Benchmarks¶
PyDbLite has been measured by the time taken by various operations for 3 pure-Python database modules (PyDbLite, buzhug and Gadfly) and compared them with SQLite.
The tests are those described on the SQLite comparisons pages, which compares performance of SQLite to that of MySQL and PostGreSQL
insert¶
create the base and insert n elements (n= 1000, 25,000 or 100,000) in it
The database has 3 fields:
- a (integer, from 1 to n)
- b (random integer between 1 and 100000)
- c (a string, value = ‘fifty nine’ if b=59)
For PyDbLite, gadfly and SQLite two options are possible : with an index on field a, or without index
The values of a, b, c are stored in a list recs
SQL statements¶
cursor.execute("CREATE TABLE t1(a INTEGER, b INTEGER, c VARCHAR(100))")
if make_index:
cursor.execute("CREATE INDEX i3 ON t1(a)")
for a, b, c in recs:
cursor.execute("INSERT INTO t1 VALUES(%s,%s,'%s')" %(a, b, c))
conn.commit()
PyDbLite code¶
db = PyDbLite.Base(name).create('a','b','c')
if index:
db.create_index('a')
for a,b,c in recs:
db.insert(a=a,b=b,c=c)
db.commit()
buzhug code¶
db=Base('t1').create(('a', int), ('b', int), ('c', str))
for rec in recs:
db.insert(*rec)
db.commit()
gadfly code¶
conn = gadfly.gadfly()
conn.startup(folder_name,folder_name)
cursor = conn.cursor()
cursor.execute("CREATE TABLE t1(a INTEGER, b INTEGER, c VARCHAR(100))")
if make_index:
cursor.execute("CREATE INDEX i3 ON t1(a)")
insertstat = "INSERT INTO t1 VALUES(?,?,?)"
for a, b, c in recs:
cursor.execute(insertstat,(a, b, c))
conn.commit()
select1¶
100 selections to count the number of records and the average of field b for values of b between 10*n and 10*n + 1000 for n = 1 to 100
SQL statements¶
for i in range(100):
sql = 'SELECT count(*), avg(b) FROM t1 WHERE b>=%s AND b<%s' % (100 * i, 1000 + 100 * i)
cursor.execute(sql)
nb,avg = cursor.fetchall()[0]
buzhug code¶
for i in range(100):
recs = db.select(['b'], b=[100 * i, 999 + 100 * i])
nb = len(recs)
if nb:
avg = sum([r.b for r in recs]) / nb
select2¶
100 selections to count the number of records and the average of field b for values of c with the string ‘one’, ‘two’, ...,’ninety nine’ inside. It uses the keyword LIKE for SQL database (I couldn’t do the test for Gadfly which doesn’t support LIKE) ; for buzhug I use regular expressions. The strings for each number between 0 and 99 are stored in the list num_strings
SQL statements¶
for num_string in num_strings:
sql = "SELECT count(*), avg(b) FROM t1 WHERE c LIKE '%%%s%%'" %num_string
cursor.execute(sql)
nb,avg = cursor.fetchall()[0]
buzhug code¶
for num_string in num_strings:
pattern = re.compile(".*"+num_string+".*")
recs = db.select(['b'], 'p.match(c)', p=pattern)
nb = len(recs)
if nb:
avg = sum([r.b for r in recs]) / nb
delete1¶
delete all the records where the field c contains the string ‘fifty’. There again I couldn’t do the test for gadfly
SQL statements¶
sql = "DELETE FROM t1 WHERE c LIKE '%fifty%';"
cursor.execute(sql)
conn.commit()
buzhug code¶
db.delete(db.select(['__id__'], 'p.match(c)', p=re.compile('.*fifty.*')))
delete2¶
delete all the records for which the field a is > 10 and < 20000
SQL statements¶
sql="DELETE FROM t1 WHERE a > 10 AND a < 20000;"
cursor.execute(sql)
conn.commit()
buzhug code¶
db.delete(db.select(['__id__'], 'x < a < y', x=10, y=20000))
update1¶
1000 updates, multiply b by 2 for records where 10*n <= a < 10 * (n + 1) for n = 0 to 999
SQL statements¶
for i in range(100):
sql="UPDATE t1 SET b = b * 2 WHERE a>=%s AND a<%s;" % (10 * i, 10 * (i + 1))
cursor.execute(sql)
conn.commit()
buzhug code¶
for i in range(100):
for r in db.select(a=[10 * i, 10 * i + 9]):
db.update(r, b=r.b * 2)
update2¶
1000 updates to set c to a random value where a = 1 to 1000 New values of field c are stored in a list new_c
SQL statements¶
for i in range(0, 1000):
sql="UPDATE t1 SET c='%s' WHERE a=%s" % (new_c[i], i)
cursor.execute(sql)
conn.commit()
buzhug code¶
recs = db.select_for_update(['a','c'], a=[1,999])
for r in recs:
db.update(r, c=new_c[r.a])
The tests were made on a Windows XP machine, with Python 2.5 (except gadfly : using the compile kjbuckets.pyd requires Python 2.2)
Versions : PyDbLite 2.5, buzhug 1.6, gadfly 1.0.0, SQLite 3.0 embedded in Python 2.5 Results
Here are the results
1000 records
PyDbLite sqlite gadfly buzhug
no index index no index index no index index
size (kO) 79 91 57 69 60 154
create 0.04 0.03 1.02 0.77 0.71 2.15 0.29
select1 0.06 0.07 0.09 0.09 1.50 1.49 0.21
select2 0.04 0.04 0.16 0.16 - - 0.51
delete1 0.01 0.02 0.49 0.50 - - 0.04
delete2 0.08 0.01 0.56 0.26 0.04 0.05 0.17
update1 0.07 0.07 0.52 0.37 1.91 1.91 0.49
update2 0.20 0.03 0.99 0.45 7.72 0.54 0.72
25,000 records
PyDbLite sqlite gadfly buzhug
no index index no index index no index index
size 2021 2339 1385 1668 2948 2272
create 0.73 1.28 2.25 2.20 117.04 7.04
select1 2.31 2.72 2.69 2.67 153.05 3.68
select2 1.79 1.71 4.53 4.48 - 12.33
delete1 0.40 0.89 1.88 0.98 - (1) 0.84
delete2 0.22 0.35 0.82 0.69 1.78 2.88
update1 2.85 3.55 2.65 0.45 183.06 1.23
update2 18.90 0.96 10.93 0.47 218.30 0.81
100,000 records
PyDbLite sqlite buzhug
no index index no index index
size 8290 9694 5656 6938 8881
create 4.07 7.94 5.54 7.06 28.23
select1 9.27 13.73 9.86 9.99 14.72
select2 7.49 8.00 16.86 16.64 51.46
delete1 2.97 4.10 2.58 3.58 3.48
delete2 3.00 4.23 0.98 1.41 3.31
update1 13.72 15.80 9.22 0.99 1.87
update2 24.83 5.95 69.61 1.21 0.93
(1) not tested with index, creation time is +INF
Conclusions PyDblite is as fast, and even faster than SQLite for small databases. It is faster than gadfly in all cases. buzhug is faster on most operations when size grows
