What is AWS Elastic Load Balancing (ELB)?
Who uses AWS Elastic Load Balancing (ELB)?
AWS Elastic Load Balancing (ELB) Integrations
Here are some stack decisions, common use cases and reviews by companies and developers who chose AWS Elastic Load Balancing (ELB) in their tech stack.
The 350M API requests we handle daily include many processing tasks such as image enhancements, resizing, filtering, face recognition, and GIF to video conversions.
Tornado is the one we currently use and aiohttp is the one we intend to implement in production in the near future. Both tools support handling huge amounts of requests but aiohttp is preferable as it uses asyncio which is Python-native. Since Python is in the heart of our service, we initially used PIL followed by Pillow. We kind of still do. When we figured resizing was the most taxing processing operation, Alex, our engineer, created the fork named Pillow-SIMD and implemented a good number of optimizations into it to make it 15 times faster than ImageMagick
Thanks to the optimizations, Uploadcare now needs six times fewer servers to process images. Here, by servers I also mean separate Amazon EC2 instances handling processing and the first layer of caching. The processing instances are also paired with AWS Elastic Load Balancing (ELB) which helps ingest files to the CDN.
We chose AWS because, at the time, it was really the only cloud provider to choose from.
We tend to use their basic building blocks (EC2, ELB, Amazon S3, Amazon RDS) rather than vendor specific components like databases and queuing. We deliberately decided to do this to ensure we could provide multi-cloud support or potentially move to another cloud provider if the offering was better for our customers.
We’ve utilized c3.large nodes for both the Node.js deployment and then for the .NET Core deployment. Both sit as backends behind an nginx instance and are managed using scaling groups in Amazon EC2 sitting behind a standard AWS Elastic Load Balancing (ELB).
While we’re satisfied with AWS, we do review our decision each year and have looked at Azure and Google Cloud offerings.
#CloudHosting #WebServers #CloudStorage #LoadBalancerReverseProxy
We build a Slack app using the Bolt framework from slack https://api.slack.com/tools/bolt, a Node.js express app. It allows us to easily implement some administration features so we can easily communicate with our backend services, and we don't have to develop any frontend app since Slack block kit will do this for us. It can act as a Chatbot or handle message actions and custom slack flows for our employees.
This app is deployed as a microservice on Amazon EC2 Container Service with AWS Fargate. It uses very little memory (and money) and can communicate easily with our backend services. Slack is connected to this app through a ALB ( AWS Elastic Load Balancing (ELB) )
We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.
We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.
In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.
Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache
We use Terraform because we needed a way to automate the process of building and deploying feature branches. We wanted to hide the complexity such that when a dev creates a PR, it triggers a build and deployment without the dev having to worry about any of the 'plumbing' going on behind the scenes. Terraform allows us to automate the process of provisioning DNS records, Amazon S3 buckets, Amazon EC2 instances and AWS Elastic Load Balancing (ELB)'s. It also makes it easy to tear it all down when finished. We also like that it supports multiple clouds, which is why we chose to use it over AWS CloudFormation.
We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.
We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.
We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.
You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?
AWS Elastic Load Balancing (ELB)'s Features
- Distribution of requests to Amazon EC2 instances (servers) in multiple Availability Zones so that the risk of overloading one single instance is minimized. And if an entire Availability Zone goes offline, Elastic Load Balancing routes traffic to instances in other Availability Zones.
- Continuous monitoring of the health of Amazon EC2 instances registered with the load balancer so that requests are sent only to the healthy instances. If an instance becomes unhealthy, Elastic Load Balancing stops sending traffic to that instance and spreads the load across the remaining healthy instances.
- Support for end-to-end traffic encryption on those networks that use secure (HTTPS/SSL) connections.
- The ability to take over the encryption and decryption work from the Amazon EC2 instances, and manage it centrally on the load balancer.
- Support for the sticky session feature, which is the ability to "stick" user sessions to specific Amazon EC2 instances.
- Association of the load balancer with your domain name. Because the load balancer is the only computer that is exposed to the Internet, you don’t have to create and manage public domain names for the instances that the load balancer manages. You can point the instance's domain records at the load balancer instead and scale as needed (either adding or removing capacity) without having to update the records with each scaling activity.
- When used in an Amazon Virtual Private Cloud (Amazon VPC), support for creation and management of security groups associated with your load balancer to provide additional networking and security options.
- Supports use of both the Internet Protocol version 4 (IPv4) and Internet Protocol version 6 (IPv6).