[dy] Record memory and cpu usage for pipeline runs #4761
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| self.logger.info( | ||
| f'Pipeline {self.pipeline.uuid} for run {self.pipeline_run.id} ' | ||
| f'in schedule {self.pipeline_run.pipeline_schedule_id} is alive.', | ||
| **tags, | ||
| ) | ||
|
|
||
| if memory_usage and memory_usage >= MEMORY_USAGE_MAXIMUM: | ||
| self.memory_usage_failure(tags=tags) | ||
| def __measure_and_record_usage(self) -> Tuple[float, float]: |
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This will work with local_python executor but not other executors.
| self.pipeline.type in [PipelineType.INTEGRATION, PipelineType.STREAMING] | ||
| or self.pipeline.run_pipeline_in_one_process | ||
| ): | ||
| if job_manager.has_pipeline_run_job(self.pipeline_run.id): |
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this will not work when you have multiple schedulers
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Description
This PR adds memory and cpu tracking for pipeline runs in the backend. There will be a follow up PR to surface the metrics in the frontend.
Calculating memory is relatively straightforward, we can get the memory usage for all processes for a pipeline run. To get the memory usage per pipeline, we can use
psutil.Process.memory_percent(). CPU can be done in a similar way, but getting the cpu usage percentage per process is more complicated. The comments in the code explains more in depth how it's implemented, but basically we need to track the cpu usage at various points in time and compare the numbers to get the cpu usage percent.Right now, we get memory and cpu usage every time a heartbeat is run for the pipeline run, so roughly every 10 seconds. The usage is stored in the
PipelineRun.metricsfield. The pipeline run cpu and memory usage will also be included in the tags for the heartbeat log.Example:
How Has This Been Tested?
Checklist
docs/mint.jsoncc: @wangxiaoyou1993