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Storage caching is the process of temporarily storing data that is frequently accessed in a cache memory, so that it can be retrieved quickly when requested by the user. Caching is used to reduce the latency and increase the throughput of data access, especially for disk or network-based storage systems that have slower access times compared to memory-based storage.
When a user requests data, the caching system checks if the data is already in cache memory. If it is, the data is retrieved from the cache instead of the slower storage device. If the data is not cached, it is retrieved from the slower storage device and stored in the cache to enable easy reuse. Caching can improve the overall performance of storage systems and reduce the workload on the underlying storage devices.
This is part of a series of articles about block storage.
In this article:
Storage Caching: Benefits and Use Cases
Caching provides several benefits that can significantly improve the performance of applications and reduce costs associated with database and backend operations. Here are some key benefits of caching:
- Improving application performance: Caching can significantly accelerate the response time of applications by minimizing the latency associated with accessing data from slower, longer-term storage devices. This can enhance user experience and increase the efficiency of critical business processes.
- Reducing database costs: Caching can reduce the load on the database by serving frequently accessed data from cache memory. This reduces the overall number of requests made to the database, thereby reducing the cost associated with database licensing, maintenance, and hardware upgrades.
- Increasing IOPS (read throughput): Caching can increase the input/output operations per second (IOPS) of storage systems by serving frequently accessed data from cache memory. This improves the read throughput of the system and reduces the response time for read-intensive applications.
- Reducing the backend load: Caching can reduce the workload on the backend by serving frequently accessed data from cache memory. This reduces the number of requests made to the backend, thereby improving the scalability and performance of the backend infrastructure.
Here are some common caching use cases:
- Database caching: This involves storing frequently used data in a cache memory to decrease the load on the database and improve the application's response time. The database caching can be implemented in different layers of the application stack, such as in-memory cache, query cache, or result cache. By caching frequently accessed data, the database can reduce the number of requests made to the disk-based storage system, leading to significant improvements in read throughput and query response time.
- DNS caching: This is the process of storing frequently accessed DNS records in a cache memory to reduce the latency associated with resolving domain names. DNS caching can be implemented in both client-side and server-side applications, improving the performance of applications that require frequent DNS lookups. DNS caching can significantly reduce the DNS query response time, which can improve the user experience of web applications.
- Content Delivery Networks (CDNs): CDNs are a type of caching system that stores frequently accessed content (such as images, videos, and static files) in a cache memory distributed across multiple geographical locations. By caching content closer to the end-users, CDNs can improve the performance of web-based applications by reducing the latency associated with fetching content from the source server. CDNs also decrease the load on the source server, increase the scalability of web applications, and provide a better user experience to geographically dispersed users.
Types of Caching
There are several storage caching strategies based on how the application reads or writes data. For write-heavy applications, the caching approach will be different from a read-heavy application.
Side Caches
A side cache, also known as cache-aside, is a caching pattern used in read-heavy applications. The frequently used data is cached on the application side rather than within the data store.
When a request for data is made, the application checks the cache for the data, and if it's present, returns it directly. If the data isn't present within the cache, the app fetches it from the data store, caches it for future use, and returns it to the user. This approach takes some of the load off the data store, speeds up read operations, and improves the overall application performance.
Read-Through and Write-Through Caches
Read-through and write-through caches are caching strategies that manage how data is loaded into the cache and how it is updated. Both strategies aim to enhance the performance of applications, with each strategy tailored to different types of workloads:
- A read-through cache is a strategy used primarily in read-heavy applications. In this approach, when a cache miss occurs (i.e., the requested data is not in the cache), the cache retrieves the data from the underlying data store, stores it in the cache, and then returns it to the user. By doing so, subsequent requests for the same data will be served directly from the cache, minimizing the number of reads from the data store and improving the application's performance.
- A write-through cache is a strategy that is more suitable for write-heavy applications. In this approach, when the application writes data, the cache updates both the data in the cache and the underlying data store simultaneously. This ensures that the cache and data store are always in sync, which is essential for data consistency.
Write-Behind Caches
Write-behind (write-back) caches are a type of cache in which data is first written to the cache before being asynchronously written to the main storage later. They are used to improve write performance by allowing applications to continue writing data without waiting for the data to be written to the main storage. This can help reduce latency and improve overall system performance.
Storage Caching in the Cloud
Storage caching is an important technique for improving the performance of cloud storage systems, just as it is for on-premises storage systems. In fact, storage caching is particularly important in the cloud, where network latency and bandwidth limitations can make accessing storage slower than on-premises storage.
Cloud providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, offer several storage caching options for their customers.
Amazon Web Services
AWS offers Amazon ElastiCache, a fully-managed caching service that supports popular open-source caching engines such as Memcached and AWS ElastiCache for Redis. ElastiCache allows customers to easily deploy and manage caching clusters, and it can be used to boost the performance of databases, web applications, and other workloads that rely on frequently accessed data.
Microsoft Azure
Azure offers several caching options as well, including Azure Cache for Redis, a fully managed caching service that supports in-memory data storage, and Azure Managed Disks, which include caching options that can be configured to improve disk performance.
Google Cloud
Google Cloud offers several storage caching options to improve the performance of storage systems, including Transfer Appliance, which is a storage caching appliance that can be used to transfer and cache large datasets before being uploaded to Google Cloud. Google Kubernetes Engine (GKE) supports several caching solutions, including Redis, Memcached, and Varnish. These caching solutions can be used to improve the performance of workloads deployed on GKE.
In addition to these managed caching services, cloud customers can also implement caching solutions on their own infrastructure, either in virtual machines or containers, using open-source caching engines such as Redis, Memcached, or Varnish.
Storage Caching with Cloud Volumes ONTAP
NetApp Cloud Volumes ONTAP, the leading enterprise-grade storage management solution, delivers secure, proven storage management services on AWS, Azure, and Google Cloud. Cloud Volumes ONTAP capacity can scale into the petabytes, and it supports various use cases such as file services, databases, DevOps, or any other enterprise workload, with a strong set of features including high availability, data protection, storage efficiencies, Kubernetes integration, and more.
These NetApp caching features help minimize access latencies:
- Edge Caching for caching data at the edge of the network reduces access latencies and enables collaboration among distributed users accessing the same file from remote locations. Central file locking makes sure collaboration happens in real time, preventing multiple users from making changes separately so file data stays consistent.
- FlexCache Volumes create local caches of frequently accessed data, reducing the need to access remote file storage and improving overall data access times. Cloud bursting through FlexCache helps scale storage infrastructure dynamically by using cloud resources during periods of high demand. This can help ensure optimal performance and reduce access latencies, even during periods of peak usage.
