I would like to build a customer billing tool using signalR.
I am unsure if SignalR is the correct technology really, I currently use a scheduled service that runs twice a day.
I would like to send a request to connected clients to perform a billing routine anytime I want. The client will run a number of activities (generate reports, call some stored procedures etc... which can take up to 5 minutes to complete.
I have built a simple system but I am concerned about the long running task on the client. Whilst the long process is running the client is unable to retrieve other requests from the server, once the long task is complete all the requests appear.
Is there any way to just ignore requests on the client until the long task is complete? Or a better way to handle the requests? Maybe SignalR isn’t the best approach.
Related
We have a bus reservation system running in GKE in which we are handling the creation of such reservations with different threads. Due to that, CRUD java methods can sometimes run simultaneously referring to the same bus, resulting in the save in our DB of the LAST simultaneous update only (so the other simultaneous updates are lost).
Even if the probabilities are low (the simultaneous updates need to be really close, 1-2 seconds), we need to avoid this. My question is about how to address the solution:
Lock the bus object and return error to the other simultaneous requests
In-memory map or Redis caché to track the bus requests
Use GCP Pub/Sub, Kafka or RabbitMQ as a queue system.
Try to focus the efforts on reducing the simultaneous time window (reduce from 1-2 seconds up to milliseconds)
Others?
Also, we are worried if in the future the GKE requests handling scalability may be an issue. If we manage a relatively higher number of buses, should we need to implement a queue system between the client and the server? Or GKE load balancer & ambassador will already manages it for us? In case we need a queue system in the future, could it be used also for the collision problem we are facing now?
Last, the reservation requests from the client often takes a while. Therefore, we are changing the requests to be handled asynchronously with a long polling approach from the client to know the task status. Could we link this solution to the current problem? For example, using the Redis caché or the queue system to know the task status? Or should we try to keep the requests synchronous and focus on reducing the processing time (it may be quite difficult).
I have a situation where I host a high RPS highly available service that receives requests aka commands. These commands have to be sent to N downstream clients, who actually execute them. Each downstream client is separate microsevice and has different constraints like mode (sync,async), execution cadence etc.
Should a slow downstream client build the logic to receive all requests and execute them in batches as they want ? Or my service should build logic to talk to slow and fast clients by maintaining state for commands across downstream clients. Share your opinions
Not enough info to give any prescriptive advice, but I'd start with dividing the tasks into async and sync first. Those are 2 completely different workloads that, most likely, would require different implementation stacks. I'll give you an idea of what you can start with in the world of AWS...
Not knowing what you mean by async, I'd default to a message-bus setup. In that case you can use something like Amazon Kinesis or Kafka for ingestion purposes, and kicking off Lambda or EC2 instance. If the clients need to be notified of a finished job they can either long-poll an SQS queue, subscribe to an SNS topic, or use MQTT with websockets for a long-running connection.
The sync tasks are easier, since it's all about processing power. Just make sure you have your EC2 instances in an auto-scaling group behind an ALB or API Gateway to scale out, and in, appropriately.
This is a very simple answer since I don't have any details needed to be more precise, but this should give you an idea of where to get started.
I'd like to develop a simple solution using .NET for the following problem:
We have several computers in a local network:
10 client computers that may need to execute a program that is only installed on two workstations
The two workstations that are only used to execute the defined program
A server that can be used to install a service available from all previously described computers
When a client computer needs to execute the program, he would send a request to the server, and the server would distribute the job to a workstation when available for execution, and inform the client computer when the execution has been performed.
I'm not very used to web and services development so I'm not sure if it's the best way to go, but below is a possible solution I thought about:
A web service on the server stores in queues or in a database the list of tasks with their status
The client computer calls the web service to execute a program and gets a task id. Then calls it every second with the task id to know if the execution has been performed.
The workstations that are available call the web service every second to know if there is something to execute. If yes, the server assigns the task, and the workstation calls the web service when the execution is completed.
I summarized this in the below figure:
Do you think to a simpler solution?
Have a look at signalr! You could use it as messaging framework and you would not need to poll the service from 2 different diretions. With signalR you would be able to push execution orders to the service and the service will notify the client once the execution has been processed. The workstation would be connected with signalR, too. They would not need to ask for execution orders as the webservice would be able to push execution orders to either all or a specific workstation.
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.
I need to build a Windows Service in VB.net under Visual Studio 2003. This Windows service should read the flat file (Huge file of about a million records) from the local folder and upload it to the corresponding database table. This should be done in Rollback mode (Database transaction). While transferring data to table, the service should also be listening to additional client requests. So, if in between client requests for a cancel operation, then the service should rollback the transactions and give feedback to the client. This windows service also keeps writing continuously to two log files about the status and error records.
My client is ASPX page (A website).
Can somebody help me explain how to organize and achieve this functionality in a windows service(Processing and listening for additional client requests simultaneously. Ex. Cancellation request).
Also could you suggest me the ideal way of achieving this (like if it is best to implement it as web service or windows service or just a remote object or some other way).
Thank you all for your help in advance!
You can architect your service to spawn "worker threads" that do the heavy lifting, while it simply listens for additional requests. Because future calls are likely to have to deal with the current worker, this may work better than, say, architecting it as a web service using IIS.
The way I would set it up is: service main thread is listening on a port or pipe for a communication. When it gets a call to process data, it spawns a worker thread, giving it some "status token" (could be as simple as a reference to a boolean variable) which it will check at regular intervals to make sure it should still be running. Thread kicks off, service goes back to listening (network classes maintain a buffer of received data so calls will only fail if they "time out").
If the service receives a call to abort, it will set the token to a "cancel" value. The worker thread will read this value on its next poll and get the message, rollback the transaction and die.
This can be set up to have multiple workers processing multiple files at once, belonging to callers keyed by their IP or some unique "session" identifier you pass back and forth.
You can design your work like what FTP do. FTP use two ports, one for commands and another for data transfer.
You can consider two classes, one for command parsing and another for data transfer, each one on separate threads.
Use a communication channel (like a privileged queue) between threads. You can use Syste.Collections.Concurrent if you move to .NET 4.0 and more threading features like CancellationTokens...
WCF has advantages over web service, but comparing it to windows service needs more details of your project. In general WCF is easier to implement in compare to windows service.