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Visualization, storage and comparison results of performance testing by using graphana and influx db

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Visualization, storage and comparison results of performance testing by using graphana and influx db

In our presentation we describe approach to solve following problems in performance testing:
- Visualization of test results.
- Visualization of real time metrics.
- Storage results of different test runners.
- Comparison results of different test runners.

In our presentation we describe approach to solve following problems in performance testing:
- Visualization of test results.
- Visualization of real time metrics.
- Storage results of different test runners.
- Comparison results of different test runners.

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Visualization, storage and comparison results of performance testing by using graphana and influx db

  1. 1. 1 Visualization, storage and comparison results of performance testing by using Grafana and InfluxDB. Shapin Anton Klykov Denis May 27, 2017 #ITsubbotnik Saint Petersburg
  2. 2. 2 • Lead Software Test Automation Engineer • 10+ years in IT • Areas of my competency: manual, automation, performance and etc. • Successfully completed more than 10 BDD projects. Email: anton_shapin@epam.com Skype: anton_shapin GIT: http://github.com/kirlionik Shapin Anton
  3. 3. 3 • Lead Performance Analyst • 9+ years in IT • Areas of my competency: performance and etc. Email: Denis_Klykov@epam.com Skype: denis_klykov Klykov Denis
  4. 4. 4 How it works Questions AGENDA Instruments Problems#1 #2 #3 #4
  5. 5. 5 LET`S START !
  6. 6. 6 Problems: Visualization of test results.1 Visualization of real time metrics. 2 Storage results of different test runners. 3 Comparison results of different test runners. 4
  7. 7. 7 Instruments
  8. 8. 8 How it works Tests Server metrics Data Sources …
  9. 9. 9 Grafana: Allows you to query your metrics1 Metrics Visualization2 Define alerts where it makes sense3 Collaborate4 The analytics platform for all your metrics
  10. 10. 10 InfluxDB: Built-in HTTP API1 Data can be tagged, allowing very flexible querying 2 Simple to install and manage, and fast to get data in and out 3 It aims to answer queries in real-time.4 InfluxDB is an open source time series database with no external dependencies.
  11. 11. 11 JMeter -> InfluxDB Create BeanShell Listener
  12. 12. 12 InfluxDB -> Grafana Add Data Source
  13. 13. 13 InfluxDB -> Grafana Create Dashboard and Graphs
  14. 14. 14 InfluxDB -> Grafana Graphs
  15. 15. 15 InfluxDB -> Grafana Graphs
  16. 16. Dashboards «Spaceship»
  17. 17. Dashboards «Spaceship»
  18. 18. 18 Comparison Create snapshots
  19. 19. 19 Comparison Compare snapshots
  20. 20. 20 TRY IT AT HOME docker build -t tutum/influxdb docker push tutum/influxdb docker run -d -p 8083:8083 -p 8086:8086 tutum/influxdb Open http://localhost:8083/ Create database with name Demo, login/pass: admin/admin 1 2 3 4 5 Install InfluxDB
  21. 21. 21 TRY IT AT HOME docker pull grafana/grafana docker run -i -d -p 3000:3000 -e "GF_SERVER_ROOT_URL=http://grafana.localhost" -e "GF_SECURITY_ADMIN_PASSWORD=secret" grafana/grafana Open http://localhost:3000/ Add dataSource InfluxDB 1 2 4 5 Install Grafana
  22. 22. 22 CONS: Separate instances of InfluxDB and Grafana Network issues Specific SQL syntax 1 2 3
  23. 23. 23 PROS: Visualization of test results. Visualization of real time metrics. Comparison results of different test runners. Storage results of different test runners. Sharing test results with all team members. 1 2 3 4 5
  24. 24. 24 1.It is a very good approach. But this is not a silver bullet. Summary 2.Separate instances of InfluxDB and Grafana. 3. It reduce time for analysis and finding bottlenecks.
  25. 25. 25 Thank you for attention! Email: Denis_Klykov@epam.com Skype: denis_klykov Email: anton_shapin@epam.com Skype: anton_shapin GIT: http://github.com/kirlionik

Notes de l'éditeur

  • Built-in HTTP API so you don't have to write any server side code to get up and running.
    Data can be tagged, allowing very flexible querying.
    SQL-like query language.
    Simple to install and manage, and fast to get data in and out.
    It aims to answer queries in real-time. That means every data point is indexed as it comes in and is immediately available in queries that should return in < 100ms.

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