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In this series of articles, I will tell the story of how you can face a rather simple problem precisely on large installations in Atlassian products, in particular on Jira.
The methods for analyzing and finding bottlenecks are:
I hope the idea of sampling is clear.
Also, I do recommend reading the post from Dan Luu (https://danluu.com/perf-tracing/).
I will do more with the aspect of tracing requests, which shows almost the entire segment of the request, namely from the beginning of the request from the client's browser, to the transition to the reverse proxy, if it exists, to the application server and from it to caches, Lucene search indexes, DBMS.
To analyze the system on the part of the customer, there were strict requirements for the use of tools.
The first criterion was the price, especially during the pandemic, and it was not budgeted, so the main criterion was for the open-source review. Because the next aspect was to check for vulnerabilities and malicious code. Also, one of the requirements was that the data did not go beyond the limited contour, and as an additional opportunity, integration with the existing infrastructure.
Therefore, the selection criteria are as follows:
As a starting point, the site https://openapm.io/landscape was used, which collected almost all the current APM (performance monitoring tool) tools. During the benchmarking analysis, grow root met the requirements for 2020.
Glowroot (https://github.com/glowroot/glowroot) is an open-source agent that is well written and looks forward to your feedback on improvement. And I think after that the community will quickly make changes to the product.
In next step: I will share how to install on your on-prem installation and share practical user cases. if you want faster observe the use cases you can checkout the next slides (https://www.slideshare.net/gonchik/tsymzhitov-gonchik-atlassian-apm).