Python is an open-source, dynamically typed, and cross-platform programming language. Its high-level built-in data structures, along with dynamic binding and typing, make it ideal for Rapid Application Development. Additionally, it serves as a primary scripting language for automating tasks and conducting data analysis.
NCache, an in-memory, distributed caching solution, significantly boosts the application scalability and performance by storing data in-memory, reducing database load, and ensuring faster access. While NCache is designed for .NET applications, it supports client-side APIs for multiple languages, including Python. This means Python applications can take advantage of NCache’s powerful features, such as seamless cache operations, advanced data expiration techniques, and robust locking mechanisms. By integrating NCache, Python applications can achieve improved response times, reduced strain on databases, and enhanced overall efficiency—making it a must-have for high-performance systems.
Why Use Python With NCache?
NCache’s robust caching features provide seamless integration with Python, enhancing performance, reliability, and scalability for your applications in the following ways:
- Improved Performance: In-memory data caching reduces latency, yields maximum performance, and faster response times.
- Reliability: NCache provides data reliability through replication, ensuring continued data access even if any server node fails.
- Scalability: NCache allows linear scalability by adding cache servers, without requiring downtime as the transitional load increases.
- High-Availability and Fault Tolerance: In a web farm, NCache enhances fault tolerance by distributing data across several nodes to prevent a single point of failure scenario.
- Cluster Topologies: NCache offers different caching topologies like Replicated and Partition-Replica for load balancing, reliability, and scalability to manage high-traffic Python applications.
Setting Up NCache Python Client
To use NCache with Python, ensure you have Python version 3.7 or higher installed. Follow the steps below on how to get started with the Python client:
Step 1: Installing NCache
First, install NCache in your development environment. It is available for both Windows and Linux and comes in two editions: Enterprise and Professional. You can check out the detailed comparison of these versions, to choose which version suits you best. Once the edition is decided, you can refer to the installation guide for step-by-step guidance on how to install NCache.
Step 2: Creating a Clustered Cache
The next step will be to create a clustered cache. Depending on your requirements, you can choose to add as many or as few nodes as you want. A 2-node setup is recommended as a single-node setup will suffice but lacks replication features. NCache offers various topologies and cache configuration options that vary across different versions. After you have successfully created a cache, ensure the cache is started before configuring the Python client.
Step 3: Configuring Python Client With NCache
To install the Python package (ncache-client) for NCache, use the following commands. This package installs all the required client libraries for your Python application.
For Enterprise:
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pip install ncache-client |
For Professional:
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pip install ncache-professional-client |
Next, import the necessary modules:
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import NCache |
Now, connect to the cache using the cache name:
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# Specify the cache name cache_name = "demoCache" # Connect to cache cache = ncache.CacheManager.get_cache(cache_name) |
Once your Python application is connected to NCache, you’re ready to start utilizing NCache’s powerful caching capabilities to enhance performance and scalability.
Some Basic Operations
The code below shows how you can add, get, and remove an object from NCache. The data can be a single item or multiple items and can be synchronously or asynchronously inserted or removed from the cache.
Searching Cached Data
NCache allows you to search cached data using Groups, Named Tags, and SQL-like queries. The Group feature allows you to logically group related cache items or those falling under the same category, optimizing data retrieval. With Tags, you can associate multiple keywords with cache items, which serve as identifying markers for retrieval. Moreover, advanced queries that need to target specific tagged data make use of Named Tags.
Cached data can be indexed and queried using NCache’s SQL-like query mechanism. This feature enables you to search for keys that meet specific criteria and return them in a result set.
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# Get product from database against given product ID product = fetch_product_from_db(1001) # Generate a unique cache key for this product key = "Product:" + product.get_product_id() # Add Product object to cache version = cache.add(key, product) # Get item against key from cache in given Class type retrieved_item = cache.get(key, Product) # Remove specified item item_removed = cache.remove(key, Product) |
The following example adds a CacheItem containing the object customer to the cache using the Add method. It then sets an additional tag property against it by adding tags.
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# Get customer from database customer = fetch_customer_from_db("ALFKI") # Create a unique cache key for this customer. key = "Customers:" + customer.get_customer_id() # Specify tags tags = [ ncache.Tag("East Coast Customers"), ncache.Tag("Important Customers") ] # Create a new CacheItem cache_item = ncache.CacheItem(customer) # Setting the tag property of the cacheItem cache_item.set_tags(tags) # Add customer object to cache version = cache.add(key, cache_item) # CacheItem added successfully |
Conclusion
Integrating NCache with your Python application enhances scalability, performance, and reliability with ease. Download NCache today and to elevate your Python application’s efficiency, optimize data access, and ensure fault tolerance.