2023-02-05 09:36:16 +03:00
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# Summary
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One of the main objectives to deliver satisfactory user experience when using
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Enso is to be fast when getting ready to work. This requires the engine to
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initialize all services the IDE needs in proper order and to make sure the
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essential ones are ready as quickly as possible. In short - to **start fast**.
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This document describes how to measure, record and analyze the startup of the
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Enso engine.
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## Collecting the data
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2023-11-08 22:30:28 +03:00
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### Via the Project Manager
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2023-02-05 09:36:16 +03:00
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Start `project-manager` with following options to record first 20s of the Enso
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engine startup sequence:
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```
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2023-11-20 19:01:25 +03:00
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$ project-manager --profiling-path=start.npss --profiling-time=20
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2023-02-05 09:36:16 +03:00
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```
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Let the IDE connect to just launched `project-manager` - e.g. start the IDE with
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`--no-backed` option. Once the `start.log` and `start.npss` files are generated
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(next to each other), open them in GraalVM's VisualVM:
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```
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$ graalvm/bin/jvisualvm --openfile start.npss
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```
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Use VisualVM to analyze to recorded data.
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2023-11-08 22:30:28 +03:00
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### Via the runner
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Runner executable also supports the profiling with the `--profiling-path`
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option. For example, you can run the Enso project with the profiling enabled:
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```
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$ enso --profiling-path=/tmp/run.npss --run ~/enso/project/New_Project_1
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```
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And then open it in the VisualVM:
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```
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$ visualvm --openfile /tmp/run.npss
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```
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2023-02-05 09:36:16 +03:00
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### Interactively Analyze
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VisualVM offers two timelines. A "stackdepth" one and also _"UI Actions"_ line.
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Hovering over boxes in _"UI Actions"_ shows the messages describing what happens
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in the engine - what has been logged into `start.log`. One can then select an
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interval and get profiling information for that interval:
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This picture shows that 2.7s is spend in `EnsoCompiledJob` task. Overall the
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goal is to log enough information to help us navigate thru the long startup
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sequence. Select appropriate interval based on the displayed _UI Actions_ - e.g.
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logged events - and analyze what has happened there based on the sampling of JVM
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stack traces.
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