I am trying to learn deployment topology and data centre topology for complex application. I know that answer to this question may vary depending on different scenarios. But I am talking about most probable and general case.
I browsed over the internet and came to know that complex large scale e-commerce J2EE applications are deployed on multiple application servers (e.g. weblogic cluster) and these weblogic application servers are connected to multiple data centres (where customer/application specific data is stored). I would like to know how these servers talk to multiple data centres (in terms of JDBC Connection and data source).
Also if there are multiple data centres then there is possibility of inconsistent data.
My knowledge about data centre is zero and would like to know more about how communication between application server and data centres takes place. In short I want to know typical production deployment topology along with data centers.
Thanks in advance.
This is a very complex and broad question that is open to interpretation. You might want to start with something like this to get enough information to ask more specific questions that are a better fit for Stack Overflow: http://www.javaworld.com/article/2077094/soa/construct-java-applications-through-distributed-object-technology.html
Related
I want to build an application. The application will have three microservice like content-service1, content-service2, content-service3. Also, each microservice will have its own database. And the application will have a load balancer, Load balancer's mission is distribution (the first request will go to the first container, the second request will go to the second container...). The question is coming... How can ı provide consistency between different databases? I looked at some topics like partitioning, eventual consistency, saga... But I don't understand. Are these the right solution?
system design that ı want image
It looks like you are mixing up microservices and application instances.
If content-service1, content-service2 and content-service3 is backed by the same application (same code) you only have one application (one service).
If you want to have high availability you surely need multiple instances of the same application (simply the same application run 3 times on different servers).
In this case, you don't need to have a database per application instances because all of them will be connected to the same database and you won't have inconsistency issues.
If you don't want to have a single point of failure you should also need to ensure you can have redundancy for your database. Depending on the provider you use, you might have master/slaves or multi-master topology, data replication between database nodes will probably be handled by the database itself.
Microservice is a solution to split big applications in smaller ones. Each microservice will store it's own data in a dedicated database. Microservices are application like others, they can themselves have multiple instances for high availability.
This is rather a set of questions than one very specific question. In the last couple weeks/days I puzzled together information regarding how to properly host a JAVA PLAY application "in the cloud", as lots of this information is scattered over different services, I felt like gathering up all these small pieces to one, because lots of things are important to be seen in full context. However, I moved my considerations to the bottom of the question, as they are mainly my opinions and subjective findings, which I don't want to be held responsible for. If I got something wrong, please don't hesitate to point that out.
Hosting Java PLAY + MySQL on AWS for world wide accessibility
Our Scenario: we have a quite straight forward application written within the Java PLAY framework (https://www.playframework.com/), working on iOS and Android as well as with a backend-system (for administration, content management and API), storing data in a MySQL DB. While most of the users' interactions with the server is quick and easy (login, sync some data) there are also some more data-intensive tasks (download some <100mb data zips to the mobile phone, upload a couple of mb to the server). Therefore we were looking for a solution to properly provide users far away from our servers with reasonable response times. The obvious next step was hosting in the cloud.
Hosting setup within AWS:
Horizontal scaling: for the start, only 1 EC2 instance with our app will be running in eu-1a. We will need to evaluate how much resources one instance actually requires, if more instances are needed and if more instances would actually benefit to quicker response times.
Horizontal scaling across regions: once the app generates heavy user load from another region, the whole EC2 instance should be duplicated and put to another region, running a db read replica (see Setting up a globally available web app on amazon web services and https://aws.amazon.com/de/blogs/aws/cross-region-read-replicas-for-amazon-rds-for-mysql/ ).
Vertical scaling of EC2 instances: in recent tests of the old hosting setup, the database proved to be the bottleneck rather than the play app and its server's hardware specifications. Therefore it is not yet fully clear how much vertical scaling would affect response times. If a t2.micro instance serves as good as a m3.xlarge instance, of course we would rather climb our way up from the bottom here.
Vertical scaling of RDS: we will need to estimate how much traffic hits the DB server and what CPU/RAM/etc will be required. Probably we will work our way up here aswell.
Global Redirection: done using Amazon Route 53 (?). A user from Tokio should be redirected to the EC2 instance running in Asia; a user from Rome to the EC2 instance in Europe. This does not only affect API calls within the app, but also content delivery (in both directions).
Open Questions regarding the setup
Is this setup conclusive? Am I missing crucial components?
Regarding global redirection: is Amazon Route 53 the right tool? How does it differ from CloudFront (which strikes me to be purely for content / media distribution?).
How do I define correct data/api endpoints for my app? Of course I don't want to define the database endpoint of a db read replica during app deployment. Will this also happen during the AR53 (question 2) setup? Same goes for API calls, of course the app should direct it's calls to https://myurl.com/api and from there it should be redirected. Is this realistic?
I would highly appreciate all kinds of thoughts (!), also regarding the background info written below. If you can point me to further reading to solve my questions on my own, I am also very thankful - there is simply a huge load of information regarding this, but this makes it hard to narrow the answers down. I do have knowledge in hosting/servers, but I am pretty sure there are true experts out there waiting to slap me with knowledge. :)
Background-Information
Current Hosting Setup: a load balancer distributes the traffic on 2 root linux servers, both of them running the PLAY app, one of them also holding the MySQL installation.
The current hosting setup has 3 big flaws:
No vertical scalability: the hosting company would take money for each scaling step. Currently the servers are running idle, but if the app booms, we could run short on capacity quickly. Running idle is still paid as if permanently under full load. This is expensive!
No deployment support: currently, we connect through SSH, manually deploy the correct folders to the file system, recompile on the server, set privileges, apply database evolutions; do the same for the second server (with different db connection parameters). What could possibly go wrong. ;)
No worldwide availability: to set up another server in another region of the world would mean a huge effort. To have a synchronized replica of our DB can be done, but once again deploying would mean downtime, room for errors and therefore time and money.
Hosting Options for Java PLAY:
There are lot of different blog posts about this. In short:
AWS: Amazon Web Services is one of the first places you start looking. Here you get everything that's possible, at a flexible price. You set yourself up an EC2 instance, a MySQL RDS and you're good to go - all of this in the free tier, so you can experiment, play around, test your stuff.
Microsoft Azure: similar to AWS regarding pricing and possibilities. However, I did not dive into setting up and deploying our application for test purposes.
Heroku: super easy deployment from within PLAY, scalable servers. However (on the first glance?) lacks possibility to supply remote regions with high speed content.
Jelastic: even easier deployment from within PLAY / IntelliJ IDEA. You push your app image to jelastic, jelastic distributes it further to their infrastructure providers.
RedHat OpenShift (https://www.openshift.com/): sounds promising, yet not as complete as AWS.
Lots of choices and possible setups/prices. Especially after finding out about deployment using boxfuse (https://cloudcaptain.sh/) I made my choice for AWS, as it offers absolutely all we need from 1 source. Boxfuse has low monthly costs but is perfectly integrated into AWS. Scaling is supported as well as the 3 common environments (dev/test/prod). Support is outstanding.
The setup looks good. I would however make one change: your large up- & downloads. As mobile speeds may not be ideal, have your app serve long-running requests is something you should avoid as this will needlessly tie up server threads. Instead consider having users upload and download straight from S3 using presigned URLs. You can then later add CloudFront to the mix when it makes financial sense to do so.
R53 will work just fine for picking the best server(s) for each end user.
For EC2 consider having an ELB + Auto-Scaling Group setup. Even just for a single instance you get the benefit of permanent health monitoring and auto-respawns. If you expect more load you can then auto-scale based on your expected bottleneck (cpu, network i/o). This will give you a more autonomous and robust setup than manually having to scale up and down based on your own monitoring analysis (even though the scaling part is very easy if you stick with immutable infrastructure & blue/green deployments like what Boxfuse offers).
Your focus on vertical server scaling might not serve you well on AWS. I would start thinking about horizontal scaling of app servers behind an Elastic Load Balancer, and possibly look into Elastic Beanstalk.
I'm not sure you can setup a read replica in another region via RDS, you might have to set that up via MySQL servers running on standard EC2 instances. And even if you can, that's going to be some expensive and high-latency data transfer.
If file uploads and downloads are all you are worried about, you just need to put CloudFront (Amazon's CDN service) in front of your application, and allow it to handle file uploads and downloads via its global edge servers. You could even do this without moving your entire application into AWS. I would recommend reading this blog post as a start.
I am doing a system architecture and my knowledge from college doesn't help me when it comes to understand the subtle differences between centralized, distributed and service oriented architecture/application.
If I take a typical client/server architecture, the client sends requests to a server, the server then sends responses to the client. That is a centralized architecture.
An application that handles both server and client sides will be a distributed application (because working on different platforms), but that is still a centralized architecture.
Therefore, a distributed architecture must involve a distributed application.
Questions: am I right? What does all that become when it comes to service oriented architectures / applications?
Distributed: The whole process involved in a computation task is divided into pieces and assigned to multiple computational nodes. Each node when doing its part of the processing does not have access to the whole information of the system that is necessary to achieve a globally optimized result. The aggregate of the results from multiple nodes will converge towards a global optimal result through usually multiple iterations of computations distributed across multiple nodes.
A good example is a router system in which each router has only the information it exchanges with its neighbours. At the start the neighbours known only part of the whole network system. Once a router gets more information from its neighbours it incorporates the new information into its view of the whole system, then spreads its view to its neighbours. Through multiple iteration of these steps, each separately computed by individual routers, all routers would settle on a consistent global view of the whole network system.
Another example could be a web ordering system where the browser initially gets a list of commonly order goods. The browser may have logic to track user viewing behaviour and make a decision to fetch from the server a different category of goods list, but does not send all the user behaviour parameters to the server. In this imaged example, the browser knows something the serer does not know, and the server does too. Thus the whole application would be a distributed system. In addition to this part of distributed nature, the user authentication could be done on one of the servers, the inventory is done on another server, and reservation on yet another one. Each of the servers involved would not have the whole information of the specific user browsing and ordering instance. But the aggregate work from all these nodes will fulfill the business need to sell more goods and satisfy more customers.
Opposite to the distributed, is the centralized, in that the computation logic would be always able to get the information of the whole picture.
Given this view, a client-server application can be a viewed as a distributed system if you think the client side involves non-trivial decision making. Or can be a centralized system if you think the client is dumb.
The service-oriented term is more about how the functional processing power is integrated into the system. In a service-oriented system, new capability may be introduced into the system at runtime by new API functionality discovery, or new logic capability discovery behind an unchanged API. Think about it, you could build an application that initially has no much built-in capability, then it expands its capability by discovering and incorporating new capability on the service providers. In contrast a traditional system needs to be built at build time, typically as a consequence of human-involving discussion-design-documentation activity. A service-oriented design is a good fit to a distributed system.
I would like to disregard web applications here, because to scale them horizontally, ie to use multiple server instances together, it is "sufficient" to just duplicate the server software over the machines and just use a sort of router that forwards requests to the "less busy" server machine.
But what if my server application allows users to engage together in realtime ?
If the response to the request of a certain client X depends on the context of a client Y whose connection is managed by another machine then "inter machines" communication is needed.
I'd like to know the kind of "design solutions" that people has used in such cases.
For example, the people at Facebook must have already encountered such situation when enabling the chat feature of their social app.
Thank you in advance for any advise.
One solution to achive that is to use distibuted caches like memcache (Facebook also uses that aproach).
Then all the information which is needed on all nodes is stored in that cache (and a database if it needs to be permanent) an so all nodes can access that information (with a very small latency between the nodes).
regards
You should consider some solutions that provide transparent horizontal database scalability and guarantee ACID semantics. There are many solutions that offer this at various levels. People at Facebook which you reference have solved the problem by accepting eventual consistency but your question leads me to believe that you can't accept eventual consistency.
We're developing a server system in Scala + Akka for a game that will serve clients in Android, iPhone, and Second Life. There are parts of this server that need to be highly available, running on multiple machines. If one of those servers dies (of, say, hardware failure), the system needs to keep running. I think I want the clients to have a list of machines they will try to connect with, similar to how Cassandra works.
The multi-node examples I've seen so far with Akka seem to me to be centered around the idea of scalability, rather than high availability (at least with regard to hardware). The multi-node examples seem to always have a single point of failure. For example there are load balancers, but if I need to reboot one of the machines that have load balancers, my system will suffer some downtime.
Are there any examples that show this type of hardware fault tolerance for Akka? Or, do you have any thoughts on good ways to make this happen?
So far, the best answer I've been able to come up with is to study the Erlang OTP docs, meditate on them, and try to figure out how to put my system together using the building blocks available in Akka.
But if there are resources, examples, or ideas on how to share state between multiple machines in a way that if one of them goes down things keep running, I'd sure appreciate them, because I'm concerned I might be re-inventing the wheel here. Maybe there is a multi-node STM container that automatically keeps the shared state in sync across multiple nodes? Or maybe this is so easy to make that the documentation doesn't bother showing examples of how to do it, or perhaps I haven't been thorough enough in my research and experimentation yet. Any thoughts or ideas will be appreciated.
HA and load management is a very important aspect of scalability and is available as a part of the AkkaSource commercial offering.
If you're listing multiple potential hosts in your clients already, then those can effectively become load balancers.
You could offer a host suggestion service and recommends to the client which machine they should connect to (based on current load, or whatever), then the client can pin to that until the connection fails.
If the host suggestion service is not there, then the client can simply pick a random host from it internal list, trying them until it connects.
Ideally on first time start up, the client will connect to the host suggestion service and not only get directed to an appropriate host, but a list of other potential hosts as well. This list can routinely be updated every time the client connects.
If the host suggestion service is down on the clients first attempt (unlikely, but...) then you can pre-deploy a list of hosts in the client install so it can start immediately randomly selecting hosts from the very beginning if it has too.
Make sure that your list of hosts is actual host names, and not IPs, that give you more flexibility long term (i.e. you'll "always have" host1.example.com, host2.example.com... etc. even if you move infrastructure and change IPs).
You could take a look how RedDwarf and it's fork DimDwarf are built. They are both horizontally scalable crash-only game app servers and DimDwarf is partly written in Scala (new messaging functionality). Their approach and architecture should match your needs quite well :)
2 cents..
"how to share state between multiple machines in a way that if one of them goes down things keep running"
Don't share state between machines, instead partition state across machines. I don't know your domain so I don't know if this will work. But essentially if you assign certain aggregates ( in DDD terms ) to certain nodes, you can keep those aggregates in memory ( actor, agent, etc ) when they are being used. In order to do this you will need to use something like zookeeper to coordinate which nodes handle which aggregates. In the event of failure you can bring the aggregate up on a different node.
Further more, if you use an event sourcing model to build your aggregates, it becomes almost trivial to have real-time copies ( slaves ) of your aggregate on other nodes by those nodes listening for events and maintaining their own copies.
By using Akka, we get remoting between nodes almost for free. This means that which ever node handles a request that might need to interact with an Aggregate/Entity on another nodes can do so with RemoteActors.
What I have outlined here is very general but gives an approach to distributed fault-tolerance with Akka and ZooKeeper. It may or may not help. I hope it does.
All the best,
Andy