Loading Data from ClickHouse Cloud
to Azure Cosmos DB
with dlt
in Python
We will be using the dlt PostgreSQL destination to connect to Azure Cosmos DB. You can get the connection string for your Azure Cosmos DB database as described in the Azure Cosmos DB Docs.
Join our Slack community or book a call with our support engineer Violetta.
Loading data from ClickHouse Cloud
to Azure Cosmos DB
using the open-source python library dlt
offers a seamless and efficient way to manage and analyze large datasets. ClickHouse Cloud
is a high-performance, scalable data warehousing solution designed for real-time analytics. It allows businesses to run complex queries with exceptional speed and efficiency. On the other hand, Azure Cosmos DB
is a fully managed NoSQL and relational database tailored for modern app development. By leveraging dlt
, users can automate the data transfer process, ensuring robust security, automated backups, and integration with various data sources. This enables data-driven decision-making and supports advanced analytics capabilities. For more information on ClickHouse Cloud
, visit ClickHouse Cloud.
dlt
Key Features
- Pipeline Metadata:
dlt
pipelines leverage metadata to provide governance capabilities, including load IDs that enable incremental transformations and data vaulting. Read more about lineage. - Schema Enforcement and Curation:
dlt
empowers users to enforce and curate schemas, ensuring data consistency and quality. Read more about adjusting a schema. - Schema Evolution:
dlt
alerts users to schema changes, allowing them to review and validate modifications in the source data’s schema. This proactive governance helps maintain data integrity. - Scaling and Finetuning:
dlt
offers several mechanisms and configuration options to scale up and finetune pipelines, such as running extraction, normalization, and load in parallel. Read more about performance. - Advanced Features and Community Support:
dlt
is a constantly growing library that supports many features and use cases needed by the community. Join our Slack to find recent releases or discuss what you can build withdlt
. Build a pipeline tutorial.
Getting started with your pipeline locally
dlt-init-openapi
0. Prerequisites
dlt
and dlt-init-openapi
requires Python 3.9 or higher. Additionally, you need to have the pip
package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.
1. Install dlt and dlt-init-openapi
First you need to install the dlt-init-openapi
cli tool.
pip install dlt-init-openapi
The dlt-init-openapi
cli is a powerful generator which you can use to turn any OpenAPI spec into a dlt
source to ingest data from that api. The quality of the generator source is dependent on how well the API is designed and how accurate the OpenAPI spec you are using is. You may need to make tweaks to the generated code, you can learn more about this here.
# generate pipeline
# NOTE: add_limit adds a global limit, you can remove this later
# NOTE: you will need to select which endpoints to render, you
# can just hit Enter and all will be rendered.
dlt-init-openapi clickhouse_cloud --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/click_house_cloud.yaml --global-limit 2
cd clickhouse_cloud_pipeline
# install generated requirements
pip install -r requirements.txt
The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt
:
dlt>=0.4.12
You now have the following folder structure in your project:
clickhouse_cloud_pipeline/
├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── rest_api/ # The rest api verified source
│ └── ...
├── clickhouse_cloud/
│ └── __init__.py # TODO: possibly tweak this file
├── clickhouse_cloud_pipeline.py # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)
1.1. Tweak clickhouse_cloud/__init__.py
This file contains the generated configuration of your rest_api. You can continue with the next steps and leave it as is, but you might want to come back here and make adjustments if you need your rest_api
source set up in a different way. The generated file for the clickhouse_cloud source will look like this:
Click to view full file (229 lines)
from typing import List
import dlt
from dlt.extract.source import DltResource
from rest_api import rest_api_source
from rest_api.typing import RESTAPIConfig
@dlt.source(name="clickhouse_cloud_source", max_table_nesting=2)
def clickhouse_cloud_source(
base_url: str = dlt.config.value,
) -> List[DltResource]:
# source configuration
source_config: RESTAPIConfig = {
"client": {
"base_url": base_url,
},
"resources":
[
# Returns a list of all organization activities.
{
"name": "organization_id_activities",
"table_name": "activity",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "result",
"path": "/v1/organizations/:organizationId/activities",
"paginator": "auto",
}
},
# Returns a single organization activity by ID.
{
"name": "organization_id_activities_activity_id",
"table_name": "activity_id",
"primary_key": "requestId",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/v1/organizations/:organizationId/activities/:activityId",
"paginator": "auto",
}
},
# Returns a list of all keys in the organization.
{
"name": "organization_id_keys",
"table_name": "api_key",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "result",
"path": "/v1/organizations/:organizationId/keys",
"paginator": "auto",
}
},
# Returns a list of all backups for the service. The most recent backups comes first in the list.
{
"name": "organization_id_services_service_id_backups",
"table_name": "backup",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "result",
"path": "/v1/organizations/:organizationId/services/:serviceId/backups",
"paginator": "auto",
}
},
# Returns a single backup info.
{
"name": "organization_id_services_service_id_backups_backup_id",
"table_name": "backup_id",
"primary_key": "requestId",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/v1/organizations/:organizationId/services/:serviceId/backups/:backupId",
"paginator": "auto",
}
},
# Returns list of all organization invitations.
{
"name": "organization_id_invitations",
"table_name": "invitation",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "result",
"path": "/v1/organizations/:organizationId/invitations",
"paginator": "auto",
}
},
# Returns details for a single organization invitation.
{
"name": "organization_id_invitations_invitation_id",
"table_name": "invitation_id",
"primary_key": "requestId",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/v1/organizations/:organizationId/invitations/:invitationId",
"paginator": "auto",
}
},
# Returns a single key details.
{
"name": "organization_id_keys_key_id",
"table_name": "key_id",
"primary_key": "requestId",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/v1/organizations/:organizationId/keys/:keyId",
"paginator": "auto",
}
},
# Returns a list of all members in the organization.
{
"name": "organization_id_members",
"table_name": "member",
"endpoint": {
"data_selector": "result",
"path": "/v1/organizations/:organizationId/members",
"paginator": "auto",
}
},
# Returns a list with a single organization associated with the API key in the request.
{
"name": "",
"table_name": "organization",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "result",
"path": "/v1/organizations",
"paginator": "auto",
}
},
# Returns details of a single organization. In order to get the details, the auth key must belong to the organization.
{
"name": "organization_id",
"table_name": "organization_id",
"primary_key": "requestId",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/v1/organizations/:organizationId",
"paginator": "auto",
}
},
# Information required to set up a private endpoint
{
"name": "organization_id_services_service_id_private_endpoint_config",
"table_name": "private_endpoint_config",
"primary_key": "requestId",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/v1/organizations/:organizationId/services/:serviceId/privateEndpointConfig",
"paginator": "auto",
}
},
# Information required to set up a private endpoint
{
"name": "organization_id_private_endpoint_config",
"table_name": "private_endpoint_config",
"primary_key": "requestId",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/v1/organizations/:organizationId/privateEndpointConfig",
"params": {
"Cloud provider identifier": "FILL_ME_IN", # TODO: fill in required query parameter
"Cloud provider region": "FILL_ME_IN", # TODO: fill in required query parameter
},
"paginator": "auto",
}
},
# Returns prometheus metrics for a service. Please contact support to enable this feature.
{
"name": "organization_id_services_service_id_prometheus",
"table_name": "prometheu",
"endpoint": {
"path": "/v1/organizations/:organizationId/services/:serviceId/prometheus",
"paginator": "auto",
}
},
# Returns a list of all services in the organization.
{
"name": "organization_id_services",
"table_name": "service",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "result",
"path": "/v1/organizations/:organizationId/services",
"paginator": "auto",
}
},
# Returns a service that belongs to the organization
{
"name": "organization_id_services_service_id",
"table_name": "service_id",
"primary_key": "requestId",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/v1/organizations/:organizationId/services/:serviceId",
"paginator": "auto",
}
},
# Returns a single organization member details.
{
"name": "organization_id_members_user_id",
"table_name": "user_id",
"primary_key": "requestId",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/v1/organizations/:organizationId/members/:userId",
"paginator": "auto",
}
},
]
}
return rest_api_source(source_config)
2. Configuring your source and destination credentials
dlt-init-openapi
will try to detect which authentication mechanism (if any) is used by the API in question and add a placeholder in your secrets.toml
.
The dlt
cli will have created a .dlt
directory in your project folder. This directory contains a config.toml
file and a secrets.toml
file that you can use to configure your pipeline. The automatically created version of these files look like this:
generated config.toml
[runtime]
log_level="INFO"
[sources.clickhouse_cloud]
# Base URL for the API
base_url = "https://api.clickhouse.cloud"
generated secrets.toml
[sources.clickhouse_cloud]
# secrets for your clickhouse_cloud source
# example_api_key = "example value"
2.1. Adjust the generated code to your usecase
At this time, the dlt-init-openapi
cli tool will always create pipelines that load to a local duckdb
instance. Switching to a different destination is trivial, all you need to do is change the destination
parameter in clickhouse_cloud_pipeline.py
to postgres and supply the credentials as outlined in the destination doc linked below.
3. Running your pipeline for the first time
The dlt
cli has also created a main pipeline script for you at clickhouse_cloud_pipeline.py
, as well as a folder clickhouse_cloud
that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.
The main pipeline script will look something like this:
import dlt
from clickhouse_cloud import clickhouse_cloud_source
if __name__ == "__main__":
pipeline = dlt.pipeline(
pipeline_name="clickhouse_cloud_pipeline",
destination='duckdb',
dataset_name="clickhouse_cloud_data",
progress="log",
export_schema_path="schemas/export"
)
source = clickhouse_cloud_source()
info = pipeline.run(source)
print(info)
Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:
python clickhouse_cloud_pipeline.py
4. Inspecting your load result
You can now inspect the state of your pipeline with the dlt
cli:
dlt pipeline clickhouse_cloud_pipeline info
You can also use streamlit to inspect the contents of your Azure Cosmos DB
destination for this:
# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline clickhouse_cloud_pipeline show
5. Next steps to get your pipeline running in production
One of the beauties of dlt
is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:
The Deploy section will show you how to deploy your pipeline to
- Deploy with GitHub Actions: Learn how to deploy a pipeline using GitHub Actions, a CI/CD runner that you can use for free.
- Deploy with Airflow and Google Composer: Follow the guide on how to deploy a pipeline with Airflow and Google Composer, a managed Airflow environment provided by Google.
- Deploy with Google Cloud Functions: Discover how to deploy a pipeline using Google Cloud Functions.
- Explore Other Deployment Options: Check out other deployment methods and guides in the deployment walkthroughs.
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to effectively monitor your
dlt
pipeline to ensure smooth operation and quick identification of issues. How to Monitor your pipeline - Set up alerts: Set up alerts to be notified of any issues or important events in your
dlt
pipeline, ensuring you can respond promptly. Set up alerts - Set up tracing: Implement tracing to get detailed insights into the execution of your
dlt
pipeline, helping you understand and optimize performance. And set up tracing
Available Sources and Resources
For this verified source the following sources and resources are available
Source ClickHouse Cloud
Streams various organizational, user activity, and configuration data from ClickHouse Cloud.
Resource Name | Write Disposition | Description |
---|---|---|
activity | append | Logs and tracks user activities within the ClickHouse Cloud platform. |
api_key | append | Stores API keys used for authenticating and authorizing API requests. |
invitation_id | append | Unique identifiers for invitations sent to users for accessing the platform. |
organization_id | append | Unique identifiers for different organizations using the ClickHouse Cloud service. |
prometheu | append | Stores Prometheus monitoring data for performance and health metrics. |
invitation | append | Contains details of invitations sent to users for joining the platform. |
activity_id | append | Unique identifiers for specific activities logged within the platform. |
member | append | Information about members of various organizations within ClickHouse Cloud. |
private_endpoint_config | append | Configuration settings for private endpoints used to access ClickHouse Cloud securely. |
service | append | Details about various services provided by ClickHouse Cloud. |
service_id | append | Unique identifiers for different services within the platform. |
backup | append | Information about backups created for data stored in ClickHouse Cloud. |
user_id | append | Unique identifiers for users accessing the ClickHouse Cloud platform. |
key_id | append | Unique identifiers for API keys used within the platform. |
backup_id | append | Unique identifiers for backups created within the platform. |
organization | append | Details about organizations using ClickHouse Cloud, including names and contact information. |
Additional pipeline guides
- Load data from Mux to Google Cloud Storage in python with dlt
- Load data from ClickHouse Cloud to BigQuery in python with dlt
- Load data from Chess.com to Neon Serverless Postgres in python with dlt
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