Advanced Hard Drive Caching Techniques
With the introduction of the solid-state Flash drive, performance came to the forefront for data storage technologies. Prior to that, software developers and server administrators needed to devise methods for which they could increase I/O throughput to storage, most of which resulted in low capacity caching to random access memory (RAM) or a RAM drive. Although not as fast as RAM, the Flash drive was almost a dream come true, but it had its limitations—one of which was its low capacities packaged in the NAND-based chips. The traditional spinning disk drive provided users' desired capacities but lacked in speedy accessibility. Even with the 6Gb SATA protocol, sequential data access at best performed at approximately 150MB per second (or MB/s) for both read and write operations, while random access varied between 2–5MB/s as the seeking across multiple sectors laid out in multiple tracks across multiple spinning platters proved to be an extremely disruptive bottleneck. The solid-state drive (SSD) with no movable components significantly decreased these access latencies, thus rendering this bottleneck almost nonexistent.
Even today, the consumer SSD cannot compare to the capacities provided by the magnetic hard disk drive (or HDD), which is why in this article I intend to introduce readers to proven methods for obtaining near SSD performance with the traditional HDD. Multiple open-source projects exist that can achieve this, all but one of which utilizes an SSD as a caching node, and the other caches to RAM. The device drivers I cover here are dm-cache, FlashCache and the RapidDisk/RapidCache suite; I also briefly discuss bcache and EnhanceIO.
To build the kernel modules shown in this article, you need to have either the full kernel source or the kernel headers installed for your current kernel image revision.
In my examples, I am using a commercial SATA III (6Gbps) SSD with an average performance of the following:
Sequential read: 231MB/s
Sequential write: 74MB/s
Random read: 230MB/s
Random write: 72MB/s
This SSD provides the caching layer for a slower mechanical SATA III HDD that performs at the following:
Sequential read: 115MB/s
Sequential write: 72MB/s
Random read: 2MB/s
Random write: 2MB/s
In my environment, the SSD is labeled as /dev/sdb, and the HDD is /dev/sda3. These are non-intrusive transparent caching solutions intended to achieve the performance benefits of SSDs. They can be added and removed to existing storage targets without issue or data loss (assuming that all cached data has been flushed to disk successfully). Also, all the examples here showcase a write-back caching scheme with the exception of RapidCache, which instead will be used in write-through mode. In write-back mode, newly written data is cached but not immediately written to the destination target. Write-through mode always will write new data to the target while still maintaining it in cache for future reads.
The benchmarks shown here were obtained by using FIO, a file I/O benchmarking and test tool designed for data storage technologies. It is maintained by Linux kernel developer Jens Axboe. Unless noted otherwise, all captured I/O is written at the typical 4KB page size, asynchronously to the storage target 32 transfers at a time (that is, queue depth).
dm-cache has been around for quite some time—at least since 2006. It originally made its debut as a research project developed by Dr Ming Zhao through his summer internship at IBM research. The dm-cache module just recently was integrated into the Linux kernel tree as of version 3.9. Whether you choose to enable it in a recently downloaded kernel or compile it from the official project site, the results will be the same. To load the module, you need to invoke modprobe or insmod:
$ sudo modprobe dm-cache
Now that the module is loaded, you need to inform that module about which drive to point to for the cache and which to point to for the destination. The dm-cache project site provides a Perl script to simplify this process called dmc-setup.pl. For example, if I wanted to use the entire SSD in write-back caching mode with a 4KB block size, I would type:
$ sudo perl dmc-setup.pl -o /dev/sda3 -c /dev/sdb -n cache -b 8 -w
This script is a wrapper to the equivalent dmsetup command below:
$ echo 0 20971520 cache /dev/sda3 /dev/sdb 0 8 65536 16 1 | ↪dmsetup create cache
The dm-cache documentation hosted on the project site provides details on each parameter field, so I don't cover them here.
You may notice that in both examples, I named the mapping to both drives "cache". So, when I need to access the drive mapping, I must refer to it as "cache".
The following mapping passes all data requests to the caching driver, which in turn performs the necessary magic to process the requests either by handling it entirely out of cache or both the cache and the slower device:
$ ls -l /dev/mapper total 0 lrwxrwxrwx 1 root root 7 Jun 30 12:10 cache -> ../dm-0 crw------- 1 root root 10, 236 Jun 30 11:52 control
Just like with any other device-mapper-enabled target, I also can pull up detailed mapping data:
$ sudo dmsetup status cache 0 20971520 cache stats: reads(83), writes(0), ↪cache hits(0, 0.0),replacement(0), replaced dirty blocks(0) $ sudo dmsetup table cache 0 20971520 cache conf: capacity(256M), associativity(16), ↪block size(4K), write-back
If the target drive already is formatted with data on it, you just need to mount it; otherwise, format it to your specified filesystem:
$ sudo mke2fs -F /dev/mapper/cache
Remember, these solutions are non-intrusive, so if you have existing data that needs to remain on that disk drive, skip the above step and go straight to mounting it for data accessibility:
$ sudo mount /dev/mapper/cache /mnt/cache $ df|grep cache /dev/mapper/cache 10321208 1072632 8724288 11% /mnt/cache
Petros Koutoupis is currently a senior software developer at Cleversafe, an IBM Company. He is also the creator and maintainer of the RapidDisk Project. Petros has worked in the data storage industry for more than a decade.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
Join Linux Journal's Mike Diehl and Pat Cameron of Help Systems.
Free to Linux Journal readers.Register Now!
- Stunnel Security for Oracle
- SourceClear Open
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- SUSE LLC's SUSE Manager
- My +1 Sword of Productivity
- Managing Linux Using Puppet
- Google's SwiftShader Released
- Non-Linux FOSS: Caffeine!
- Parsing an RSS News Feed with a Bash Script
- Doing for User Space What We Did for Kernel Space
With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide