I'm currently learning how blockchain works, just out of personal curiosity. I'm going through this course and now I've setup the peer-to-peer connectivity using web sockets. Multiple instances of the blockchain application can now run and communicate with each other using these sockets.
The one downside of the course implementation is how the instances know how to find each other. Essentially they need to be explicitly configured to communicate. In my current project, I have it setup with 3 instances. One opens a socket on port 5001 and connects to nothing else. The other opens a socket on port 5002 and connects to the instance on 5001. And the third opens a socket on port 5003 and connects to the instances on 5002 and 5001.
The point is, all three are explicitly configured like that. All three must be started in exactly that order so they can properly connect to the others. While this is fine for a practice implementation, I know that's not how a real blockchain implementation must be working out there in the wild. There must be some mechanism of discovery that takes place which would allow any of these instances to locate whichever others are currently running.
Networking is not my area of expertise, so I'm at a loss on how this could be done.
P2P cryptocurrency clients usually have hardcoded list of peers. These peers are managed by community. If you start you client for the first time, these peers is all you have.
When you connect to another node, it saves your IP in its internal list. Any node can request this list from another node. In this way your client can discover other nodes. Client application saves list of nodes to disk. On the next startup you have hardcoded nodes and nodes you were connected last time. Some nodes may be offline, but this is ok.
More detailed explanation in case of Bitcoin: https://developer.bitcoin.org/devguide/p2p_network.html#peer-discovery
Related
We are currently transitioning our apps to Kubernetes and I have two apps, appP and appH, that I need to communicate with each other over a port unknown at start up time.
Unlike most of our apps, we don't have a set port for them will to communicate over. Before Kubernetes, third party app (out of my control) would tell appP to start processing an item, itemA, identified with a unique id and it would also tell appH to handle the processed data produced by appP.
To coordinate communications between appP and appH, appH would generate a port based on the unique id and publish the host and port info to connect on to an intermediate app (IA). appP, once done with it's processing queries IA for the connection information based on the unique id and sends it over.
Now we have to adapt this to kubernetes. Each app runs in its own deployment, as does the IA. So how can I setup appH to accept the connection over a port without being able to specify it in the service definition?
Note: I've seen some posts say that pods should be able to communicate to any other pods in the cluster regardless of specifying the ports in the service definition but I can't seem to find a ton of confirming information on this and I don't have a ton of time on our cluster where it is free to bang my head against.
Would it would just fine as is regardless? My biggest worry is the ip resolution. Currently appH grabs its ip based on the host it's running on (using boost). Not sure how this resolves within a container.
If not, my next thought would be if I could setup a headless service with selector for appH in order to allow for ip resolution. What I am unsure of then is if I could have appP connect to <appH_Service>:<arbitrary_port>?
Would the service even have to be headless in this scenario? I mostly say headless w/ selector because I saw in one specific post that it is the only one you don't need a port in the spec for it. Also because I am unsure if the connection would go through unless it was the actual pod's ip it was connecting with, rather than the services.
Any info or clarification is appreciated. For the most part, I can't really change the architecture of these apps right now, I just have to get them talking to each other as is and haven't found a ton of clear information on this type of case.
Note: We use helm and coredns if anyone is curious.
The Kubernetes networking model is as follows: a Pod is a group of containers that share a single network identity (a cluster IP). Any port exposed by a container is thus automatically exposed on the Pod. The model demands that each Pods can communicate with other Pods.
This means that your current design can work without modifications.
What Services bring to the table is that you can bring a stable network identity to a group of Pods that is otherwise very volatile. It does not apply to your appP/appH coupling, I think.
I am developing a multiplayer game (client-server model) and I am stuck when it comes to scaling its servers.
I understand that most games never even reach 10 000+ players, and I don't think mine will either.
Though if I would be very lucky to get that I want to develop the servers so they cannot become a huge obsticle later.
I have searched a lot for a solution to my problem on the internet, checking GDC talks about it and checking other posts on this website, but none of them seems to solve my specific problem.
My current setup is below and all servers are written in C++ using ENet as my network library.
Game server
This server handles the actual gameplay of the game and requires quite a lot of CPU and packages being sent between the server and its connected clients.
But this dedicated server is hosted by the players themselves, so I don't have to think about scaling it at all.
Lobby server
This server handles the server list, containing all servers currently up.
All game servers are sending a UDP package to this server every 5 seconds to say they are still alive.
This is so the lobby server can keep an updated list of all servers currently online.
All clients are sending a UDP package to this server when they want to fetch all servers (which is only in the server
list screen), and the lobby server sends back a list of all servers.
This does not happen that often and the lobby server is limited to send 4 servers per second to a client (and not a huge package containing all servers).
Login server
This server handles creating accounts, lost password, logins, friends and their current game status,
private messages to other logged in players and player profiles that specifies what in-game items they have.
All clients are sending a UDP package to this server every 5 seconds to say they are still alive, while also
sending what game they are currently in. The server then sends back their friend lists online/offline/in-game statuses.
This is so their friends can keep an updated list of which friend is online/offline/in-game.
It sends messages only with player actions otherwise, like creating an account, logging in, changing/resetting password,
adding/removing/ignoring a friend, private messages to friends, etc.
My questions
What I am worried about is that my lobby and login server might not be scalable and that they would have too much traffic on them.
1. Could they in theory be hosted on just a single computer? Or would it be too much traffic for 10 000+ players?
2. If they can be hosted on a single computer, will the servers still not have issues for people that live far away?
Would it be better to have the lobby and login servers per region of the world in that case?
The bad thing about that is that the players would not be able to see servers in the US if they live in Europe, and that their account and items would not exist on the other servers.
3. Might be far-fetched, but if I would rewrite both servers to instead be on a website with a database and make the client/game server do
web requests instead (such as HTTPS or calling a php with specific headers),
would it help in solving my problems somehow?
All your problems and questions are solved by serverless cloud based solution AWS Lambda e.g. or similar. In this case the scalability is not your problem. Just develop the logic. This will save you much time.
If you would like to make servers as single app hosted by your own server. Consider using something like e.g. Go instead of C++. It's designed exactly for these purposes. I mean highly loaded web/network services.
Well, this is c++ and i code in java, but maybe the logic is useful for you any way so i will tell you how i end up implementing something similar but in a casino.
In my case I have 2 diferrent sockets in the same server program, one of the sockets is TCP and it handles all logins, registers and payments, while the second socket is UDP and it handle the actual game multiple payers are playing, then you could group internally all those UDP connection in groups (probably arrays of sockets) to generate those lobbies. Doing that all your server is just one class that could run in a single pc using 2 ports (one for each socket) However this do not solve the problem of the ping for people who live far away.
If ping is a problem (not my case in a casino) you could probably host your server region base but removing the login, registration and paymets of your server and replace it for a connection to a central server (this central server should be TCP and you could also implement a https socket to also allow your webpage to connect to this server and create accounts or pay you directly from the browser)
sorry to mess your life even more, but i hope it helps
I've been reading around on the www but just can't get the most important basics of P2P.
The diagram is like this:
[peer1]<-->[dsl-router1]<-->[central server]<-->[dsl-router2]<-->[peer2]
I'm developing a chat software on the central server. Chat messages being transfered thru' the central server well by now, however, I need to make the p2p file sharing feature because the bandwidth (the cable bandwith, not the transfer limit) of the server supposed for transfering chat messages only.
The problem is that, my software on central server knows the IPs and ports of router1 and router2, but not the peer1 and peer2 as these peers are behind the routers and don't have IP addresses.
How to actually transfer some data from peer1 to peer2 and vice versa without having this data passing thru' central server?
(and the worst case is that there is a wireless router between peer and dsl-router)
There are two basic ways of doing this. The new way is to use IGDP (opening a port via uPnP). This is described quite well here:
http://www.codeproject.com/Articles/13285/Using-UPnP-for-Programmatic-Port-Forwardings-and-N
If neither of the two nodes have a router supporting uPnP then another alternative is TCP hole punching, which is not perfect but works quite well in practice. This is described here:
http://www.brynosaurus.com/pub/net/p2pnat/
During some situations, "routers" supplied by the ISP may run on bridge mode, which directly exposes the peer computer on the internet (the computer gets a public internet address). If at least one side has this configuration (or in a similar situation that the peer client is not behind another device), then things should be rather straight forward: simply assign the central server's job to whoever that have this privilege.
In the other case where both peers only have a local address (e.g. 192.168.0.2) assigned to their computers, it would then be rather difficult to get through the routers; clients behind routers are for the most part unreachable from the outside unless they originated the request. Then, one solution to the problem is port forwarding. By doing port forwarding, either through explicitly written rules or UPnP, some ports on the peer computer is exposed to the public internet, as in the first situation where instead of only some ports the entire computer is exposed.
If you are without either of these, then there is no simple way to avoid sending through the central server. Though you could, potentially, find other peers who have the capability to transfer for others.
If it possible to provide a service to multiple clients whereby if the server providing this service goes down, another one takes it's place- without some sort of centralised "control" which detects whether the main server has gone down and to redirect the clients to the new server?
Is it possible to do without having a centralised interface/gateway?
In other words, its a bit like asking can you design a node balancer without having a centralised control to direct clients?
Well, you are not giving much information about the "service" you are asking about, so I'll answer in a generic way.
For the first part of my answer, I'll assume you are talking about a "centralized interface/gateway" involving ip addresses. For this, there's CARP (Common Address Redundancy Protocol), quoted from the wiki:
The Common Address Redundancy Protocol or CARP is a protocol which
allows multiple hosts on the same local network to share a set of IP
addresses. Its primary purpose is to provide failover redundancy,
especially when used with firewalls and routers. In some
configurations CARP can also provide load balancing functionality. It
is a free, non patent-encumbered alternative to Cisco's HSRP. CARP is
mostly implemented in BSD operating systems.
Quoting the netbsd's "Introduction to CARP":
CARP works by allowing a group of hosts on the same network segment to
share an IP address. This group of hosts is referred to as a
"redundancy group". The redundancy group is assigned an IP address
that is shared amongst the group members. Within the group, one host
is designated the "master" and the rest as "backups". The master host
is the one that currently "holds" the shared IP; it responds to any
traffic or ARP requests directed towards it. Each host may belong to
more than one redundancy group at a time.
This might solve your question at the network level, by having the slaves takeover the ip address in order, without a single point of failure.
Now, for the second part of the answer (the application level), with distributed erlang, you can have several nodes (a cluster) that will give you fault tolerance and redundancy (so you would not use ip addresses here, but "distributed erlang" -a cluster of erlang nodes- instead).
You would have lots of nodes lying around with your Distributed Applciation started, and your application resource file would contain a list of nodes (ordered) where the application can be run.
Distributed erlang will control which of the nodes is "the master" and will automagically start and stop your application in the different nodes, as they go up and down.
Quoting (as less as possible) from http://www.erlang.org/doc/design_principles/distributed_applications.html:
In a distributed system with several Erlang nodes, there may be a need
to control applications in a distributed manner. If the node, where a
certain application is running, goes down, the application should be
restarted at another node.
The application will be started at the first node, specified by the
distributed configuration parameter, which is up and running. The
application is started as usual.
For distribution of application control to work properly, the nodes
where a distributed application may run must contact each other and
negotiate where to start the application.
When started, the node will wait for all nodes specified by
sync_nodes_mandatory and sync_nodes_optional to come up. When all
nodes have come up, or when all mandatory nodes have come up and the
time specified by sync_nodes_timeout has elapsed, all applications
will be started. If not all mandatory nodes have come up, the node
will terminate.
If the node where the application is running goes down, the
application is restarted (after the specified timeout) at the first
node, specified by the distributed configuration parameter, which is
up and running. This is called a failover
distributed = [{Application, [Timeout,] NodeDesc}]
If a node is started, which has higher priority according to
distributed, than the node where a distributed application is
currently running, the application will be restarted at the new node
and stopped at the old node. This is called a takeover.
Ok, that was meant as a general overview, since it can be a long topic :)
For the specific details, it is highly recommended to read the Distributed OTP Applications chapter for learnyousomeerlang (and of course the previous link: http://www.erlang.org/doc/design_principles/distributed_applications.html)
Also, your "service" might depend on other external systems like databases, so you should consider fault tolerance and redundancy there, too. The whole architecture needs to be fault tolerance and distributed for "the service" to work in this way.
Hope it helps!
This answer is a general overview to high availability for networked applications, not specific to Erlang. I don't know too much about what is available in the OTP framework yet because I am new to the language.
There are a few different problems here:
Client connection must be moved to the backup machine
The session may contain state data
How to detect a crash
Problem 1 - Moving client connection
This may be solved in many different ways and on different layers of the network architecture. The easiest thing is to code it right into the client, so that when a connection is lost it reconnects to another machine.
If you need network transparency you may use some technology to sync TCP states between different machines and then reroute all traffic to the new machine, which may be entirely invisible for the client. This is much harder to do than the first suggestion.
I'm sure there are lots of things to do in-between these two.
Problem 2 - State data
You obviously need to transfer the session state from the crashed machine unto the backup machine. This is really hard to do in a reliable way and you may lose the last few transactions because the crashed machine may not be able to send the last state before the crash. You can use a synchronized call in this way to be really sure about not losing state:
Transaction/message comes from the client into the main machine.
Main machine updates some state.
New state is sent to backup machine.
Backup machine confirms arrival of the new state.
Main machine confirms success to the client.
This may potentially be expensive (or at least not responsive enough) in some scenarios since you depend on the backup machine and the connection to it, including latency, before even confirming anything to the client. To make it perform better you can let the client check with the backup machine upon connection what transactions it received and then resend the lost ones, making it the client's responsibility to queue the work.
Problem 3 - Detecting a crash
This is an interesting problem because a crash is not always well-defined. Did something really crash? Consider a network program that closes the connection between the client and server, but both are still up and connected to the network. Or worse, makes the client disconnect from the server without the server noticing. Here are some questions to think about:
Should the client connect to the backup machine?
What if the main server updates some state and send it to the backup machine while the backup have the real client connected - will there be a data race?
Can both the main and backup machine be up at the same time or do you need to shut down work on one of them and move all sessions?
Do you need some sort of authority on this matter, some protocol to decide which one is master and which one is slave? Who is that authority? How do you decentralise it?
What if your nodes loses their connection between them but both continue to work as expected (called network partitioning)?
See Google's paper "Chubby lock server" (PDF) and "Paxos made live" (PDF) to get an idea.
Briefly,this solution involves using a consensus protocol to elect a master among a group of servers that handles all the requests. If the master fails, the protocol is used again to elect the next master.
Also, see gen_leader for an example in leader election which works with detecting failures and transferring service ownership.
I have an application server. At a high level, this application server has users and groups. Users are part of one or more groups, and the server keeps all users aware of the state of their groups and other users in their groups. There are three major functions:
Updating and broadcasting meta-data relating to users and their groups; for example, a user logs in and the server updates this user's status and broadcasts it to all online users in this user's groups.
Acting as a proxy between two or more users; the client takes advantage of peer-to-peer transfer, but in the case that two users are unable to directly connect to each other, the server will act as a proxy between them.
Storing data for offline users; if a client needs to send some data to a user who isn't online, the server will store that data for a period of time and then send it when the user next comes online.
I'm trying to modify this application to allow it to be distributed across multiple servers, not necessarily all on the same local network. However, I have a requirement that backwards compatibility with old clients cannot be broken; essentially, the distribution needs to be transparent to the client.
The biggest problem I'm having is handling the case of a user connected to Server A making an update that needs to be broadcast to a user on Server B.
By extension, an even bigger problem is when a user on Server A needs the server to act as a proxy between them and a user on Server B.
My initial idea was to try to assign each user a preferred server, using some algorithm that takes which users they need to communicate with into account. This could reduce the number of users who may need to communicate with users on other servers.
However, this only minimizes how often users on different servers will need to communicate. I still have the problem of achieving the communication between users on different servers.
The only solution I could come up with for this is having the servers connect to each other, when they need to deal with a user connected to a different server.
For example, if I'm connected to Server A and I need a proxy with another user connected to Server B, I would ask Server A for a proxy connection to this user. Server A would see that the other user is connected to Server B, so it would make a 'relay' connection to Server B. This connection would just forward my requests to Server B and the responses to me.
The problem with this is that it would increase bandwidth usage, which is already extremely high. Unfortunately, I don't see any other solution.
Are there any well known or better solutions to this problem? It doesn't seem like it's very common for a distributed system to have the requirement of communication between users on different servers.
I don't know how much flexibility you have in modifying the existing server. The way I did this a long time ago was to have all the servers keep a TCP connection open to each other. I used a UDP broadcast which told the other servers about each other and allowed them to connect to new servers and remove servers that stopped sending the broadcast.
Then everytime a user connects to a server that server Unicasts a TCP message to all the servers it is connected to, and all the servers keeps a list of users and what server they are on.
Then as you suggest if you get a message from one user to another user on another server you have to relay that to the other server. The servers really need to be on the same LAN for this to work well.
You can run the server to server communications in a thread, and actually simulate the user being on the same server.
However maintaining the user lists and sending messages is prone to race conditions (like a user drops off while you are relaying the message from one server to another etc).
Maintaining the server code was a nightmare and this is really not the most efficient way to implement scalable servers. But if you have to use the legacy server code base then you really do not have too many options.
If you can look into using a language that supports remote processes and nodes like Erlang.
An alternative might be to use a message queue system like RabbitMQ or ActiveMQ, and have the servers talk to each other through that. Those system are designed to be scalable, and usually work off a Publish/Subscribe mechanism.