https://github.com/matteogioioso/lambda-flame
Extract a flame graph directly from your lambda function. Supported runtimes: Nodejs. 🔥
https://github.com/matteogioioso/lambda-flame
aws aws-lambda flame-graph lambda nodejs serverless
Last synced: 10 months ago
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Extract a flame graph directly from your lambda function. Supported runtimes: Nodejs. 🔥
- Host: GitHub
- URL: https://github.com/matteogioioso/lambda-flame
- Owner: MatteoGioioso
- License: mit
- Created: 2020-12-03T04:19:45.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-05-22T07:56:23.000Z (almost 5 years ago)
- Last Synced: 2024-05-01T20:08:44.633Z (almost 2 years ago)
- Topics: aws, aws-lambda, flame-graph, lambda, nodejs, serverless
- Language: JavaScript
- Homepage:
- Size: 12.7 MB
- Stars: 13
- Watchers: 2
- Forks: 0
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Extract flame graph for Nodejs directly from an AWS lambda function.
Lambda flame is a custom Nodejs runtime that collects and creates a flame graph of your lambda function process.
## Usage
### Deployment
The first thing is to deploy the Serverless application into your account.
You can do it via the AWS console: [lambda-flame](https://serverlessrepo.aws.amazon.com/applications/ap-southeast-1/164102481775/lambda-flame),
or directly into Cloudformation: (**make sure you specify the latest version**)
```yaml
lambdaflame:
Type: AWS::Serverless::Application
Properties:
Location:
ApplicationId: arn:aws:serverlessrepo:ap-southeast-1:164102481775:applications/lambda-flame
SemanticVersion: 1.0.0
```
### Setup
After the application has been deployed go to the lambda console,
click on layers and copy the lambda-flame layer arn;
#### SAM/Cloudformation
You can now paste the layer in your SAM/Cloudformation template:
```yaml
AWSTemplateFormatVersion: "2010-09-09"
Transform: "AWS::Serverless-2016-10-31"
Description: "Lambda flame test template"
Resources:
ExampleFunction:
Type: AWS::Serverless::Function
Properties:
Runtime: provided // Runtime must be set to provided
Timeout: 30
MemorySize: 1024
CodeUri: ./
Handler: handler.handler
Environment:
Variables:
LAMBDA_FLAME_DEST_BUCKET: !Ref FlameBucket
LAMBDA_FLAME_DEBUG: ALL
Policies:
- AWSLambdaBasicExecutionRole
- S3WritePolicy: // Or simply s3:PutObject
BucketName: !Ref FlameBucket
Layers:
- !Sub arn:aws:lambda:ap-southeast-1:{AWS::AccountId}:layer:lambda-flame:13 // Layer arn from the Lambda Flame application
Events:
HTTP:
Type: Api
Properties:
Path: /
Method: Get
FlameBucket:
Type: AWS::S3::Bucket
Properties:
BucketName: lambda-flame-graph-test
```
#### Serverless framework
Paste the arn in your `serverless.yaml`:
```yaml
service: lambda-flame-test-sls
provider:
name: aws
stackName: lambda-flame-test-sls
region: ap-southeast-1
iamRoleStatements:
- Effect: Allow
Action:
- s3:PutObject
Resource:
- arn:aws:s3:::lambda-flame-graph-test
- arn:aws:s3:::lambda-flame-graph-test/*
package:
individually: true
functions:
exampleFunction:
memorySize: 1024
timeout: 30
handler: handler.handler
runtime: provided
environment:
LAMBDA_FLAME_DEST_BUCKET: lambda-flame-graph-test
LAMBDA_FLAME_DEBUG: ALL
layers:
- !Sub arn:aws:lambda:ap-southeast-1:${AWS::AccountId}:layer:lambda-flame:13
events:
- http:
method: get
path: /
resources:
Resources:
FlameBucket:
Type: AWS::S3::Bucket
Properties:
BucketName: lambda-flame-graph-test
```
If you are using the **webpack plugin**, the custom runtime option will not work for versions < 5.4.0
Just add the `allowCustomRuntime: true` option to your function:
```yaml
exampleFunction:
handler: handler.handler
runtime: provided
allowCustomRuntime: true
layers:
- !Sub arn:aws:lambda:ap-southeast-1:${AWS::AccountId}:layer:lambda-flame:13
...
```
### Some important notes:
- The `Runtime` property **must be set to `provided`**
- All the result of the v8 profiling and the flamegraph creation will be sent to an S3 bucket of your choice. You **must specify a valid S3 Bucket name**.
You can use any bucket you want and one bucket for all your functions,
lambda flame will name space all the output files under `s3:///lambda-flame///`
- In order to send the profiling output and flame graph files to your S3 bucket **make sure you have the required write permissions `s3:PutObject`**
- The v8 profile output processing happens outside your handler invocation therefore it won't impact your function profiling. However, is a CPU bound operation,
if you want to make it a bit faster just temporarily add more memory
- **This tool (for the time being) is not meant to run in a production environment, use it as a debugging purposes only**
### Run
After the invocation of your function has terminated, you can navigate to your S3 Bucket under `s3:///lambda-flame///`
and open the `flamegraph.html`
## Config
You can set all the parameters via environmental variables
| Property | Type | Description | Default |
| -------- | ---- | ----------- | ------- |
| LAMBDA_FLAME_DEST_BUCKET | `String` | Valid S3 bucket name **required** | `undefined` |
| LAMBDA_FLAME_DEBUG | `String` | Activate debugging logs, they will appear in Cloudwatch, leave it empty if you don't want them | `ALL` |
## Roadmap
There are a lot of new features I want to add, feel free to contribute, open issues and share your ideas 🙏.
- Provide multiple runtimes other than nodejs (planning to support Go)
- Add more control over via configuration
- Automate builds of Nodejs runtimes
- Possibility to capture a heap dump
- Dramatically improve flame graph visualization and interactivity
## Notice
This tool (for the time being) is partially using a modified version [flamebearer](https://github.com/mapbox/flamebearer) all the credits goes to the [author](https://www.mapbox.com/) of this library.
In the future I am planning to build a custom visualization similar to [Clinic flame](https://clinicjs.org/flame/).