Chapter 2: Methodologies

The Learning objectives of this chapter:

  • Understand key performance metrics: latency, utilization, and saturation

  • Develop a sense for the scale of measured time, down to nanoseconds

  • Learn tuning trade-offs, targets, and when to stop analysis

  • Identify problems of workload versus architecture

  • Consider resource versus workload analysis

  • Follow different performance methodologies, including: the USE method, workload characterization, latency analysis, static performance tuning, and performance mantras

  • Understand the basics of statistics and queueing theory


  • IOPS: Input/output operations per second is a measure of the rate of data transfer operations. For disk I/O, IPOS refers to reads and writes per second.

  • Throughput: The rate of work performed. It can refer to the data rate (bytes per second or bits per second), or the operation rate (operations per second or transactions per second)

  • Response time: The time for an operation to complete.

  • Latency: A measure of time an operation spends waiting to be serviced. It also refers to the entire time for an operation.

  • Utilization: For resources that service request, utilization is a measure of how busy a resource is, based on how much time in a given interval it was actively performing work. (disk I/O, CPU, Memory, storage etc)

  • Saturation: The degree to which a resource has queued work it cannot service.

  • Bottleneck: it is a resource that limits the performance of the system.

  • Workload: The input to the system or the load applied is the workload. For a database, the workload consists of the database queries and commmands send by the clients.

  • Cache: A fast storage area that can duplicate or buffer a imited amount of data, to avoid communicating directly with a slower tier of storage, thereby improving performance.


System Under Test

Queueing System



The latency is the time spent waiting before an operation is performed.

Time Scales


A common trade-off in performance tuning is the one between CPU and memory, as memory can be used to cache results, reducing CPU usage.

Tunable parameters often come with trade-offs. Examoles:

  • File system record size (or block size):

    • Small reocrd sizes, close to the application I/O size, will perform better for random I/O workloads and make more efficient use of the file system cache while the application is running.

    • Large record sizes will improve streaming workloads, including file system backups.

  • Network buffer size:

    • Small buffer sizes will reduce the memory overhead per connection, helping the system scale.

    • Large sizes will improve network throughput.

Tunning Efforts

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