I have a project which needs to make a tcp connection to an external source. Each worker thread will be sending messages to this external service.
I'm wondering how I can do this without having a connection be brought up and torn down for every request. I'm pretty sure the pymongo module does something similar but I can't find any documentation on it. Would it be possible to set up some kind of thread-safe queue and have a separate thread consume that queue? I understand I could probably use gearman for this, but I'd like to avoid having another moving part in the system.
uWSGI has a thread-safe process-shared queueing system (http://projects.unbit.it/uwsgi/wiki/QueueFramework) but are you sure using simple python threading.Queue class is not enough ?
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I am pretty new to gRPC. I am thinking of using gRPC(Java) to do inter node(server) communication in my use case:
I have my own app logic to do some bookkeeping work on each node;
a node would need to communicate with others to reach some consensus(part of app logic) and this means a node need to both have client and server;
so how could I achieve this? server seems to be blocking after I call server.awaitTerminate(), right? but do we also have the async version of the gRPC server in java? I bet yes, but I am not yet sure how could I leverage it.
for example, I have node A, B, C. I will need to have gRPC serverA, serverB, serverC start first, and for each server say A, I need client to connect to B and C. and in addition to communication part, app (say in node A)logic would be able to send out msg to other nodes(say B and C) via corresponding clients(to server B and C) if needed;and of course app logic would be notified when requests coming from B and C(because itself is a server).
I've been searching online for days and have gone through grpc/grpc-java related material and code example. however, i find there's not that much code example to show what is best practice and pattern to leverage gRPC...i'd really like to hear whatever suggestion you may have...
thanks in advance!
Calling server.awaitTermination() in your main() is not required. The examples do so because grpc-java uses daemon threads by default. Thus, in the examples the only non-daemon thread is the main thread, and you need at least one non-daemon thread to keep the JVM running. See the documentation for java.lang.Thread.
awaitTermination() is not a serve_forever() method that processes new requests; awaitTermination() simply blocks the current thread until the grpc server has terminated. Processing happens on other threads.
I'd like to know if there's a way to communicate directly between two (or more) flask-socketio servers. I want to pass information between servers, and have clients connect a single web socket server, which would have all the combined logic and data from the other servers.
I found this example in JS Socket IO Server to Server where the solution was to use a socket.io-client to connect to another server.
I've looked through the Flask-SocketIO documentation, as well as other resources, however it doesn't appear that Flask-SocketIO has a client component to it.
Any suggestions or ideas?
Flask-SocketIO 2.0 can (maybe) do what you want. This is explained in the Using Multiple Workers section of the documentation.
Basically, the servers are configured to connect to a shared message queue service (redis, for example), and then a load balancer in front of them assigns clients to any of the servers in the pool using sticky sessions. Broadcasting operations are coordinated automatically among the servers by passing messages on the queue.
As an additional feature, if you use this set up, you can have any process connect to the message queue to post messages for clients, so for example, you can emit events to clients from a worker or other auxiliary process that is not a SocketIO server.
From your question it is unclear if you were looking to implement something like this, or if you wanted to have the servers communicate for a different reason. Sending of custom messages on the queue is currently not supported, but your question gave me the idea, this might be useful for some scenarios.
As far as using a SocketIO client as in the question you referenced, that shouud also work. You can use this Python package: https://pypi.python.org/pypi/socketIO-client. If you go this route, you can have a server be a client and receive events or join rooms.
I am implementing an ASP.NET application that needs to service conventional http requests but the responses require data that I need to acquire from providers that are executables that provide their data over sockets. My plan to implement was:
1) In Application_Start, start a new thread that starts a socket server
2) In Session_Start, launch the session-specific process that will ultimately connect to the socket server, and from there do a Monitor.Wait on a session-specific lock object which I've stored in Application.Contents by Session key
3) When the socket server sees a new connection, make the data available to the appropriate session Contents and do a Monitor.Pulse on the session-specific lock object
Is this technically feasible in IIS? Can this concept function as a stable system?
Before answering, please bear in mind I am not asking "is this the recommended approach", I am aware it is not and if I had the option to write this system from scratch I would do this differently. I'm also not able to change the fact that the programs communicate using sockets.
Given the constraints this approach makes sense.
Shutdown and recycling of IIS worker processes are always throny issues when it comes to keeping state in a web app. Note, that your worker process can recycle pretty much at any time for many reasons. Some of those reasons are unavoidable: Server reboot, app deployment, bug leading to a process crash. So you need to think through what happens in those cases: All sessions will be lost while the child processes still run. Suggested solution: Add the children into a Windows Job Object and configure the Job to be killed when the parent exits.
With overlapped IIS worker recycling you can have two functioning workers running at the same time. You must deal with that possibility.
Consider the possibility that the child process immediately crashes. It will never make a connection. Make sure your app doesn't hang waiting for the connection forever.
I'm fairly new to Akka and writing concurrent applications and I'm wondering what's a good way to implement an actor that would wait for a redis list and once an item becomes available it will process it, or send it to a different actor to process?
Would using the blocking function BRPOPLPUSH be better, or would a scheduler that will ask the actor to poll redis every second be a better way?
Also, on a normal system, how many of these actors can I spawn concurrently without consuming all the resource the system has to offer? How does one decide how many of each Actor type should an actor system be able to handle on the system its running on?
As a rule of thumb you should never block inside receive. Each actor should rely only on CPU and never wait, sleep or block on I/O. When these conditions are met you can create even millions of actors working concurrently. Each actor is suppose to have 600-650 bytes memory footprint (see: Concurrency, Scalability & Fault-tolerance 2.0 with Akka Actors & STM).
Back to your main question. Unfortunately there is no official Redis client "compatible" with Akka philosophy, that is, completely asynchronous. What you need is a client that instead of blocking will return you a Future object of some sort and allow you to register callback when results are available. There are such clients e.g. for Perl and node.js.
However I found fyrie-redis independent project which you might find useful. If you are bound to synchronous client, the best you can do is either:
poll Redis periodically without blocking and inform some actor by sending a message to with a Redis reply or
block inside an actor and understand the consequences
See also
Redis client library recommendations for use from Scala
BRPOPLPUSH with block for long time (up to the timeout you specify), so I would favour a Scheduler instead which still blocks, but for a shorter amount of time every second or so.
Whichever way you go, because you are blocking, you should read this section of the Akka docs which describes methods for working with blocking libraries.
Do you you have control over the code that is inserting the item into redis? If so you could get that code to send your akka code a message (maybe over ActiveMQ using the akka camel support) to notify it when the item has been inserted into redis. This will be a more event driven way of working and prevent you from having to poll, or block for super long periods of time.
Node.JS seems limited in its ability to live-update code and in its ability to automatically isolate exceptions. Both of which are practically by default in Java.
One very effective way to live-update is to have a listener process that simply echos communication to/from the child process. Then to update, the listener starts up a new child (which reads the updated code automatically) and then starts sending requests to the new child,, ending the old child when all requests are complete.
Is there already a system that provides this http functionality through stdout/stdin.
Is there a system that provides TCP server or UDP server functionaility through stdout/stdin.
By this I mean, providing a module that looks like the http or net module with the exception that it uses stdout/stdin for the underlying I/O.
Similar to This CGI module
some applications will only have to change require('http') to require('cgi')
I intend to do something similar. I hope to re-use code if it is already out there, and also to easily convert a small or single purpose webserver, into this listener layer which runs many webapps. It is important that cleanup occurs properly. Connections that end or error should be freed up and the end/error events/commands should be properly echoed both ways.
(I believe a common way is to have the children listen on ports and the parent communicate with those ports, but I think an stdout/stdin solution will be more efficient)
Use nginx (HttpUpstreamModule) or HAProxy. In both cases you'd run them in front and mark a backend as down and then bring it back up when you need to do a live upgrade.
I'm not certain that this is what you're looking for (indeed, I'm not certain that I understand your question), but Remy Sharp has written a very helpful node module called nodemon. It promises to "monitor for any changes in your node.js application and automatically restart the server." This may help with the issue of live updating code.