While AWS Lambda offers default metrics for basic monitoring, sometimes we need to look closely at function performance and resource use. Here is where CloudWatch Lambda Insights helps, providing enhanced observability designed for our Lambda functions.
What is CloudWatch Lambda Insights?
Lambda Insights collects, aggregates, and summarizes a new set of metrics for our functions. It does this by using a CloudWatch Lambda Extension (provided as a Lambda layer), which runs alongside our function in the same execution environment. This extension gathers detailed performance data for each invocation and sends it as log events using the embedded metric format to a dedicated CloudWatch Log Group (/aws/lambda-insights). CloudWatch then processes these logs to generate metrics.
What Metrics Does Lambda Insights Track?
Lambda Insights provides a new set of metrics. Key metrics include:
duration: The overall duration of the invocation.
cpu_total_time: Total CPU time used by the function.
memory_utilization: Maximum memory used as a percentage of allocated memory.
total_network: The number of bytes received and sent by the function.
fd_use: The maximum number of file descriptors in use.
init_duration: The time spent in the init phase of the Lambda execution environment lifecycle.
total_memory: The amount of memory allocated to our function.
billed_duration: Includes the duration plus a small amount of time needed to initialize the runtime.
billed_mb_ms: The total amount of compute resources used by our function is calculated by multiplying total_memory by the billed_duration.
Use Cases for Lambda Insights
Enabling Lambda Insights is particularly beneficial in the following scenarios:
Cold Start Analysis: The detailed breakdown of initialization times helps identify and reduce cold start latency.
Performance Optimization: Lambda Insights offers detailed memory and CPU usage metrics that help identify resource-related bottlenecks.
Cost Optimization: By identifying inefficient resource utilization, Lambda Insights helps optimize function configurations to reduce costs while maintaining performance.
Anomaly Detection: Lambda Insights enables easier identification of unexpected behavior patterns, such as memory leaks, excessive CPU utilization, running out of file descriptors, or unusual network activity.
Troubleshooting Issues: The detailed metrics make it easier to diagnose issues occurring in our functions.
Lambda Insights vs. Default Lambda Metrics
The default Lambda metrics are aggregated per minute, while Lambda Insights allows us to see the values for each invocation.
Lambda Insights offers detailed system-level resource usage metrics (CPU, Memory, Network).
The default Lambda metrics are enabled by default, while Lambda Insights needs to be activated for each function.
Lambda Insights is not free. It incurs costs based on the volume of log data ingested into CloudWatch Logs.
Let's deploy a simple .NET Lambda function with Lambda Insights enabled using the SAM.
Pre-requisites
Have an IAM User with programmatic access.
Install the Amazon Lambda Templates (dotnet new -i Amazon.Lambda.Templates)
Install the Amazon Lambda Tools (dotnet tool install -g Amazon.Lambda.Tools)
Install AWS SAM CLI.
The Lambda Function
Run the following commands to set up our Lambda function:
dotnet new lambda.EmptyFunction -n MyLambda -o .
dotnet add src/MyLambda package Amazon.Lambda.APIGatewayEvents
dotnet new sln -n MyApplications
dotnet sln add --in-root src/MyLambda
Open the Program.cs file and update the content as follows:
using Amazon.Lambda.Core;
using Amazon.Lambda.APIGatewayEvents;
[assembly: LambdaSerializer(typeof(Amazon.Lambda.Serialization.SystemTextJson.DefaultLambdaJsonSerializer))]
namespace MyLambda;
public class Function
{
public APIGatewayHttpApiV2ProxyResponse FunctionHandler(APIGatewayHttpApiV2ProxyRequest request, ILambdaContext context)
{
return new APIGatewayHttpApiV2ProxyResponse
{
Body = @"{""Message"":""Hello World""}",
StatusCode = 200,
Headers = new Dictionary<string, string> { { "Content-Type", "application/json" } }
};
}
}
AWS SAM template
At the solution level, create a template.yaml file with the following content:
AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Description: >
SAM
Resources:
MyFunction:
Type: AWS::Serverless::Function
Properties:
Timeout: 60
MemorySize: 512
Tracing: Active
Runtime: dotnet8
Architectures:
- x86_64
Handler: MyLambda::MyLambda.Function::FunctionHandler
CodeUri: ./src/MyLambda/
Policies:
- CloudWatchLambdaInsightsExecutionRolePolicy
Layers:
- !Sub arn:aws:lambda:${AWS::Region}:580247275435:layer:LambdaInsightsExtension:55
Events:
get:
Type: Api
Properties:
Path: /
Method: get
Outputs:
MyApiEndpoint:
Description: "API endpoint"
Value: !Sub "https://${ServerlessRestApi}.execute-api.${AWS::Region}.amazonaws.com/Prod"
To add Lambda Insights to our functions, we need to configure two things:
Add the CloudWatchLambdaInsightsExecutionRolePolicy policy. This policy provides the required permissions to send metrics and logs specifically for Lambda Insights.
Add the specific layer with the Lambda Insights extension. You can find all the layers here.
Run the following commands to deploy the resources to AWS:
sam build
sam deploy --guided
Final Words
Do not enable Lambda Insights on all functions by default. Enable it only for:
Critical production functions where deep visibility is essential.
Functions currently undergoing performance tuning or troubleshooting.
During the development and testing phases.
Configure an appropriate data retention period for the /aws/lambda-insights log group. Storing insights data indefinitely may incur ongoing CloudWatch Logs storage costs. You can find all the code here. Thanks, and happy coding.
