I'm writing a script to SELECT query a database and parse through ~33,000 records. Unfortunately I'm running into problems at the cursor.fetchone()
/cursor.fetchall()
phase of things.
I first tried iterating through the cursor a record at a time like so:
# Run through every record, extract the kanji, then query for FK and weight
printStatus("Starting weight calculations")
while True:# Get the next row in the cursorrow = cursor.fetchone()if row == None:break# TODO: Determine if there's any kanji in row[2]weight = float((row[3] + row[4]))/2printStatus("Weight: " + str(weight))
Based on the output of printStatus
(it prints out a timestamp plus whatever string is passed to it), the script took approximately 1 second to process each row. This lead me to believe that the query was being re-run each time the loop iterated (with a LIMIT 1 or something), as it took ~1 second for the same query to run once in something like SQLiteStudio [i]and[/i] return all 33,000 rows. I calculated that, at that rate, it would take around 7 hours to get through all 33,000 records.
Instead of sitting through that, I tried to use cursor.fetchall() instead:
results = cursor.fetchall()# Run through every record, extract the kanji, then query for FK and weight
printStatus("Starting weight calculations")
for row in results:# TODO: Determine if there's any kanji in row[2]weight = float((row[3] + row[4]))/2printStatus("Weight: " + str(weight))
Unfortunately, the Python executable locked up at 25% CPU and ~6MB of RAM when it got to the cursor.fetchall()
line. I left the script running for ~10 minutes, but nothing happened.
Is ~33,000 returned rows (about 5MB of data) too much for Python to grab at once? Am I stuck iterating through one at a time? Or is there something I can do to speed things up?
EDIT: Here's some console output
12:56:26.019: Adding new column 'weight' and related index to r_ele
12:56:26.019: Querying database
12:56:28.079: Starting weight calculations
12:56:28.079: Weight: 1.0
12:56:28.079: Weight: 0.5
12:56:28.080: Weight: 0.5
12:56:28.338: Weight: 1.0
12:56:28.339: Weight: 3.0
12:56:28.843: Weight: 1.5
12:56:28.844: Weight: 1.0
12:56:28.844: Weight: 0.5
12:56:28.844: Weight: 0.5
12:56:28.845: Weight: 0.5
12:56:29.351: Weight: 0.5
12:56:29.855: Weight: 0.5
12:56:29.856: Weight: 1.0
12:56:30.371: Weight: 0.5
12:56:30.885: Weight: 0.5
12:56:31.146: Weight: 0.5
12:56:31.650: Weight: 1.0
12:56:32.432: Weight: 0.5
12:56:32.951: Weight: 0.5
12:56:32.951: Weight: 0.5
12:56:32.952: Weight: 1.0
12:56:33.454: Weight: 0.5
12:56:33.455: Weight: 0.5
12:56:33.455: Weight: 1.0
12:56:33.716: Weight: 0.5
12:56:33.716: Weight: 1.0
And here's the SQL query:
//...snip (it wasn't the culprit)...
The output of EXPLAIN QUERY PLAN from SQLiteStudio:
0 0 0 SCAN TABLE r_ele AS re USING COVERING INDEX r_ele_fk (~500000 rows)
0 0 0 EXECUTE CORRELATED SCALAR SUBQUERY 1
1 0 0 SEARCH TABLE re_pri USING INDEX re_pri_fk (fk=?) (~10 rows)
0 0 0 EXECUTE CORRELATED SCALAR SUBQUERY 2
2 0 0 SEARCH TABLE ke_pri USING INDEX ke_pri_fk (fk=?) (~10 rows)
2 0 0 EXECUTE CORRELATED SCALAR SUBQUERY 3
3 0 0 SEARCH TABLE k_ele USING AUTOMATIC COVERING INDEX (value=?) (~7 rows)
3 0 0 EXECUTE CORRELATED SCALAR SUBQUERY 4
4 0 0 SEARCH TABLE k_ele USING COVERING INDEX idx_k_ele (fk=?) (~10 rows)
0 0 0 EXECUTE CORRELATED SCALAR SUBQUERY 5
5 0 0 SEARCH TABLE k_ele USING COVERING INDEX idx_k_ele (fk=?) (~10 rows)
0 0 0 EXECUTE CORRELATED SCALAR SUBQUERY 6
6 0 0 SEARCH TABLE re_pri USING INDEX re_pri_fk (fk=?) (~10 rows)
0 0 0 EXECUTE CORRELATED SCALAR SUBQUERY 7
7 0 0 SEARCH TABLE ke_pri USING INDEX ke_pri_fk (fk=?) (~10 rows)
7 0 0 EXECUTE CORRELATED SCALAR SUBQUERY 8
8 0 0 SEARCH TABLE k_ele USING AUTOMATIC COVERING INDEX (value=?) (~7 rows)
8 0 0 EXECUTE CORRELATED SCALAR SUBQUERY 9
9 0 0 SEARCH TABLE k_ele USING COVERING INDEX idx_k_ele (fk=?) (~10 rows)