1. Low-Latency Predictions Caching with GCP Redis


    In this program, we will provision a Redis instance in Google Cloud Platform (GCP) for low-latency predictions caching. Redis is an in-memory data structure store, used as a distributed, in-memory key–value database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, and sorted sets with range queries.

    In scenarios like machine learning predictions, where latency directly affects the user experience, caching the results of expensive computations in Redis can reduce access times from potentially hundreds of milliseconds (when fetching from a relational database or a computation service) to just a few milliseconds when fetching from in-memory stores like Redis.

    To achieve this, we will use the gcp.redis.Instance resource from Pulumi's GCP provider. This will create a Redis instance that you can then configure to connect to your application. Here is how you might create such an instance:

    import pulumi import pulumi_gcp as gcp # Create a GCP Redis instance for caching redis_instance = gcp.redis.Instance("predictions-cache-instance", memory_size_gb=1, # The amount of memory allocated to this instance in GB authorized_network="default", # The network where this instance will be accessible. Replace with your VPC network name if needed. redis_version="REDIS_4_0", # The version of Redis software. Choose the one that suits your needs. tier="STANDARD_HA", # STANDARD_HA as an example. This indicates a high availability (HA) instance. Choose the tier based on your needs. location_id="us-central1-f", # The zone to create the instance in. Change this to suitable GCP region according to your geography or redundancy needs. display_name="PredictionsCache", # A human-readable display name for the Redis instance. ) pulumi.export("redis_instance_name", redis_instance.name) pulumi.export("redis_instance_host", redis_instance.host) pulumi.export("redis_instance_port", redis_instance.port)

    Let's go through some of the key aspects of this program:

    • Memory Size: This is specified in GBs. The right amount of memory allocated depends on your usage pattern and the size of your data set.
    • Authorized Network: This controls which network has access to the Redis instance. Usually, this will be a VPC (Virtual Private Cloud) network where your application servers reside.
    • Redis Version: You should specify the Redis version that is compatible with your application.
    • Tier: The service tier determines the availability and redundancy of your instance. STANDARD_HA means that it's a high availability instance.
    • Location ID: This should ideally be geographically close to your application servers or your user base to minimize latency.

    The output of the Pulumi program includes the instance name, host, and port, which you can use to configure your application to connect to the Redis instance.

    You should plug in your application that handles the machine learning predictions into this Redis cache based on the host and port provided. You'll typically use a Redis client library for your programming language to set up the connection, read from the cache, and write prediction results back into the cache.

    This simple setup ensures that frequently accessed prediction data is served with minimal latency, which is crucial for real-time applications that rely on fast response times.