Optimizing Amazon Simple Queue Service (SQS) for speed and scale
Optimizing Amazon Simple Queue Service (SQS) for speed and scale
by Jeff Barr | on 26 JUN 2024 | in Amazon Simple Queue Service (SQS), Featured, Launch, News | Permalink | Comments | Share
Voice by Polly
After several public betas, we launched Amazon Simple Queue Service (Amazon SQS) in 2006. Nearly two decades later, this fully managed service is still a fundamental building block for microservices, distributed systems, and serverless applications, processing over 100 million messages per second at peak times.
Because there’s always a better way
we continue to look for ways to Europe Cell Phone Number List improve performance, security, internal efficiency, and so forth. When we do find a potential way to do something better, we are careful to preserve existing behavior, and often run new and old systems in parallel to allow us to compare results.
Today I would like to tell you how we recently made improvements to Amazon SQS to reduce latency, increase fleet capacity, mitigate an approaching scalability cliff, and reduce power consumption.
Improving SQS Optimizing Amazon Simple Queue Service (SQS) for speed and scale
Like many AWS services, Amazon SQS is implemented using a collection of internal microservices. Let’s focus on two of them today:
Customer Front-End – The customer-facing front-end accepts, authenticates, and authorizes API calls such as CreateQueue and SendMessage. It then routes each request to the storage back-end.
Storage Back-End -This internal microservice is Afghanistan Phone Number List responsible for persisting messages sent to standard (non-FIFO) queues. Using a cell-based model, each cluster in the cell contains multiple hosts, each customer queue is assigned to one or more clusters, and each cluster is responsible for a multitude of queues:
Connections – Old and New
The original implementation used a connection per request between these two services. Each front-end had to connect to many hosts, which mandated the use of a connection pool, and also risked reaching an ultimate, hard-wired limit on the number of open connections. While it is often possible to simply throw hardware at problems like this and scale out, that’s not always the best way. It simply moves the moment of truth (the “scalability cliff”) into the future and does not make efficient use of resources.
