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

Terminology

• 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.

Models

System Under Test

2.1 Block diagram of system uder test

Queueing System

2.2 Simple queueing model

Concepts

Latency

2.3 Network connection latency

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

Time Scales

Table 2.2 Example time scale of system latencies

Trade-Offs

The good/fast/cheap "pick two" trade-off.

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