1. Kubernetes Managed Elasticsearch for Log Analytics


    To set up managed Elasticsearch on Kubernetes for log analytics, you can use a combination of Kubernetes resources and a managed Elasticsearch service. Here's how you can do it:

    1. Deploy a Kubernetes cluster where you will host your log analytics stack.
    2. Use a Managed Elasticsearch service such as AWS Elasticsearch Service, Elastic Cloud on Kubernetes (ECK) by Elastic or similar services provided by other cloud providers.
    3. Configure your Kubernetes pods to send logs to the Elasticsearch cluster.
    4. Use Kibana or a similar tool to visualize and analyze the logs.

    Below is a Pulumi program written in Python that provisions an Elasticsearch domain using the AWS provider. This program assumes that you already have a Kubernetes cluster running. For the purposes of this example, I'm using AWS Elasticsearch Service, but you can substitute this with the managed Elasticsearch service of your choice by adjusting the provider and resource types accordingly.

    import pulumi import pulumi_aws as aws # Create an AWS Elasticsearch Domain, which provides a managed Elasticsearch cluster. # The configuration options below are a minimal example; you will need to adjust them # according to your specific use case and requirements. managed_elasticsearch = aws.elasticsearch.Domain("managed-elasticsearch", elasticsearch_version="7.10", # Specify the Elasticsearch version you want to use. cluster_config=aws.elasticsearch.DomainClusterConfigArgs( instance_type="r5.large.elasticsearch", # Choose an instance size suitable for your workload. ), ebs_options=aws.elasticsearch.DomainEbsOptionsArgs( ebs_enabled=True, volume_size=10, # Define the volume size (in GB). volume_type="gp2", # Define the volume type. ), access_policies=pulumi.Output.all( pulumi.resource.Constant("arn:aws:iam::123456789012:root") # Replace with the correct principal ARN. ).apply(lambda arn: { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": { "AWS": arn }, "Action": "es:*", "Resource": "*" }] }), # Enable automated snapshots. snapshot_options=aws.elasticsearch.DomainSnapshotOptionsArgs( automated_snapshot_start_hour=23, ), # Apply any tags as necessary. tags={ "Name": "ManagedElasticsearch", "Environment": "Production", } ) # Export the domain endpoint to be used by applications like Kibana, Fluentd, etc. pulumi.export('elasticsearch_endpoint', managed_elasticsearch.endpoint) # To complete the setup, you will need to configure your Kubernetes deployments # to send logs to the provisioned Elasticsearch domain endpoint. # This typically involves setting up Fluentd or a similar log shipper as a DaemonSet # within your Kubernetes cluster, which collects logs and sends them to Elasticsearch.

    In the program above, we first import the required Pulumi modules. We then define a managed Elasticsearch domain using pulumi_aws.elasticsearch.Domain and configure its version, instance type, volume size, and access policies. You'll need to replace the placeholder values, such as the Elasticsearch version, instance type, and volume size with ones that match your specific needs.

    The access_policies argument configures the access policies for the Elasticsearch domain. You should replace the principal ARN with the correct ARN that represents the users or services that should have access to this Elasticsearch domain.

    Finally, we export the domain endpoint, which you'll use to integrate with logging agents or visualization tools like Kibana.

    This program will get you started with managed Elasticsearch in AWS for log analytics with Pulumi. Adjustments would be needed if you decide to choose a different cloud provider or if you have specific network, security, or data requirements.