The aiohttp library provides an asynchronous HTTP client built on top of asyncio. Starting with version 3.5, Python offers asynchronicity as its core using asyncio. 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. That blocks the application while waiting for the server to reply, slowing down the program. AsynchronicityĪs explained earlier, requests is entirely synchronous. The usage of Session objects is made transparent to the developer: from requests_futures import sessionsīy default a worker with two threads is created, but a program can easily customize this value by passing the max_workers argument or even its own executor to the FuturSession object – for example like this: FuturesSession(executor=ThreadPoolExecutor(max_workers=10)).
This pattern being quite useful, it has been packaged into a library named requests-futures. Print("Results: %s" % results) Using futures with requests With futures.ThreadPoolExecutor(max_workers=4) as executor: It allows parallelizing the HTTP requests in a very rapid way. It is possible that the program could do something else rather than sitting idle.Ī smart application can mitigate this problem by using a pool of threads like the ones provided by concurrent.futures. Having the application waiting and doing nothing can be a drawback here. Calling requests.get("") blocks the program until the HTTP server replies completely. Requests also has one major drawback: it is synchronous. Indeed, the HTTP 1.1 protocol forces the replies to be sent in the same order as the requests were sent – first-in first-out. However, pipelining requests may not be as fast as sending them in parallel. Unfortunately, this is not supported by the requests library. The HTTP protocol also provides pipelining, which allows sending several requests on the same connection without waiting for the replies to come (think batch). Exceptions can be raised without the penalty of closing the TCP connection.Reduced latency in subsequent requests (no TCP handshaking).Lower CPU and memory usage (fewer connections opened simultaneously).Reusing the TCP connection to send out several HTTP requests offers a number of performance advantages: Response = session.get("") Changing pool size Which is also configurable: import requests Session.get("") Using Session with requestsĮach connection is stored in a pool of connections (10 by default), the size of To avoid that, an application needs to use a Session object that allows reusing an already opened connection. with the get function) the connection is closed on return. This lack of optimization is simple to explain if you know that when using requests in its simple mode (e.g. Persistent connections are a standard since HTTP 1.1 though many applications do not leverage them. The first optimization to take into account is the use of a persistent connection to the Web server. There are many HTTP clients in Python the most widely used and easy to That's why knowing optimization patterns are a prerequisite. The ubiquity of REST API makes HTTP a first class citizen. Nowadays, it is more than likely that you will have to write an HTTP client for your application that will have to talk to another HTTP server.
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