aws-lambda-power-tuning
autoscaler
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aws-lambda-power-tuning | autoscaler | |
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36 | 89 | |
5,145 | 7,622 | |
- | 1.8% | |
8.7 | 9.7 | |
7 days ago | 3 days ago | |
JavaScript | Go | |
Apache License 2.0 | Apache License 2.0 |
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aws-lambda-power-tuning
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Optimizing Costs in the Cloud: Embracing a FinOps Mindset
Sometimes, changing services, like opting for HTTP over REST API Gateway, leveraging tools like Lambda Powertuning to optimize functions, or reducing a CloudWatch log retention and changing log level, can lead to significant savings.
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AWS SnapStart - Part 13 Measuring warm starts with Java 21 using different Lambda memory settings
In case of not enabling SnapStart for the Lambda function we observed that increasing memory reduces the warm execution time for our use case especially for p>90. As adding more memory to the Lambda function is also a cost factor, the sweet spot between cold and warm start time and cost is somewhere between 768 and 1204 MB memory setting for the Lambda function for our use case. You can use AWS Lambda Power Tuning for very nice visualisations.
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How to enhance your Lambda function performance with memory configuration?
The aws lambda power tuning tool helps optimise the Lambda performance and cost in a data-driven manner. Let's try it out:
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Controlling Cloud Costs: Strategies for keeping on top of your AWS cloud spend
For Lambda, a very useful tool to help optimise is the AWS Lambda Power Tuning tool, released by Alex Casalboni, Developer Advocate at AWS: https://github.com/alexcasalboni/aws-lambda-power-tuning
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Best way to decrease latency (API <-> Lambda <-> Dynamodb)
Lambda memory affects not only the CPU performance and and host execution priority, but also network performance. Be wary though as the price scales linearly. You can use a tool like Lambda Power Tuning to find the sweet spot for your application. https://github.com/alexcasalboni/aws-lambda-power-tuning
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How to optimize your lambda functions with AWS Lambda power tuning
This tool, which is open source and available here, takes the form of a Step Function that is deployed on your AWS account. The purpose of this Step Function is to run your lambda with different memory configurations several times and output a comparison in the form of a graph (or JSON) to try to find the optimal balance between cost and execution time. There are three possible optimization modes: cost, execution time, or a "balanced" mode where it tries to find a balance between the two.
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Developers Journey to AWS Lambda
The AWS Documentation's Memory and Computing Power page is a good starting point. To avoid configuring it manually, it's worth checking out AWS Lambda Power Tuning, which will help you find the sweet spot.
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Guide to Serverless & Lambda Testing — Part 2 — Testing Pyramid
Utilizing tools such as AWS X-Ray, AWS Lambda Power Tuning, and AWS Lambda Powertools tracer utility is recommended. Read more about it here.
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Tunea tus funciones Lambda
Install the AWS SAM CLI in your local environment. Configure your AWS credentials (requires AWS CLI installed): $ aws configure Clone this git repository: $ git clone https://github.com/alexcasalboni/aws-lambda-power-tuning.git Build the Lambda layer and any other dependencies (Docker is required): $ cd ./aws-lambda-power-tuning $ sam build -u sam build -u will run SAM build using a Docker container image that provides an environment similar to that which your function would run in. SAM build in-turn looks at your AWS SAM template file for information about Lambda functions and layers in this project. Once the build has completed you should see output that states Build Succeeded. If not there will be error messages providing guidance on what went wrong. Deploy the application using the SAM deploy "guided" mode: $ sam deploy -g
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AWS Serverless Production Readiness Checklist
Use AWS Lambda Power Tuning to balance cost and performance.
autoscaler
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Upgrading Hundreds of Kubernetes Clusters
We use Cluster Autoscaler to automatically adjust the number of nodes (cluster size) based on your actual usage to ensure efficiency. Additionally, we deploy Vertical and Horizontal Pod Autoscalers to scale your applications' resources as their needs change automatically.
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Not Everything Is Google's Fault (Just Most Things)
> * Hetzner: cheap, good service, the finest pets in the world, no cattle
You can absolutely do cattle with Hetzner. They support imaging and immutable infrastructure. They don't have a native auto scaling equivalent, but if you're using Kubernetes, they have a cluster autoscaler: https://github.com/kubernetes/autoscaler/blob/master/cluster...
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Kubernetes(K8s) Autoscaler — a detailed look at the design and implementation of VPA
Here we take the VPA as a starting point to analyze the design and implementation principles of the VPA in Autoscaler. The source code for this article is based on Autoscaler HEAD fbe25e1.
- Scaling with Karpenter and Empty Pod(A.k.a Overprovisioning)
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Reducing Cloud Costs on Kubernetes Dev Envs
Autoscaling over EKS can be accomplished using either the cluster-autoscaler project or Karpenter. If you want to use Spot instances, consider using Karpenter, as it has better integrations with AWS for optimizing spot pricing and availability, minimizing interruptions, and falling back to on-demand nodes if no spot instances are available.
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☸️ Managed Kubernetes : Our dev is on AWS, our prod is on OVH
Autoscaling is already provided on OVH, but we don't use it for now. Autoscaler has to be manually installed on the AWS/EKS cluster.
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relevant way of scaling pods
do you mean this: https://github.com/kubernetes/autoscaler/blob/master/vertical-pod-autoscaler/pkg/recommender/README.md
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Kubernetes Cluster Maintenance
Read more about this scaler in detail here!
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Anyone running Windows nodes in your clusters?
We have a default node group of Linux hosts, but there's a secondary nodegroup of Windows hosts that is typically scaled down to 0. When a team's build runs, a pod is scheduled based on their definition. Cluster-autoscaler will check the nodeSelector and automatically spin up a node from that nodegroup if necessary.
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How to make sure Kubernetes autoscaler not deleting the nodes which runs specific pod
I am running a Kubernetes cluster(AWS EKS one) with Autoscaler pod So that Cluster will autoscale according to the resource request within the cluster.
What are some alternatives?
json-schema-to-ts - Infer TS types from JSON schemas 📝
karpenter-provider-aws - Karpenter is a Kubernetes Node Autoscaler built for flexibility, performance, and simplicity.
dynamodb-toolbox - A simple set of tools for working with Amazon DynamoDB and the DocumentClient
cluster-proportional-autoscaler - Kubernetes Cluster Proportional Autoscaler Container
middy - 🛵 The stylish Node.js middleware engine for AWS Lambda 🛵
aws-ebs-csi-driver - CSI driver for Amazon EBS https://aws.amazon.com/ebs/
aws-sam-cli - CLI tool to build, test, debug, and deploy Serverless applications using AWS SAM
keda - KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
aws-graviton-getting-started - Helping developers to use AWS Graviton2 and Graviton3 processors which power the 6th and 7th generation of Amazon EC2 instances (C6g[d], M6g[d], R6g[d], T4g, X2gd, C6gn, I4g, Im4gn, Is4gen, G5g, C7g[d][n], M7g[d], R7g[d]).
descheduler - Descheduler for Kubernetes
failure-lambda - Module for fault injection into AWS Lambda
k3s-aws-terraform-cluster - Deploy an high available K3s cluster on Amazon AWS