i'm still relatively new to NodeJS, but I'm interesting in making > 100k simultaneous connections to the API of several stock exchanges.
In the node docs it mentions that the number of simultaneous outgoing calls/connections is infinity - is that accurate?
This info on the changelog is more related to the fact that before the maximum number of concurrent sockets node could handle was set to 5 (it was changeable of course) but now it is set to Infinity by default.
This has nothing to do with the capacity of connexions an application can handle, that will be limited by the network's characteristics, the OS specifications, the available resources on the server, etc.
In short, you are looking at something (the blog post) that has nothing to do with your question.
Related
See the backlog parameter mentioned here:
All listen() methods can take a backlog parameter to specify the maximum length of the queue of pending connections. The actual length will be determined by the OS through sysctl settings such as tcp_max_syn_backlog and somaxconn on Linux. The default value of this parameter is 511 (not 512).
A more thorough explanation of this parameter would be appreciated. Some questions that come to mind after reading that description:
When is a connection 'pending' vs accepted?
How do you max out this backlog limitation?
What happens when the backlog parameter is exceeded, is an error returned?
This parameter will help you throttle connections to your server and, hence, protect it from "too many connections" issues.
Without this parameter, your server will (theoretically speaking and without OS limitations) accepts any number of connections. And since each connection consumes memory from your underlying machine, you will get to an "out of memory" situation when there are too many connections to your server and your server will stop running or, at least, will behaving in unexpected manner.
Receiving too many connections can be either normal (too many valid users trying to use your server) or abnormal (a result of a DOS/DDOS attack). In all cases, it is a good practice to put a limit to the number of connections your server can handle to ensure a high quality of service (connections above the limit will be "polity" declined).
Hopefully, Linux put some system-wide limitations that are tcp_max_syn_backlog and somaxconn. Even if you set the backlog parameter to a high value, Linux will review it to align it with the value set for these two system parameters. You can read more about this feature in this post.
I am developing a TCP server in Linux which initially can handle thousands of concurrent clients, which are intendeed to be long living. However, after starting to implement some functionallity, I made a thread pool for that calls which are blocking and should be done apart, like database or disk access.
After some tests, under high load requesting "many" asynchronous functions my server starts to lag due to many tasks being enqueued, as they arrive faster than they can be processed. These tasks are solved in the nanoseconds, but there are thounsands. I do understand this is totally normal.
I could of course grow behind a load balancer or buying better servers with more cores, however, in practice and as standard in the industry, how many concurrent long-lived TCP sessions are consideer a "good" number in such a server like this one I'm describing? How can I say that the number of concurrent connections I got is "good enough"?
Unfortunately there's no a magic number to answer your question but I have some considerations for you to find your number:
First of all, every operational system has its own max number of
simultaneous connections because the port numbers are finite. So
check if you're not trespassing this number, else every new
connection will be refused by your server.
In order to identify how many simultaneous connections are okay to you, you must to establish a max time of response for your service.
Keep in mind that even having simultaneous connections, multicore CPUs etc... the response will come out by the same network and get bottle necked. Thus I advice you to do a load and a stress test over your architecture in order to find your acceptable latency limit.
TL;DR: There's no a magic answer, you should do a load and a stress test to find it.
I'm implementing a websocket-secure (wss://) service for an online game where all users will be connected to the service as long they are playing the game, this will use a high number of simultaneous connections, although the traffic won't be a big problem, as the service is used for chat, storage and notifications... not for real-time data synchronization.
I wanted to use Alchemy-Websockets, but it doesn't support TLS (wss://), so I have to look for another service like Fleck (or other).
Alchemy has been tested with high number of simultaneous connections, but I didn't find similar tests for Fleck, so I need to get some real info from users of fleck.
I know that Fleck is non-blocking and uses Async calls, but I need some real info, cuz it might be abusing threads, garbage collector, or any other aspect that won't be visible to lower number of connections.
I will use c# for the client as well, so I don't need neither hybiXX compatibility, nor fallback, I just need scalability and TLS support.
I finally added Mono support to WebSocketListener.
Check here how to run WebSocketListener in Mono.
10K connections is not little thing. WebSocketListener is asynchronous and it scales well. I have done tests with 10K connections and it should be fine.
My tests shows that WebSocketListener is almost as fast and scalable as the Microsoft one, and performs better than Fleck, Alchemy and others.
I made a test on a Windows machine with Core2Duo e8400 processor and 4 GB of ram.
The results were not encouraging as it started delaying handshakes after it reached ~1000 connections, i.e. it would take about one minute to accept a new connection.
These results were improved when i used XSockets as it reached 8000 simultaneous connections before the same thing happened.
I tried to test on a Linux VPS with Mono, but i don't have enough experience with Linux administration, and a few system settings related to TCP, etc. needed to change in order to allow high number of concurrent connections, so i could only reach ~1000 on the default settings, after that he app crashed (both Fleck test and XSocket test).
On the other hand, I tested node.js, and it seemed simpler to manage very high number of connections, as node didn't crash when reached the limits of tcp.
All the tests where echo test, the servers send the same message back to the client who sent the message and one random other connected client, and each connected client sends a random ~30 chars text message to the server on a random interval between 0 and 30 seconds.
I know my tests are not generic enough and i encourage anyone to have their own tests instead, but i just wanted to share my experience.
When we decided to try Fleck, we have implemented a wrapper for Fleck server and implemented a JavaScript client API so that we can send back acknowledgment messages back to the server. We wanted to test the performance of the server - message delivery time, percentage of lost messages etc. The results were pretty impressive for us and currently we are using Fleck in our production environment.
We have 4000 - 5000 concurrent connections during peak hours. On average 40 messages are sent per second. Acknowledged message ratio (acknowledged messages / total sent messages) never drops below 0.994. Average round-trip for messages is around 150 miliseconds (duration between server sending the message and receiving its ack). Finally, we did not have any memory related problems due to Fleck server after its heavy usage.
I need to send identical information to 100's of clients over the Internet. I currently maintain a list of client connections and iterate over the list. Obviously the longer the list gets the more latency there is toward the end of the list.
I have looked at multicasting. However unless I am missing something it is only good for LAN-based communications at present. It requires routers that support multicasting and most routers do not. There is no mechanism that I can see where one requests an available multicast address to avoid broadcasting to an address already in use.
So my questions are:
1) Am I missing something and can I use multicasting to accomplish this? (have tried without success)
2) Other than multicasting, is there a short cut to sending identical packets to many recipients?
I solved the problem by multicasting between threads in the server. Every client connection results in the creation of an object. These objects are stored in a queue. Each object has its own thread and joins the multicast group. When the server multicasts a string to the client objects the delay that arose from the list iteration no longer occurs.
Every now and then there is huge latency (nearly a second). I suspect that this is a JVM thing.
If you need high performing low latency IO, you shoud try http://nodejs.org/
You may be also interested in some cache http://memcached.org/
I'm considering using bittorrent for a large data dissemination problem where the data source is petascale and users will want up to several terabytes. Some details
Number of torrents potentially in the millions
torrent sizes ranging from 100Mb to 100Gb
A stable set of clusters around the world capable of acting as seeders each holding a large subset of the total torrents (say 60% on average)
A relatively small number of simultaneous users (less than 100) wanting to download on average a few terabytes of data.
I expect the number of active torrents to be small compared to the total available but quality of service is important so there must be several seeders for each torrent or some mechanism for launching new seeders.
My question is can bittorrent clients handle seeding huge numbers of torrents, most of which are idle? Would I need to stripe torrents across the seeders in a cluster or could each node be seeding all torrents it has access to? Which client would do the best job? Are there any tools for managing clusters of seeders?
I am assuming that trackers can be made to scale to this level.
There are 2 main problems:
Each torrent (typically) needs to announce to a tracker periodically, this might end up using a significant amount of bandwidth.
The bittorrent client itself need to be written in a way to scale with a large number of torrents
As for the tracker traffic, let's assume you have 1 million torrents, the typical re-announce interval is 30 minutes, but some tracker has it set to 1 hour. Let's be conservative and assume your tracker uses 1 hour announce intervals. You will have to make 1 million GET requests per hour, let's say each request is 400 bytes up and 100 bytes down (assuming most responses will not contain any peers), that's about 111 kB/s up and 28 kB/s down constantly. That's not so bad, but keep in mind that TCP requires an extra round-trip for establishing connections, so that's another 40 bytes down and 40 bytes up.
This can be mitigated by only using UDP trackers. Then you would only need a single connect-message, and you can reuse the connection ID for each announce. Each announce message would then be 100 bytes, and the returned message would be a bit more compact as well, let's assume 60 bytes. That would get you 28 kB/s up and 16kB/s down, just to keep the torrents announced. For this you would need a client with decent udp tracker support (one that caches the connection ID for instance).
Not too bad, assuming that's insignificant compared to the actual data your seeds would send.
However, you don't necessarily need to stripe your torrents across separate data centers, you could also use an HTTP server to seed the torrents. All major bittorrent clients support http seeding, and you wouldn't have to worry about announcing to the tracker (the URL is burned into the .torrent itself).
As for a client that scales well with torrents, I don't know for sure, I haven't done any measurements. It should be fairly straightforward to just generate a million random torrents and try to load it up.
I have done some optimization work in libtorrent rasterbar to make it scale well with many torrents, I haven't tried millions though.
I've written a blog post on this topic, here.
You may be looking for Hekate
It's in, at best, pre-alpha right now, but it's quite nearly what you're describing.
To not collapse under the overhead of useless tracker announces and scrapes in the millions (and that in every announce interval), you have to restrict your seeding clusters to only load the current working set of items that are requested right now. Downloaders need to get (download) the .torrent file from a central place anyway, and that could trigger loading it into the seeding clusters. Alternatively, determine activity for a particcular info-hash by recognizing announces that do NOT originate from one of your seed clusters.
rTorrent has fast-resume (meaning no hashing happens when an appropriately prepared .torrent is loaded), and is controllable via xmlrpc so you can decommission idle items. That way, a .torrent download can trigger the actual data to be available for the next 24 hours, or as long as there's activity in the swarm.
The protocol allows for this, but I do not know which clients would scale to millions of torrents. In the worst case, you would have to write your own seed-only client.
The protocol feature most relevant to your use case is that, when a peer connects to another, the connecting peer is supposed to send the torrent's info-hash first. This means that a single listening TCP port could be used to seed an unlimited amount of torrents, with almost zero resources used when idle.
This can be found on The BitTorrent Protocol Specification:
If both sides don't send the same value, they sever the connection. The one possible exception is if a downloader wants to do multiple downloads over a single port, they may wait for incoming connections to give a download hash first, and respond with the same one if it's in their list.
I also found the same on this Bittorrent Protocol Specification v1.0:
The initiator of a connection is expected to transmit their handshake immediately. The recipient may wait for the initiator's handshake, if it is capable of serving multiple torrents simultaneously (torrents are uniquely identified by their info_hash).
However, there is one thing that would increase your load, and it is the tracker. With the normal tracker protocol, each client has to periodically announce to the tracker each torrent it has, together with information like how much it has uploaded. With millions of torrents, this would present a somewhat high load. If you were writing your own mass-seed-only client, a separate protocol to announce your seeders to the tracker would be a good idea.