Experiencing memory leaks when updating 500k records using either of the
following routines.
Quite reproducible, and running tests on both windows 7 and 2008 servers
(servers with 8 and 12G ram)
Initial speed tests suggest 100 recs per second, but routines fail to
complete after 3 hours.
Have tried with and without caching.
Using current trunk and stable versions.
Currently exporting the entire db, deleting the database and reimporting
(have tried rebuilds through SQLite Manager).
If that doesn't work, looking at swapping databases, but before we do that
... Any comments or ideas?
Wondering if the initial selects are too big and need to be split?
(haven't tried caching to the disk yet)
If that is the issue, are there any recommended strategies?
def update_TABLE_A_1():
import datetime
a = datetime.datetime.now()
q = db1(db1.TABLE_A.ITEM_ID == db1.TABLE_B.id).select(cache=(cache.ram,
600),cacheable=True, limitby=(0,1000000)) #limitby merely to test speed and
ranges, tried with and without caching
for row in q:
db1.TABLE_A.update_or_insert(db1.TABLE_A.ITEM_ID ==
row.TABLE_B.id,FIELD_A1
= row.TABLE_B.FIELD_B2, FIELD_A2 = row.TABLE_B.FIELD_B3, FIELD_A3 = row.
TABLE_B.FIELD_B4, FIELD_A4 = row.TABLE_B.FIELD_B5)
b = datetime.datetime.now()
b1 = b-a
return str(b1)
def update_TABLE_A_2():
import datetime
a = datetime.datetime.now()
q = db1(db1.TABLE_A > 0).select(limitby=(0,1000000))
for row in q:
q1 = db1(db1.TABLE_B == row.id).select(cache=(cache.ram,600),
cacheable=True, limitby=(0,1))
row.update_record(FIELD_A = q1[0].FIELD_B1, FIELD_B = q1[0].FIELD_B2
, FIELD_C = q1[0].FIELD_B3, FIELD_D = q1[0].FIELD_B4)
b = datetime.datetime.now()
b1 = b-a
b2 = b1.seconds/1000
return (str(b1), str(b2))
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