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Added sleep in dataframe_memory_leak test after gc collection #494

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merged 2 commits into from
Feb 4, 2025
Merged

Added sleep in dataframe_memory_leak test after gc collection #494

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7226a05
Added sleep in dataframe_memory_leak test after gc collection
tomuben Jan 30, 2025
14a3735
Added comment + added sleep in test_dataframe_set_emits
tomuben Feb 1, 2025
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6 changes: 6 additions & 0 deletions test_container/tests/test/pandas/all/dataframe_memory_leak.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,6 +93,7 @@ def test_dataframe_scalar_emits(self):

import tracemalloc
import gc
import time
snapshot_begin = None
memory_check_executed = False
tracemalloc.start()
Expand All @@ -111,6 +112,7 @@ def run(ctx):
assert memory_check_executed == False #Sanity check for row number
print("Checking memory usage", flush=True)
gc.collect()
time.sleep(1)
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In case it works we should a comment to the sleeps

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Done.
The CI Build failed because of Docker.

snapshot_end = tracemalloc.take_snapshot()
top_stats_begin_end = snapshot_end.compare_to(snapshot_begin, 'lineno')
first_item = top_stats_begin_end[0] #First item is always the largest one
Expand Down Expand Up @@ -203,6 +205,7 @@ def test_dataframe_scalar_returns(self):

import tracemalloc
import gc
import time
snapshot_begin = None
memory_check_executed = False
tracemalloc.start()
Expand All @@ -220,6 +223,7 @@ def run(ctx):
assert memory_check_executed == False #Sanity check for row number
print("Checking memory usage", flush=True)
gc.collect()
time.sleep(1)
snapshot_end = tracemalloc.take_snapshot()
top_stats_begin_end = snapshot_end.compare_to(snapshot_begin, 'lineno')
first_item = top_stats_begin_end[0] #First item is always the largest one
Expand Down Expand Up @@ -254,6 +258,7 @@ def test_dataframe_set_emits(self):

import tracemalloc
import gc
import time
tracemalloc.start()

def process_df(ctx):
Expand All @@ -272,6 +277,7 @@ def run(ctx):

if batch_idx == ({batch_count} - 1):
gc.collect()
time.sleep(1)
snapshot_end = tracemalloc.take_snapshot()
top_stats_begin_end = snapshot_end.compare_to(snapshot_begin, 'lineno')
first_item = top_stats_begin_end[0] #First item is always the largest one
Expand Down
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