#concurrent connections

In today's data-driven Porfiles, the dependence of online business on Proxies is becoming more and more evident. Whether it is real-time data crawling, fast traffic management, or concurrent request support in a multi-user environment, Proxies IP's multi-threading and concurrent connection technologies are core tools. These technologies not only enhance the operational efficiency of the Proxies system, but also bring more flexibility in business operations.

For Proxies IP management systems, understanding and effectively utilizing multithreading and concurrent connections is key to staying competitive. Multi-threading techniques allow the system to process multiple requests at the same time, while concurrent connections can further increase the number of simultaneously active connections, which together ensure a smoother user experience. This article will explain the practical application of these concepts step-by-step, delving into their unique advantages in Proxies IP management, as well as the technical challenges that organizations may face during their use and how to deal with them. In this way, it will not only help users better understand the practical application of Proxies IP technology, but also provide guidance for enterprises to optimize their business processes.

Basic concepts of multithreading and concurrency

Multi-threading and concurrent connections are core concepts that enhance the performance of Proxies IP management. With multithreading, a Proxy Service can handle multiple requests at the same time, and this parallel processing significantly improves the efficiency of task execution.

For example, web servers are able to cope with a large number of client requests during high loads by multithreading, while maintaining fast responses and avoiding the congestion caused by single threads.

Concurrent connections, on the other hand, refer to the number of simultaneous connections maintained within a Proxies system at a given moment. Highly concurrent connections are especially important in Proxies management, especially when the system needs to handle a large number of requests at the same time. With multi-threading and concurrent connections, Proxy Services are not only able to handle more traffic, but also allocate resources more flexibly to provide a smoother experience for users.

Limitations of Single-Threaded Proxies Management

The core function of Proxy Service is to act as an intermediary between the client and the target server, guaranteeing the security and privacy of data transmission and improving the overall efficiency. However, in single-threaded mode, the processing power and response speed of Proxies management will be significantly limited. The single-threaded management model can only process each request one by one, waiting for the completion of the previous task before processing the next request. This design leads to higher latency, which reduces the response speed of the Proxies service and affects the user experience.

In high-frequency request scenarios such as data collection and application testing, it is especially difficult to meet the demand in single-threaded mode. When a large number of client requests are centrally influxed, single-threaded systems are often overloaded, resulting in higher latency and affecting system performance and user experience.

Efficiency Benefits of Multi-Threaded Proxies Management

Multi-threading technology is an effective way for Proxies management to break through the bottleneck of single-threading. Multi-threading allows Proxy Services to handle multiple requests at the same time and is no longer limited to the mode of completing tasks one by one, which significantly improves the system's operational efficiency and response speed.

In scenarios such as data collection, multi-threading can assign requests to different Proxies IPs, dispersing the request pressure and avoiding high-frequency requests concentrated on a single IP to trigger the server's blocking mechanism. By assigning independent Proxies IPs to each thread, the risk of being blocked can be significantly reduced, accelerating data collection and improving crawling efficiency. It is worth noting that although multi-threading significantly improves speed, an unreasonable number of requests may lead to server blocking. Therefore, it is necessary to reasonably configure the number of concurrent connections and follow the principle of compliant crawling to ensure that business operations are legal and compliant.

The Important Role of Concurrent Connections

The number of concurrent connections indicates the number of active connections within the Proxies management system at the same moment. The upper limit of this value is not only affected by Proxy Service bandwidth and processing capacity, but also constrained by network resource configuration and proxy type (e.g., HTTP, SOCKS, etc.). In a highly concurrent operating Porfiles, a reasonable concurrent connection configuration can help to improve the overall system performance and user experience.

Concurrent connections have a direct impact on the speed and efficiency of the system and are closely related to the balance between supply and demand of Proxies resources. When there are sufficient Proxies resources, a higher number of concurrent connections can ensure fast task completion and improve system response speed; conversely, when Proxies resources are in short supply, too high a number of concurrent connections may increase latency or even trigger system congestion. Therefore, when choosing Proxies, it is crucial to ensure that the system has a reasonable upper limit of concurrent connections to cope with large-scale data requests.

The Challenges of Multi-Threading and Concurrent Connections

Although the application of multithreading and concurrent connections improves the performance of Proxies, it also poses a number of technical challenges, notably:

Resource Competition: Multiple threads may request the same resource at the same time, resulting in system performance degradation.

Data Consistency: Multi-threaded access to shared data can easily trigger data inconsistency issues that need to be managed synchronously.

Thread Leakage: Threads that are not properly terminated may exhaust system resources and affect system stability.

To address the above challenges, Proxies management systems often use a variety of technical strategies to optimize thread management and concurrent connection configuration. For example, the establishment of a thread pool can reduce the frequent creation and destruction of threads and lower the system load; thread scheduling and resource allocation priority management can ensure that critical tasks are given higher priority in resource allocation; data synchronization management prevents concurrent modification of shared resources by multiple threads through the use of thread locks and other mechanisms, thus ensuring data consistency. With these techniques, the stability and performance of Proxies in a highly concurrent Porfiles environment will be effectively guaranteed.

How to Optimize Multithreading and Concurrency

For effective management of multithreading and concurrent connections, here are a few common optimization strategies:

Thread Pool Management: Reduce resource consumption and ensure that threads can be reused cyclically through a fixed number of thread pools.

Thread scheduling: By setting the priority of different threads, resources are reasonably allocated and important tasks are prioritized.

Data synchronization: With the help of thread locks and other mechanisms to achieve synchronized access to shared data, to avoid conflicts caused by resource competition.

Exception handling: error handling mechanisms are set up in multi-threaded code to avoid system interruptions by catching and handling exceptions.

Real-time monitoring: Detect thread execution status and system load through monitoring tools to identify and resolve performance bottlenecks in a timely manner.

These strategies not only have significant results in optimizing the configuration of multi-threaded and concurrent connections, but also help Proxies management systems maintain stable performance in complex Porfiles environments.

Multithreading and Concurrent Connections in Proxies Applications

Taking data capture as an example, Proxies IP combined with multi-threading technology can effectively improve the efficiency of web page data capture. Multi-threading technology can assign different Proxies IPs to each crawling request, thus dispersing the request pressure and reducing the risk of being blocked by the target server. In addition, the concurrent connection configuration of the Proxies management system can also significantly increase the request speed, realizing efficient and fast high-volume data capture.

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Proxy4Free also provides users with high anonymity and strong security to ensure that data privacy is not compromised. Proxies are easy to configure and use, making your network operations more efficient. For multi-threaded and concurrent connections that require stable support, Proxy4Free is the ideal choice for an efficient network experience.

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Choosing a high-quality Proxies service is crucial for applications with multiple threads and concurrent connections. With professional Proxies, organizations and developers can more easily handle large-scale requests while safeguarding the security of data transmission. Our Proxies not only come with high standards of encryption and Secure Proxy, but also support seamless integration with a wide range of proxy management tools to help users automate data collection and efficient network operations.

For users with high demand for globalized data capture, social media monitoring or content management, we provide Proxies with no extra charge on concurrent connections, so users don't need to worry about the extra expenses increased by concurrent connections and enjoy a more efficient and stable service experience.

Conclusion

Multi-threading and concurrent connection technologies are key elements in the Proxies IP management system. Properly configured, these technologies enable Proxies to maintain efficient and stable responses when dealing with high-volume requests. Multi-threading technology improves the request processing speed, and concurrent connections guarantee smooth operation in a multi-tasking Porfiles environment. By optimizing the configuration of multithreading and concurrent connections, Proxies can provide users with more efficient network support and meet the needs of rapid business expansion.

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In Part One of this article, we started our tour of the NodeFly Agent Dashboard graphs with some ideas of how to utilize it effectively. Today we continue that tour to look at the best ways to use it with your product development.

Concurrent Connections

The “Concurrent connections” graph shows the number of connections you have established, as well as an average throughput. Considerations for your product will include how many people will be using your product at the same time, what they will be doing while connected and how much data might be moved in either direction, and how long they will be connected. Again, it means being aware of what your product needs to do.

How many people are actively using your product at any given moment can be a good metric for tuning as well as for understanding other metrics.  If you your Concurrent Connections go up you can expect your heap size to increase as well.

You can highlight or hide Concurrent Connections and Throughput.

Event Loop

Our Event metric is measuring the time from calling nextTick to when the function is executed.  This can be different from app to app but we've set a default threshold of 10ms(configurable).  Any wait longer than that will register as a a 'blocked' event loop.  We measure this by how often the threshold is reached and by how much.

The key to remember is that since Node.js can only do one thing at once, the amount of requests your product gets will impact your Event Loop and efficiency.

Node.js needs a fast event loop to be able to deliver those lightning-fast speeds you're looking for.

Heap Size

This graph will display Heap (Current heap size), RSS (Resident set size) and V8 GC (Heap size sampled immediately after a full garbage collection). As with our other graphs, you can customize what you see by highlighting or hiding any of these three sets of data.

Heap size is the designated amount of virtual memory being used, while RSS is the portion of physical RAM being used from the process’s memory.

Overall Usage

With any of these graphs, we encourage you to use the real-time insight into capacity utilization and instantly see how changing usage affects performance. That is, as you develop and change your code and product, see how its performance is affected. By seeing what your app is talking to you can see what might be slowing it down. This is especially critical in production but also valuable if something goes wrong with your product and you need to fix or improve how your app functions.  And you can make it easier by configuring alerts for the graphed values or by adding subusers to your monitoring team.

Future Plans

The NodeFly team keeps busy building upon the agent. We have some exciting plans to make the Dashboard even more useful to a broader audience. Here’s a tease of some upcoming features we have planned:

TV mode for at-a-glance monitoring

Embeddable graphs

Customizable Dashboard layout

Improved event loop monitoring with libuv

Of course, we will announce more as we get closer to launching these new features!

Conclusion

There are many ways to utilize the NodeFly dashboard. But at the end of the day it is your familiarity with your product that allows you to make the most of the real-time data the NodeFly dashboard provides. Monitoring don’t necessarily mean you will get information that you can put to use immediately, but it can help you compile good intel to help shape your product and know what is happening as you go into production as well as assist if something goes wrong. 

We’re hoping our feedback provides some new ideas as to how to make the most of our dashboard metrics. But we know we have incredible users who already put our dashboard to use in very productive ways. NodeFly users – if you have tips and tricks that you would like to share, don’t hesitate to write us at [email protected] so we can spread the word.

Benchmarking Node.js seems to be hot topic lately, not week passes without a benchmark being posted on HackerNews All these benchmarks highlight different parts and usages of Node.js. On some of them Node.js performs well and on others it sucks, badly. Both types of benchmarks are useful as it shows other users what Node.js is capable off. But it also shows us, developers, what needs to be worked and could potentially become a bottleneck in our application stack. But either way, it's a win/win situation.

Real time frameworks

When people start building real time applications they always want the fasted, best and the most bad-ass system there is available as it needs to handle a large amount of concurrent connected users. There isn't much known about the performance of Node.js when handling real time connections. You can create a real time application on any language and stack you want but developers usually prefer to libraries and frameworks that do the hard lifting for them. The frameworks that they usually consider are Twisted or Tornado, a evented server written Python, Netty the Java powered asynchronous event framework or Node.js using Socket.IO.

Because I spend allot my free time working with and on Socket.IO I started wondering what the limits would be for Node.js based real time system. Can it handle 20k, 50k, 100k or even 500k of connected users? What is the limit and when will it break?

Felix Geisendörfer recently tweeted that Node.js can allocate 1.6 million concurrent HTTP server requests through a single socket before running out of memory. Resulting in a 1.53kb memory allocation per request. This was done by flushing a huge buffer of GET /\n\n in one HTTP connection. 1.6 million is quite impressive, but than again Node.js is known for it's first class and high quality HTTP parser (Thanks Ry!).

Measuring connected sockets

It's nice to know that your server can handle 1.6 million concurrent requests over one single connection, the possibility of this occurring in real life is 0.0%. A more realistic test would be to measure how many concurrent connected connections a single Node.js process could handle and much memory one single connection allocates. Creating a benchmark that would generate 1.6 million concurrent connected requests would require me to buy allot of IP addresses to distribute the requests as we are tied to 64k ephemeral ports per IP address. So instead of generating the actual load of 1.6 million sockets I decided to calculate the theoretical performance of a single node process.

I started coding up a small script that allows to create a bunch of real time connections:

/** * Dependencies */ var http = require('http') , agent = http.getAgent('127.0.0.1', 8080); // Agent default to max sockets of 5, we need more agent.maxSockets = Infinity; // create more, agents un till we have enough for(var i = 0; i < 4000; i++){ http.get({ agent: agent , path: '/' , port: 8080 , host: '127.0.0.1' }); console.log("Client connected: " + i); }

While this doesn't follow any best practices of doing a proper http benchmark it's enough for this particular test as we are not testing processing performance of Node.js here but it's HTTP / TCP socket limits. Now that the simple benchmark script ready I started building the script that handles the incoming the requests, to make it more realistic I made sure that both the request and the response object where stored in Node.js process memory so we could communicate with the connected sockets like you would normally do with a Comet / real time server. I decided to test of different storage backends for the request objects. First Array storage:

/** * Dependencies */ var http = require('http') , host = 'localhost' , port = '8080'; // the stuff var connections = [] , i = 0; // process title so I can find it back more easily process.title = "connection"; var server = http.createServer(function(req, res){ connections[connections.length] = {req: req, res:res}; res.writeHead(200); res.write('ping'); console.log('established connections: ' + ++i); }); /** * Send messages to the stored connections * * @api public */ function message(){ var i = connections.length , connection; while(i--){ connection = connections[i]; connection.res.write('wut'); console.log('pew'); } }; // spam each minute setInterval(function(){ message() }, 1000 * 60 ); server.listen(port); console.log('listening on 8080');

And another server instance that would use an Object to store the requests and responses.

/** * Dependencies */ var http = require('http') , host = 'localhost' , port = '8080'; // the stuff var connections = {} , i = 0; // process title so I can find it back more easily process.title = "connection"; var server = http.createServer(function(req, res){ connections[Object.keys(connections).length] = {req: req, res:res}; res.writeHead(200); res.write('ping'); console.log('established connections: ' + ++i); }); /** * Send messages to the stored connections * * @api public */ function message(){ var arr = Object.keys(connections) , i = arr.length , connection; while(i--){ connection = connections[arr[i]]; connection.res.write('wut'); console.log('pew'); } }; // spam each minute setInterval(function(){ message(); }, 1000 * 60 ); server.listen(port); console.log('listening on 8080');

Running the benchmark

So I stared testing out the Array based storage first as V8 is know for it's high performing arrays. I started up the Node server, waited a while until the server was idle and fired off the simulation script. I got peak memory of 43.5mb while it was connecting all the sockets, after a few seconds (the garbage collected kicked in?) and the memory dropped back to 28.7mb RSS. Messaging the server gave it a small spike of memory but that was excepted. I re-ran the test 10x to confirm the findings and they produced the following averages:

Array:

Start: 12.6mb

Peak: 43.5mb

Idle: 28.7mb

messaging: 34.8mb / 11% cpu

Up next was the Object based store, surprisingly it came really close to Array based storage. It used a bit more memory, but for some use cases it would be worth to store the response/requests in a object because you can do easy key/client lookups. I re-ran the test 10x to confirm the findings and they produced the following averages:

Object:

Start: 12.6mb

Peak: 48.3mb

idle: 28.7mb

messaging: 35mb / 10.9% cpu

OMG what does it mean??

Now that we have these stats we can calculate the the theoretical limits of the one single process. We know that once single process is limited to a V8 heap of 1.7 gb. When you get near that limit your process already starts to die. We had a start up RSS memory of 12.6mb and the servers memory stabilized to 28.7mb so for the 4000 connections it spend 16.1mb thats 16.1mb / 4000 = 4.025kb for each connection. To fill up the server with stabilized connections it could reach a total of 1.7gb / 4.025kb = 422.000 connections. These findings come really close the 512000 concurrent Web Socket connections on groovy++ which took 2.5gb for the Java heap according the article.

I'm impressed. Node.js is the awesome sause that you should be eating when want to take a bite out of the real time web.

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