Autocommit with ceODBC is slow

February 10, 2015

(This article was first published on Heuristic Andrew, and kindly contributed to R-bloggers)

You already know that a SQL INSERT is faster with bulk loading than inserting a record at a time, but what about the effect of autocommit on performance? While this is probably not specific to ceODBC, using autocommit is astonishingly slow. Here is how slow.

First, the Python code to run the benchmark:

import ceODBC
import datetime
import os
import time

connection_string="driver=sql server;database=database;server=server;"
print connection_string

conn = None
cursor = None
def init_db():
import ceODBC
global conn
global cursor
conn = ceODBC.connect(connection_string)
cursor = conn.cursor()

def table_exists():
cursor.execute("select count(1) from information_schema.tables where table_name='zzz_ceodbc_test'")
return cursor.fetchone()[0] == 1

def create_table():
CREATE TABLE zzz_ceodbc_test (
col1 INT,
col2 VARCHAR(50)
) """
import traceback

rows = []
for i in xrange(0,10000):

def log_speed(start_time, end_time, records):
elapsed_seconds = end_time - start_time
if elapsed_seconds > 0:
records_second = int(records / elapsed_seconds)
# make elapsed_seconds an integer to shorten the string format
elapsed_str = str(
print("{:,} records; {} records/sec; {} elapsed".format(records, records_second, elapsed_str))
print("counter: %i records " % records)

def benchmark(bulk, autocommit):
global conn
global cursor
cursor.execute('truncate table zzz_ceodbc_test')

conn.autocommit = autocommit
insert_sql = 'insert into zzz_ceodbc_test (col1, col2) values (?,?)'

start_time = time.time()
if bulk:
cursor.executemany(insert_sql, rows)
for row in rows:
cursor.execute(insert_sql, row)
end_time = time.time()

cursor.execute("select count(1) from zzz_ceodbc_test")
assert cursor.fetchone()[0] == len(rows)

log_speed(start_time, end_time, len(rows))

del cursor
del conn
return end_time - start_time

def benchmark_repeat(bulk, autocommit, repeats=5):
description = "%s, autocommit=%s" % ('bulk' if bulk else 'one at a time', autocommit)
print 'n******* %s' % description
results = []
for x in xrange(0, repeats):
results.append(benchmark(bulk, autocommit))
print results

benchmark_repeat(True, False)
benchmark_repeat(True, True)
benchmark_repeat(False, True)

And to graph the results in R:

results_table <- 'group seconds
bulk_manual 0.6710000038146973
bulk_manual 0.6710000038146973
bulk_manual 0.9830000400543213
bulk_manual 0.7330000400543213
bulk_manual 0.6710000038146973
bulk_auto 8.486999988555908
bulk_auto 8.269000053405762
bulk_auto 8.980999946594238
bulk_auto 8.453999996185303
bulk_auto 8.480999946594238
one_at_a_time 24.391000032424927
one_at_a_time 23.70300006866455
one_at_a_time 71.66299986839294
one_at_a_time 23.58899998664856
one_at_a_time 37.18400001525879'

results <- read.table(textConnection(results_table), header = TRUE)

ggplot(results, aes(group, seconds)) + geom_boxplot()

Conclusion: bulk loading with autocommit is 76% faster than inserting records one-at-a-time, and turning off autocommit is 91% faster than bulk loading with autocommit. Also, bulk loading gives more consistent performance.

Ran on Windows 7 Pro 64-bit, Python 2.7.9 32-bit, ceODBC 2.0.1, Microsoft SQL Server 11.0 SP1, R 3.1.2.

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