The document provides an overview of performance testing, outlining key concepts such as types of tests (load, stress, endurance, spike, and scalability testing) and common performance issues. It emphasizes the importance of collecting metrics, understanding app behavior, and designing tests with clear goals, urging developers to focus on actionable results rather than just identifying flaws. Key takeaways include the need for a balanced approach to testing, failing builds on performance issues, and recognizing performance testing as a broad field.

1. How Fast Is My App?
Performance Testing 101
Gene Gotimer
Principal DevOps Engineer at Praeses, LLC
@OtherDevOpsGene
June 27, 2024 #KCDC2024

2. What is performance testing?
“I Love Lucy | Lucy And Ethel At The Chocolate Factory (S2, E1)”
Paramount Plus, © 1952
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3. • Time
• Resources
• Capacity
What are we measuring?
• Time- How long does it take?
• Resources- How big a system do we need?
• Capacity- How much can we handle at once?
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4. Common performance problems
• Long load times
• Poor response time
• Poor scalability
• Bottlenecks
• CPU
• memory
• network
• storage
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5. Types of performance testing
• Load testing
• Stress testing
• Endurance testing
• Spike testing
• Scalability testing
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6. Load testing
• Can we handle the realistic,
expected load?
• with some margin, possibly
• Could be very quick as a
smoke test
• Or longer as a
non-functional quality gate
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load
time
expected load
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7. Stress testing
• How much traffic can we
handle until we break?
• aka breakpoint testing
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load
time
expected load
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8. Endurance testing
• How long can we handle typical traffic and usage for?
• aka soak testing
• This should be a long test- overnight or over a weekend
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load
time
expected load
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9. Spike testing
• Can we support a sudden
peak load?
• sudden transient traffic
• not seasonable,
predictable growth
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load
time
expected load
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10. Scalability testing
• Can we provide acceptable
service as the traffic, number
of users, amount of data
grows?
• time can be built-in to allow
additional resources to start
• especially for horizontal scaling
• Could be combined with
stress testing
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load
time
expected load
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11. What metrics should we collect?
• Response time
• Requests or transactions per second
• Error rate
• System behavior
• CPU, memory, network, storage
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12. How do we design perf tests?
• Identify the environment
• isolated?
• production-sized?
• Pick the scenarios
• representative actions?
• full-throttle load or more realistic?
• Select the metrics
• Decide on the goal
• what is passing?
• measure the metrics or pass a threshold?
• or just an experiment to understand the behavior?
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13. Collecting data, not finding flaws
• Goal should be learning the app’s behavior under different
scenarios
• So that you can plan how to deal if/when it happens
• Not focused on trying to flag shortcomings
• Not all performance “failures” will be real problems
• We won’t be able to do much about some limits
• Some will result in business discussions
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14. Taylor Swift and Ticketmaster
When the sale went online on November 15, 2022,
the website crashed in an hour,
with users logged out or in a frozen queue;
however, 2.4 million tickets were sold,
breaking the record for the highest single-day ticket
sales ever by an artist.
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“
”
“Taylor Swift–Ticketmaster controversy”,
https://en.wikipedia.org/wiki/Taylor_Swift%E2%80%93Ticketmaster_controversy

15. Demo app
https://github.com/OtherDevOpsGene/sorting
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16. Gatling demo
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17. JMeter demo
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18. Unit test demo
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19. Balance
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Early rapid
feedback
No late
surprises
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20. Testing pyramid
Full transaction metrics
via the UI, endurance, and scalability
User performance testing, stress, and spike testing
Load testing via the UI
Load testing via the API
Baseline and trends of a subset of functional tests
Unit test performance
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early and fast
late
and
slower
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21. Not just web performance
• We were adding a lot of audit logging to the database
• Used pgBadger to analyze PostgreSQL query logs
• 4 queries represented 85% of the traffic
• Wrote 4 JDBC Sampler tests in JMeter to match those queries
• Looping in roughly the same proportion
• Created baseline, then watched trends
• Database changes were tested for performance impacts
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22. Actionable tests
• Make the early tests actionable.
• Metrics aren’t actionable unless you have a cutoff.
• Fail builds if the performance slows.
• Gathering performance metrics is important for planning.
• Testing is a means to an end, not a goal.
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23. Wrap up
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24. Key takeaways
• Understand what you are trying to test for.
• Performance testing is a field, not a particular kind of test.
• Use a balance of types and trends to add confidence that
you won’t find performance issues late in the process.
• Fail builds for performance issues.
• Testing is a means to an end, not a goal.
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25. Questions?
Gene Gotimer
Principal DevOps Engineer at Praeses, LLC
@OtherDevOpsGene
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