When reading the Systems Performance: Enterprise and the Cloud, 2nd Edition
by Brendan Gregg, I saw that each chapter has an
Exercises section with a set
of questions. This series of blogs will be my attempt to answer them and also
give additional links I’ve read whilst reading the chapter. The answers might
not be correct, not detailed but this is to help me explain what I learned to
make sure I understand it.
You can follow system-performance-book for this series.
Answer the following questions about key performance terminology:
What are IOPS?
Input/Output operations per second to measure the rate of the data transfer, for example, the disk IOPS refers to the number of reads and writes per second. The higher the number the better performance.
What is utilization?
Defines how busy a resource was performing work for a specific set of time, for example to serve a request 20% of the CPU is utilized.
What is saturation?
When a resource is utilized and there is no free resources left and can no longer use the resource, which leads to queueing.
What is latency?
The time taken to finish a specific request, for example, the time taken to respond to a specific request.
What is micro-benchmarking?
Create an artificial workload to test the throughput for a small part of the
application or system, for example, using
iperf to test the TCP throughput of
Choose five methodologies to use for your (or a hypothetical) environment. Select the order in which they can be conducted, and explain the reason for choosing each.
- Problem Statement: First define the problem by asking questions like when the problem showed up if there were any changes to the environment.
- Request, Errors, Duration (RED): After you have the problem statement, then you need to understand how the user is perceiving this problem, are they seeing a bunch of errors or slow requests. Like this, we can understand the severity of the problem.
- Utilization, Saturation, Error (USE): See the underlying machine of the service and see you can find the bottleneck from a hardware level.
- Scientific Method: Now that we know the severity of the defined problem and how the service is behaving you can start creating hypothesis and test that the hypothesis with the next step which is drill-down.
- Drill-down analysis: When we have a hypothesis, start drill-down analysis of that problem statement until you reach a dead-end or disprove your hypothesis.
Summarize problems when using average latency as a sole performance metric. Can these problems be solved by including the 99th percentile?
Averages hide details and outliers, time is also important, for example, if you take the CPU utilization 5-minute-average and it’s 50% it’s possible that the CPU spiked at 100% for 2 seconds and it doesn’t show in the average.
99th percentile can solve this since it measures the variance from the average and shows the minimum and maximum variance.