The key fact about disk performance is that the throughput of hard drives has been diverging from the latency of a disk seek for the last decade. As a result the performance of linear writes on a 6 7200rpm SATA RAID-5 array is about 300MB/sec but the performance of random writes is only about 50k/sec—a difference of nearly 10000X. These linear reads and writes are the most predictable of all usage patterns, and hence the one detected and optimized best by the operating system using read-ahead and write-behind techniques that prefetch data in large block multiples and group smaller logical writes into large physical writes. A further discussion of this issue can be found in this ACM Queue article; they actually find that sequential disk access can in some cases be faster than random memory access!
磁盘性能方面最关键的一个事实是，在过去的十几年中，硬盘的吞吐量正在变得和磁盘寻道时间严重不一致了。结果，在一个由6个7200rpm的SATA硬盘组成的RAID-5磁盘阵列上，线性写入（linear write）的速度大约是300MB/秒，但随即写入却只有50k/秒，其中的差别接近10000倍。线性读取和写入是所有使用模式中最具可预计性的一种方式，因而操作系统采用预读（read-ahead）和后写（write-behind）技术对磁盘读写进行探测并优化后效果也不错。预读就是提前将一个比较大的磁盘块中内容读入内存，后写是将一些较小的逻辑写入操作合并起来组成比较大的物理写入操作。关于这个问题更深入的讨论请参考这篇文章ACM Queue article；实际上他们发现，在某些情况下，顺序磁盘访问能够比随即内存访问还要快！
分布式发布订阅消息系统 Kafka 架构设计 - 开源中国社区 https://www.oschina.net/translate/kafka-design?cmp&p=1#
Apache Kafka http://kafka.apache.org/documentation/#design_quotasnecessary
Don't fear the filesystem!
Kafka relies heavily on the filesystem for storing and caching messages. There is a general perception that "disks are slow" which makes people skeptical that a persistent structure can offer competitive performance. In fact disks are both much slower and much faster than people expect depending on how they are used; and a properly designed disk structure can often be as fast as the network.
The key fact about disk performance is that the throughput of hard drives has been diverging from the latency of a disk seek for the last decade. As a result the performance of linear writes on a JBOD configuration with six 7200rpm SATA RAID-5 array is about 600MB/sec but the performance of random writes is only about 100k/sec—a difference of over 6000X. These linear reads and writes are the most predictable of all usage patterns, and are heavily optimized by the operating system. A modern operating system provides read-ahead and write-behind techniques that prefetch data in large block multiples and group smaller logical writes into large physical writes. A further discussion of this issue can be found in this ACM Queue article; they actually find that sequential disk access can in some cases be faster than random memory access!
To compensate for this performance divergence, modern operating systems have become increasingly aggressive in their use of main memory for disk caching. A modern OS will happily divert all free memory to disk caching with little performance penalty when the memory is reclaimed. All disk reads and writes will go through this unified cache. This feature cannot easily be turned off without using direct I/O, so even if a process maintains an in-process cache of the data, this data will likely be duplicated in OS pagecache, effectively storing everything twice.
Furthermore, we are building on top of the JVM, and anyone who has spent any time with Java memory usage knows two things:
- The memory overhead of objects is very high, often doubling the size of the data stored (or worse).
- Java garbage collection becomes increasingly fiddly and slow as the in-heap data increases.
As a result of these factors using the filesystem and relying on pagecache is superior to maintaining an in-memory cache or other structure—we at least double the available cache by having automatic access to all free memory, and likely double again by storing a compact byte structure rather than individual objects. Doing so will result in a cache of up to 28-30GB on a 32GB machine without GC penalties. Furthermore, this cache will stay warm even if the service is restarted, whereas the in-process cache will need to be rebuilt in memory (which for a 10GB cache may take 10 minutes) or else it will need to start with a completely cold cache (which likely means terrible initial performance). This also greatly simplifies the code as all logic for maintaining coherency between the cache and filesystem is now in the OS, which tends to do so more efficiently and more correctly than one-off in-process attempts. If your disk usage favors linear reads then read-ahead is effectively pre-populating this cache with useful data on each disk read.
This suggests a design which is very simple: rather than maintain as much as possible in-memory and flush it all out to the filesystem in a panic when we run out of space, we invert that. All data is immediately written to a persistent log on the filesystem without necessarily flushing to disk. In effect this just means that it is transferred into the kernel's pagecache.
This style of pagecache-centric design is described in an article on the design of Varnish here (along with a healthy dose of arrogance).