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Delete all instead of using a loop.

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SergeantPanda 2025-05-20 20:36:04 -05:00
parent 422bd0577a
commit 72783090cd
1 changed files with 9 additions and 23 deletions
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32
apps/epg/tasks.py
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@ -918,14 +918,10 @@ def parse_programs_for_tvg_id(epg_id):
if not acquire_task_lock('parse_epg_programs', epg_id):
logger.info(f"Program parse for {epg_id} already in progress, skipping duplicate task")
return "Task already running"
source_file = None
program_parser = None
programs_to_create = None
epg = None
epg_source = None
programs_processed = 0
mem_before = 0 # Initialize with default value to avoid UnboundLocalError
programs_to_create = []
try:
# Add memory tracking only in trace mode or higher
try:
@ -957,18 +953,9 @@ def parse_programs_for_tvg_id(epg_id):
logger.info(f"Refreshing program data for tvg_id: {epg.tvg_id}")
# First, remove all existing programs - use chunked delete to avoid memory issues
# Delete old programs
chunk_size = 5000
last_id = 0
while True:
ids = list(ProgramData.objects.filter(epg=epg, id__gt=last_id).order_by('id').values_list('id', flat=True)[:chunk_size])
if not ids:
break
ProgramData.objects.filter(id__in=ids).delete()
last_id = ids[-1]
del ids
gc.collect()
# Optimize deletion with a single delete query instead of chunking
# This is faster for most database engines
ProgramData.objects.filter(epg=epg).delete()
file_path = epg_source.file_path
if not file_path:
@ -1106,11 +1093,10 @@ def parse_programs_for_tvg_id(epg_id):
if len(programs_to_create) >= batch_size:
ProgramData.objects.bulk_create(programs_to_create)
logger.debug(f"Saved batch of {len(programs_to_create)} programs for {epg.tvg_id}")
del programs_to_create
del custom_props
del custom_properties_json
gc.collect()
#continue
programs_to_create = []
# Only call gc.collect() every few batches
if programs_processed % (batch_size * 5) == 0:
gc.collect()
except Exception as e:
logger.error(f"Error processing program for {epg.tvg_id}: {e}", exc_info=True)