Read CSV file into MySQL and use executemany instead of execute

Read CSV file into MySQL and use executemany instead of execute - Printable Version

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Read CSV file into MySQL and use executemany instead of execute - bluethundr - May-04-2019

I have a python script that reads a large (4GB!!!) CSV file into MySQL. It works as is, but is DOG slow. The CSV file has over 4 million rows. And it is taking forever to insert all the records into the database.

Could I get an example of how I would use executemany in this situation?

source = os.path.join('source_files', 'aws_bills', 'march-bill-original-2019.csv')
try:
	with open(source) as csv_file:
		csv_reader = csv.reader(csv_file, delimiter=',')
		next(csv_reader)
		insert_sql = """ INSERT INTO billing_info (InvoiceId, PayerAccountId, LinkedAccountId, RecordType, RecordId, ProductName, RateId, SubscriptionId, PricingPlanId, UsageType, Operation, AvailabilityZone, ReservedInstance, ItemDescription, UsageStartDate, UsageEndDate, UsageQuantity, BlendedRate, BlendedCost, UnBlendedRate, UnBlendedCost, ResourceId, Engagement, Name, Owner, Parent) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) """
		#for row in csv_reader:
		for row_idx, row in enumerate(csv_reader):
			try:
				cursor.execute(insert_sql,row)
				#cursor.executemany(insert_sql, 100)
				mydb.commit()
				print('row', row_idx, 'inserted with LinkedAccountId', row[2], 'at', datetime.now().isoformat())
			except Exception as e:
				print("MySQL Exception:", e)
		print("Done importing data.")

Again, that code works to insert the records into the database. But I am hoping to speed this up with executemany if I can get an example of how to do that.

RE: Read CSV file into MySQL and use executemany instead of execute - MvGulik - May-04-2019

For (mysql)executemany one need to pre-collect the data that one wants to commit.
In such a case one would loop to pre-collect multiple records, after which the pre-collected data would be committed(executemany) after the loop.

Completely loading/pre-processing a large amount of data is generally a bad idea. Processing the source data/file in chunks generally works better with large amounts of data. (ie: read N records, pre-process while you go, commit, do next N records, ...)

Note sure here (not played around with DB's for a relative long time), but I think one could try to delay the actual cursor.execute(). Like only commit after processing N records. (make sure to commit-check after normal loop ending)