Whether you’re using Python to scrape data or build an app, your programming process may take a while. To see results faster, consider leveraging mechanisms that streamline and expedite Python programming. Read more on Python Caches.
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Speeding up the programming process in Python goes beyond the benefit of convenience. First and foremost, it frees up time and resources for you and your employees to focus on revenue-boosting tasks, such as refining marketing strategies and interacting with clients.
One of the best mechanisms for accelerating large-scale programming projects is Python caches. Developers can use Python caches to minimize unneeded computations and create smoother user experiences.
What Is a Cache in Python?
Python cached property or caches store frequently accessed data to accelerate system performance and decrease access times. Python programmers can use Python caches to keep recent or often-used data in faster and more accessible memory locations.
Developers can use various methods to create caches in Python, including the following:
- Built-in “dict” data structure: You can use Python’s built-in “dict” data structure to create small to moderately-sized caches.
- Python disk caches: “Diskcache” is a Python library that provides a dictionary-like data structure. You can use it to store and retrieve data efficiently on your local disk and ensure data is accessible even after a program exits.
- Cachetools library: The “cachetools” library provides several caching algorithms, including Least Frequently Used (LFU) and Least Recently Used (LRU).
- Combining “diskcache” with “cachetools”: You can combine these libraries to create an in-memory (storing data in the system’s memory) cache with optional disk persistence.
- Third-party caching libraries: These libraries provide different caching features and strategies. Examples include “cachepy” and “cachier.”
Companies usually implement Python caches through a distributed cache. Multiple app servers distribute cache and maintain it as an external service to the app server. Distributed caching can significantly boost the scalability and performance of an app, especially if a server farm or cloud service hosts the app.
Python Cache Use Cases
You can use Python disk caches in several ways. Most companies use them to streamline web applications, machine learning models, and web scraping.
You can use Python caches on the server (back end) and client-side (front end) of Python web applications.
On the server side, web applications use in-memory caching between the web servers and the database. This can drastically reduce the request load to the database, leading to quicker load times. On the client side, web apps store static resources and user preferences in the client’s browser cache, which also results in faster load times.
For instance, suppose you run an eCommerce website with hundreds of items. Because there are so many items to choose from, users usually move between different product pages. To load pages faster, developers can cache the list of products with in-memory caching. As a result, the web application does not have to repeatedly access the database to fetch product information every time a user changes pages.
Machine learning applications
Machine learning applications often require large datasets. Using caches to prefetch parts of the dataset can decrease the data access time, reducing model training time.
Besides model training, you can also use Python caches for the following machine-learning applications:
- Feature engineering usually involves a complex transformation of raw data. As a result, it can create a bottleneck in the machine-learning pipeline. Developers can cache engineered and preprocessed features to avoid repetitive computations.
- Hyperparameter tuning or optimization involves choosing a set of optimal hyperparameters for machine learning algorithms. A hyperparameter is a parameter whose value controls the learning process. Developers can cache the results of hyperparameter runs, including best-performing ones, to speed up machine learning model selection.
- Real-time predictions and interference are often present in machine learning models. In such situations, low latency is critical. Developers can lower latency by caching the results of model inference or predictions for frequently occurring input data.
Last but not least, you can use Python caches to streamline Python web scraping. Web scraping involves pulling data from the internet into Excel or Google Sheets for analysis. Marketers, data scientists, and small business owners often use web scraping to derive actionable insights about their companies, products, services, industries, and clients.
There are two main web scraping tools: web scraping bots or web scrapers and web scraping application programming interfaces (APIs). Web scrapers are programs that automatically pull information from source websites. Meanwhile, web scraping APIs are web scraping funnels for specific websites, programs, and databases. Unlike web scrapers, web scraping APIs only provide access to data that website owners want you to have.
How To Cache Data in Python
There are several ways to cache data in Python. Before we dive into these methods, you must install Python and the required libraries. Read Python’s actions/setup-python documentation to set up Python.
Then, install the Python requests library using the “python -m pip install requests” command in your terminal. You will use the Python requests library to make HTTP requests to a source website.
Now that you have Python installed, here are three Python caching methods for web scraping.
Using a manual decorator to cache data in Python
A decorator is a Python design pattern that allows you to add new functionalities to an existing object without changing its structure. Developers often use decorators to store functions’ results and save them in the cache for future usage.
You can perform Python caching using manual decorators by following these steps:
- Create a function with a URL as a function argument. This function should request the URL and return the response text.
- Create a memoized version of this function. Memoization is a type of caching that ensures a function does not run for the same inputs more than once. Developers often use memoization to make applications more efficient.
Using LRU cache decorator for Python caching
Another way to perform Python caching is to use the @lru_cache decorator that comes with Python. The @lru_cache decorator implements caching using the Least Recently Used (LRU) method. This cache is fixed in size and removes data not recently used from the cache.
You can use @lru_cache by creating a new function for pulling content and putting “lru_cache” at the top. Use “maxsize” to set the size of the cache. The value reads in kilobytes (kbs), and setting it to “none” will allow the cache to grow indefinitely.
Using external caching services
Lastly, you can use external caching services that integrate with Python. These tools are powerful and provide a wide range of features. Examples include:
- Redis is an open-source, in-memory data structure store. Besides serving as a cache, it can be a message broker, database, or queue.
- Memcached is a distributed, high-performance, in-memory key-value store. It has fewer features than Redis.
- Couchbase Server has a fully integrated caching layer that provides high-speed data access.
Python Disable Cache and Python Clear Cache
If your Python cache becomes too large, the application may use more memory than it requires, leading to poor performance. Fortunately, you can disable Python caches or clear them.
To disable Python caching, remove the code you used to cache data in Python. For instance, if you used a manual decorator to cache Python data, just remove that code. To clear a Python cache, you can use cache_clear() to invalidate or clear the cache after the caching function.
Implementing Cache Python Eviction Policies
Disabling and clearing Python caches can be time-consuming, especially if you have a lot of Python projects. The most efficient way to clear caches is through cache eviction policies. These algorithms automatically remove data from caches to make space for new data.
There are several types of Python cache eviction policies:
- LRU (Least Recently Used) removes the least recently used element when a cache runs out of space.
- LFU (Least Frequently Used) removes the least frequently used data when a cache runs out of space. It takes the age and frequency of data into account. The main drawback of LFU is that it keeps stagnant data in the cache for long periods.
- MRU (Most Recently Used) removes the item used most recently. It is great for scenarios where users have just seen something but are unlikely to see the same thing again — for instance, streaming services. Most companies use MRU along with LRU and LFU to cover their bases.
- FIFO (First In, First Out) evicts elements in the same order as they enter the pipeline.
Python caches store frequently accessed data to make user experiences faster and more streamlined for apps, websites, and more. Marketers and CEOs can also use them to accelerate Python web scraping for market research and data-driven decision-making.
However, even if you are familiar with Python caching, Python requests cache web scraping can still cause a headache. For instance, you may come across anti-scraping tools that block you from accessing source websites. To avoid anti-scraping technologies, you may have to hire staff to implement and manage proxies, which can greatly affect your bottom line.
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