I am looking at Uber architecture picture : https://imgur.com/a/c1Nkuvf and I am wondering In the center there is a box with DISCO and Supply and Demand services and the idea is that the Demand Service calls the Supply Service which calls one of the servers -Region1 to Region5 gets the information and sends it back to the Demand service and then it is send to the client.My question is where do these services reside and this box with the 3 of them is it some kind of module,message bus or something else?
The box with words Disco, Supply and Demand represents the three major services that work together to match the riders with drivers. Demand service receives the demand from the riders and keeps track of their gps locations while supply service keeps track of the drivers and their vehicle locations. The disco service performs the calculations so that riders are matched with drivers optimally in terms of distance, time and other factors. There would be a set of sub services within each major service to perform several low level tasks. These services run inside numerous geo distributed app servers as depicted by Regions 1 to 5 in the diagram. So to answer your question, the box is just the representation of these geo distributed app servers. Hope this helps!
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I am trying to find the best way to architect a low cost solution to provide an on-demand web server for a certain amount of time.
The context is as follows: I have some large amount of data sitting on S3. From time to time, users will want to consult that data. I've written a Flask app that can display the data in a nice way for them. Beign poorly written, it really only accepts a single user session at the time. Currently therefore they have to download the Flask app and run it on their own machine.
I would like to find a way for users to request a cloud-based web server that would run the Flask app (through a docker container for example) on-demand, and give them access to it quickly, without having to do much if anything on their own machine.
Every user wanting to view the data would have their own web server created on demand (to avoid multiple users sharing the same web server, which wouldn't work with my Flask app)
Critically, and in order to avoid cost, the web server would terminate itself automatically after some (configurable) idle time (possibly with the Flask app informing the user that it's about to shut down, so that they can "renew" the lease).
Initially I thought that maybe AWS Fargate would be good: it can run docker instances, is quite configurable in terms of CPU/disk it can get (my Flask app is resource-hungry), and at least on paper could be used in a way that there is zero cost when users are not consulting the data (bar S3 costs naturally). But it's when it comes to the detail that I'm not sure...
How to ensure that every new user gets their own Fargate instance?
How to shut-down the instance automatically after idle time?
Is Fargate quick enough in terms of boot time?
The closest I can think is AWS App Runner. It's built on top of Fargate and it provides an intelligent scale out mechanism (probably you are not interested in this) as well as a scale to (almost) 0 capability. The way it works is that when the endpoint is solicited and it's doing work you pay for the entire fargate task (cpu/memory) you have selected in the configuration. If the endpoint is doing nothing you only pay for the memory (note the memory cost is roughly 20% of the entire cost so it's not scale to 0 but "quasi"). Checkout the pricing examples at the bottom of this page.
Please note you can further optimize costs by pausing/starting the endpoint (when it's paused you pay nothing) but in that case you need to create the logic that pauses/restarts it.
Another option you may want to explore is using Lambda this way (which would allow you to use the same container image and benefit from the intrinsic scale to 0 of Lambda). But given your comment "Lambda doesn’t have enough power, and the timeout is not flexible" may be a show stopper.
I have a product for which I would like to create a dashboard to show
its availability/uptime over time and display any outages.
Specifically I am looking for
ability to report historical information on service uptime
provide details on any service outages
The product is running on a fleet of linux servers and connects to a DB running
on a separate instance, also we have some dedicated instances that run nightly
batch jobs. My system also relies on some external services to provide
additional functionality for select customers. There is redis cache also for
caching data for multiple customers.
We replicate all the above setup (application servers, DB, jobs servers, redis
cache etc) into dedicated clusters for large customers. Small customers are put
on one of the shared clusters to keep costs low.
Currently we are running health checks on application servers only and providing
that information in a simple HTML page. This is a go to page for end-users/customers
and support teams.
Since the product is constructed using multiple systems/services our current HTML
page often times says that the system is up and running fine while can be experiencing
issues with some of its components or external services.
Current health check is using a simple HTTP request and looks for a 200
status code, this check runs every minute and we plot this data into a simple
chart to show last 30 days. We also show a list of outages with timestamp and
additional static information that is manually added.
We would like to build a more robust solution that monitors much more than the HTTP port
and where we have more details like what part
of the system is having issues and how those issues are impacting the system and
which customers are impacted.
Appreciate any guidance or help. We prefer to build the solution using
open source tools since we dont have much budget. Goal is to improve things for
my team members who are already overloaded.
I'm not sure if this will be overkill or not for your setup, given that I don't know your product, but have a look at the ELK Stack and see if you can use some components or at least some ideas from there:
What is the ELK Stack?
The Complete Guide to the ELK Stack
I recently learned, traditionally, interbank payment systems need following features to carry out tasks:
need a central bank to prevent parties involved from going bust.
need a clearing house to perform netting algorithms to minimise liquidity requirement.
If we use corda to implement a similar payment system:
do we still need central banks and clearing houses appearing in the networks as independent nodes?
If so, what do they do?
Do they serve as notary nodes or something else?
What relationships do they have with commercial banks?
Why this kind of corda-based design is better than traditional interbank payment system?
Corda has been used to develop a real-time gross settlement pilot in association with the Monetary Authority of Singapore. See the report here: http://www.mas.gov.sg/~/media/ProjectUbin/Project%20Ubin%20Phase%202%20Reimagining%20RTGS.pdf and the source code here: https://github.com/project-ubin/ubin-corda.
Using Corda removes the need for clearing houses. Netting and delivery-versus-payment/atomic asset swaps can be achieved without the need for a centralised party. Corda also removes the need for reconciliation, which happens automatically via the platform.
More importantly, Corda is driving towards a vision of global interoperability. See
https://medium.com/corda/universal-interoperability-why-enterprise-blockchain-applications-should-be-deployed-to-shared-3d4daff97754. In this vision, assets are not trapped in silos, and can move freely across the network. For example:
BankNode receives cash via the interbank payment system
BankNode lends this money to SupplierNode in exchange for an obligation
SupplierNode uses this money to purchase goods from FactoryNode
FactoryNode uses the money to pay the suppliers of its raw materials
And so on, and so on...
Coordinating things using a clearing house remains possible when looking at a single area like interbank payments. However, as the network grows to support many different business use-cases - supply chain, lending, assets, payments... - it becomes increasingly difficult to find a coordinating party that can be trusted by all parties, across industries and regions. Corda removes the need to identify such a coordinating party.
In this vision, central banks are likely to continue to exist as trusted issuers of fiat currency.
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.
Hello I am a newbie to n-tier architecture and was trying to find out the difference between what an application server hosting application layer of code does, and what a web service does?
So I'll tell you people my understanding of the whole n-tier concept, we have the UI -> Web Server -> business logic/application logic on an Application server -> Database Server. (Of course load balancers and multiple server instances would also be existent to fasten and store the state of processes)
But to be specific, the business logic layer would not be tied to a UI, so it is more or less independent and can be reused.
A web service on an other hand too provides functionality similar to the business logic, where it is not tied to a UI, and can be reused for different cases.
Could anybody explain if what I just explained above is right? And as I did mention earlier, I am a newbie to this, so if this sounds stupid or naive please do not bash me :)
Here's a quick, dirty, and very general explanation of a 4-tier architecture, which I'm assuming may best apply to your application:
Presentation Layer : The interface to the outside world (web site)
Application Layer : The mechanics necessary to create the interface(s) to the outside world (web application frameworks, web services)
Business Logic Layer : The actual logic that embodies/simulates/emulates your business's processes and workflows (algorithms, transformations, approval processes, etc.)
Database Layer : The database and the logic needed to query information from it
In general, web services are not part of the business logic layer. That layer is usually protected as much as the database layer, because there could be trade secrets or confidential ways of doing things in there, and you usually don't want anyone accessing that directly, except programatically or through approved interfaces (such as web services).
Web services, application layers, and business logic can be aptly compared to Coca-Cola and it's business. Bottles and cans are usually how Joe Blow consumes Coke's product (e.g. web site in the presentation layer), but other businesses want to be able to serve Coca-Cola to their customers as well, so Coke lets them use carbonated water and Coca-Cola syrup (e.g. web services in the application layer). Coke's secret formula (e.g. business logic layer), and Coke's distribution processes to get it into the store (e.g. application layer) are all hidden from the consumer. Joe Blow doesn't care how it gets into the store, he just knows he can get Coke from a variety of sources (web site, mobile client, etc.). And Coke doesn't want people knowing its secret formula (business logic). If you want a Coke, you have to go through a store or a restaurant (approved interfaces).