@theapexlab/serverless-icebreaker

Serverless Icebreaker

Introduction:

The serverless Icebreaker is a utility designed to analyze pre-built AWS Lambdas and mitigate cold start duration. Cold start duration can result in user experience issues, such as lengthy page loading times. By optimizing the size of the lambda build, you can reduce cold start duration and improve overall performance.

Features

• Lambda Size Analysis: The Serverless Icebreaker examines the file size of AWS Lambdas and identifies if the build is not compacted.
• Library Usage: It identifies the three most frequently utilized or imported libraries in the Lambda function.
• Metrics Generation: The tool generates metrics for all analyzed Lambdas, allowing you to monitor their sizes and track improvements over time.
• Threshold Errors: If a Lambda's size exceeds a specified threshold, the tool generates an error, indicating the need for optimization.
• Framework Optimization: The default configuration of the Serverless Icebreaker is optimized for the SST and Serverless frameworks, making it easy to integrate and use within your projects.

About cold start duration

The chart illustrates the correlation between lambda build size and cold start duration. As the lambda build size increases, the cold start duration also tends to be longer. This relationship highlights the importance of optimizing the lambda build size to reduce cold start latency and enhance overall performance.

Our mission is to minimize cold start duration and improve user experience. One of the most effective practices we recommend is optimizing your lambda build size

Lambda build size	Cold start duration
1 MB	150 ms
19.6 MB	692 ms
30.2 MB	1716 ms
52.8 MB	2515 ms

Here some examples how to optimize your lambda imports:

// Instead of const AWS = require('aws-sdk'), use:
const DynamoDB = require('aws-sdk/clients/dynamodb')

// Instead of const AWSXRay = require('aws-xray-sdk'), use:
const AWSXRay = require('aws-xray-sdk-core')

// Instead of const AWS = AWSXRay.captureAWS(require('aws-sdk')), use:
const dynamodb = new DynamoDB.DocumentClient()
AWSXRay.captureAWSClient(dynamodb.service)

Usage

Icons:

• ✅ - SUCCESS / The lambda build size is lower than the error threshold
• 🚧 - WARNING / The lambda build size is within 10% of the error threshold
• ❌ - ERROR / The lambda build size is higher than error threshold

Installation:

npm install @theapexlab/serverless-icebreaker --save-dev

Run:

npx sib
or
npm run sib

Uninstall:

npm uninstall @theapexlab/serverless-icebreaker

Behind the scenes

Upon first run, it creates a sib-config.json with the default settings for SST in the root of the project.

If the lambda is not minified on build time the imported node-modules are commented like this // node_modules/... , so this app basically counts the occurrences of the same imports, and if the file size is over 20MB (can be changed in sib-config.json) the developer gets an error, and the three most used libs in the lambda.

Configuration

The configuration file sib-config.json can be found at the root of the project. Here you can change a few things:

• buildPath: default folder where the built lambdas are located
• errorThresholdMB: the maximum acceptable size of the lambda in megabytes
• showOnlyErrors: show only the files that exceed the error threshold
• filterByName: search filter for files
• ignorePattern: term, either complete or partial, to exclude from file names
• detailedReport: gives you a detailed report and the end

Custom arguments

Search for something specific in a lambda's name:

npx sib --filterByName=get

Add string to ignore in file names:

npx sib --ignore-pattern=redis

Overwrite the error threshold:

npx sib --errorThresholdMB=30

To show only the files that exceed the error threshold:

npx sib --showOnlyErrors

To run a detailed report:

npx sib --detailed-report

To see all available options:

npx sib --help

Pipeline Mode

When using the --pipeline flag, (a sib-config.json configuration file is required). In the absence of any errors, no output will be generated. However, if an error does occur, the program will exit with code 1.

This feature allows you to seamlessly integrate it into your existing pipeline, such as Husky or GitHub Actions, for efficient error handling and continuous integration.

For optimal results it is advisable to perform a build before very run.

npx sib --pipeline
or
npm run sib --pipeline

1. Add to husky.

npx husky add .husky/pre-commit "npm run sib --pipeline"

1. Add to Github Action

jobs:
    ...
    steps:
    ...
      - name: sib
        run: npm run sib --pipeline

Examples of how to use Serverless Icebreaker with

• SST framework
• Serverless framework

Support

Ask a question

If you have any questions or need clarification about SIB, feel free to ask in the repository. Other community members and maintainers can provide insights, solutions, and guidance to help you out.

👉 Ask a question

Create a bug report

Encountered an error or facing an issue with SIB? Make sure to create a bug report. By reporting bugs, you contribute to the improvement of the tool and help the maintainers identify and address any problems.

👉 Create bug report

Submit a feature request

Have a brilliant idea for a new feature or enhancement in SIB? Submit a feature request to share your suggestions with the community. It's an opportunity to shape the future of the tool and contribute to its growth.

👉 Submit feature request

Created by Apex lab

We are digital product experts with a vision of delivering top-quality solutions focusing on serverless.