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Fix memory leak by not piling up breadcrumbs forever in Spark workers. #4167

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Merged
merged 2 commits into from
Mar 20, 2025

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@antonpirker antonpirker commented Mar 19, 2025

We now clear all existing breadcrumbs when a job is started. If an error happens in a job, only breadcrumbs created in this job will be shown.

Fixes #1245.

@antonpirker antonpirker changed the title Do not pile up breakcrumbs forever Fix memory leak by not piling up breadcrumbs forever in Spark workers. Mar 19, 2025
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codecov bot commented Mar 19, 2025

Codecov Report

Attention: Patch coverage is 66.66667% with 1 line in your changes missing coverage. Please review.

Project coverage is 79.54%. Comparing base (eb189ef) to head (22ef829).
Report is 3 commits behind head on master.

✅ All tests successful. No failed tests found.

Files with missing lines Patch % Lines
sentry_sdk/integrations/spark/spark_driver.py 66.66% 1 Missing ⚠️
Additional details and impacted files
@@           Coverage Diff           @@
##           master    #4167   +/-   ##
=======================================
  Coverage   79.53%   79.54%           
=======================================
  Files         141      141           
  Lines       15735    15736    +1     
  Branches     2675     2675           
=======================================
+ Hits        12515    12517    +2     
  Misses       2371     2371           
+ Partials      849      848    -1     
Files with missing lines Coverage Δ
sentry_sdk/integrations/spark/spark_driver.py 64.32% <66.66%> (+0.20%) ⬆️

... and 1 file with indirect coverage changes

@antonpirker antonpirker marked this pull request as ready for review March 20, 2025 11:06
@antonpirker antonpirker requested a review from sentrivana March 20, 2025 11:07
@antonpirker antonpirker merged commit 5715734 into master Mar 20, 2025
139 of 140 checks passed
@antonpirker antonpirker deleted the antonpirker/fix/spark-memory-leak branch March 20, 2025 13:06
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Successfully merging this pull request may close these issues.

Spark integration causes significant slowdowns or even the entire job to run out of memory and fail
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