speed up api requests python

speed up api requests python

How much do you think rewriting this code using threading or asyncio will speed this up? And nope, payload has to be the way it is :l, Speed Up API Requests & Overall Python Code, Building a safer community: Announcing our new Code of Conduct, Balancing a PhD program with a startup career (Ep. First the amount of time taken by your programme to retrieve the info from the mentioned URL (this will be affected by the internet speed and the time taken by the web server to send the response) + time taken by the python to analyse that information. Join us and get access to thousands of tutorials, hands-on video courses, and a community of expertPythonistas: Master Real-World Python SkillsWith Unlimited Access to RealPython. I also use these requests at the start to load up the pool_connections(idk if this is how you use them) but essentially to make the requests faster. Finally, a quick note about picking the number of threads. rev2023.6.2.43473. One of the cool advantages of asyncio is that it scales far better than threading. The execution timing diagram looks like this: Unlike the I/O-bound examples, the CPU-bound examples are usually fairly consistent in their run times. The big difference is that this time it is clearly the best option. I would suggest to compute these two times separately and see which time is taking longer and how much variation is there.. keep in mind that at some point you will hit Google maps' API rate limits ;), I mean't to ask, what does everything contained within the if. As you can imagine, bringing up a separate Python interpreter is not as fast as starting a new thread in the current Python interpreter. It is more suitable to perform CPU-Bound tasks, because it enables full CPU utilization. This is all running on a single CPU with no concurrency. During I/O operation, your CPU will only wait until it finishes. In Germany, does an academia position after Phd has an age limit? Note that this program requires the requests module. Rationale for sending manned mission to another star? await semaphore.acquire() . Youve now seen the basic types of concurrency available in Python: Youve got the understanding to decide which concurrency method you should use for a given problem, or if you should use any at all! You see this all the time when you search: When the data of the request is hidden, its called a POST request. Remember, this is just a placeholder for your code that actually does something useful and requires significant processing time, like computing the roots of equations or sorting a large data structure. You see this when you submit a form on the web and the submitted data does not show on the URL. The URL is called the endpoint and the often invisibly submitted extra part of the request is called the payload or data. Async IO in Python and Speed Up Your Python Program With Concurrency [2] It is not strictly concurrent execution. In addition, youve achieved a better understanding of some of the problems that can arise when youre using concurrency. Asking for help, clarification, or responding to other answers. How to show a contourplot within a region? Your simplified event loop maintains two lists of tasks, one for each of these states. If youre following along and dont have the requests library installed, you can do so with the following command from the same terminal environment from which you run Python: Often times Jupyter will have the requests library installed already, but in case it doesnt, you can install it with the following command from inside a Notebook cell: And now we can put it all together. Before you jump into examining the asyncio example code, lets talk more about how asyncio works. This is in common with countless other APIs out there but trips me up every time because its so much more convenient to work with structured dicts than flattened strings. Optimize Python Requests for Faster Performance - SkillsHats Find traffic-driving keywords with our 1.25 billion+ keyword index. Youll see more as you step into the next section and look at CPU-bound examples. Implementing threading Sending 1000 requests. Aiolimiter: The request rate limit (e.g. Adding concurrency to your program adds extra code and complications, so youll need to decide if the potential speed up is worth the extra effort. Is there any way for me to increase the speed of API calls? Names are set equal to values using the = sign. Those of you who are unfamiliar with the concept of race conditions might want to expand and read the section below. Head to our Q&A section to start a new conversation. It looks a little odd, but you only want to create one of these objects, not one for each thread. I said that the data structure you were looking at above was JSON. It actually slowed things down because the cost for setting up and tearing down all those processes was larger than the benefit of doing the I/O requests in parallel. The web is a giant API that takes URLs as input and returns pages. So each thread will create a single session the first time it calls get_session() and then will simply use that session on each subsequent call throughout its lifetime. The ready state will indicate that a task has work to do and is ready to be run, and the waiting state means that the task is waiting for some external thing to finish, such as a network operation. So as you might imagine, its easy for JSON data to flow in and out of Python. For example, the JSON data above might be converted to a string. SERP tracking and analytics for enterprise SEO experts. The code has a few small changes from our synchronous version. Studying Data Science while working in automobile industry as PLM expert. Then use a thread pool as the number of request grows, this will avoid the overhead of repeated thread creation. Upskill and get certified with on-demand courses & certifications. It knows that the tasks in the ready list are still ready because it knows they havent run yet. b) The concept of Concurrent Programming: Concurrent Programming is a form of computing, where multiple tasks will be executed simultaneously. You may want to send requests in parallel. Well it is certainly better than the previous code, then during await asyncio.sleep(0.125) the code can switch to another task. Speeding it up involves overlapping the times spent waiting for these devices. 5 threads for 5 downloaders. The great thing about this version of code is that, well, its easy. Well to be 100% correct, race condition can still happen, but you will have to try very hard to get it. How much of the power drawn by a chip turns into heat? But be careful, not every operation be combined with await due to compatibility issue. We are discussing Python here. This is my first post in this sub. Ask Question Asked 2 years, 10 months ago Modified 2 years, 10 months ago Viewed 584 times 0 For a detailed understanding, I have attached a link of file: Actual file I have data in the list, that has similar syntax like: i = [a.b>c.d , e.f.g>h.i.j ] e.g. Negative R2 on Simple Linear Regression (with intercept), Word to describe someone who is ignorant of societal problems. One exception to this that youll see in the next code is the async with statement, which creates a context manager from an object you would normally await. An important point of asyncio is that the tasks never give up control without intentionally doing so. Then, youll get to see some code dealing with CPU-bound programs. See which Moz SEO solution best meets your business needs. While working on a client's project I had a task where I needed to integrate a third-party API for the project. We'll repeat the . I find it interesting that requests.post() expects flattened strings for the data parameter, but expects a tuple for the auth parameter. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Learn more about Stack Overflow the company, and our products. Not the answer you're looking for? Its just easier to visualize and set up with web pages. https://www.sec.gov/os/accessing-edgar-data, 1.https://leimao.github.io/blog/Python-Concurrency-High-Level/, Using Asyncio in Python: Understanding Pythons Asynchronous Programming Features, https://www.deviantart.com/mondspeer/art/happy-monk-506670247. Efficiently match all values of a vector in another vector. advanced Hold out on adding concurrency until you have a known performance issue and then determine which type of concurrency you need. Youll get a syntax error otherwise. It was comparatively easy to write and debug. The requests docsare simple and straight forwardfor humans. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. If the new code that is running modifies counter as well, then the first thread has a stale copy of the data and trouble will ensue. When running this script, I saw the times vary from 14.2 to 21.9 seconds. Each task takes far fewer resources and less time to create than a thread, so creating and running more of them works well. There is one small but important change buried in the details here, however. Also, many solutions require more communication between the processes. You dont have to worry about making your code thread-safe. This switch can happen in the middle of a single Python statement, even a trivial one like x = x + 1. Instead of simply calling download_site() repeatedly, it creates a multiprocessing.Pool object and has it map download_site to the iterable sites. When the running task gives control back to the event loop, the event loop places that task into either the ready or waiting list and then goes through each of the tasks in the waiting list to see if it has become ready by an I/O operation completing. This allows us to share resources a bit more easily in asyncio than in threading. I bring this up not to cast aspersions on requests but rather to point out that these are difficult problems to resolve. I'm not asking for help solving a problem but rather asking for help for possible ways to improve the speed of my program. So long as its in memory, you can do stuff with it (often just saving it to a file). The initializer function parameter is built for just this case. Again, heres the example request we made above: Now that you understand what the variable name json_string is telling you about its contents, you shouldnt be surprised to see this is how we populate that variable: and the contents of json_string looks like this: This is one of my key discoveries in learning the Moz Links API. It takes 2.5 seconds on my machine: Thats much better than we saw with the other options. If you have any questions or any improvement suggestion (this is my first medium article), you can contact me in commentar section:-) If you read this far and like it, please clap this article. Lets start by focusing on I/O-bound programs and a common problem: downloading content over the network. In your example, this is done with thread_local and get_session(): local() is in the threading module to specifically solve this problem. The following scenario would help to understand what race condition is. Tried to submit an edit but the edit queue is full? Reason is it will block everything else until the time.sleep command finishes. As you can imagine, hitting this exact situation is fairly rare. its a matter of latency between client and servers , you can't change anything in this way unless you use multiple server location ( the near server to the client are getting the request ) . There are a couple of issues with asyncio at this point. If the program youre running takes only 2 seconds with a synchronous version and is only run rarely, its probably not worth adding concurrency. Learn more here: https://prettyprinted.com/coachingGet the code here: https://prettyprinted.com/l/vZJWeb Development Courses: https://prettyprinted.comSubscribe: http://www.youtube.com/channel/UC-QDfvrRIDB6F0bIO4I4HkQ?sub_confirmation=Twitter: https://twitter.com/pretty_printedGithub: https://github.com/prettyprinted So I will use it too. The way the threads or tasks take turns is the big difference between threading and asyncio. Now that you have an idea of what concurrency and parallelism are, lets review their differences, and then we can look at why they can be useful: Each of these types of concurrency can be useful. Now arguments are assigned to variable names right in the function definition, so you can refer to either argument1 or argument2 anywhere inside this function. Hey, thats exactly what I said the last time we looked at multiprocessing. That means that the one CPU is doing all of the work of the non-concurrent code plus the extra work of setting up threads or tasks. The best answers are voted up and rise to the top, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site. How to speed up http calls in python? With Examples How to join two one dimension lists as columns in a matrix, Negative R2 on Simple Linear Regression (with intercept). Lets start at the top of the example. This means we can do non I/O blocking operations separately. In Python: X wants to borrow one hammer for table-making: Next person also wants to borrow one hammer: X doesnt need the hammer and gives it back to its owner. The keys are like the column names in a spreadsheet. We are using the = sign to assign the value of the right side of the equation to the variable on the left side of the equation. This scenario assumes no rate limiter is applied. The realtime speed is measured both on stdout, using get_net_speed () function, and conky. Its also easy for computers to read and write. But first, some basics. Gain a competitive edge in the ever-changing world of search. This is frequently the best answer, and it is in our case. the main coroutine, you need to execute: asyncio.run(main()). First off, it does not specify how many processes to create in the Pool, although that is an optional parameter. I found the fastest results somewhere between 5 and 10 threads. Youll see more of how they are different as you progress through the examples. It is created this way so that it could be combined together with await later in the next part. They arise frequently when your program is working with things that are much slower than your CPU. Threading is utterly simple to implement with Python. I've monitored the download process is slower on an ethernet connected box. The #1 most popular introduction to SEO, trusted by millions. 8 requests/second). Assignment is different from equality. A race condition will happen, when all monks start at the same time grab the chopstick right to him. Asyncio, on the other hand, uses cooperative multitasking. Your program spends most of its time talking to a slow device, like a network connection, a hard drive, or a printer. at the same time. This is the concept of portable, interoperable data. A tuple is a list of values that don't change. Heres an example of what the final output gave on my machine: Note: Your results may vary significantly. On average, the time taken to make the API call is 1.3 to . You program spends most of its time doing CPU operations. In light of the discussion above, you can view await as the magic that allows the task to hand control back to the event loop. In another word, await keyword is the point where Asyncio can transfer the control of execution to another courotines/tasks. I have a programming project that needs to read a bulk amount of insider transactions (form 4s) from sec.gov daily and build a rank list of the most successfull insiders in the US. Unlike the previous approaches, the multiprocessing version of the code takes full advantage of the multiple CPUs that your cool, new computer has. This function computes the sum of the squares of each number from 0 to the passed-in value: Youll be passing in large numbers, so this will take a while. Making statements based on opinion; back them up with references or personal experience. Its just that the I/O requests are all done by the same thread: The lack of a nice wrapper like the ThreadPoolExecutor makes this code a bit more complex than the threading example. I'm very new to API's so I'm not even sure what sort of things can/cannot be sped up, which sort of things are left to the webserver servicing the API and what I can change myself. You can't do it so easily with strings. Await keyword is applied at for example line 8, because this is the command line, where the CPU will have to wait idle. ___________________________________________________________________. The line that creates Pool is worth your attention. a) Difference between CPU-Bound and I/O-Bound tasks: CPU-Bound task: a kind of task which completion speed determines by the speed of your processor. What does it mean that a falling mass in space doesn't sense any force? Asyncio: Python has 3 main libraries that allow concurrent programming. Unlike standard blocking operations, an Async operation will send off a request, then allow your program to do other tasks whilst it waits for a response. Heres what the same program looks like with threading: When you add threading, the overall structure is the same and you only needed to make a few changes. Those of you coming from other languages, or even Python 2, are probably wondering where the usual objects and functions are that manage the details youre used to when dealing with threading, things like Thread.start(), Thread.join(), and Queue. In this situation noone can eat his noodle and they are basically blocking each other. Look into aiohttp or httpx for Async http libraries 36 Citing my unpublished master's thesis in the article that builds on top of it. Examples of things that are slower than your CPU are legion, but your program thankfully does not interact with most of them. Some people might call this part of the restful API movement, but the much more difficult XML format is also considered restful and everyone seems to have their own interpretation. What if it takes hours to run? 576), AI/ML Tool examples part 3 - Title-Drafting Assistant, We are graduating the updated button styling for vote arrows. . Show. It also takes full advantage of the CPU power in your computer. The variable response is now a reference to the object that was returned from the API. Speed Up API Requests & Overall Python Code Thats what makes this type of problem quite difficult to debug as it can be quite hard to reproduce and can cause random-looking errors to show up. The browser is a type of software known as a client. Part 3: Threading API requests with Python - Medium Because each process has its own memory space, the global for each one will be different. The multiprocessing version of this example is great because its relatively easy to set up and requires little extra code. After initiating the requests, Asyncio will not switch back to for example pep-8015, until it gets a response from the request and ready for the next job/step. Key-names and string values get double-quotes. Now lets talk about two new keywords that were added to Python: async and await. The values are like the cells in the spreadsheet. Begin by importing the required libraries. With this limit, 8 requests/second will be initiated. This article aims to provide the basics of how to use asyncio for making asynchronous requests to an API. I like to think of them as different trains of thought. Theyre just a bunch of characters. In threading, the operating system actually knows about each thread and can interrupt it at any time to start running a different thread. And nope, payload has to be the way it is :l The rest of the code is quite similar to what youve seen before. I forge 100 links for the test by this magic python list operator: url_list = ["https://www.google.com/","https://www.bing.com"]*50 The code: import requests import time def download_link ( url: str) -> None: result = requests. One way to think about it is that each process runs in its own Python interpreter. As you probably guessed, writing a threaded program takes more effort. It is a whole different story, if you have a script that needs to perform million requests daily for example. Step-by-step guides to search success from the authority on SEO. Afterward a web_scrape_task task will be created (not started) for each number from 8010 to 8016 and appended into a tasks-list. Because the operating system is in control of when your task gets interrupted and another task starts, any data that is shared between the threads needs to be protected, or thread-safe. Im here to tell you theres so much more to them than that if youre willing to take just a few little steps. As you have probably already noticed because you decided to visit this page, requests can take forever to run, so here's a nice blog written while I was an intern at NLMatics to show you how to use asyncio to speed them up.. What is asyncio?. Now lets look at the non-concurrent version of the example: This code calls cpu_bound() 20 times with a different large number each time. What one-octave set of notes is most comfortable for an SATB choir to sing in unison/octaves? Theres only a few things here that are new. However, you may be able to speed it along using parallelization. In Portrait of the Artist as a Young Man, how can the reader intuit the meaning of "champagne" in the first chapter? Explore all the free SEO tools Moz has to offer. The JSONPlaceholder website is perfect for the task, as it serves as a dummy API. In Python, both threads and tasks run on the same CPU in the same process. The processes all run at the same time on different processors. Its easier than you think. Microsoft Build 2023 Book of News With that in mind, lets step up to a radically different approach to concurrency, multiprocessing. The json.dumps() function is called a dumper because it takes a Python object and dumps it into a string. First story of aliens pretending to be humans especially a "human" family (like Coneheads) that is trying to fit in, maybe for a long time? Its easiest to think of async as a flag to Python telling it that the function about to be defined uses await. semaphore.release() . Our focus remains the same - to make it as easy as possible for you to navigate the latest news and offer critical details on the . The tasks must cooperate by announcing when they are ready to be switched out. (Therefore when I have to do Pandas operation for example to combine new and existing content after every download, I have to use asyncio.Queue to save the result temporarily.). Install requests library using pip install requests. The Problems With the Synchronous Version. Once youve decided that you should optimize your program, figuring out if your program is CPU-bound or I/O-bound is a great next step. multiprocessing in the standard library was designed to break down that barrier and run your code across multiple CPUs. When I'm testing with 6 items it takes anywhere from 4.86s to 1.99s and I'm not sure why the significant change in time. Its also more straight-forward to think about. Theres only one train of thought running through it, so you can predict what the next step is and how it will behave. Finally, the Executor is the part thats going to control how and when each of the threads in the pool will run. These objects use low-level primitives like threading.Lock to ensure that only one thread can access a block of code or a bit of memory at the same time. On the flip side, there are classes of programs that do significant computation without talking to the network or accessing a file. The s stands for string. By clicking Post Your Answer, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct. There are several strategies for making data accesses thread-safe depending on what the data is and how youre using it. Being slower isnt always a big issue, however. I'm still getting outpaced by some other people occasionally. But, starting with Python 3.2, the standard library added a higher-level abstraction called Executors that manage many of the details for you if you dont need that fine-grained control. I suggest the reader to read the below well written article, because it explained the above points very well. A tuple is a list of values that don't change. By clicking Post Your Answer, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct. But special data services like the Moz Links API have their own set of rules. API Python script slows down after a while, how to make the code faster? Thats a high-level view of whats happening with asyncio. Threading and asyncio both run on a single processor and therefore only run one at a time. A web browser is what traditionally makes requests of websites for web pages. They dont really show up in this simple example, but splitting your problem up so each processor can work independently can sometimes be difficult. This should look familiar from the threading example. There is no need to do multithreading yourself. Heres the fastest run of my tests. Its a subtle point, but worth understanding as it will help with one of the largest stumbling blocks with the Moz Links (and most JSON) APIs. CSS codes are the only stabilizer codes with transversal CNOT? Speed Up Your API Requests With Thread Pools - Medium This is a simple toy downloader using python's requests library. Asyncio? Find centralized, trusted content and collaborate around the technologies you use most. There is not a way to pass a return value back from the initializer to the function called by the process download_site(), but you can initialize a global session variable to hold the single session for each process. The reason for what appear to be singular and plural options are actually binary and string options. Heres what its execution timing diagram looks like: Little of this code had to change from the non-concurrent version. By the end of this article, you should have enough info to start making that decision. if the thread is still waiting for a response from HTML-Request). Making statements based on opinion; back them up with references or personal experience. Frankly speaking, none of the arguments below will matter if you only need to execute one thousand requests just for once. If youve updated to Python 3.7, the Python core developers simplified this syntax for you. Therefore I need to build a Python script that could make millions URL-requests efficiently, remove unneccessary form 4s and evaluate the remaining datas as Pandas DataFrame. Commenting Tips: The most useful comments are those written with the goal of learning from or helping out other students. (136s -> 91s, for reference), Dead link. Create a separate thread for each request as a start. It will not be swapped out in the middle of a Python statement unless that statement is marked. The big problem here is that its relatively slow compared to the other solutions well provide. Most of the time isn't spent computing your request. The tasks decide when to give up control. download_all_sites() creates the Session and then walks through the list of sites, downloading each one in turn. Heres how Python functions get defined. Feel free to play around with this number and see how the overall time changes. Because the operating system knows nothing about your code and can swap threads at any point in the execution, its possible for this swap to happen after a thread has read the value but before it has had the chance to write it back. This thread swapping can occur at any point, even while doing sub-steps of a Python statement. Can you be arrested for not paying a vendor like a taxi driver or gas station? Aiohttp: This library is compatible with Asyncio and will be used to perform asynchronous HTML-Requests. Normal text strings on the other hand are compatible with almost everything and can be passed on web-requests with ease. python - How to speed up API requests? - Stack Overflow A minor mistake in code can cause a task to run off and hold the processor for a long time, starving other tasks that need running. I strongly suggest the reader to read it, especially if you are considering to apply this concept. I also live very close to the API hosting actually so that's a bonus I have over others already.

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