cribl-control-plane 0.0.22

Python Client SDK Generated by Speakeasy.

cribl_control_plane_sdk_python

Summary

Cribl API Reference: This API Reference lists available REST endpoints, along with their supported operations for accessing, creating, updating, or deleting resources. See our complementary product documentation at docs.cribl.io.

Table of Contents

• cribl_control_plane_sdk_python
  • SDK Installation
  • IDE Support
  • SDK Example Usage
  • Authentication
  • Available Resources and Operations
  • Retries
  • Error Handling
  • Custom HTTP Client
  • Resource Management
  • Debugging

SDK Installation

[!NOTE] Python version upgrade policy

Once a Python version reaches its official end of life date, a 3-month grace period is provided for users to upgrade. Following this grace period, the minimum python version supported in the SDK will be updated.

The SDK can be installed with either pip or poetry package managers.

PIP

PIP is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.

pip install cribl-control-plane

Poetry

Poetry is a modern tool that simplifies dependency management and package publishing by using a single pyproject.toml file to handle project metadata and dependencies.

poetry add cribl-control-plane

Shell and script usage with uv

You can use this SDK in a Python shell with uv and the uvx command that comes with it like so:

uvx --from cribl-control-plane python

It's also possible to write a standalone Python script without needing to set up a whole project like so:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.9"
# dependencies = [
#     "cribl-control-plane",
# ]
# ///

from cribl_control_plane import CriblControlPlane

sdk = CriblControlPlane(
  # SDK arguments
)

# Rest of script here...

Once that is saved to a file, you can run it with uv run script.py where script.py can be replaced with the actual file name.

IDE Support

PyCharm

Generally, the SDK will work well with most IDEs out of the box. However, when using PyCharm, you can enjoy much better integration with Pydantic by installing an additional plugin.

• PyCharm Pydantic Plugin

SDK Example Usage

Example

# Synchronous Example
from cribl_control_plane import CriblControlPlane, models
import os


with CriblControlPlane(
    server_url="https://api.example.com",
    security=models.Security(
        bearer_auth=os.getenv("CRIBLCONTROLPLANE_BEARER_AUTH", ""),
    ),
) as ccp_client:

    res = ccp_client.lake.create_cribl_lake_dataset_by_lake_id(lake_id="<id>", id="<id>", accelerated_fields=[
        "<value 1>",
        "<value 2>",
    ], bucket_name="<value>", cache_connection={
        "accelerated_fields": [
            "<value 1>",
            "<value 2>",
        ],
        "backfill_status": models.CacheConnectionBackfillStatus.PENDING,
        "cache_ref": "<value>",
        "created_at": 7795.06,
        "lakehouse_connection_type": models.LakehouseConnectionType.CACHE,
        "migration_query_id": "<id>",
        "retention_in_days": 1466.58,
    }, deletion_started_at=8310.58, description="pleased toothbrush long brush smooth swiftly rightfully phooey chapel", format_=models.CriblLakeDatasetFormat.DDSS, http_da_used=True, retention_period_in_days=456.37, search_config={
        "datatypes": [
            "<value 1>",
        ],
        "metadata": {
            "earliest": "<value>",
            "enable_acceleration": True,
            "field_list": [
                "<value 1>",
                "<value 2>",
            ],
            "latest_run_info": {
                "earliest_scanned_time": 4334.7,
                "finished_at": 6811.22,
                "latest_scanned_time": 5303.3,
                "object_count": 9489.04,
            },
            "scan_mode": models.ScanMode.DETAILED,
        },
    }, storage_location_id="<id>", view_name="<value>")

    # Handle response
    print(res)

The same SDK client can also be used to make asychronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from cribl_control_plane import CriblControlPlane, models
import os

async def main():

    async with CriblControlPlane(
        server_url="https://api.example.com",
        security=models.Security(
            bearer_auth=os.getenv("CRIBLCONTROLPLANE_BEARER_AUTH", ""),
        ),
    ) as ccp_client:

        res = await ccp_client.lake.create_cribl_lake_dataset_by_lake_id_async(lake_id="<id>", id="<id>", accelerated_fields=[
            "<value 1>",
            "<value 2>",
        ], bucket_name="<value>", cache_connection={
            "accelerated_fields": [
                "<value 1>",
                "<value 2>",
            ],
            "backfill_status": models.CacheConnectionBackfillStatus.PENDING,
            "cache_ref": "<value>",
            "created_at": 7795.06,
            "lakehouse_connection_type": models.LakehouseConnectionType.CACHE,
            "migration_query_id": "<id>",
            "retention_in_days": 1466.58,
        }, deletion_started_at=8310.58, description="pleased toothbrush long brush smooth swiftly rightfully phooey chapel", format_=models.CriblLakeDatasetFormat.DDSS, http_da_used=True, retention_period_in_days=456.37, search_config={
            "datatypes": [
                "<value 1>",
            ],
            "metadata": {
                "earliest": "<value>",
                "enable_acceleration": True,
                "field_list": [
                    "<value 1>",
                    "<value 2>",
                ],
                "latest_run_info": {
                    "earliest_scanned_time": 4334.7,
                    "finished_at": 6811.22,
                    "latest_scanned_time": 5303.3,
                    "object_count": 9489.04,
                },
                "scan_mode": models.ScanMode.DETAILED,
            },
        }, storage_location_id="<id>", view_name="<value>")

        # Handle response
        print(res)

asyncio.run(main())

Authentication

Per-Client Security Schemes

This SDK supports the following security schemes globally:

Name	Type	Scheme	Environment Variable
bearer_auth	http	HTTP Bearer	CRIBLCONTROLPLANE_BEARER_AUTH
client_oauth	oauth2	OAuth2 token	CRIBLCONTROLPLANE_CLIENT_OAUTH

You can set the security parameters through the security optional parameter when initializing the SDK client instance. The selected scheme will be used by default to authenticate with the API for all operations that support it. For example:

from cribl_control_plane import CriblControlPlane, models
import os


with CriblControlPlane(
    server_url="https://api.example.com",
    security=models.Security(
        bearer_auth=os.getenv("CRIBLCONTROLPLANE_BEARER_AUTH", ""),
    ),
) as ccp_client:

    res = ccp_client.lake.create_cribl_lake_dataset_by_lake_id(lake_id="<id>", id="<id>", accelerated_fields=[
        "<value 1>",
        "<value 2>",
    ], bucket_name="<value>", cache_connection={
        "accelerated_fields": [
            "<value 1>",
            "<value 2>",
        ],
        "backfill_status": models.CacheConnectionBackfillStatus.PENDING,
        "cache_ref": "<value>",
        "created_at": 7795.06,
        "lakehouse_connection_type": models.LakehouseConnectionType.CACHE,
        "migration_query_id": "<id>",
        "retention_in_days": 1466.58,
    }, deletion_started_at=8310.58, description="pleased toothbrush long brush smooth swiftly rightfully phooey chapel", format_=models.CriblLakeDatasetFormat.DDSS, http_da_used=True, retention_period_in_days=456.37, search_config={
        "datatypes": [
            "<value 1>",
        ],
        "metadata": {
            "earliest": "<value>",
            "enable_acceleration": True,
            "field_list": [
                "<value 1>",
                "<value 2>",
            ],
            "latest_run_info": {
                "earliest_scanned_time": 4334.7,
                "finished_at": 6811.22,
                "latest_scanned_time": 5303.3,
                "object_count": 9489.04,
            },
            "scan_mode": models.ScanMode.DETAILED,
        },
    }, storage_location_id="<id>", view_name="<value>")

    # Handle response
    print(res)

Available Resources and Operations

Available methods

auth

• login - Log in and obtain Auth token

destinations

• list_destination - Get a list of Destination objects
• create_destination - Create Destination
• get_destination_by_id - Get Destination by ID
• update_destination_by_id - Update Destination
• delete_destination_by_id - Delete Destination
• delete_destination_pq_by_id - Clears destination persistent queue
• get_destination_pq_by_id - Retrieves status of latest clear PQ job for a destination
• get_destination_samples_by_id - Retrieve samples data for the specified destination. Used to get sample data for the test action.
• create_destination_test_by_id - Send sample data to a destination to validate configuration or test connectivity

distributed

• get_summary - Get summary of Distributed deployment

groups

• get_groups_config_version_by_id - Get effective bundle version for given Group
• create_products_groups_by_product - Create a Fleet or Worker Group
• get_products_groups_by_product - Get a list of ConfigGroup objects
• delete_groups_by_id - Delete a Fleet or Worker Group
• get_groups_by_id - Get a specific ConfigGroup object
• update_groups_by_id - Update a Fleet or Worker Group
• update_groups_deploy_by_id - Deploy commits for a Fleet or Worker Group
• get_groups_acl_by_id - ACL of members with permissions for resources in this Group

health

• get_health_info - Provides health info for REST server

lake

• create_cribl_lake_dataset_by_lake_id - Create a Dataset in the specified Lake
• get_cribl_lake_dataset_by_lake_id - Get the list of Dataset contained in the specified Lake
• delete_cribl_lake_dataset_by_lake_id_and_id - Delete a Dataset in the specified Lake
• get_cribl_lake_dataset_by_lake_id_and_id - Get a Dataset in the specified Lake
• update_cribl_lake_dataset_by_lake_id_and_id - Update a Dataset in the specified Lake

packs

• create_packs - Install Pack
• get_packs - Get info on packs
• update_packs - Upload Pack
• delete_packs_by_id - Uninstall Pack from the system
• update_packs_by_id - Upgrade Pack

pipelines

• list_pipeline - Get a list of Pipeline objects
• create_pipeline - Create Pipeline
• get_pipeline_by_id - Get Pipeline by ID
• update_pipeline_by_id - Update Pipeline
• delete_pipeline_by_id - Delete Pipeline

routes

• list_routes - Get a list of Routes objects
• get_routes_by_id - Get Routes by ID
• update_routes_by_id - Update Routes
• create_routes_append_by_id - Appends routes to the end of the routing table

sources

• list_source - Get a list of Source objects
• create_source - Create Source
• get_source_by_id - Get Source by ID
• update_source_by_id - Update Source
• delete_source_by_id - Delete Source
• create_source_hec_token_by_id - Add token and optional metadata to an existing HEC Source
• update_source_hec_token_by_id_and_token - Update token metadata on existing HEC Source

teams

• get_products_groups_acl_teams_by_product_and_id - ACL of team with permissions for resources in this Group

versioning

• get_version_branch - get the list of branches
• create_version_commit - create a new commit containing the current configs the given log message describing the changes.
• get_version_count - get the count of files of changed
• get_version_current_branch - returns git branch that the config is checked out to, if any
• get_version_diff - get the textual diff for given commit
• get_version_files - get the files changed
• get_version_info - Get info about versioning availability
• create_version_push - push the current configs to the remote repository.
• create_version_revert - revert a commit
• get_version_show - get the log message and textual diff for given commit
• get_version_status - get the the working tree status
• create_version_sync - syncs with remote repo via POST requests
• create_version_undo - undo the last commit

workers

• get_summary_workers - get worker and edge nodes count
• get_workers - get worker and edge nodes
• update_workers_restart - restarts worker nodes

Retries

Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.

To change the default retry strategy for a single API call, simply provide a RetryConfig object to the call:

from cribl_control_plane import CriblControlPlane, models
from cribl_control_plane.utils import BackoffStrategy, RetryConfig
import os


with CriblControlPlane(
    server_url="https://api.example.com",
    security=models.Security(
        bearer_auth=os.getenv("CRIBLCONTROLPLANE_BEARER_AUTH", ""),
    ),
) as ccp_client:

    res = ccp_client.lake.create_cribl_lake_dataset_by_lake_id(lake_id="<id>", id="<id>", accelerated_fields=[
        "<value 1>",
        "<value 2>",
    ], bucket_name="<value>", cache_connection={
        "accelerated_fields": [
            "<value 1>",
            "<value 2>",
        ],
        "backfill_status": models.CacheConnectionBackfillStatus.PENDING,
        "cache_ref": "<value>",
        "created_at": 7795.06,
        "lakehouse_connection_type": models.LakehouseConnectionType.CACHE,
        "migration_query_id": "<id>",
        "retention_in_days": 1466.58,
    }, deletion_started_at=8310.58, description="pleased toothbrush long brush smooth swiftly rightfully phooey chapel", format_=models.CriblLakeDatasetFormat.DDSS, http_da_used=True, retention_period_in_days=456.37, search_config={
        "datatypes": [
            "<value 1>",
        ],
        "metadata": {
            "earliest": "<value>",
            "enable_acceleration": True,
            "field_list": [
                "<value 1>",
                "<value 2>",
            ],
            "latest_run_info": {
                "earliest_scanned_time": 4334.7,
                "finished_at": 6811.22,
                "latest_scanned_time": 5303.3,
                "object_count": 9489.04,
            },
            "scan_mode": models.ScanMode.DETAILED,
        },
    }, storage_location_id="<id>", view_name="<value>",
        RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))

    # Handle response
    print(res)

If you'd like to override the default retry strategy for all operations that support retries, you can use the retry_config optional parameter when initializing the SDK:

from cribl_control_plane import CriblControlPlane, models
from cribl_control_plane.utils import BackoffStrategy, RetryConfig
import os


with CriblControlPlane(
    server_url="https://api.example.com",
    retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
    security=models.Security(
        bearer_auth=os.getenv("CRIBLCONTROLPLANE_BEARER_AUTH", ""),
    ),
) as ccp_client:

    res = ccp_client.lake.create_cribl_lake_dataset_by_lake_id(lake_id="<id>", id="<id>", accelerated_fields=[
        "<value 1>",
        "<value 2>",
    ], bucket_name="<value>", cache_connection={
        "accelerated_fields": [
            "<value 1>",
            "<value 2>",
        ],
        "backfill_status": models.CacheConnectionBackfillStatus.PENDING,
        "cache_ref": "<value>",
        "created_at": 7795.06,
        "lakehouse_connection_type": models.LakehouseConnectionType.CACHE,
        "migration_query_id": "<id>",
        "retention_in_days": 1466.58,
    }, deletion_started_at=8310.58, description="pleased toothbrush long brush smooth swiftly rightfully phooey chapel", format_=models.CriblLakeDatasetFormat.DDSS, http_da_used=True, retention_period_in_days=456.37, search_config={
        "datatypes": [
            "<value 1>",
        ],
        "metadata": {
            "earliest": "<value>",
            "enable_acceleration": True,
            "field_list": [
                "<value 1>",
                "<value 2>",
            ],
            "latest_run_info": {
                "earliest_scanned_time": 4334.7,
                "finished_at": 6811.22,
                "latest_scanned_time": 5303.3,
                "object_count": 9489.04,
            },
            "scan_mode": models.ScanMode.DETAILED,
        },
    }, storage_location_id="<id>", view_name="<value>")

    # Handle response
    print(res)

Error Handling

CriblControlPlaneError is the base class for all HTTP error responses. It has the following properties:

Property	Type	Description
err.message	str	Error message
err.status_code	int	HTTP response status code eg 404
err.headers	httpx.Headers	HTTP response headers
err.body	str	HTTP body. Can be empty string if no body is returned.
err.raw_response	httpx.Response	Raw HTTP response
err.data		Optional. Some errors may contain structured data. See Error Classes.

Example

from cribl_control_plane import CriblControlPlane, errors, models
import os


with CriblControlPlane(
    server_url="https://api.example.com",
    security=models.Security(
        bearer_auth=os.getenv("CRIBLCONTROLPLANE_BEARER_AUTH", ""),
    ),
) as ccp_client:
    res = None
    try:

        res = ccp_client.lake.create_cribl_lake_dataset_by_lake_id(lake_id="<id>", id="<id>", accelerated_fields=[
            "<value 1>",
            "<value 2>",
        ], bucket_name="<value>", cache_connection={
            "accelerated_fields": [
                "<value 1>",
                "<value 2>",
            ],
            "backfill_status": models.CacheConnectionBackfillStatus.PENDING,
            "cache_ref": "<value>",
            "created_at": 7795.06,
            "lakehouse_connection_type": models.LakehouseConnectionType.CACHE,
            "migration_query_id": "<id>",
            "retention_in_days": 1466.58,
        }, deletion_started_at=8310.58, description="pleased toothbrush long brush smooth swiftly rightfully phooey chapel", format_=models.CriblLakeDatasetFormat.DDSS, http_da_used=True, retention_period_in_days=456.37, search_config={
            "datatypes": [
                "<value 1>",
            ],
            "metadata": {
                "earliest": "<value>",
                "enable_acceleration": True,
                "field_list": [
                    "<value 1>",
                    "<value 2>",
                ],
                "latest_run_info": {
                    "earliest_scanned_time": 4334.7,
                    "finished_at": 6811.22,
                    "latest_scanned_time": 5303.3,
                    "object_count": 9489.04,
                },
                "scan_mode": models.ScanMode.DETAILED,
            },
        }, storage_location_id="<id>", view_name="<value>")

        # Handle response
        print(res)


    except errors.CriblControlPlaneError as e:
        # The base class for HTTP error responses
        print(e.message)
        print(e.status_code)
        print(e.body)
        print(e.headers)
        print(e.raw_response)

        # Depending on the method different errors may be thrown
        if isinstance(e, errors.Error):
            print(e.data.message)  # Optional[str]

Error Classes

Primary errors:

• CriblControlPlaneError: The base class for HTTP error responses.
  • Error: Unexpected error. Status code 500. *

Less common errors (6)

Network errors:

• httpx.RequestError: Base class for request errors.
  • httpx.ConnectError: HTTP client was unable to make a request to a server.
  • httpx.TimeoutException: HTTP request timed out.

Inherit from CriblControlPlaneError:

• HealthStatusError: Healthy status. Status code 420. Applicable to 1 of 63 methods.*
• ResponseValidationError: Type mismatch between the response data and the expected Pydantic model. Provides access to the Pydantic validation error via the cause attribute.

* Check the method documentation to see if the error is applicable.

Custom HTTP Client

The Python SDK makes API calls using the httpx HTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with your own HTTP client instance. Depending on whether you are using the sync or async version of the SDK, you can pass an instance of HttpClient or AsyncHttpClient respectively, which are Protocol's ensuring that the client has the necessary methods to make API calls. This allows you to wrap the client with your own custom logic, such as adding custom headers, logging, or error handling, or you can just pass an instance of httpx.Client or httpx.AsyncClient directly.

For example, you could specify a header for every request that this sdk makes as follows:

from cribl_control_plane import CriblControlPlane
import httpx

http_client = httpx.Client(headers={"x-custom-header": "someValue"})
s = CriblControlPlane(client=http_client)

or you could wrap the client with your own custom logic:

from cribl_control_plane import CriblControlPlane
from cribl_control_plane.httpclient import AsyncHttpClient
import httpx

class CustomClient(AsyncHttpClient):
    client: AsyncHttpClient

    def __init__(self, client: AsyncHttpClient):
        self.client = client

    async def send(
        self,
        request: httpx.Request,
        *,
        stream: bool = False,
        auth: Union[
            httpx._types.AuthTypes, httpx._client.UseClientDefault, None
        ] = httpx.USE_CLIENT_DEFAULT,
        follow_redirects: Union[
            bool, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
    ) -> httpx.Response:
        request.headers["Client-Level-Header"] = "added by client"

        return await self.client.send(
            request, stream=stream, auth=auth, follow_redirects=follow_redirects
        )

    def build_request(
        self,
        method: str,
        url: httpx._types.URLTypes,
        *,
        content: Optional[httpx._types.RequestContent] = None,
        data: Optional[httpx._types.RequestData] = None,
        files: Optional[httpx._types.RequestFiles] = None,
        json: Optional[Any] = None,
        params: Optional[httpx._types.QueryParamTypes] = None,
        headers: Optional[httpx._types.HeaderTypes] = None,
        cookies: Optional[httpx._types.CookieTypes] = None,
        timeout: Union[
            httpx._types.TimeoutTypes, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
        extensions: Optional[httpx._types.RequestExtensions] = None,
    ) -> httpx.Request:
        return self.client.build_request(
            method,
            url,
            content=content,
            data=data,
            files=files,
            json=json,
            params=params,
            headers=headers,
            cookies=cookies,
            timeout=timeout,
            extensions=extensions,
        )

s = CriblControlPlane(async_client=CustomClient(httpx.AsyncClient()))

Resource Management

The CriblControlPlane class implements the context manager protocol and registers a finalizer function to close the underlying sync and async HTTPX clients it uses under the hood. This will close HTTP connections, release memory and free up other resources held by the SDK. In short-lived Python programs and notebooks that make a few SDK method calls, resource management may not be a concern. However, in longer-lived programs, it is beneficial to create a single SDK instance via a context manager and reuse it across the application.

from cribl_control_plane import CriblControlPlane, models
import os
def main():

    with CriblControlPlane(
        server_url="https://api.example.com",
        security=models.Security(
            bearer_auth=os.getenv("CRIBLCONTROLPLANE_BEARER_AUTH", ""),
        ),
    ) as ccp_client:
        # Rest of application here...


# Or when using async:
async def amain():

    async with CriblControlPlane(
        server_url="https://api.example.com",
        security=models.Security(
            bearer_auth=os.getenv("CRIBLCONTROLPLANE_BEARER_AUTH", ""),
        ),
    ) as ccp_client:
        # Rest of application here...

Debugging

You can setup your SDK to emit debug logs for SDK requests and responses.

You can pass your own logger class directly into your SDK.

from cribl_control_plane import CriblControlPlane
import logging

logging.basicConfig(level=logging.DEBUG)
s = CriblControlPlane(server_url="https://example.com", debug_logger=logging.getLogger("cribl_control_plane"))

You can also enable a default debug logger by setting an environment variable CRIBLCONTROLPLANE_DEBUG to true.