High-Availability Postgres Clusters for Machine Learning Metadata

Question

Pulumi · Accepted Answer

Creating a high-availability (HA) PostgreSQL cluster for machine learning metadata involves setting up a database environment that can handle failover, replication, and is designed to maximize uptime. For this purpose, we'll use Pulumi in combination with cloud resources that provide managed PostgreSQL services with built-in high-availability features.

In this example, we'll use AWS Aurora which is a MySQL and PostgreSQL-compatible relational database built for the cloud. It provides a high-performance and highly available database service.

With AWS Aurora, you don't have to manually set up replication or failover mechanisms as it automatically handles these to ensure high availability. Aurora clusters generally consist of one primary instance which performs read/write operations and optionally several replica instances which can serve read requests and promote to be the new primary in case of failover.

Here's a program in Python using Pulumi to create a high-availability PostgreSQL cluster that could be used to store machine learning metadata:

```python
import pulumi
import pulumi_aws as aws
import pulumi_aws_quickstart_aurora_postgres as aws_quickstart

# This is the AWS region where all resources will be deployed.
aws_region = "us-west-2"

# Create an Amazon Aurora PostgreSQL cluster that is highly available.
# The cluster will be created with one writer and two readers in different
# availability zones to ensure high availability.
aurora_cluster = aws_quickstart.Cluster("auroraCluster",
    # The identifier for the cluster. This must be unique across all Aurora clusters in the region.
    db_cluster_identifier="machine-learning-metadata-cluster",
    # Provide the engine version for the Aurora PostgreSQL cluster.
    engine="aurora-postgresql",
    engine_version="11.7",
    # The number of days to retain backups for the Aurora PostgreSQL cluster.
    backup_retention_period=7,
    # The instance class to use for the Aurora cluster instances.
    instance_class=aws.rds.InstanceType.T3_Medium,
    # Whether to enable storage encryption.
    storage_encrypted=True,
    # Setting up the VPC, subnets and security groups. This assumes we have predefined these.
    # Alternatively, you could create new ones using the pulumi_aws library.
    vpc_id=pulumi.Config("vpc_id").require(),
    # Pass two subnet IDs for different availability zones.
    subnet_ids=[
        pulumi.Config("subnet1_id").require(),
        pulumi.Config("subnet2_id").require()
    ],
    # Pass the ID of the security group that allows access to the cluster.
    vpc_security_group_ids=[pulumi.Config("security_group_id").require()],
    # The number of reader instances that should be available in the cluster.
    replication_source_identifier=pulumi.Config("replication_source_id").optional(),
    skip_final_snapshot=True
)

# Export the cluster endpoint URL so it can be used to connect to the database.
pulumi.export("cluster_endpoint", aurora_cluster.cluster.cluster_endpoint)
```

In this program, we're using the `pulumi_aws` library to provision AWS resources and `pulumi_aws_quickstart_aurora_postgres` to create a quickstart Aurora PostgreSQL cluster which is managed by AWS and comes with HA features out of the box. We specify the instance size, number of instances, VPC and subnets for the deployment, encryption settings, and other database parameters such as engine version.

Please note that you have to configure Pulumi with the required AWS configuration settings and credentials beforehand. Use the `pulumi config` command to set the required configuration values like `vpc_id`, `subnet1_id`, `subnet2_id`, and `security_group_id`.

You would need to run this Pulumi program using the Pulumi CLI after adjusting configs as prescribed above. If you use non-default AWS profile, make sure to configure that as well before running the Pulumi program.

Once the Pulumi program is applied, it will create an Aurora PostgreSQL cluster. The exported `cluster_endpoint` can be used by your applications or machine learning services to interact with the database cluster.

Remember to never post sensitive information such as passwords or secret keys within your Pulumi code or configuration files. Always use secure ways to manage secrets, such as using Pulumi's secret management or AWS Secrets Manager.