Model Inference Result Caching with Upstash Redis

Question

Pulumi · Accepted Answer

To set up model inference result caching using Upstash Redis with Pulumi, you will deploy an instance of Redis which is an in-memory data structure store used as a distributed cache. Upstash provides Redis-compatible serverless database instances that can scale automatically and provide a fast response time suitable for caching purposes.

Here is a Python program using Pulumi that demonstrates how to create a Redis instance on Upstash. This program uses the pulumi_aiven provider, as Upstash isn't directly supported by Pulumi as of now, so we'll simulate the equivalent with Aiven's Redis service.

First, we'll import the necessary module (pulumi_aiven) and then create a new Redis instance. You must have the pulumi_aiven provider installed and configured to authenticate with Aiven's API.

In the aiven.Redis resource, we specify the plan and location for the Redis service. Additionally, we'll configure necessary properties, such as public_access, to allow access to Redis over the internet (not recommended for production environments due to security concerns). The redisUserConfig holds configuration options for the Redis service like enabling SSL and setting timeout settings.

After completing the setup, you can connect to the Redis instance using its hostname and port, as provided by the service_uri output.

Here is the program:

import pulumi
import pulumi_aiven as aiven

# Set up the Redis instance on Aiven, which will act as a cache for our model inference results.
redis_project = aiven.Redis(
    "my-redis-cache",
    project="<YOUR_AIVEN_PROJECT_NAME>",  # Replace with your Aiven project name
    cloud_name="google-europe-west1",     # Specify the cloud and region you want to use
    plan="business-4",                    # Specify the plan for your Redis service
    service_name="my-redis-service",      # Set the name for your Redis service
    redis_user_config=aiven.RedisUserConfigArgs(
        redis_version="6",              # Specify the Redis version
        public_access=aiven.RedisUserConfigPublicAccessArgs(
            redis=True,                 # Enable public access over the Redis protocol/port
            prometheus=False
        ),
        redis_persistence="none",       # Do not persist data (this is a cache)
    )
)

# Export the connection URI of the Redis instance which will be used to connect to it from your application.
# This URI contains the hostname and port of the redis instance.
# In an actual application, you would typically inject this URI into your application's configuration.
pulumi.export("redis_connection_uri", redis_project.service_uri)

Please replace <YOUR_AIVEN_PROJECT_NAME> with your project name on Aiven. In a production setup, you would want to lock down public access and configure your resources more securely. You might also need to create an aiven.Project resource if you haven't already got an Aiven project set up.

Remember to consider security best practices when using and exposing your Redis server, such as enabling encryption in transit (TLS/SSL) and authorized access controls.

Now, with your Redis instance deployed, your application can start caching model inference results to improve performance and reduce the load on the model inference service. You connect to Redis with the redis_connection_uri provided in the exported output, and use Redis commands to get/set cache entries.