GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

If nothing happens, download the GitHub extension for Visual Studio and try again. The multiprocessing package itself is a renamed and updated version of R Oudkerk's pyprocessing package. The documentation for billiard is available on Read the Docs. Please report bugs related to multiprocessing at the Python bug tracker. The maintainers of billiard and thousands of other packages are working with Tidelift to deliver commercial support and maintenance for the open source dependencies you use to build your applications.

Save time, reduce risk, and improve code health, while paying the maintainers of the exact dependencies you use. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Multiprocessing Pool Extensions. Python C Other. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.We have built-in modules like urllib, urllib2 to deal with HTTP requests. Also, we have third-party tools like Requests. Many developers use Requests because it is high level and designed to make it extremely easy to send HTTP requests.

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But choosing the tool which is most suitable for your needs is just one thing. In the web scraping world, there are many obstacles we need to overcome. One huge challenge is when your scraper gets blocked. To solve this problem, you need to use proxies. In this part we're going to cover how to configure proxies in Requests.

To get started we need a working proxy and a URL we want to send the request to. The proxies dictionary must follow this scheme.

python http pool

It is not enough to define only the proxy address and port. You also need to specify the protocol. You can use the same proxy for multiple protocols. If you need authentication use this syntax for your proxy:. In the above example you can define proxies for each individual request. Just make the request and it will work. Sometimes you need to create a session and use a proxy at the same time to request a page. In this case, you first have to create a new session object and add proxies to it then finally send the request through the session object:.

As discussed earlier, a common problem that we encounter while extracting data from the web is that our scraper gets blocked. The solution for this is to use some kind of proxy or rather multiple proxies. A proxy solution will let us get around the IP ban. To be able to rotate IPs, we first need to have a pool of IP addresses. We can use free proxies that we can find on the internet or we can use commercial solutions for this.

If a high success rate and data quality are important for you, you should choose a paid proxy solution like Crawlera. Then, we can randomly pick a proxy to use for our request.

If the proxy works properly we can access the given site. There are multiple ways you can handle connection errors. Because sometimes the proxy that you are trying to use is just simply banned. Implementing your own smart proxy solution which finds the best way to deal with errors is very hard to do.In order to prevent conflicts between threads, it executes only one statement at a time so-called serial processing, or single-threading.

Depending on the application, two common approaches in parallel programming are either to run code via threads or multiple processes, respectively. This approach can easily lead to conflicts in case of improper synchronization, for example, if processes are writing to the same memory location at the same time.

A safer approach although it comes with an additional overhead due to the communication overhead between separate processes is to submit multiple processes to completely separate memory locations i.

python http pool

If you want to read about all the nitty-gritty tips, tricks, and details, I would recommend to use the official documentation as an entry point. In the following sections, I want to provide a brief overview of different approaches to show how the multiprocessing module can be used for parallel programming. The most basic approach is probably to use the Process class from the multiprocessing module. Here, we will use a simple queue function to generate four random strings in s parallel.

The order of the obtained results does not necessarily have to match the order of the processes in the processes list. Since we eventually use the. In this case, we could also simply use the values from our range object as position argument. The modified code would be:. To make sure that we retrieved the results in order, we could simply sort the results and optionally get rid of the position argument:. A simpler way to maintain an ordered list of results is to use the Pool.

Another and more convenient approach for simple parallel processing tasks is provided by the Pool class. The Pool.

Before we come to the async variants of the Pool methods, let us take a look at a simple example using Pool. Here, we will set the number of processes to 4, which means that the Pool class will only allow 4 processes running at the same time. In contrast, the async variants will submit all processes at once and retrieve the results as soon as they are finished. In the following approach, I want to do a simple comparison of a serial vs.

Here, I define a function for performing a Kernel density estimation for probability density functions using the Parzen-window technique. Sometimes I receive comments about whether I used this for-else combination intentionally or if it happened by mistake.

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So what this function does in a nutshell: It counts points in a defined region the so-called windowand divides the number of those points inside by the number of total points to estimate the probability of a single point being in a certain region. Below is a simple example where our window is represented by a hypercube centered at the origin, and we want to get an estimate of the probability for a point being in the center of the plot based on the hypercube.

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In the section below, we will create a random dataset from a bivariate Gaussian distribution with a mean vector centered at the origin and a identity matrix as covariance matrix. And our goal is here to use the Parzen-window approach to predict this density based on the sample data set that we have created above.I was recently confronted with a problem: I needed to build a large number order of of Docker containers and then push them to the registry.

However, after some initial testing I discovered that pushing multiple images to registry got stalled likely due to an overload of simultaneous uploads. In my testing, I was only able to run 2—3 simultaneous docker push commands until all the new ones I add got stalled. At that point I decided to limit the simultaneous uploads to the small number of parallel threads, while still utilizing large number of threads to facilitate image builds. Combination of queue multiprocessing.

Queue for passing down the work from builder threads to pusher threads and thread pool multiprocessing. Pool looked like a best candidate. Yet, there are small nuances and gaps in documentation which took me some time to understand especially when using multiprocessing on Windows. Below, I provide a small tutorial on how to use these data structures and objects. Problem formulation. In this toy problem we have a large array of parallel Processes writing results into the Queue.

Alongside them, there is a single-threaded reader Process checking for new items in the Queue and assigning them to new Processes in the Pool, such that only a small fixed number of these Processes are running at the same time. For our large array of parallel threads on the left we are going to use multithreading.

From the official reference :.

python http pool

Process objects represent activity that is run in a separate process. Starting a process es requires 2 things: the target function called and the Process call itself. In the example above we created 10 Process es and launched them all at the same time. Each process is running an instance of proc function with arguments taken from arg. Because the order of execution is not guaranteed, when we run it, we get something like:. Now, it is time to introduce a multithreading.

python http pool

According to reference :. Queue allow us to put objects to it and process them elsewhere asynchronously. Importantly, queues are thread and process safe. Keep in mind that Queue. The next step in solving our problem is to switch to a parallel reads from the queue. We could just spawn the reader processes in the same way we spawned writers, but that will permit up 10 threads run in parallel. What should we do if we are limited by the smaller number of readers like in the original problem description?

Enter multithreading. Pool :. However, if we run the code above, we will get no output. What happened? Thankfully, multiprocessing reference provides a way to wait for the execution results:. This time, if we run the code we will get the following error: RuntimeError: Queue objects should only be shared between processes through inheritance.

The multiprocessing. Manager will enable us to manage the queue and to also make it accessible to different workers:.

Finally, we are able to get the results we expect:. I initially started working on this problem on a Linux-based machine, but later continued on Windows.That's why knowing optimization patterns are a prerequisite.

It is the de-factor standard nowadays. The first optimization to take into account is the use of a persistent connection to the Web server. Persistent connections are a standard since HTTP 1. This lack of optimization is simple to explain if you know that when using requests in its simple mode e.

To avoid that, an application needs to use a Session object that allows reusing an already opened connection. Each connection is stored in a pool of connections 10 by defaultthe size of which is also configurable:. The HTTP protocol also provides pipeliningwhich allows sending several requests on the same connection without waiting for the replies to come think batch.

Unfortunately, this is not supported by the requests library. However, pipelining requests may not be as fast as sending them in parallel. Indeed, the HTTP 1. Calling requests. Having the application waiting and doing nothing can be a drawback here. It is possible that the program could do something else rather than sitting idle. A smart application can mitigate this problem by using a pool of threads like the ones provided by concurrent. It allows parallelizing the HTTP requests in a very rapid way.

This pattern being quite useful, it has been packaged into a library named requests-futures. The usage of Session objects is made transparent to the developer:. As explained earlier, requests is entirely synchronous. That blocks the application while waiting for the server to reply, slowing down the program.

Making HTTP requests in threads is one solution, but threads do have their own overhead and this implies parallelism, which is not something everyone is always glad to see in a program.

Starting with version 3. The aiohttp library provides an asynchronous HTTP client built on top of asyncio. This library allows sending requests in series but without waiting for the first reply to come back before sending the new one.

In contrast to HTTP pipelining, aiohttp sends the requests over multiple connections in parallel, avoiding the ordering issue explained earlier. All those solutions using Sessionthreadsfutures or asyncio offer different approaches to making HTTP clients faster. The snippet below is an HTTP client sending requests to httpbin.

This example implements all the techniques listed above and times them. Without any surprise, the slower result comes with the dumb serialized version, since all the requests are made one after another without reusing the connection — 12 seconds to make 10 requests.

Minimally, you should always use a session. If your system and program allow the usage of threads, it is a good call to use them to parallelize the requests.

However threads have some overhead, and they are not weight-less. They need to be created, started and then joined. Unless you are still using old versions of Python, without a doubt using aiohttp should be the way to go nowadays if you want to write a fast and asynchronous HTTP client.Multiprocessing is a great way to improve the performance.

We came across Python Multiprocessing when we had the task of evaluating the millions of excel expressions using python code. In such scenario, evaluating the expressions serially becomes imprudent and time-consuming. Generally, in multiprocessing you execute your task using process or thread. To get the better advantage of multiprocessing, we decided to use thread.

But while doing research, we got to know that GIL Lock disables the multi-threading functionality in Python. On further digging we got to know that Python provides two classes for multiprocessing i.

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Process and Pool class. In the following sections, I have narrated the brief overview of our experience while using pool and process class. And the performance comparison using both the classes.

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I have also detailed out the performance comparison, which will help to choose the appropriate method for your multiprocessing task. Though Pool and Process both executes the task parallelly, but their way executing task parallelly is different.

Python Pool

The pool distributes the tasks to the available processors using a FIFO scheduling. It works like a map reduce architecture. It maps the input to the different processors and collects the output from all the processors. After the execution of code, it returns the output in form of a list or array. It waits for all the tasks to finish and then returns the output. The processes in execution are stored in memory and other non-executing processes are stored out of memory. The process class puts all the processes in memory and schedules execution using FIFO policy.

When the process is suspended, it pre-empts and schedules new process for execution. I think choosing an appropriate approach depends on the task in hand.

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Pool allows you to do multiple jobs per process, which may make it easier to parallelize your program. The pool will distribute those tasks to the worker processes typically same in number as available cores and collects the return values in the form of list and pass it to the parent process.

Launching separate million processes would be much less practical it would probably break your OS. We used both, Pool and Process class to evaluate excel expressions. Following are our observations about pool and process class:.

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As we have seen, the Pool allocates only executing processes in memory and process allocates all the tasks in memory, so when the task number is small, we can use process class and when the task number is large, we can use pool.Read them. No copying and pasting! Fix your mistakes. Watch the programs run. Ever wished you could learn Python from a book? This is the second edition of the best selling Python book in the world.

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