https://linen.dev logo
Join Slack
Powered by
<@U04SE163WC8> can you use prometheus to monitor a...
# ask-ai
m
@kapa.ai can you use prometheus to monitor airbyte deployments in kubernetes? If so, can you walk me through an example?
k
Yes, you can use Prometheus to monitor Airbyte deployments in Kubernetes. Here is a step-by-step guide to set it up: ### Step 1: Deploy an OpenTelemetry Collector First, you need to deploy an OpenTelemetry Collector in your Kubernetes cluster. Follow the official [Kubernetes Getting Started documentation](https://opentelemetry.io/docs/collector/getting-started/#kubernetes) to deploy the collector. ### Step 2: Update the Helm Chart Values Next, you need to update your 
values.yaml
file in the Airbyte repository to include the 
metrics-reporter
container. This will submit Airbyte metrics to the OpenTelemetry collector. Here is an example of how to modify your 
values.yaml
file:
Copy code
yaml global:     metrics:       metricClient: "otel"       otelCollectorEndpoint: "<http://otel-collector.opentelemetry.svc:4317>"      metrics:     enabled: true
Make sure to update the value of 
otelCollectorEndpoint
with your collector URL. ### Step 3: Re-deploy Airbyte With the updated chart values, you are ready to deploy your Airbyte application by upgrading the chart. ### Step 4: Configure Prometheus to Scrape Metrics Ensure that Prometheus is configured to scrape data from the OpenTelemetry collector. You can do this by adding a scrape configuration in your Prometheus configuration file:
Copy code
yaml scrape_configs:   - job_name: 'opentelemetry-collector'     static_configs:       - targets: ['otel-collector.opentelemetry.svc:4317']
### Step 5: Visualize Metrics in Grafana Finally, you can set up Grafana to visualize the metrics collected by Prometheus. Add Prometheus as a data source in Grafana and create dashboards to display the metrics. ### Additional Information - If you encounter warnings like 
Instrument has recorded multiple values for the same attributes
, it might be due to the OpenTelemetry SDK emitting multiple values for the same attributes. This is a known issue and can be monitored for further investigation. For more detailed information, you can refer to the following resources: - [Collecting Metrics | Airbyte Documentation](https://docs.airbyte.com/operator-guides/collecting-metrics#helm-chart-setup-instructions) - [How can I monitor Airbyte OSS on Kubernetes using Prometheus?](https://discuss.airbyte.io/t/how-can-i-monitor-airbyte-oss-on-kubernetes-using-prometheus/172) By following these steps, you should be able to monitor your Airbyte deployment in Kubernetes using Prometheus.
m
@kapa.ai what is the best practice for automating the creation of custom data sources and data destinations and connections within airbyte? e.g. kubernetes, airflow, terraform. Where should that automation live?
k
A message from kapa.ai
The best practice for automating the creation of custom data sources, data destinations, and connections within Airbyte involves using infrastructure-as-code tools like Terraform, orchestration tools like Apache Airflow, and container orchestration platforms like Kubernetes. Here’s a detailed guide on how to achieve this: ### Using Terraform Terraform allows you to manage your Airbyte connectors and connections as code, making it easy to automate and version control your setup. 1. Set Up Terraform Configuration: Create a Terraform configuration file to define your Airbyte sources, destinations, and connections. Here’s an example snippet:
Copy code
hcl
   provider "airbyte" {
     server_url = "<http://localhost:8000>"
   }

   resource "airbyte_source" "example_source" {
     name = "example-source"
     source_type = "file"
     configuration = {
       url = "<https://example.com/data.csv>"
     }
   }

   resource "airbyte_destination" "example_destination" {
     name = "example-destination"
     destination_type = "postgres"
     configuration = {
       host = "localhost"
       port = 5432
       database = "example_db"
       username = "user"
       password = "password"
     }
   }

   resource "airbyte_connection" "example_connection" {
     name = "example-connection"
     source_id = airbyte_source.example_source.id
     destination_id = airbyte_destination.example_destination.id
     sync_mode = "full_refresh"
     destination_sync_mode = "append"
   }
2. Apply the Configuration: Run the following commands to apply the Terraform configuration:
Copy code
sh
   terraform init
   terraform apply
### Using Apache Airflow Apache Airflow can be used to orchestrate and automate the execution of Airbyte syncs. 1. Create an Airflow DAG: Define a DAG in Airflow to trigger Airbyte syncs. Here’s an example snippet:
Copy code
python
   from airflow import DAG
   from airflow.providers.airbyte.operators.airbyte import AirbyteTriggerSyncOperator
   from datetime import datetime

   default_args = {
       'owner': 'airflow',
       'start_date': datetime(2023, 1, 1),
       'retries': 1,
   }

   with DAG('airbyte_sync_dag', default_args=default_args, schedule_interval='@daily') as dag:
       airbyte_sync = AirbyteTriggerSyncOperator(
           task_id='airbyte_sync_task',
           airbyte_conn_id='airbyte_default',
           connection_id='YOUR_AIRBYTE_CONNECTION_ID',
           asynchronous=False,
           timeout=3600,
           wait_seconds=3
       )
2. Deploy the DAG: Place the DAG file in the Airflow DAGs directory and restart the Airflow scheduler. ### Using Kubernetes Kubernetes can be used to deploy and manage Airbyte instances, ensuring scalability and reliability. 1. Deploy Airbyte on Kubernetes: Use Helm charts to deploy Airbyte on Kubernetes. Update the 
values.yaml
file to include your custom configurations.
The best practice for automating the creation of custom data sources, data destinations, and connections within Airbyte involves using infrastructure-as-code tools like Terraform, orchestration tools like Apache Airflow, and container orchestration platforms like Kubernetes. Here’s a detailed guide on how to achieve this: ### Using Terraform Terraform allows you to manage your Airbyte connectors and connections as code, making it easy to automate and version control your setup. 1. Set Up Terraform Configuration: Create a Terraform configuration file to define your Airbyte sources, destinations, and connections. Here’s an example snippet:
Copy code
hcl    provider "airbyte" {      server_url = "<http://localhost:8000>"    }     resource "airbyte_source" "example_source" {      name = "example-source"      source_type = "file"      configuration = {        url = "<https://example.com/data.csv>"      }    }     resource "airbyte_destination" "example_destination" {      name = "example-destination"      destination_type = "postgres"      configuration = {        host = "localhost"        port = 5432        database = "example_db"        username = "user"        password = "password"      }    }     resource "airbyte_connection" "example_connection" {      name = "example-connection"      source_id = airbyte_source.example_source.id      destination_id = airbyte_destination.example_destination.id      sync_mode = "full_refresh"      destination_sync_mode = "append"    }
2. Apply the Configuration: Run the following commands to apply the Terraform configuration:
Copy code
sh    terraform init    terraform apply
### Using Apache Airflow Apache Airflow can be used to orchestrate and automate the execution of Airbyte syncs. 1. Create an Airflow DAG: Define a DAG in Airflow to trigger Airbyte syncs. Here’s an example snippet:
Copy code
python    from airflow import DAG    from airflow.providers.airbyte.operators.airbyte import AirbyteTriggerSyncOperator    from datetime import datetime     default_args = {        'owner': 'airflow',        'start_date': datetime(2023, 1, 1),        'retries': 1,    }     with DAG('airbyte_sync_dag', default_args=default_args, schedule_interval='@daily') as dag:        airbyte_sync = AirbyteTriggerSyncOperator(            task_id='airbyte_sync_task',            airbyte_conn_id='airbyte_default',            connection_id='YOUR_AIRBYTE_CONNECTION_ID',            asynchronous=False,            timeout=3600,            wait_seconds=3        )
2. Deploy the DAG: Place the DAG file in the Airflow DAGs directory and restart the Airflow scheduler. ### Using Kubernetes Kubernetes can be used to deploy and manage Airbyte instances, ensuring scalability and reliability. 1. Deploy Airbyte on Kubernetes: Use Helm charts to deploy Airbyte on Kubernetes. Update the 
values.yaml
file to include your custom configurations.
Copy code
yaml    global:      metrics:        metricClient: "otel"        otelCollectorEndpoint: "<http://otel-collector.opentelemetry.svc:4317>"     metrics:      enabled: true
2. Apply the Helm Chart: Run the following command to deploy Airbyte:
Copy code
sh    helm install airbyte airbyte/airbyte -f values.yaml
### Where Should the Automation Live? - Infrastructure-as-Code (IaC) Repositories: Store your Terraform configurations in a dedicated
m
@kapa.ai is there a docker image I can use that already has airbyte-api-python-sdk installed?
k
A message from kapa.ai
As of the information available in the provided knowledge sources, there is no specific mention of a pre-built Docker image that already includes the 
airbyte-api-python-sdk
installed. However, you can easily create a custom Docker image that includes the 
airbyte-api-python-sdk
by using a Dockerfile. Here’s an example of how you can create such a Docker image: ### Step-by-Step Guide to Create a Custom Docker Image 1. Create a Dockerfile: Create a file named 
Dockerfile
in your project directory with the following content:
Copy code
Dockerfile    FROM python:3.9-slim     # Install the airbyte-api-python-sdk    RUN pip install airbyte-api-python-sdk     # Set the working directory    WORKDIR /app     # Copy your application code to the container    COPY . /app     # Set the entrypoint for your application    CMD ["python", "your_script.py"]
2. Build the Docker Image: Run the following command in your terminal to build the Docker image:
Copy code
sh    docker build -t custom-airbyte-api-sdk-image .
3. Run the Docker Container: Once the image is built, you can run a container using the following command:
Copy code
sh    docker run -it --rm custom-airbyte-api-sdk-image
### Additional Resources For more detailed information on building Docker images and using Docker with Airbyte, you can refer to the following resources: - [Developing on Docker | Airbyte Documentation](https://docs.airbyte.com/contributing-to-airbyte/resources/developing-on-docker#building-the-docker-images) - [Building a Source Connector: The Hard Way | Airbyte Documentation](https://docs.airbyte.com/connector-development/tutorials/the-hard-way/build-a-connector-the-hard-way#1-publish-the-docker-image) By following these steps, you can create a Docker image that includes the 
airbyte-api-python-sdk
and use it for your custom applications.
m
@kapa.ai can you provide a concrete example of creating a custom data source using the airbyte-api-python-sdk using a JSON blob as config:
Copy code
res = s.sources.create_source(request=models.SourceCreateRequest(
    configuration=models.SourceAha(
        api_key='<value>',
        url='<https://complicated-seat.org>',
    ),
    name='My Source',
    workspace_id='744cc0ed-7f05-4949-9e60-2a814f90c035',
))
k
A message from kapa.ai
Certainly! Below is a concrete example of creating a custom data source using the 
airbyte-api-python-sdk
with a JSON blob as the configuration. This example assumes you have already installed the 
airbyte-api-python-sdk
and have the necessary credentials and workspace ID. ### Example: Creating a Custom Data Source First, ensure you have the 
airbyte-api-python-sdk
installed:
Copy code
sh
pip install airbyte-api-python-sdk
Next, you can use the following Python script to create a custom data source:
Copy code
python
import airbyte_api_client
from airbyte_api_client import Configuration, ApiClient
from airbyte_api_client.api import source_api
from airbyte_api_client.model.source_create_request import SourceCreateRequest
from airbyte_api_client.model.source_aha import SourceAha

# Configure API key authorization: apiKeyAuth
configuration = Configuration(
    host="<http://localhost:8000/api>",
    api_key={
        'Authorization': 'Bearer YOUR_API_KEY'
    }
)

# Enter a context with an instance of the API client
with ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = source_api.SourceApi(api_client)
    
    # Define the source configuration
    source_configuration = {
        "api_key": "<value>",
        "url": "<https://complicated-seat.org>"
    }
    
    # Create the source request
    source_create_request = SourceCreateRequest(
        name="My Source",
        source_definition_id="YOUR_SOURCE_DEFINITION_ID",
        workspace_id="744cc0ed-7f05-4949-9e60-2a814f90c035",
        connection_configuration=source_configuration
    )
    
    try:
        # Create the source
        api_response = api_instance.create_source(source_create_request)
        print("Source created successfully:", api_response)
    except airbyte_api_client.ApiException as e:
        print("Exception when calling SourceApi->create_source: %s\n" % e)
### Explanation 1. Install the SDK: Ensure the 
airbyte-api-python-sdk
is installed using 
pip install airbyte-api-python-sdk
. 2. Configure the API Client: Set up the API client with the appropriate host and API key. 3. Define the Source Configuration: Create a dictionary for the source configuration, which includes the necessary parameters like 
api_key
and 
url
. 4. Create the Source Request: Use the 
SourceCreateRequest
model to create a request object, including the source name, source definition ID, workspace ID, and the configuration dictionary. 5. Create the Source: Call the 
create_source
method from the 
SourceApi
class to create the source. Handle any exceptions that may occur. ### Additional Resources For more detailed information, you can refer to the [Airbyte API Documentation](https://airbyte-public-api-docs.s3.us-east-2.amazonaws.com/rapidoc-api-docs.html#post-/v1/sources/create).
Certainly! Below is a concrete example of creating a custom data source using the 
airbyte-api-python-sdk
with a JSON blob as the configuration. This example assumes you have already installed the 
airbyte-api-python-sdk
and have the necessary credentials and workspace ID. ### Example: Creating a Custom Data Source First, ensure you have the 
airbyte-api-python-sdk
installed:
Copy code
sh pip install airbyte-api-python-sdk
Next, you can use the following Python script to create a custom data source:
Copy code
python import airbyte_api_client from airbyte_api_client import Configuration, ApiClient from airbyte_api_client.api import source_api from airbyte_api_client.model.source_create_request import SourceCreateRequest from airbyte_api_client.model.source_aha import SourceAha  # Configure API key authorization: apiKeyAuth configuration = Configuration(     host="<http://localhost:8000/api>",     api_key={         'Authorization': 'Bearer YOUR_API_KEY'     } )  # Enter a context with an instance of the API client with ApiClient(configuration) as api_client:     # Create an instance of the API class     api_instance = source_api.SourceApi(api_client)          # Define the source configuration     source_configuration = {         "api_key": "<value>",         "url": "<https://complicated-seat.org>"     }          # Create the source request     source_create_request = SourceCreateRequest(         name="My Source",         source_definition_id="YOUR_SOURCE_DEFINITION_ID",         workspace_id="744cc0ed-7f05-4949-9e60-2a814f90c035",         connection_configuration=source_configuration     )          try:         # Create the source         api_response = api_instance.create_source(source_create_request)         print("Source created successfully:", api_response)     except airbyte_api_client.ApiException as e:         print("Exception when calling SourceApi->create_source: %s\n" % e)
### Explanation 1. Install the SDK: Ensure the 
airbyte-api-python-sdk
is installed using 
pip install airbyte-api-python-sdk
. 2. Configure the API Client: Set up the API client with the appropriate host and API key. 3. Define the Source Configuration: Create a dictionary for the source configuration, which includes the necessary parameters like 
api_key
and 
url
. 4. Create the Source Request: Use the 
SourceCreateRequest
model to create a request object, including the source name, source definition ID, workspace ID, and the configuration dictionary. 5. Create the Source: Call the 
create_source
method from the 
SourceApi
class to create the source. Handle any exceptions that may occur. ### Additional Resources For more detailed information, you can refer to the [Airbyte API Documentation](https://airbyte-public-api-docs.s3.us-east-2.amazonaws.com/rapidoc-api-docs.html#post-/v1/sources/create). By following this example, you should be able to create a custom data source using the 
airbyte-api-python-sdk
with a JSON blob as the configuration.
m
@kapa.ai can you show me a curl statement to list workspaces in an airbyte deployment
k
A message from kapa.ai
Certainly! Below is a 
curl
statement to list workspaces in an Airbyte deployment. This example assumes you have the necessary 
access_token
for authentication. ### Example 
curl
Statement to List Workspaces
Copy code
sh curl -X GET "<YOUR_WEBAPP_URL>/api/public/v1/workspaces" \      -H "Authorization: Bearer <YOUR_ACCESS_TOKEN>" \      -H "Content-Type: application/json"
### Explanation - 
-X GET
: Specifies the HTTP method to use, which is 
GET
in this case. - 
<YOUR_WEBAPP_URL>
: Replace this with the URL of your Airbyte web application. - 
Authorization: Bearer <YOUR_ACCESS_TOKEN>
: Replace 
<YOUR_ACCESS_TOKEN>
with your actual access token. - 
Content-Type: application/json
: Sets the content type to JSON. ### Example Response You can expect a response similar to the following:
Copy code
json {   "data": [     {       "workspaceId": "b5367aab-9d68-4fea-800f-0000000000",       "name": "Finance Team",       "dataResidency": "auto"     },     {       "workspaceId": "b5367aab-9d68-4fea-800f-0000000001",       "name": "Analytics Team",       "dataResidency": "auto"     }   ] }
For more details, you can refer to the [Airbyte API Reference](https://reference.airbyte.com/reference/listworkspaces). By using this 
curl
statement, you can list the workspaces in your Airbyte deployment.
m
@kapa.ai how to enable api username/password with airbyte
k
A message from kapa.ai
To enable API username/password authentication with Airbyte, you need to configure basic authentication. Here’s a step-by-step guide to set it up: ### Step-by-Step Guide 1. Modify the 
.env
File: Update the 
.env
file in your Airbyte installation directory to include the 
BASIC_AUTH_USERNAME
and 
BASIC_AUTH_PASSWORD
environment variables. Here’s an example:
Copy code
env    BASIC_AUTH_USERNAME=your_username    BASIC_AUTH_PASSWORD=your_password
2. Restart Airbyte: After updating the 
.env
file, restart your Airbyte services to apply the changes. If you are using Docker, you can do this with the following commands:
Copy code
sh    docker-compose down    docker-compose up -d
3. Access Airbyte with Basic Auth: Once Airbyte is running, you can access the Airbyte UI by navigating to 
<http://localhost:8000>
in your browser. You will be prompted to enter the username and password you set in the 
.env
file. ### Example 
.env
File Here’s an example of what your 
.env
file might look like:
Copy code
env # Airbyte environment variables BASIC_AUTH_USERNAME=airbyte BASIC_AUTH_PASSWORD=password
### Additional Resources For more detailed information, you can refer to the following resources: - [Airbyte Documentation on Basic Auth](https://airbyte.com/tutorials/how-to-use-airflow-and-airbyte-together#install-airbyte) By following these steps, you can enable API username/password authentication for your Airbyte instance.
m
@kapa.ai how to do this using the airbyte helm chart
k
A message from kapa.ai
To enable API username/password authentication using the Airbyte Helm chart, you need to configure the Helm chart values to include the basic authentication settings. Here’s a step-by-step guide to achieve this: ### Step-by-Step Guide 1. Update the 
values.yaml
File: Modify your 
values.yaml
file to include the 
BASIC_AUTH_USERNAME
and 
BASIC_AUTH_PASSWORD
environment variables. Here’s an example:
Copy code
yaml    server:      extraEnv:        - name: BASIC_AUTH_USERNAME          value: "your_username"        - name: BASIC_AUTH_PASSWORD          value: "your_password"
2. Deploy or Upgrade Airbyte with Helm: Use the Helm command to deploy or upgrade your Airbyte instance with the updated 
values.yaml
file.
Copy code
sh    helm upgrade --install airbyte airbyte/airbyte -n <NAMESPACE> --values /path/to/values.yaml
Replace 
<NAMESPACE>
with the namespace where you want to deploy Airbyte, and 
/path/to/values.yaml
with the path to your 
values.yaml
file. ### Example 
values.yaml
File Here’s an example of what your 
values.yaml
file might look like:
Copy code
yaml server:   extraEnv:     - name: BASIC_AUTH_USERNAME       value: "airbyte"     - name: BASIC_AUTH_PASSWORD       value: "password"
### Additional Resources For more detailed information on deploying Airbyte using Helm, you can refer to the following resources: - [Deploy Airbyte on Kubernetes using Helm | Airbyte Documentation](https://docs.airbyte.com/deploying-airbyte/on-kubernetes-via-helm#default-deployment) By following these steps, you can enable API username/password authentication for your Airbyte instance deployed using the Helm chart.
m
@kapa.ai how to configure airbyte to run in enterprise mode
k
A message from kapa.ai
To configure Airbyte to run in Enterprise mode, you need to follow a series of steps to set up the necessary configurations and deploy Airbyte using Helm. Below are the detailed steps: ### Step 1: Create and Configure 
values.yaml
1. Create an Empty 
values.yaml
File: Inside your 
airbyte
directory, create an empty 
values.yaml
file. 2. Add Basic Enterprise Configuration: Paste the following into your newly created 
values.yaml
file. This is required to deploy Airbyte Self-Managed Enterprise:
Copy code
yaml    global:        edition: enterprise        airbyteUrl: # e.g. <https://airbyte.company.example>
3. Enable SSO Authentication (Optional): To enable SSO authentication, add instance admin details and SSO auth details to your 
values.yaml
file, under 
global
. Here’s an example:
Copy code
yaml    global:        edition: enterprise        airbyteUrl: # e.g. <https://airbyte.company.example>      auth:          instanceAdmin:            firstName: ## First name of admin user.            lastName: ## Last name of admin user.          identityProvider:            type: oidc            secretName: airbyte-config-secrets ## Name of your Kubernetes secret.            oidc:              domain: ## e.g. company.example              app-name: ## e.g. airbyte              clientIdSecretKey: client-id              clientSecretSecretKey: client-secret
### Step 2: Deploy Airbyte Using Helm 1. Add the Airbyte Helm Repository: If you haven't already, add the Airbyte Helm repository:
Copy code
sh    helm repo add airbyte <https://airbytehq.github.io/helm-charts>    helm repo update
2. Install or Upgrade Airbyte: Use the Helm command to install or upgrade your Airbyte instance with the 
values.yaml
file:
Copy code
sh    helm upgrade --install airbyte airbyte/airbyte --namespace airbyte --values /path/to/values.yaml
Replace 
/path/to/values.yaml
with the path to your 
values.yaml
file. ### Step 3: Verify the Deployment 1. Check the Status: Verify that the Airbyte pods are running correctly:
Copy code
sh    kubectl get pods -n airbyte
2. Access the Airbyte UI: Navigate to the URL specified in the 
airbyteUrl
field of your 
values.yaml
file to access the Airbyte UI. ### Additional Resources For more detailed information, you can refer to the following resources: - [Deploy Airbyte Enterprise using Kubernetes](https://docs.airbyte.com/enterprise-setup/implementation-guide) - [Configure Okta for Single Sign-On (SSO) with Airbyte Self-Managed Enterprise](https://docs.airbyte.com/access-management/sso) By following these steps, you can configure and deploy Airbyte in Enterprise mode using Helm.
Open in Slack
PreviousNext
Powered by