I am using aws for my cloud infrastructure. I use ecs fargate as my compute machine. I am currently maintaining 10-20 apis which interact with members who have my application downloaded on their phone. Obviously one or two of these apis are my "main" apis and these are the ones which are really personalised to my users and honestly, these are the only two apis which members really access (by navigating to those screens).
My business team wants to send push notifications to members to alert them on certain new events which lands them on a screen where these APIs need to be called. Due to this, my application has mini crashes during this time period.
I've thought of a couple of ideas for the same, but since this is obviously an issue across industries and a solved problem, I wanted the standard solutions.
The ideas I have:
Sending notifications in batches. This seems like the best solution though it requires a bit of effort though I'm not sure how much.
Have a serverless machine run my requests (aws lambda functions) for those APIs which need to scale instantly. I have a lot of other APIs which I keep in fargate because I don't want my lambda function to be too heavy and then take a while to start up.
Scale machines all the time to handle the load I get during push notifications. This seems suboptimal due to cost reasons.
Scale machines up just during those periods where I want to send push notifications and them scale them back down. This seems like a decent solution if I can automate the entire process. I can have a flow which I follow for each push notification which will cause the system to scale and then start sending the notifications.
Is there a better way to do this. This seems like a relatively straightforward problem for people to have, but I don't see too much information on this topic.
I like your second option best because it's by far the easiest to manage (because you don't have to manage it). After that I'd go with your last option. I would use step functions to manage this, where the first step is to scale up the number of instances in Fargate. Once that has reached the desired level you would send the notifications. Add autoscaling to your services in Fargate to have it handle coming down automatically.
Related
I have a set of calculations that needs to run in a batch, and the workload is easily parallelized across machines. The work to be done is already done within a Docker container. I'm trying to understand the easiest way for me to run this workload in a highly parallel way on AWS. However, in trying to figure out where to begin I'm having trouble finding the right entrypoint. I read about AWS Batch and AWS Fargate, but each time I try to go down one of those paths to learn about them in more detail, more AWS services start popping up (Lamdas, Step Functions, ECS, AutoScaling groups), with each article having a different combination. Furthermore, I start thinking about the problem as a Batch vs Fargate problem, and then I find another article that talks about Batch + Fargate, or X + ECS + ....
I'm having trouble finding the appropriate introduction to the choices so I can get started with setting something up and getting some experience. Any pointers on which direction I might go or some resources for me to look at?
AWS containers services team member here. Your question triggers all my button cause I have been working on a deliverable to address some of this confusion ("where do I start with xyz?"). I can try to answer your question briefly here but if you want to read more (perhaps way more than you'd need feel free to contact me offline (mreferre at amazon dot com will work).
First and foremost it's not a Vs but it's an AND. Think of all these products you mention being distributed at different layers of the stack (this is a draft visual in the deliverable):
Fargate represents capacity (where your container is running), ECS represents a core containers orchestrator and Batch is one of the provisioners on top of the container orchestrator. Lambda is something separate and that live on its own. The options for your specific use case seem to be:
Lambda
ECS/Fargate
Batch/ECS/Fargate
Step Functions/ECS/Fargate (this one is outside of analysis and you don't see it in my visual - wondering if I should add it).
As others have hinted you probably want to use Lambda if your model is event-driven (e.g. if you want to fire up a dedicated function for every event like a new file uploaded to S3).
You probably do not want to use a naked ECS/Fargate solution because it would require more work to deal with the triggering and the scheduling of your batch jobs.
You probably want to use either Batch or Step Functions to schedule jobs on ECS/Fargate. I'd argue SF is good if you have basic workflows that you need to deal with and Batch if you need to manage complex jobs at scale. Perhaps this 35 mins presentation that I did last year can provide a bit more background on these Batch Vs SF differences.
Let me know if you have any additional questions because this discussion is super useful for the positioning I am trying to build.
I am new to AWS and havebeen reading about aws lambda. Its very useful but you still have to write individual lambda functions instead of as a whole. i am wondering practically if its possible AWS Lambda can replace an entire Rest Api layer in an enterprise web application
Of course, everything is possible in the computer world but you need to answer lambda-serverless is the best way for me?
For example, you need smaller business flow per lambda(lambda have some hardware limits and need short computing and starting time for cost savings), that's mean you must separate your flow, its success depends on your business area and implementation. is your working area fit for this? But Lambda can handle almost everything with other AWS services(to be honest, maybe in some cases, lambda is a bit harder than the current system and community support is less than traditional systems but it also has lots of advantages as you know). You can check this repo, it full-serverless booking app and this serverless e-commerce repo.
To sum up, if your team is ready for it, you can start the conversion from some part of your application and check everything is ok. This answer totally depends on your team and business BCS nothing is impossible and that's engineering.
That's my opinion because your question looks like a comment question.
I am starting a project where I want to create a website which will display LIVE flight information and status. We all have seen this at airport. An example is given here - http://www.computronics.biz/productimages/prodairport4.jpg. As you can see this information changes continuously. The website will talk to a backend api and the this backend api will talk to database. Now the important part is that the flight information in the database will be updated by the airline itself. There could be several airlines and they will update their data respectively. I have drawn a diagram and uploaded here - https://imgur.com/a/ssw1S.
Now those airlines will obviously have an interface (website talking to some backend API) through which they will update the database.
Now here is my attempt to solve it. We need to have some sort of trigger such that if any airline updates a flight detail in the database between current time - 1 hour to current + 4 hours (website will only display few hours of flights), we need to call the web api and then send the update to the website in the real time. The user must not refresh the page at all. At the same time the website needs to scale well i.e. if 1 million users are on the website, and there is an update in the database in the correct time range, all 1 million user's website should get updated within a decent amount of time.
I did some research and it looks like we need to have an event based approach. For example - we need to create a function (AWS lambda or Azure function) that should be called whenever there is an update in the database (Dynamo DB for example) within the correct time range. This function then should call an API which should then update the website through web socket technology for example.
I am not looking for any code but just some alternative suggestions on how this can be solved in a scalable way. Also how do we test scalability?
Dont use serverless functions(Lambda/Azure functions)
Although I am a huge fan of serverless functions, and currently running a full web app in Lambda, I don't think its needed for your use case and doesn't make sense economically. As you've answered in the comments, each airline will not write directly to the database, they'll push to an API, meaning you are explicitly told when flights have changed. When an airline has sent you new data you can simply propagate this to all the browser endpoints via websockets. This keeps the design very simple. There is no need to artificially create a database event that then triggers a function that will then tell you a flight has been updated. Thats like removing your doorbell and replacing it with a motion detector that triggers a doorbell :)
Cost
Money always deserves its own section. Lambda is more of an economic break through than a technological one. You have to know when its cost effective. You pay per request so if your dealing with a process that handles 10,000 operations a month, or something that only fires 1,000 times a day, than lambda is dirt cheap and practically free. You also pay for the length of time the function is executing and the memory consumed while executing. Generally, it makes sense to use lambda functions where a dedicated server would be sitting idle for most of the time. So instead of a whole EC2 instance, AWS provides you with a container on demand. There are points at which high requests rates and constantly running processes makes lambda more expensive than EC2. This article discusses how generally its cheaper to use lambda up to a point -> https://www.trek10.com/blog/lambda-cost/ The same applies to Azure functions and googles equivalent. They are all just containers offered on demand.
If you're dealing with flight information I would imagine you will have thousands of flights being updated every minute so your lambda functions will be firing constantly as if you were running an EC2 instance. You will end up paying a lot more than EC2. When you have a service that needs to stay up 24/7 and run 24/7 with high activity that is most certainly a valid use case for a dedicated server or servers.
Proposed Solution
These are the components I would use below:
Message Queue of some sort (RabbitMQ or AWS SQS with SNS perhaps)
Web Socket Backend (The choice will depend on programming language)
Airline input API (REST,GraphQL, or maybe AWS Kinesis Data Firehose)
The airlines publish their data to a back-end api. The updates are stored on a message queue and the web applicaton that actually displays the results to users, via websockets, reads from the queue.
Scalability
For scalability you can run the websocket application on multiple EC2 instances (all reading from the same queuing service) in an autoscaling group, so with extra load more instances will be created automatically hence the name "autoscaling". And those instances can sit behind an elastic load balancer. Lots of AWS documentation on how to do this and its their flagship design pattern. If you use AWS SQS you don't have to manage the scalability details yourself, aws handles that. The only real components to scale are your websocket application and the flight data input endpoint. You can run the flight api in an autoscaling group as well but AWS does offer an additional tool for high traffic data processing. I detail that below.
Testing Scalability
It would be fairly easy to have a mock airline blast your service with thousands and thousands of fake updates and on the other end you can easily run multiple threads of selenium tests simulating browser clicks and validating that the UI is still operational.
Additional tools
If it ends up being large amounts of data, rather than using a conventional REST api for your flight update service you could consider a service AWS offers specifically for dealing with large amounts of real time updates (Kinessis Data Firehose) https://aws.amazon.com/kinesis/data-firehose/ But I've never used it.
First, please don't over think this. This is a trivial problem to solve and doesn't require any special techniques, technologies or trendy patterns & frameworks.
You actually have three functional areas you can address almost separately.
Ingestion - Collection and normalization of the data from the various sources. For this, you'll need a process and transformation engine, LogicApps or such.
Your databases. You'll quickly learn that not all flights are the same ;). While it might seem so, the amount of data isn't that much. Instances of MySQL/SQL Server tuned for a particular function will work just fine. Hint, you don't need to have data for every movement ready to present all the time.
Presentation. The data API and UIs. This, really, is the easy part. I would suggest you use basic polling at first. For reasons you will never have any control over, the SLA for flight data is ~5 minutes so a real-time client notification system is time you should spend elsewhere at first.
We currently run a Java backend which we're hoping to move away from and switch to Node running on AWS Lambda & Serverless.
Ideally during this process we want to build out a fully service orientated architecture.
My question is if our frontend angular app requests the current user's ordered items to get that information it would need to hit three services, the user service, the order service and the item service.
Does this mean we would need make three get requests to these services? At the moment we would have a single endpoint built for that specific request, which can then take advantage of DB joins for optimal performance.
I understand the benefits SOA, but how to do we scale when performing more compex requests such as this? Are there any good resources I can take a look at?
Looking at your question I would advise to align your priorities first: why do you want to move away from the Java backend that you're running on now? Which problems do you want to overcome?
You're combining the microservices architecture and the concept of serverless infrastructure in your question. Both can be used in conjunction, but they don't have to. A lot of companies are using microservices, even bigger enterprises like Uber (on NodeJS), but serverless infrastructures like Lambda are really just getting started. I would advise you to read up on microservices especially, e.g. here are some nice articles. You'll also find answers to your question about performance and joins.
When considering an architecture based on Lambda, do consider that there's no state whatsoever possible in a Lambda function. This is a step further then stateless services that we usually talk about; they generally target 'client state' that does not exist anymore. But a Lambda function cannot have any state, so e.g. a persistent DB-connection pool is not possible. For all the downsides, there's also a lot of stuff you don't have to deal with which can be very beneficial, especially in terms of scalability.
I have a web app running on php, mysql, apache on a virtual windows server. I want to redesign it so it is scalable (for fun so I can learn new things) on AWS.
I can see how to setup an EC2 and dump it all in there but I want to make it scalable and take advantage of all the cool features on AWS.
I've tried googling but just can't find a simple guide (note - I have no command line experience of Linux)
Can anyone direct me to detailed resources that can lead me through the steps and teach me? Or alternatively, summarise the steps in an answer so I can research based on what you say.
Thanks
AWS is growing and changing all the time, so there aren't a lot of books to help. Amazon offers training that's excellent. I took their three day class on Architecting with AWS that seems to be just what you're looking for.
Of course, not everyone can afford to spend the travel time and money to attend a class. The AWS re:Invent conference in November 2012 had a lot of sessions related to what you want, and most (maybe all) of the sessions have videos available online for free. Building Web Scale Applications With AWS is probably relevant (slides and video available), as is Dissecting an Internet-Scale Application (slides and video available).
A great way to understand these options better is by fiddling with your existing application on AWS. It will be easy to just move it to an EC2 instance in AWS, then start taking more advantage of what's available. The first thing I'd do is get rid of the MySql server on your own machine and use one offered with RDS. Once that's stable, create one or more read replicas in RDS, and change your application to read from them for most operations, reading from the main (writable) database only when you need completely current results.
Does your application keep any data on the web server, other than in the database? If so, get rid of all local storage by moving that data off the EC2 instance. Some of it might go to the database, some (like big files) might be suitable for S3. DynamoDB is a good place for things like session data.
All of the above reduces the load on the web server to just your application code, which helps with scalability. And now that you keep no state on the web server, you can use ELB and Auto-scaling to automatically run multiple web servers (and even automatically launch more as needed) to handle greater load.
Does the application have any long running, intensive operations that you now perform on demand from a web request? Consider not performing the operation when asked, but instead queueing the request using SQS, and just telling the user you'll get to it. Now have long running processes (or cron jobs or scheduled tasks) check the queue regularly, run the requested operation, and email the result (using SES) back to the user. To really scale up, you can move those jobs off your web server to dedicated machines, and again use auto-scaling if needed.
Do you need bigger machines, or perhaps can live with smaller ones? CloudWatch metrics can show you how much IO, memory, and CPU are used over time. You can use provisioned IOPS with EC2 or RDS instances to improve performance (at a cost) as needed, and use difference size instances for more memory or CPU.
All this AWS setup and configuration can be done with the AWS web console, or command-line tools, or SDKs available in many languages (Python's boto library is great). After learning the basics, look into CloudFormation to automate it better (I've written a couple of posts about that so far).
That's a bit of the 10,000 foot high view of one approach. You'll need to discover the details of each AWS service when you try to use them. AWS has good documentation about all of them.
Depending on how you look at it, this is more of a comment than it is an answer, but it was too long to write as a comment.
What you're asking for really can't be answered on SO--it's a huge, complex question. You're basically asking is "How to I design a highly-scalable, durable application that can be deployed on a cloud-based platform?" The answer depends largely on:
The specifics of your application--what does it do and how does it work?
Your tolerance for downtime balanced against your budget
Your present development and deployment workflow
The resources/skill sets you have on-staff to support the application
What your launch time frame looks like.
I run a software consulting company that specializes in consulting on Amazon Web Services architecture. About 80% of our business is investigating and answering these questions for our clients. It's a multi-week long project each time.
However, to get you pointed in the right direction, I'd recommend that you look at Elastic Beanstalk. It's a PaaS-like service that abstracts away the underlying AWS resources, making AWS easier to use for developers who don't have a lot of sysadmin experience. Think of it as "training wheels" for designing an autoscaling application on AWS.