Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables you to decouple and scale micro services, distributed systems, and server less applications. SQS eliminates the complexity and overhead associated with managing and operating message oriented middleware, and empowers developers to focus on differentiating work. Using SQS, you can send, store, and receive messages between software components at any volume, without losing messages or requiring other services to be available. Get started with SQS in minutes using the AWS console, Command Line Interface or SDK of your choice, and three simple commands.
SQS offers two types of message queues. Standard queues offer maximum throughput, best-effort ordering, and at-least-once delivery. SQS FIFO queues are designed to guarantee that messages are processed exactly once, in the exact order that they are sent.
a. Eliminate Administrative Overhead
AWS manages all in progress operations and underlying infrastructure required to produce an extremely accessible and scalable message queuing service.
With AWS SQS, there’s no direct value, no ought to acquire, install, and assemble messaging package, and no long build-out and maintenance of supporting infrastructure.
Amazon SQS queues are dynamically created and scale automatically, therefore, you’ll be able to build and grow applications quickly and with efficiency.
b. Reliably Deliver Messages
Use AWS SQS to transmit any volume of data, at any level of output, while not losing messages or requiring alternative services to be accessible. Amazon SQS helps you to decouple application parts in order that they run and fail severally, increasing the fault tolerance of the system.
Multiple copies of each message area unit hold on redundantly across multiple accessible zones in order that they’re out there whenever required.
c. Keep Sensitive Information Secure
You can use Amazon SQS to exchange sensitive data between applications using server-side secret writing (SSE) to inscribe every message body.
The AWS SQS compass point integration with AWS Key Management Service (KMS) permits you to centrally manage the keys that defend SQS messages together with keys that defend your alternative AWS resources.
AWS KMS logs each use of your encryption keys to AWS CloudTrail to assist meet your restrictive and compliance wants.
d. Scale Elastically and Cost-Effectively
AWS SQS leverages the AWS cloud to dynamically scale supported demand. Amazon SQS scales elastically together with your application. Therefore, you don’t have to worry regarding capability designing and pre-provisioning.
There’s no limit to the number of messages per queue, and commonplace queues offer nearly unlimited output. Prices area unit supported usage that provides important value saving versus the “always-on” model of self-managed electronic messaging middleware.
Industrial Use Cases-
The BMW Group is using AWS for its new connected-car application that collects sensor data from BMW 7 Series cars to give drivers dynamically updated map information. BMW Group is one of the leading manufacturers of premium cars and mobility services in the world, with brands such as Rolls Royce, BMW, and Mini. BMW built its new car-as-a-sensor (CARASSO) service in only six months leveraging Amazon Simple Storage Service (Amazon S3), Amazon Simple Queue Service (Amazon SQS), Amazon DynamoDB, Amazon Relational Database Service (Amazon RDS), and AWS Elastic Beanstalk. By running on AWS, CARASSO can adapt to rapidly changing load requirements that can scale up and down by two orders of magnitude within 24 hours. By 2018 CARASSO is expected to process data collected by a fleet of 100,000 vehicles traveling more than eight billion kilometers.
redBus is an Indian travel agency that specializes in bus travel throughout India by selling bus tickets throughout the country. Tickets are purchased through the company’s Website or through the Web services of its agents and partners. The company also offers software, on a Software as a Service (SaaS) basis, which gives bus operators the option of handling their own ticketing and managing their own inventories. To date, the company says they have sold over 30 million bus tickets and has more than 1750 bus operators using the software to manage their operations.
The company previously ran its operations from a traditional data center by purchasing and renting its systems and infrastructure. In addition to the expense, several logistical problems evolved from this arrangement. The biggest problem was that the infrastructure could not effectively handle processing fluctuations, which had a negative impact on productivity. Additionally, the procurement of servers or upgrading the server configuration was an extremely time-consuming endeavor. Over time, redBus realized that a better solution was imperative — a solution that offered scalability to handle the company’s processing fluctuations. redBus looked to Amazon Web Services (AWS) for a solution.
Why Amazon Web Services
After testing the AWS solution on a small application for several months, the travel agency determined that it was very workable and convenient. Although redBus was quite enthusiastic about the on-demand instances and variety of instance types, several other features cemented the company’s decision to migrate completely to AWS. These features included the ability to easily manage access to servers through security groups, the easy-to-use, self-service management console, the concept of Elastic IPs, and superior support.
The company has incorporated many of the AWS products into its solution, including Amazon Elastic Compute Cloud (Amazon EC2), Elastic Load Balancing, Amazon Relational Database Service (Amazon RDS), Amazon Simple Storage Service (Amazon S3), Amazon Elastic Block Store (Amazon EBS), and Amazon Cloud Watch. Charan Padmaraju, Chief Technology Officer believes that “with features like Elastic Load Balancing and multiple availability zones, AWS provides the required infrastructure to build for redundancy and auto-fail over. When you incorporate these in your system/application design, you can achieve high reliability and scale.”
Benefits after AWS-
- Reduced costs by up to 40%
- Reduced website latency by 4x
- Able to instantly replicate test environments, which in turn reduces time to market
Oyster’s Challenge solved
Since its 2009 launch, Oyster has published more than one million high-quality digital images. When this massive volume of images became too cumbersome to handle in-house, the company decided to offload the content to a central repository on Amazon Simple Storage Service (Amazon S3). “We migrated to Amazon S3 in 2010,” says Eytan Seidman, Co-Founder and Vice President of Product. “We chose moving to the cloud and Amazon S3 because storing images in our data center would have been too costly. Amazon S3 was a more economical solution.”
Oyster reprocesses its entire collection of photographic images a few times each year to update the copyright year and, if necessary, to change the watermarks. Using their previous solution, reprocessing the entire collection of photographs required about 800 hours to complete. In addition, Oyster often recreated existing images in new formats and sizes for mobile and tablet devices. Resizing existing images and adding new ones was slowing down the rate at which the company was able to process the collection. “Our processes were slowing down,” says Seidman. “When the iPad with Retina display came out, for example, it took us more than a week to create new sizes specifically for that resolution.” Oyster considered purchasing additional hardware, but found the cost of new hardware and routine maintenance was too high, especially when the machines would sit idle most of the time.
Moreover, there were numerous software bugs in the multiprocessing solution that the company used, but since the solution didn’t scale, Oyster didn’t bother to fix them.
Why Amazon Web Services
“We were already using Amazon S3 to store the images, so using Amazon Elastic Compute Cloud (Amazon EC2) to process the images was a natural choice,” Seidman says. Chris McBride, a software engineer at Oyster, adds, “We wanted a cloud environment that could be ramped up for the large processing jobs and downsized for the smaller daily jobs.”
While the company is still running one local server, the bulk of the processing work now takes place on the AWS Cloud. Oyster is using a customized Amazon Linux AMI within Amazon EC2. Within this new environment, the company connects to Amazon S3 and Amazon Simple Queue Service (Amazon SQS) using boto, a Python interface to AWS. The images themselves are processed with the ImageMagick software available in the AMI package.
Oyster uses Amazon EC2 instances and Amazon SQS in an integrated workflow to generate the sizes they need for each photo. The team processes a few thousand photos each night, using Amazon EC2 Spot Instances. When Oyster processes the entire collection, it can use up to 100 Amazon EC2 instances. The team uses Amazon SQS to communicate the photos that need to be processed and the status of the jobs.
Benefits of AWS
- Reduced time to process photos by 95%
- Reduced in-house hardware expenses
- Saves $10,000 in capital expenditure and reduced operating expenses by an additional $10,000
Thank — You