QueryHistoryInterface

	// This is an evolving API that facilitates the addition and removal of
	// visualizations from existing queries in the Databricks Workspace. Data
	// structures can change over time.
	QueryVisualizations sql.QueryVisualizationsInterface

	// This is an evolving API that facilitates the addition and removal of
	// vizualisations from existing queries within the Databricks Workspace.
	// Data structures may change over time.
	//
	// **Note**: A new version of the Databricks SQL API is now available.
	// Please see the latest version. [Learn more]
	//
	// [Learn more]: https://docs.databricks.com/en/sql/dbsql-api-latest.html
	QueryVisualizationsLegacy sql.QueryVisualizationsLegacyInterface

	// The Recipient Activation API is only applicable in the open sharing model
	// where the recipient object has the authentication type of `TOKEN`. The
	// data recipient follows the activation link shared by the data provider to
	// download the credential file that includes the access token. The
	// recipient will then use the credential file to establish a secure
	// connection with the provider to receive the shared data.
	//
	// Note that you can download the credential file only once. Recipients
	// should treat the downloaded credential as a secret and must not share it
	// outside of their organization.
	RecipientActivation sharing.RecipientActivationInterface

	// A recipient is an object you create using :method:recipients/create to
	// represent an organization which you want to allow access shares. The way
	// how sharing works differs depending on whether or not your recipient has
	// access to a Databricks workspace that is enabled for Unity Catalog:
	//
	// - For recipients with access to a Databricks workspace that is enabled
	// for Unity Catalog, you can create a recipient object along with a unique
	// sharing identifier you get from the recipient. The sharing identifier is
	// the key identifier that enables the secure connection. This sharing mode
	// is called **Databricks-to-Databricks sharing**.
	//
	// - For recipients without access to a Databricks workspace that is enabled
	// for Unity Catalog, when you create a recipient object, Databricks
	// generates an activation link you can send to the recipient. The recipient
	// follows the activation link to download the credential file, and then
	// uses the credential file to establish a secure connection to receive the
	// shared data. This sharing mode is called **open sharing**.
	Recipients sharing.RecipientsInterface

	// Databricks provides a hosted version of MLflow Model Registry in Unity
	// Catalog. Models in Unity Catalog provide centralized access control,
	// auditing, lineage, and discovery of ML models across Databricks
	// workspaces.
	//
	// An MLflow registered model resides in the third layer of Unity
	// Catalog’s three-level namespace. Registered models contain model
	// versions, which correspond to actual ML models (MLflow models). Creating
	// new model versions currently requires use of the MLflow Python client.
	// Once model versions are created, you can load them for batch inference
	// using MLflow Python client APIs, or deploy them for real-time serving
	// using Databricks Model Serving.
	//
	// All operations on registered models and model versions require
	// USE_CATALOG permissions on the enclosing catalog and USE_SCHEMA
	// permissions on the enclosing schema. In addition, the following
	// additional privileges are required for various operations:
	//
	// * To create a registered model, users must additionally have the
	// CREATE_MODEL permission on the target schema. * To view registered model
	// or model version metadata, model version data files, or invoke a model
	// version, users must additionally have the EXECUTE permission on the
	// registered model * To update registered model or model version tags,
	// users must additionally have APPLY TAG permissions on the registered
	// model * To update other registered model or model version metadata
	// (comments, aliases) create a new model version, or update permissions on
	// the registered model, users must be owners of the registered model.
	//
	// Note: The securable type for models is "FUNCTION". When using REST APIs
	// (e.g. tagging, grants) that specify a securable type, use "FUNCTION" as
	// the securable type.
	RegisteredModels catalog.RegisteredModelsInterface

	// The Repos API allows users to manage their git repos. Users can use the
	// API to access all repos that they have manage permissions on.
	//
	// Databricks Repos is a visual Git client in Databricks. It supports common
	// Git operations such a cloning a repository, committing and pushing,
	// pulling, branch management, and visual comparison of diffs when
	// committing.
	//
	// Within Repos you can develop code in notebooks or other files and follow
	// data science and engineering code development best practices using Git
	// for version control, collaboration, and CI/CD.
	Repos workspace.ReposInterface

	// Unity Catalog enforces resource quotas on all securable objects, which
	// limits the number of resources that can be created. Quotas are expressed
	// in terms of a resource type and a parent (for example, tables per
	// metastore or schemas per catalog). The resource quota APIs enable you to
	// monitor your current usage and limits. For more information on resource
	// quotas see the [Unity Catalog documentation].
	//
	// [Unity Catalog documentation]: https://docs.databricks.com/en/data-governance/unity-catalog/index.html#resource-quotas
	ResourceQuotas catalog.ResourceQuotasInterface

	// A schema (also called a database) is the second layer of Unity
	// Catalog’s three-level namespace. A schema organizes tables, views and
	// functions. To access (or list) a table or view in a schema, users must
	// have the USE_SCHEMA data permission on the schema and its parent catalog,
	// and they must have the SELECT permission on the table or view.
	Schemas catalog.SchemasInterface

	// The Secrets API allows you to manage secrets, secret scopes, and access
	// permissions.
	//
	// Sometimes accessing data requires that you authenticate to external data
	// sources through JDBC. Instead of directly entering your credentials into
	// a notebook, use Databricks secrets to store your credentials and
	// reference them in notebooks and jobs.
	//
	// Administrators, secret creators, and users granted permission can read
	// Databricks secrets. While Databricks makes an effort to redact secret
	// values that might be displayed in notebooks, it is not possible to
	// prevent such users from reading secrets.
	Secrets workspace.SecretsInterface

	// Identities for use with jobs, automated tools, and systems such as
	// scripts, apps, and CI/CD platforms. Databricks recommends creating
	// service principals to run production jobs or modify production data. If
	// all processes that act on production data run with service principals,
	// interactive users do not need any write, delete, or modify privileges in
	// production. This eliminates the risk of a user overwriting production
	// data by accident.
	ServicePrincipals iam.ServicePrincipalsInterface

	// The Serving Endpoints API allows you to create, update, and delete model
	// serving endpoints.
	//
	// You can use a serving endpoint to serve models from the Databricks Model
	// Registry or from Unity Catalog. Endpoints expose the underlying models as
	// scalable REST API endpoints using serverless compute. This means the
	// endpoints and associated compute resources are fully managed by
	// Databricks and will not appear in your cloud account. A serving endpoint
	// can consist of one or more MLflow models from the Databricks Model
	// Registry, called served entities. A serving endpoint can have at most ten
	// served entities. You can configure traffic settings to define how
	// requests should be routed to your served entities behind an endpoint.
	// Additionally, you can configure the scale of resources that should be
	// applied to each served entity.
	ServingEndpoints serving.ServingEndpointsInterface

	// Serving endpoints DataPlane provides a set of operations to interact with
	// data plane endpoints for Serving endpoints service.
	ServingEndpointsDataPlane serving.ServingEndpointsDataPlaneInterface

	// Workspace Settings API allows users to manage settings at the workspace
	// level.
	Settings settings.SettingsInterface

	// A share is a container instantiated with :method:shares/create. Once
	// created you can iteratively register a collection of existing data assets
	// defined within the metastore using :method:shares/update. You can
	// register data assets under their original name, qualified by their
	// original schema, or provide alternate exposed names.
	Shares sharing.SharesInterface

	// The Databricks SQL Statement Execution API can be used to execute SQL
	// statements on a SQL warehouse and fetch the result.
	//
	// **Getting started**
	//
	// We suggest beginning with the [Databricks SQL Statement Execution API
	// tutorial].
	//
	// **Overview of statement execution and result fetching**
	//
	// Statement execution begins by issuing a
	// :method:statementexecution/executeStatement request with a valid SQL
	// statement and warehouse ID, along with optional parameters such as the
	// data catalog and output format. If no other parameters are specified, the
	// server will wait for up to 10s before returning a response. If the
	// statement has completed within this timespan, the response will include
	// the result data as a JSON array and metadata. Otherwise, if no result is
	// available after the 10s timeout expired, the response will provide the
	// statement ID that can be used to poll for results by using a
	// :method:statementexecution/getStatement request.
	//
	// You can specify whether the call should behave synchronously,
	// asynchronously or start synchronously with a fallback to asynchronous
	// execution. This is controlled with the `wait_timeout` and
	// `on_wait_timeout` settings. If `wait_timeout` is set between 5-50 seconds
	// (default: 10s), the call waits for results up to the specified timeout;
	// when set to `0s`, the call is asynchronous and responds immediately with
	// a statement ID. The `on_wait_timeout` setting specifies what should
	// happen when the timeout is reached while the statement execution has not
	// yet finished. This can be set to either `CONTINUE`, to fallback to
	// asynchronous mode, or it can be set to `CANCEL`, which cancels the
	// statement.
	//
	// In summary: - Synchronous mode - `wait_timeout=30s` and
	// `on_wait_timeout=CANCEL` - The call waits up to 30 seconds; if the
	// statement execution finishes within this time, the result data is
	// returned directly in the response. If the execution takes longer than 30
	// seconds, the execution is canceled and the call returns with a `CANCELED`
	// state. - Asynchronous mode - `wait_timeout=0s` (`on_wait_timeout` is
	// ignored) - The call doesn't wait for the statement to finish but returns
	// directly with a statement ID. The status of the statement execution can
	// be polled by issuing :method:statementexecution/getStatement with the
	// statement ID. Once the execution has succeeded, this call also returns
	// the result and metadata in the response. - Hybrid mode (default) -
	// `wait_timeout=10s` and `on_wait_timeout=CONTINUE` - The call waits for up
	// to 10 seconds; if the statement execution finishes within this time, the
	// result data is returned directly in the response. If the execution takes
	// longer than 10 seconds, a statement ID is returned. The statement ID can
	// be used to fetch status and results in the same way as in the
	// asynchronous mode.
	//
	// Depending on the size, the result can be split into multiple chunks. If
	// the statement execution is successful, the statement response contains a
	// manifest and the first chunk of the result. The manifest contains schema
	// information and provides metadata for each chunk in the result. Result
	// chunks can be retrieved by index with
	// :method:statementexecution/getStatementResultChunkN which may be called
	// in any order and in parallel. For sequential fetching, each chunk, apart
	// from the last, also contains a `next_chunk_index` and
	// `next_chunk_internal_link` that point to the next chunk.
	//
	// A statement can be canceled with
	// :method:statementexecution/cancelExecution.
	//
	// **Fetching result data: format and disposition**
	//
	// To specify the format of the result data, use the `format` field, which
	// can be set to one of the following options: `JSON_ARRAY` (JSON),
	// `ARROW_STREAM` ([Apache Arrow Columnar]), or `CSV`.
	//
	// There are two ways to receive statement results, controlled by the
	// `disposition` setting, which can be either `INLINE` or `EXTERNAL_LINKS`:
	//
	// - `INLINE`: In this mode, the result data is directly included in the
	// response. It's best suited for smaller results. This mode can only be
	// used with the `JSON_ARRAY` format.
	//
	// - `EXTERNAL_LINKS`: In this mode, the response provides links that can be
	// used to download the result data in chunks separately. This approach is
	// ideal for larger results and offers higher throughput. This mode can be
	// used with all the formats: `JSON_ARRAY`, `ARROW_STREAM`, and `CSV`.
	//
	// By default, the API uses `format=JSON_ARRAY` and `disposition=INLINE`.
	//
	// **Limits and limitations**
	//
	// Note: The byte limit for INLINE disposition is based on internal storage
	// metrics and will not exactly match the byte count of the actual payload.
	//
	// - Statements with `disposition=INLINE` are limited to 25 MiB and will
	// fail when this limit is exceeded. - Statements with
	// `disposition=EXTERNAL_LINKS` are limited to 100 GiB. Result sets larger
	// than this limit will be truncated. Truncation is indicated by the
	// `truncated` field in the result manifest. - The maximum query text size
	// is 16 MiB. - Cancelation might silently fail. A successful response from
	// a cancel request indicates that the cancel request was successfully
	// received and sent to the processing engine. However, an outstanding
	// statement might have already completed execution when the cancel request
	// arrives. Polling for status until a terminal state is reached is a
	// reliable way to determine the final state. - Wait timeouts are
	// approximate, occur server-side, and cannot account for things such as
	// caller delays and network latency from caller to service. - The system
	// will auto-close a statement after one hour if the client stops polling
	// and thus you must poll at least once an hour. - The results are only
	// available for one hour after success; polling does not extend this.
	//
	// [Apache Arrow Columnar]: https://arrow.apache.org/overview/
	// [Databricks SQL Statement Execution API tutorial]: https://docs.databricks.com/sql/api/sql-execution-tutorial.html
	StatementExecution sql.StatementExecutionInterface

	// A storage credential represents an authentication and authorization
	// mechanism for accessing data stored on your cloud tenant. Each storage
	// credential is subject to Unity Catalog access-control policies that
	// control which users and groups can access the credential. If a user does
	// not have access to a storage credential in Unity Catalog, the request
	// fails and Unity Catalog does not attempt to authenticate to your cloud
	// tenant on the user’s behalf.
	//
	// Databricks recommends using external locations rather than using storage
	// credentials directly.
	//
	// To create storage credentials, you must be a Databricks account admin.
	// The account admin who creates the storage credential can delegate
	// ownership to another user or group to manage permissions on it.
	StorageCredentials catalog.StorageCredentialsInterface

	// A system schema is a schema that lives within the system catalog. A
	// system schema may contain information about customer usage of Unity
	// Catalog such as audit-logs, billing-logs, lineage information, etc.
	SystemSchemas catalog.SystemSchemasInterface

	// Primary key and foreign key constraints encode relationships between
	// fields in tables.
	//
	// Primary and foreign keys are informational only and are not enforced.
	// Foreign keys must reference a primary key in another table. This primary
	// key is the parent constraint of the foreign key and the table this
	// primary key is on is the parent table of the foreign key. Similarly, the
	// foreign key is the child constraint of its referenced primary key; the
	// table of the foreign key is the child table of the primary key.
	//
	// You can declare primary keys and foreign keys as part of the table
	// specification during table creation. You can also add or drop constraints
	// on existing tables.
	TableConstraints catalog.TableConstraintsInterface

	// A table resides in the third layer of Unity Catalog’s three-level
	// namespace. It contains rows of data. To create a table, users must have
	// CREATE_TABLE and USE_SCHEMA permissions on the schema, and they must have
	// the USE_CATALOG permission on its parent catalog. To query a table, users
	// must have the SELECT permission on the table, and they must have the
	// USE_CATALOG permission on its parent catalog and the USE_SCHEMA
	// permission on its parent schema.
	//
	// A table can be managed or external. From an API perspective, a __VIEW__
	// is a particular kind of table (rather than a managed or external table).
	Tables catalog.TablesInterface

	// Enables administrators to get all tokens and delete tokens for other
	// users. Admins can either get every token, get a specific token by ID, or
	// get all tokens for a particular user.
	TokenManagement settings.TokenManagementInterface

	// The Token API allows you to create, list, and revoke tokens that can be
	// used to authenticate and access Databricks REST APIs.
	Tokens settings.TokensInterface

	// User identities recognized by Databricks and represented by email
	// addresses.
	//
	// Databricks recommends using SCIM provisioning to sync users and groups
	// automatically from your identity provider to your Databricks workspace.
	// SCIM streamlines onboarding a new employee or team by using your identity
	// provider to create users and groups in Databricks workspace and give them
	// the proper level of access. When a user leaves your organization or no
	// longer needs access to Databricks workspace, admins can terminate the
	// user in your identity provider and that user’s account will also be
	// removed from Databricks workspace. This ensures a consistent offboarding
	// process and prevents unauthorized users from accessing sensitive data.
	Users iam.UsersInterface

	// **Endpoint**: Represents the compute resources to host vector search
	// indexes.
	VectorSearchEndpoints vectorsearch.VectorSearchEndpointsInterface

	// **Index**: An efficient representation of your embedding vectors that
	// supports real-time and efficient approximate nearest neighbor (ANN)
	// search queries.
	//
	// There are 2 types of Vector Search indexes: * **Delta Sync Index**: An
	// index that automatically syncs with a source Delta Table, automatically
	// and incrementally updating the index as the underlying data in the Delta
	// Table changes. * **Direct Vector Access Index**: An index that supports
	// direct read and write of vectors and metadata through our REST and SDK
	// APIs. With this model, the user manages index updates.
	VectorSearchIndexes vectorsearch.VectorSearchIndexesInterface

	// Volumes are a Unity Catalog (UC) capability for accessing, storing,
	// governing, organizing and processing files. Use cases include running
	// machine learning on unstructured data such as image, audio, video, or PDF
	// files, organizing data sets during the data exploration stages in data
	// science, working with libraries that require access to the local file
	// system on cluster machines, storing library and config files of arbitrary
	// formats such as .whl or .txt centrally and providing secure access across
	// workspaces to it, or transforming and querying non-tabular data files in
	// ETL.
	Volumes catalog.VolumesInterface

	// A SQL warehouse is a compute resource that lets you run SQL commands on
	// data objects within Databricks SQL. Compute resources are infrastructure
	// resources that provide processing capabilities in the cloud.
	Warehouses sql.WarehousesInterface

	// The Workspace API allows you to list, import, export, and delete
	// notebooks and folders.
	//
	// A notebook is a web-based interface to a document that contains runnable
	// code, visualizations, and explanatory text.
	Workspace workspace.WorkspaceInterface

	// A securable in Databricks can be configured as __OPEN__ or __ISOLATED__.
	// An __OPEN__ securable can be accessed from any workspace, while an
	// __ISOLATED__ securable can only be accessed from a configured list of
	// workspaces. This API allows you to configure (bind) securables to
	// workspaces.
	//
	// NOTE: The __isolation_mode__ is configured for the securable itself
	// (using its Update method) and the workspace bindings are only consulted
	// when the securable's __isolation_mode__ is set to __ISOLATED__.
	//
	// A securable's workspace bindings can be configured by a metastore admin
	// or the owner of the securable.
	//
	// The original path
	// (/api/2.1/unity-catalog/workspace-bindings/catalogs/{name}) is
	// deprecated. Please use the new path
	// (/api/2.1/unity-catalog/bindings/{securable_type}/{securable_name}) which
	// introduces the ability to bind a securable in READ_ONLY mode (catalogs
	// only).
	//
	// Securable types that support binding: - catalog - storage_credential -
	// external_location
	WorkspaceBindings catalog.WorkspaceBindingsInterface

	// This API allows updating known workspace settings for advanced users.
	WorkspaceConf settings.WorkspaceConfInterface
	// contains filtered or unexported fields
}

func NewWorkspaceClient

func NewWorkspaceClient(c ...*Config) (*WorkspaceClient, error)

NewWorkspaceClient creates new Databricks SDK client for Workspaces or returns error in case configuration is wrong

func (*WorkspaceClient) CurrentWorkspaceID

func (w *WorkspaceClient) CurrentWorkspaceID(ctx context.Context) (int64, error)

CurrentWorkspaceID returns the workspace ID of the workspace that this client is connected to.