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How to handle streaming responses using Requests

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by Abdul Rawoof A R dot icon Updated: Oct 9, 2023

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A stream is a fundamental concept in computer science and programming. You can read or write a sequence of data elements over time. Streams help transfer data efficiently and flexibly between different parts of a program. They are particularly useful for handling data that may not fit in memory.   

Here are some different types of streams you can find:   

  • Text Streams: Text streams handle character or text-based data. You can use these to read and write text files, process log data, or send text over a network.   
  • Binary Streams: Binary streams handle raw binary data. People use them for reading and writing files. It contains non-textual information, such as images, audio, and video.   
  • Input Streams: We use these to read data from sources like files, keyboards, or network sockets.   
  • Output Streams: We use these to write data to a destination, such as a file, console output, or network socket.   
  • File Streams: File streams are those used for reading and writing files on a file system. You can use text-based or binary formats for tasks like file I/O operations.   

  

We are still deciding how to change the program for streaming tweets, but using a CSV file of tweets is simple. To make it comfortable to work with real-time tweets, Twitter offers the Twitter Streaming API. When people retweet, their tweets appear in the Twitter API data stream. You can repeat the same text hundreds or thousands of times. We can determine the supported HTTP methods on our URL using the Requests OPTIONS verb.   

  

Here is an example of how to handle streaming responses using Requests:   

Fig: Preview the output you will get on running this code from your IDE.

Code

In this solution, we are using the Requests library.

Python Requests Stream Data from API

PythondotLines of Code : 20dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import requests

def get_stream(url):
    s = requests.Session()

    with s.get(url, headers=None, stream=True) as resp:
        for line in resp.iter_lines():
            if line:
                print(line)

url = 'https://jsonplaceholder.typicode.com/posts/1'
get_stream(url)

b'{'
b'  "userId": 1,'
b'  "id": 1,'
b'  "title": "sunt aut facere repellat provident occaecati excepturi optio reprehenderit",'
b'  "body": "quia et suscipit\\nsuscipit recusandae consequuntur expedita et cum\\nreprehenderit molestiae ut ut quas totam\\nnostrum rerum est autem sunt rem eveniet architecto"'
b'}'

Instructions

Follow the steps carefully to get the output easily.

  1. Install PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install Requests - pip install requests.
  4. Create a new Python file(e.g.: test.py).
  5. Copy the snippet using the 'copy' button and paste it into that file(remove output lines).
  6. Run the file using the run button.


I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.


I found this code snippet by searching for 'Python Requests Stream Data from API' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in PyCharm 2022.3.3.
  2. The solution is tested on Python 3.9.7.
  3. Requests version 2.31.0.


Using this solution, we are able to handle streaming responses using Requests with simple steps. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code which would help us to handle streaming responses using Requests.

Depenedent Library

requestsby psf

Python doticonstar image 49787 doticonVersion:v2.31.0doticon
License: Permissive (Apache-2.0)

A simple, yet elegant, HTTP library.

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            requestsby psf

            Python doticon star image 49787 doticonVersion:v2.31.0doticon License: Permissive (Apache-2.0)

            A simple, yet elegant, HTTP library.
            Support
              Quality
                Security
                  License
                    Reuse

                      You can also search for any dependent libraries on kandi like 'Requests'.

                      FAQ 

                      1. How can we use the Streaming API to stream data?   

                      "Streaming API" refers to various application programming interfaces and technologies. It enables streaming data from one point to another in real-time or near-real-time. Many things like social media, finance, and IoT use streaming APIs.   

                        

                      2. How does the elegant and simple HTTP library, requests, handle streaming APIs?   

                      The requests library in Python makes it easy to make HTTP requests elegantly. When dealing with streaming APIs, it offers different features for handling streaming data. Streaming APIs are often used when you want to work with large amounts of data.   


                      3. How does the Requests API work with Chunked transfer encoding?   

                      In HTTP, Chunked Transfer Encoding transfers data in chunks instead of all at once. It's especially helpful when the content size is unknown or when it's generated. Chunked Transfer Encoding allows a server to send data to a client in smaller chunks. You can start sending data before the full response is ready.   


                      4. How can I interact with streaming APIs with a given Request object?   

                      To interact with streaming APIs, use a Request object to make HTTP requests to the API endpoint. It handles the streaming response and processes the data as it arrives.   


                      5. Is there an easier way to access large streams of data using requests in Python?   

                      To make it easier, use the requests stream feature to simplify and streamline the process. Streaming allows you to download and process data in smaller chunks. It can be useful when dealing with large files or streaming APIs.  

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