The idea of block I/O caching isn't revolutionary, but it still is an extremely complex topic. Technically speaking, caching as a whole is complicated and a very difficult solution to implement. It all boils down to the I/O profile of the ecosystem or server on which it is being implemented. Before I dive right in, I want to take a step back, so you understand what I/O caching is and what it is intended to address.

What Is I/O Caching?

A computer cache is a component (typically leveraging some sort of performant memory) that temporarily stores data for current write and future read I/O requests. In the event of write operations, the data to be written is staged and will eventually be scheduled and flushed to the slower device intended to store it. As for read operations, the general idea is to read it from the slower device no more than once and maintain that data in memory for as long as it is still needed. Historically, operating systems have been designed to enable local (and volatile) random access memory (RAM) to act as this temporary cache. Although it performs at stellar speeds, it has its drawbacks:

• It is expensive.
• Capacities are small.
• More importantly, it is volatile. If power is removed from RAM, data is lost.

As unrealistic as it might seem, the ultimate goal is never to touch the slower device storing your data with either read or write I/O requests (Figure 1). Fortunately, other forms of performant, cheap, high-density, and persistent memory devices exist that do not perform as fast as RAM but that do still perform extremely well – enough so that I will demonstrate their use in the following exercise with noticeable results.

Figure 1: The data performance gap as you move further away from the CPU.

Using I/O Caching

Solid State Drives (SSDs) brought performance to the forefront of computing technologies, and their adoption is increasing not only in the data center but also in consumer-grade products. Unlike its traditional spinning hard disk drive (HDD) counterpart, SSDs comprise a collection of computer chips (non-volatile NAND memory) with no movable parts. Therefore SSDs are not kept busy seeking to new drive locations and, in turn, introducing latency. As great as this sounds, SSDs are still more expensive than HDDs. HDD prices have settled to around $0.03/GB; SSD prices vary but sit at around $0.13-$0.15/GB. At scale, that price gap makes a world of difference.

To keep costs down and still invest in the needed capacities, one logical solution is to buy a large number of HDDs and a small number of SSDs and enable the SSDs to act as a performant cache for the slower HDDs.

Common Methods of Caching

In this discussion, note that target refers to the backing store (i.e., the slower HDD). However, you should understand that the biggest pain point for a slower HDD is not accessing sectors for read and write workloads sequentially, it is random workloads and, to be more specific, random small I/O workloads that are the issue. The purpose of a cache is to alleviate a lot of the burden for the drive to seek new sector locations for 4K, 8K, or other small I/O requests. Some of the more common caching methods or modes are:

• Writeback caching. In this mode, newly written data is cached but not immediately written to the destination target.
• Write-through caching. This mode writes new data to the target while still maintaining it in cache for future reads.
• Write-around caching or a general-purpose read cache. Write-around caching avoids caching new write data and instead focuses on caching read I/O operations for future read requests.

Many userspace libraries, tools, and kernel drivers exist to enable high-speed caching. I will describe some of those more commonly used before eventually diverting attention to just dm-writecache.