1. Storing and Indexing Large Datasets for AI with OpenSearch


    To store and index large datasets for AI with OpenSearch, we would use an OpenSearch cluster, which is a scalable search engine that allows for complex search queries and aggregations. It is commonly used for application search, log analytics, and real-time analytics of large datasets.

    Here's a Pulumi program written in Python that demonstrates how to provision an AWS OpenSearch (formerly Elasticsearch) domain using Pulumi, an infrastructure-as-code tool. The program will outline the necessary steps to configure the AWS OpenSearch service for use.

    import pulumi import pulumi_aws as aws # Create an AWS OpenSearch Domain (formerly AWS Elasticsearch Service) # This will provision a new OpenSearch cluster where you could index and store large datasets # The instance_type and instance_count are chosen based on needs. Adjust according to your scalability requirements. # To learn more about AWS OpenSearch Domain configuration options, visit: # https://www.pulumi.com/registry/packages/aws/api-docs/opensearch/domain/ opensearch_domain = aws.opensearch.Domain("ai-dataset-domain", engine_version="OpenSearch_1.0", # Specify the OpenSearch version. You can choose the version that best fits your use case. cluster_config=aws.opensearch.DomainClusterConfigArgs( instance_type="r5.large.search", # Instance types are selected based on dataset size and processing requirements. instance_count=2, # A starting point for clustering; increase this number based on data size and throughput needs. ), ebs_options=aws.opensearch.DomainEbsOptionsArgs( ebs_enabled=True, volume_size=10, # Volume size in GiBs, adjust based on the size of the dataset. volume_type="gp2", # General Purpose SSD, you can change this to io1 or io2 for higher performance. ), node_to_node_encryption=aws.opensearch.DomainNodeToNodeEncryptionArgs( enabled=True, # Enabling encryption for data transferred between nodes. ), encrypt_at_rest=aws.opensearch.DomainEncryptAtRestArgs( enabled=True, # Enabling encryption at rest for your indexes. ), advanced_security_options=aws.opensearch.DomainAdvancedSecurityOptionsArgs( enabled=True, # Enabling fine-grained access control. internal_user_database_enabled=True, master_user_options=aws.opensearch.DomainAdvancedSecurityOptionsMasterUserOptionsArgs( master_user_name="admin", # Set a master username for cluster access. master_user_password="YourSecurePassword", # CHANGE THIS: Set a secure password. ), ), access_policies="""{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": { "AWS": "*" }, "Action": "es:*", "Resource": "*" }] }""", # NOTE: This is a very open policy; you should refine this to your specific access requirements. ) # Output the endpoint of the OpenSearch domain so it can be used with applications or further processing. pulumi.export('opensearch_domain_endpoint', opensearch_domain.endpoint)

    This Pulumi program sets up an OpenSearch Domain with the following configuration:

    • Engine Version: Specifies the version of OpenSearch you want to deploy. You must choose a version compatible with your dataset and tools.
    • Cluster Configuration: Views settings for your OpenSearch cluster, such as instance types and counts, which should be determined based on your performance and scalability requirements.
    • EBS Options: Configures the EBS volumes attached to data nodes in the cluster. You can specify volume size and type according to your storage needs and budget.
    • Node-to-Node Encryption: Enables encryption for data transfer between the nodes of your cluster, enhancing security.
    • Encryption at Rest: Ensures that your indexed data is encrypted while at rest in the cluster.
    • Advanced Security Options: Enables fine-grained access control and sets an internal user database for the cluster, along with master user credentials.
    • Access Policies: Defines who can access the cluster and what they can do. The example policy allows fully open access, which might not be suitable for production use. It's highly recommended to limit access based on your organization's security policies.

    The pulumi.export line at the end of the program outputs the domain endpoint, which you can use in your applications or for additional configuration.

    Important: This program includes sensitive information such as passwords in plain text. In a production environment, you should use the Pulumi config system or a cloud provider's secrets management service to handle sensitive data securely.

    Run the Pulumi program by executing pulumi up in the command line, in the directory where this program is saved. Pulumi will provision the OpenSearch domain as per the above configuration, and once it's done, the domain endpoint will be displayed as output. You can use this endpoint to connect your applications to the OpenSearch cluster.