Distributed Session Storage for ML-enhanced Applications

Question

Pulumi · Accepted Answer

For constructing a distributed session storage system for machine learning (ML) enhanced applications, you'll want a setup that can handle large volumes of fast-changing data, often requiring low-latency access. A common approach is to use a managed Redis service, which provides in-memory data structure store, used as a database, cache, and message broker.

Google Cloud Platform (GCP) provides a managed Redis service, Cloud Memorystore, which can be suitable for this purpose. Redis is well-known for its performance and is widely used in scenarios where rapid access to data is required.

Let's walk through the process of setting up a managed Redis instance on GCP using Pulumi's infrastructure as code approach. We will use the `gcp.redis.Instance` Pulumi resource, which will provision a Redis instance in GCP.

Here's a step by step program written in Python using Pulumi to create a Google Cloud Memorystore for Redis instance:

1. We begin by importing the required Pulumi Google Cloud package.
2. We then create a Redis instance by instantiating the `gcp.redis.Instance` class.
3. We configure the Redis instance with the necessary parameters such as the tier (BASIC or STANDARD), memory size, and region.

Here is a program that creates a Redis instance configured for a distributed session storage:

```python
import pulumi
import pulumi_gcp as gcp

# Create a Redis instance for session storage
redis_instance = gcp.redis.Instance("ml-session-storage",
    # The 'tier' specifies the replication level and the service level agreement (SLA) for the Redis instance.
    # For distributed systems and improved availability, 'STANDARD_HA' tier is recommended.
    tier="STANDARD_HA",
    # 'memory_size_gb' specifies the size of Redis memory in GiB.
    # An appropriate size should be chosen based on your workload requirements.
    memory_size_gb=1,
    # Choose the GCP region where the Redis instance will be deployed.
    # It's a good practice to deploy close to where the ML services are to reduce latency.
    region="us-central1",
    # Optionally, you can also set additional configurations such as authorized networks,
    # Redis version, label, and more advanced options like persistence settings.
)

# Export the Redis instance's id and host, which can be used to connect your application to the Redis store
pulumi.export("redis_instance_id", redis_instance.id)
pulumi.export("redis_instance_host", redis_instance.host)
```

The Redis instance created by the Pulumi program above serves as a resilient and high-performing backend for session storage, which can be consumed by your ML-enhanced applications. It automatically handles failover, backups, and updates without interrupting the service.

By linking your applications to use this Redis instance for session data, you could increase the responsiveness and statefulness of your ML workflows. The application code would leverage the Redis client libraries to interact with the data stored in Redis.

Remember to replace the memory size, and region according to your specific needs and where your other cloud resources and services are located to minimize latency.

After running this Pulumi program, you will have a GCP Redis instance that you can tie into your machine learning infrastructure. Use the provided outputs, `redis_instance_id` and `redis_instance_host`, to configure your application to use this Redis instance for session storage.