Seems constant to me. I'd show you, but Windows has no protection for the OS
when system memory is exhausted so the whole computer go kaboom.
However, memory size growth was constant, and insert time was pretty constant
(which includes the overhead of generating random values etc). Go boom when
all RAM is full at 31 GB (about 140,000,000 records). Running it again and
getting it to stop at 120,000,000 inserts showed as follows:
>testinsert.py
sys.version_info(major=3, minor=6, micro=3, releaselevel='final', serial=0)
Row(journal_mode='off')
Row(journal_mode='off')
Row(synchronous=0)
insert into data values (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?);
1000000 9.751389 1000000 9.751389
2000000 19.518461 1000000 9.767072
3000000 29.595865 1000000 10.077404
4000000 40.991316 1000000 11.395450
5000000 52.543077 1000000 11.551762
6000000 64.178037 1000000 11.634959
7000000 75.347824 1000000 11.169787
8000000 86.437358 1000000 11.089534
9000000 97.772551 1000000 11.335193
10000000 109.154860 1000000 11.382308
11000000 120.132103 1000000 10.977243
12000000 131.221607 1000000 11.089504
13000000 142.555831 1000000 11.334224
14000000 153.595233 1000000 11.039402
15000000 164.749915 1000000 11.154682
16000000 175.282969 1000000 10.533054
17000000 186.272268 1000000 10.989299
18000000 197.274567 1000000 11.002299
19000000 208.608760 1000000 11.334193
20000000 219.676239 1000000 11.067479
21000000 230.995386 1000000 11.319148
22000000 242.520074 1000000 11.524688
23000000 254.029814 1000000 11.509740
24000000 265.071223 1000000 11.041409
25000000 276.094553 1000000 11.023329
26000000 286.886328 1000000 10.791775
27000000 298.175561 1000000 11.289233
28000000 308.891103 1000000 10.715541
29000000 319.565535 1000000 10.674432
30000000 330.200866 1000000 10.635331
31000000 340.765001 1000000 10.564136
32000000 351.553740 1000000 10.788738
33000000 362.221151 1000000 10.667412
34000000 373.168309 1000000 10.947158
35000000 383.733089 1000000 10.564780
36000000 394.125019 1000000 10.391931
37000000 403.954467 1000000 9.829448
38000000 414.158771 1000000 10.204304
39000000 424.097607 1000000 9.938836
40000000 434.161623 1000000 10.064016
41000000 444.116177 1000000 9.954554
42000000 454.414414 1000000 10.298238
43000000 464.478468 1000000 10.064053
44000000 474.776956 1000000 10.298488
45000000 485.001610 1000000 10.224653
46000000 495.143748 1000000 10.142138
47000000 505.129763 1000000 9.986015
48000000 515.271824 1000000 10.142061
49000000 525.554411 1000000 10.282587
50000000 536.149616 1000000 10.595204
51000000 546.135350 1000000 9.985735
52000000 556.402481 1000000 10.267131
53000000 566.919720 1000000 10.517239
54000000 578.046368 1000000 11.126648
55000000 588.990274 1000000 10.943906
56000000 599.987536 1000000 10.997262
57000000 610.828424 1000000 10.840888
58000000 621.324319 1000000 10.495896
59000000 632.009688 1000000 10.685369
60000000 642.563769 1000000 10.554081
61000000 653.049639 1000000 10.485870
62000000 664.206841 1000000 11.157203
63000000 674.797024 1000000 10.590183
64000000 685.523797 1000000 10.726773
65000000 696.855562 1000000 11.331765
66000000 708.156918 1000000 11.301356
67000000 718.994999 1000000 10.838081
68000000 729.723788 1000000 10.728789
69000000 740.643086 1000000 10.919299
70000000 751.140253 1000000 10.497167
71000000 761.664491 1000000 10.524239
72000000 772.482858 1000000 10.818367
73000000 783.014121 1000000 10.531263
74000000 794.438486 1000000 11.424365
75000000 805.444951 1000000 11.006465
76000000 816.032293 1000000 10.587342
77000000 826.511347 1000000 10.479054
78000000 837.076599 1000000 10.565252
79000000 847.528613 1000000 10.452014
80000000 857.831194 1000000 10.302581
81000000 868.286178 1000000 10.454984
82000000 878.765237 1000000 10.479059
83000000 889.114724 1000000 10.349487
84000000 900.053886 1000000 10.939162
85000000 909.930211 1000000 9.876325
86000000 919.790808 1000000 9.860597
87000000 929.651311 1000000 9.860503
88000000 939.527506 1000000 9.876195
89000000 949.356789 1000000 9.829282
90000000 959.186170 1000000 9.829381
91000000 969.031142 1000000 9.844972
92000000 978.891677 1000000 9.860535
93000000 988.752464 1000000 9.860787
94000000 998.597371 1000000 9.844907
95000000 1008.457877 1000000 9.860506
96000000 1018.318437 1000000 9.860560
97000000 1028.163434 1000000 9.844996
98000000 1038.024322 1000000 9.860888
99000000 1047.854005 1000000 9.829683
100000000 1057.761420 1000000 9.907415
101000000 1067.637771 1000000 9.876351
102000000 1078.089400 1000000 10.451629
103000000 1088.756892 1000000 10.667492
104000000 1099.582771 1000000 10.825879
105000000 1110.237257 1000000 10.654486
106000000 1121.181479 1000000 10.944222
107000000 1132.092627 1000000 10.911147
108000000 1142.934579 1000000 10.841952
109000000 1153.866774 1000000 10.932196
110000000 1164.808998 1000000 10.942224
111000000 1175.667999 1000000 10.859001
112000000 1186.503937 1000000 10.835938
113000000 1197.440143 1000000 10.936207
114000000 1208.192860 1000000 10.752717
115000000 1218.891432 1000000 10.698572
116000000 1229.737401 1000000 10.845968
117000000 1240.846065 1000000 11.108665
118000000 1251.648885 1000000 10.802819
119000000 1262.131912 1000000 10.483027
120000000 1273.303061 1000000 11.171149
Source code:
#! python3
from __future__ import print_function
import apsw
import apswrow
import random
import sys
import time
sql_create = """
create table data
(
id int,
path int,
month int,
val1 double,
val2 double,
val3 double,
val4 double,
val5 double,
val6 double,
val7 double,
val8 double,
val9 double,
val10 double,
val11 double,
val12 double,
val13 double,
val14 double,
val15 double,
val16 double,
val17 double,
val18 double,
val19 double,
val20 double
);
"""
print(sys.version_info)
db = apsw.Connection(':memory:')
db.cursor().execute(sql_create)
for row in db.cursor().execute('pragma journal_mode=off; pragma
synchronous=off;'):
print(row)
for row in db.cursor().execute('pragma journal_mode; pragma synchronous;'):
print(row)
stmt = 'insert into data values (' + ','.join('?'*23) + ');'
print(stmt)
st = time.time()
lt = time.time()
rows = 1000000
# db.cursor().execute('begin immediate;')
id = 0
while id < 120000000:
id += 1
data = [id, id, id]
for i in range(20):
data.append(random.random())
db.cursor().execute(stmt, data)
if id % rows == 0:
# db.cursor().execute('commit;')
ct = time.time()
print('%10d %12.6f %10d %12.6f' % (id, ct-st, rows, ct-lt))
lt = ct
# db.cursor().execute('begin immediate;')
# db.cursor().execute('commit;')
db.close()
---
The fact that there's a Highway to Hell but only a Stairway to Heaven says a
lot about anticipated traffic volume.
>-----Original Message-----
>From: sqlite-users [mailto:sqlite-users-
>boun...@mailinglists.sqlite.org] On Behalf Of Nelson, Erik - 2
>Sent: Thursday, 21 December, 2017 12:06
>To: SQLite mailing list
>Subject: [sqlite] generic advice for insert performance in-memory
>database optimization
>
>I've got an in-memory database with a single table that I need to
>fill with ~500 million rows. There are no indexes and the table
>definitions is
>
>create table data(id int, path int, month int, val1 double, val2
>double, val3 double... val20 double)
>
>I'm running on linux with the OS page size configured to 4096 and
>~380 GB of ram (much more than required for the table so I think I'm
>not swapping) and haven't altered the sqlite page size. I am using
>sqlite version 3.13.0 and these pragmas immediately after database
>creation.
>
>pragma temp_store = MEMORY
>pragma journal_mode = off
>
>With these settings I'm seeing nonlinear (in a bad way) times for the
>insert. Is that expected? I've fiddled about with various
>performance-related settings like described on
>
>https://blog.devart.com/increasing-sqlite-performance.html
>
>with varying results but haven't managed to arrive at fairly-linear
>insert behavior. It's a single-threaded insert on a prepared query
>with bound arguments in a tight loop.
>
>Is linear-ish insert time a reasonable goal for an in-memory
>database?
>
>---------------------------------------------------------------------
>-
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