1. OpenSearch for Real-time AI-powered Log Analytics


    OpenSearch is a community-driven, open-source search and analytics suite derived from Apache 2.0-licensed Elasticsearch 7.10.2 & Kibana 7.10.2. It provides a scalable and fast search engine that can be used to store and search a large amount of data in near real-time, offering capabilities like full-text search and data analytics.

    To create an OpenSearch cluster that will handle real-time AI-powered log analytics, we'll use Pulumi to provision the necessary cloud infrastructure. The specific resources we will create in this example are:

    1. An AWS OpenSearch Domain: This resource will be the core component, which will allow you to index, search, and analyze logs.
    2. An IAM role and policy for OpenSearch: These resources will define the permissions that OpenSearch needs to access other AWS resources, such as S3 for storing logs or Kinesis for ingesting real-time data.

    Let's walk through the Pulumi Python code that sets up an OpenSearch domain with some basic configurations suitable for log analytics. Note that this is a starting point and you would need to adjust settings like instance types, node count, and access policies based on your specific needs and best practices.

    import pulumi import pulumi_aws as aws # Create an AWS OpenSearch Domain for log analytics opensearch_domain = aws.opensearch.Domain("ai-powered-log-analytics", engine_version="OpenSearch_1.0", cluster_config=aws.opensearch.DomainClusterConfigArgs( instance_type="r6g.large.search", instance_count=2, ), node_to_node_encryption=aws.opensearch.DomainNodeToNodeEncryptionArgs( enabled=True, ), ebs_options=aws.opensearch.DomainEbsOptionsArgs( ebs_enabled=True, volume_size=20, volume_type="gp2", ), advanced_security_options=aws.opensearch.DomainAdvancedSecurityOptionsArgs( enabled=True, internal_user_database_enabled=True, master_user_options=aws.opensearch.DomainAdvancedSecurityOptionsMasterUserOptionsArgs( master_user_name="masteruser", master_user_password="MasterUserPassword123!", ), ), # Make sure to properly configure the access policies to control who can interact with your domain access_policies=( """ { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "AWS": "*" }, "Action": "es:*", "Resource": "arn:aws:es:us-west-2:123456789012:domain/ai-powered-log-analytics/*" } ] } """ ), ) # Export the endpoint of the OpenSearch domain pulumi.export("opensearch_domain_endpoint", opensearch_domain.endpoint)

    In this code:

    • We define an OpenSearch Domain named ai-powered-log-analytics.
    • We set the engine version to OpenSearch_1.0. This is the first stable release of OpenSearch.
    • We configure the cluster with instances of type r6g.large.search. This is an AWS Graviton2 instance optimized for memory-intensive applications, such as log analytics.
    • We also enable node-to-node encryption for secure communication between the nodes in the cluster.
    • For storage, we enable Elastic Block Store (EBS) with a volume size of 20GB using the general-purpose SSD gp2 volume type.
    • Advanced security options are turned on with an internal user database, and we provide initial credentials for a master user.
    • The access_policies are used to define who can access the OpenSearch domain. They are defined in the AWS IAM policy language. In this example, we have set it to allow all actions (es:*) for demonstration purposes. In a production deployment, you would want to tighten these controls to follow the principle of least privilege.

    Finally, the endpoint of the OpenSearch domain is exported so it can be accessed from outside of Pulumi for sending and querying log data.

    Please remember to replace the master_user_password with a strong, unique password and adjust access_policies to meet your specific access requirements. Additionally, you should treat sensitive information such as passwords as secrets, which Pulumi allows you to do with configuration values or using a secrets provider.