In a Django project, I'm refreshing tens of thousands of lines of data from an external API on a daily basis. The problem is that since I don't know if the data is new or just an update, I can't do a bulk_create
operation.
Note: Some, or perhaps many, of the rows, do not actually change on a daily basis, but I don't which, or how many, ahead of time.
So for now I do:
for row in csv_data:
try:
MyModel.objects.update_or_create(id=row['id'], defaults={'field1': row['value1']....})
except:
print 'error!'
And it takes.... forever! One or two lines a second, max speed, sometimes several seconds per line. Each model I'm refreshing has one or more other models connected to it through a foreign key, so I can't just delete them all and reinsert every day. I can't wrap my head around this one -- how can I cut down significantly the number of database operations so the refresh doesn't take hours and hours.
Thanks for any help.
The problem is you are doing a database action on each data row you grabbed from the api. You can avoid doing that by understanding which of the rows are new (and do a bulk insert to all new rows), Which of the rows actually need update, and which didn't change. To elaborate:
old_data = MyModel.objects.all() # if possible than do MyModel.objects.filter(...)
api_data = [...]
for row in api_data:
if is_new_row(row, old_data):
new_rows_array.append(row)
else:
if is_data_modified(row, old_data):
...
# do the update
else:
continue
MyModel.objects.bulk_create(new_rows_array)
is_new_row
- will understand if the row is new and add it to an array that will be bulk created
is_data_modified
- will look for the row in the old data and understand if the data of that row is changed and will update only if its changed
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