Are you looking for a way to improve the performance and scalability of your cloud-native applications? Caching might be just what you need. By reducing load and storing data outside of the database, caching can play a crucial role in helping developers achieve better performance and scalability in their applications. In this article, we’ll explore different caching architectures and techniques that can help you achieve these benefits.
Introduction: Benefits of Caching
Caching has been used for decades as a technique to speed up data access. In recent years, caching has become a popular approach in cloud-native architectures as it can significantly enhance performance and scalability. When used effectively, caching reduces the burden on databases, which leads to faster execution times, improved response times, and better scalability. The benefits of caching include:
- Improved application performance and responsiveness
- Reduced load on the database
- Less network traffic
- Improved data consistency
- Lower costs by reducing server requirements
- Horizontal scaling capabilities
By understanding caching architectures and techniques, developers can realize the benefits your application needs. In the next section, we’ll discuss a popular type of caching: Distributed Cache.
Distributed Cache
Distributed cache is a shared in-memory cache maintained as an external service to the app servers. A single instance of the cache server can share information across multiple app servers. This is essential in dynamic and scalable cloud-native architectures. Distributed caching techniques focus on creating copies of data that are close to the users or applications that need them. It uses a key-value format to uniquely identify cached data and can store serialized objects in XML, JSON, or binary formats.
Here are some key benefits of Distributed Cache:
* Faster response times through data caching
* Improved scalability through load balancing
* Enhanced data consistency
* Reduced database load
Distributed caches can be implemented with one of several technologies, including Redis, SQL Server, and more. Cached data can be accessed using the IDistributedCache interface, and there can be a localized implementation of the cache using SQL Server or Redis distributed caches. The configuration for distributed caching is implementation-specific, and it paves the way to achieve scalability, load reduction, and horizontal scaling. However, considerations for caching data include data staleness and invalidation.
Now let’s take a closer look at caching data outside of the database with different caching architectures.##Caching Data Outside of the Database
Caching data outside of the database provides the benefits of reducing load and improving horizontal scaling. The different caching architectures for data include:
- Local cache: Storing data in memory on the local server itself to maximize speed.
- Remote cache: Using Redis or Elasticsearch to store data in-memory and access it through an API.
- Distributed cache with a CRUD API layer: Creating and accessing cache storage with a specific API with methods, such as create, read, update, and delete, to control cached data further.
The type of caching architecture you choose will depend on the needs of your application. Data staleness and invalidation are considerations when caching data, making sure the cache remains updated and doesn’t contain stale cached content from the database.
Server-side caching is particularly vital for server-generated pages, such as per-user customization fragments or pages that have complex conditions or queries. Many developers working on the cloud use a popular approach called server-side fragment caching, which caches the application-generated pages so they can be retrieved from the cache when required. This caching technique saves the processor time and renders pages much faster. Moreover, it reduces the workload on the database.
In-Memory Caching for Fintech Applications
Fintech applications often process large volumes of data. In-memory caching can prove to be an ideal solution to help deal with and store such massive amounts of data. Additionally, querying cached data needs to scale. Indexing is a caching technique used to reduce the number of data objects to be scanned in a query. There are two primary data structures to consider for indexing data, hash maps and binary search trees.
Hash maps provide fast add, retrievals, and deletions in constant time and with high accuracy. Although, they do not naturally sort data when retrieving data, which can impair performance for some queries.
Binary search trees group the data objects into a hierarchical structure to speed up access times when querying by key. By sorting data by key, it provides a natural sort at all indexes and provides reasonably fast retrieval times from the cache.
Composite indexing can help reduce the number of objects to be scanned in a query. It involves grouping objects by filtering attributes, which can cut down on objects to be scanned.
While many in-memory caching solutions are available, Ion’s Arc technology uses indexing, among with other caching techniques, for data aggregation and reporting. Ion’s Arc technology is an in-memory solution that provides fast, real-time data access by caching data near the processor to reduce access times.
PostgreSQL Caching for Database Scalability
Caching in PostgreSQL can improve database scalability. Required data is checked for in the cache first and retrieved from the database only when needed. Data stored in the cache for later retrieval has a time-to-live (TTL) set to expire the entry after a specific amount of time to prevent data from becoming too stale.
PostgreSQL uses two memory areas for caching, one to hold data blocks read from the file system and another to hold index blocks. By default, PostgreSQL uses shared memory to store and manage cache data.
This article goes deeper into PostgreSQL caching for database scalability. It explains how the caching system was designed, how shared memory and locking work, and how buffer eviction is handled. However, caching is becoming less accurate, and other factors are becoming more prominent, such as timescale. Timescale offers added features for time-series data, automated upgrades, and more.
Conclusion
Caching is an excellent way to reduce load and improve scalability and performance in a cloud-native architecture. There are different caching architectures for storing data outside of the database, including caching the data in local caches, remote caches using Redis or Elasticsearch, and distributed caches with a CRUD API layer. In-memory caching is good for fintech applications that process large volumes of data, and PostgreSQL caching can improve database scalability.
When it comes to caching, developers must consider data staleness and invalidation, programming language, server-side page caching, and other factors. By understanding the different caching techniques and architectures available, developers can provide efficient and scalable data caching.
Naomi Porter is a dedicated writer with a passion for technology and a knack for unraveling complex concepts. With a keen interest in data scaling and its impact on personal and professional growth.