Speed Up Your Api Requests: How To Use Multi Threading In Python
Multi-threading is a popular programming concept that enables a program to run multiple threads simultaneously, resulting in faster execution and improved performance. This concept is widely used in various applications, especially when there is a need to perform multiple tasks simultaneously.
In this blog, we will explore the concept of multi-threading in Python and how it can be used to call the Minelead API. We will cover the basics of multi-threading, its advantages, and how to implement it in Python.
requirements
- Requests module installed using pip install requests
- Api key from Minelead
- Understanding what is an Api and how to use it
Outlines
What is threading
How to make API call using threading
What is threading
Multi-threading is a process of executing multiple threads simultaneously within a single program. Each thread operates independently and can execute different parts of the program at the same time. This allows for faster execution and improved performance, especially when dealing with time-consuming tasks.
There are two types of multi-threading:
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User-level threading: In this type of multi-threading, the operating system is not involved in managing the threads. The threads are managed by the user, and the operating system only provides basic support.
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Kernel-level threading: In this type of multi-threading, the operating system is responsible for managing the threads. The threads are scheduled and managed by the operating system, which provides more efficient management and improved performance.
Advantages of Multi-Threading:
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Improved performance: Multi-threading enables the program to perform multiple tasks simultaneously, resulting in faster execution and improved performance.
- Better Resource Utilization: Multi-threading allows for better utilization of system resources, such as CPU and memory, as multiple tasks can be executed simultaneously.
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Improved Responsiveness: Multi-threading can improve the responsiveness of an application by allowing multiple tasks to be executed simultaneously.
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Increased Parallelism: Multi-threading enables increased parallelism, which can lead to better performance and increased scalability.
Implementing Multi-Threading in Python: Python provides a module called 'threading' that enables multi-threading in Python. The 'threading' module provides several classes and methods to create and manage threads in Python.
To create a thread in Python, you can use the 'Thread' class from the 'threading' module. This class takes a function as an argument and creates a new thread that executes that function. To start the thread, you need to call the 'start' method of the 'Thread' class.
How to make API call using threading
In the previous section, we learned about the concept of multi-threading and how to implement it in Python using the 'threading' module. In this section, we will see how multi-threading can be used to make API calls.
Making API calls is a common task in many applications, and it can often be time-consuming. By using multi-threading, you can make multiple API calls simultaneously, resulting in faster execution and improved performance.
Here's an example of how you can use multi-threading in Python to make API calls to the minelead API :
import threading
import requests
api_url = 'https://api.minelead.io/v1/search/?'
api_key = '<Your Api Key Goes Here>'
domains = ['google.com','amazon.com','tesla.com','minelead.io']
def get_data(domain):
response = requests.get(f'{api_url}domain={domain}&key={api_key}').json()
print(response)
threads = []
for domain in domains:
thread = threading.Thread(target=get_data, args=(domain,))
thread.start()
threads.append(thread)
for thread in threads:
thread.join()
In this example, we are using the 'requests' module to make the API calls. The 'requests.get' method is used to make a GET request to the API, and the response is printed.
We have a list of domain names that we want to pass as argument for the minelead search api, and for each domain name, we are creating a new thread that executes the 'get_data' function. The 'get_data' function takes a domain as an argument and makes a GET request to the API using the 'requests.get' method.
After creating the threads, we are starting them using the 'start' method, and then appending them to the 'threads' list.
Finally, we are using the 'join' method to wait for all the threads to complete. The 'join' method blocks the execution of the main thread until the specified thread has completed.
This is how you can use multi-threading to make API calls in Python. By making multiple API calls simultaneously, you can significantly improve the performance and responsiveness of your application.
In this section, we saw how to use multi-threading to make API calls in Python. By using multi-threading, you can make multiple API calls simultaneously, resulting in faster execution and improved performance. This is an important technique that you can use in your own applications to improve performance and responsiveness.
Conclusion
In this blog, we covered the topic of multi-threading in Python, and how it can be used to make API calls. We discussed the concept of multi-threading and how it works, and also provided an example of how to implement multi-threading in Python using the 'threading' module.
Multi-threading is an important technique that can significantly improve the performance and responsiveness of your applications. By making multiple API calls simultaneously, you can reduce the overall time it takes to fetch the data from the API.
It's important to note that multi-threading is not always the best solution for every problem, and it's crucial to carefully consider the requirements of your application before deciding to use multi-threading. In some cases, multi-threading can lead to decreased performance and added complexity, so it's important to use it wisely.
In conclusion, multi-threading is a powerful technique that can greatly improve the performance of your applications. Whether you are making API calls or working with other types of tasks, understanding the basics of multi-threading and how to implement it in Python is an important skill for any developer.
