# Custom scripting technical overview

## Introduction

This page provides some technical background for those interested in how the Patchworks custom scripting feature is implemented.

## Need to know

* The maximum memory size for a custom script is 512MB
* The maximum size of a custom script is 4GB

## KubeFaas overview

Patchworks custom scripting code is packaged into containers and runs as serverless functions. 

This is achieved using our proprietary Kubefaas (Functions as a Service) project, which provides language-agnostic script deployments using lightweight templates in a given language - and packaging into Docker containers. 

By utilising Docker and Kubernetes, we can provide consistent, repeatable execution of customer scripts, whilst managing the replicas to reduce waste.

## Script creation & execution

The diagrams below provide a simplified illustration of what happens when a custom script is built and executed.

### Script invocations

This process runs every time a script is called, ensuring that the script exists and that it has sufficient instances to handle the request:

* If the script is not deployed, Core contacts the builder to build the script instance. 
* If the script is deployed but does not have sufficient instances, it communicates with the Kubernetes API to scale up the function.

The process is illustrated below:

<figure><img src="/files/bvfYCIQ52LfpvWcj76aT" alt=""><figcaption></figcaption></figure>

Here:

1. Send a request to the Kubefaas Controller (proxy).
2. (optional) If the script is not deployed, [build it](#script-builds).&#x20;
3. (optional) Deploy the new script with result from step 2 and then return to step 1.
4. Check how many instances of the script are running.
5. (optional) Scale up script instances to 1 - wait for readiness.

### Script builds

This process runs when a script is built. Script templates are designed to maximise Docker layer caching and provide fast builds.&#x20;

The most recent builds (14 days) are cached, providing a near-instant build (thus reducing idle time in  flows). The process is illustrated below:

<figure><img src="/files/1980gEJmVZ3FpCYhZphc" alt=""><figcaption></figcaption></figure>

Here:

1. Check if the function exists in the cache - respond with an image if it already exists.
2. Build a new Docker image.
3. Store the image in an S3 cache (with 14-day retention).
4. Respond with the image URL.


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