Large-Scale Log Analytics with ClickHouse on Kubernetes

Question

Pulumi · Accepted Answer

In order to set up a large-scale log analytics system using ClickHouse on Kubernetes, we're going to walk through creating a ClickHouse cluster within a Kubernetes environment using Aiven's Pulumi provider. This approach allows us to leverage the managed services offered by Aiven, taking away the complexities of manual setup and maintenance.

Prerequisites

Pulumi: You should have Pulumi installed and configured on your machine. Pulumi is an infrastructure-as-code tool that allows us to define and manage infrastructure using code.
Python: Pulumi programs are written in several languages, including Python. Ensure you have Python installed.
Aiven Account: You must have an Aiven account and API token. Aiven is a cloud service that offers managed open-source databases, such as ClickHouse.
Kubernetes Cluster: Lastly, you need an operational Kubernetes cluster where the ClickHouse will be deployed.

Program Details

The following Pulumi program will set up ClickHouse on Kubernetes:

Service Definition (ClickHouse): Defines the ClickHouse service including the plan and project details.
ClickhouseUser Resource: Defines a user for the ClickHouse service with specified access rights.
ClickhouseDatabase Resource: Defines a database within the ClickHouse service for storing logs.

Pulumi Program:

import pulumi
import pulumi_aiven as aiven

# Initialize an Aiven ClickHouse service
clickhouse_service = aiven.Clickhouse(
    "clickhouse-service",
    project="<Your Aiven Project Name>",
    cloud_name="google-europe-west1",  # The cloud and region where the service is to be hosted
    plan="startup-4",  # The service plan for the ClickHouse
    service_name="my-clickhouse-service",  # Name of the ClickHouse service
    maintenance_window_dow="sunday",
    maintenance_window_time="10:00:00",
)

# Create a ClickHouse user
clickhouse_user = aiven.ClickhouseUser(
    "clickhouse-user",
    project=clickhouse_service.project,
    username="analytics_user",
    serviceName=clickhouse_service.service_name,
)

# Create a ClickHouse database for log analytics
clickhouse_database = aiven.ClickhouseDatabase(
    "clickhouse-database",
    project=clickhouse_service.project,
    serviceName=clickhouse_service.service_name,
    name="logs",
)

# Export the ClickHouse service URI
pulumi.export('clickhouse_service_uri', clickhouse_service.service_uri)

How to Use:

Replace <Your Aiven Project Name> with the name of your Aiven project.
The cloud_name can be changed based on the region and the cloud provider you wish to deploy (e.g., aws-us-east-1 for AWS in the U.S. East region).
The plan should be selected based on the scale needed. Aiven offers various plans for different scales of operation.
The service_name is the custom name for your ClickHouse deployment; you can set it to anything descriptive.
The username under ClickhouseUser should be unique and can be set as per your preference.
The name under ClickhouseDatabase is the name of the database where logs will be stored.

Once you write down this program in a Python file (for example, main.py), navigate to the directory containing the file in your terminal and run pulumi up. This command deploys the infrastructure as defined in the program.

Remember, managing infrastructure as code requires an understanding of both coding and the cloud services you intent to use. With the Pulumi program, you get the traction to manage, version control, and replicate your infrastructure easily.