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#!/usr/bin/python
# Copyright 2010 Lars Wirzenius
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# Excercise my btree implementation, for simple benchmarking purposes.
# The benchmark gets a location and an operation count as command line
# arguments.
#
# If the location is the empty string, an in-memory node store is used.
# Otherwise it must be a non-existent directory name.
#
# The benchmark will do the given number of insertions into the tree, and
# measure the speed of that. Then it will look up each of those, and measure
# the lookups.
import cProfile
import os
import random
import shutil
import sys
import time
import btree
def measure(keys, func, final):
start = time.clock()
for key in keys:
func(key)
final()
end = time.clock()
return end - start
def profile(keys, func, final, basename):
def helper():
for key in keys:
func(key)
final()
globaldict = globals().copy()
localdict = locals().copy()
cProfile.runctx('helper()', globaldict, localdict, '%s.prof' % basename)
def main():
if False:
import logging
logging.basicConfig(filename='btree.log', level=logging.DEBUG)
location = sys.argv[1]
n = int(sys.argv[2])
do_profile = True if sys.argv[3] == 'yes' else False
key_size = 19
value_size = 128
node_size = 64*1024
codec = btree.NodeCodec(key_size)
if location == '':
ns = btree.NodeStoreMemory(node_size, codec)
else:
if os.path.exists(location):
raise Exception('%s exists already' % location)
os.mkdir(location)
ns = btree.NodeStoreDisk(location, node_size, codec)
forest = btree.Forest(ns)
tree = forest.new_tree()
# Create list of keys.
keys = ['%0*d' % (key_size, i) for i in xrange(n)]
# Calibrate.
looptime = measure(keys, lambda key: None, lambda: None)
# Measure inserts.
random.shuffle(keys)
value = 'x' * value_size
if do_profile:
profile(keys, lambda key: tree.insert(key, value),
lambda: forest.commit(), 'insert')
else:
insert_time = measure(keys, lambda key: tree.insert(key, value),
lambda: forest.commit())
# Measure lookups.
random.shuffle(keys)
if do_profile:
profile(keys, tree.lookup, lambda: None, 'lookup')
else:
lookup_time = measure(keys, lambda key: tree.lookup(key),
lambda: None) - looptime
# Report
if do_profile:
print 'See insert.prof, lookup.prof for profiling data (try viewprof)'
else:
print 'num_operations: %d' % n
print 'insert: %.3f s (%.1f/s)' % (insert_time, n/insert_time)
print 'lookup-time: %.3f s (%.1f/s)' % (lookup_time, n/lookup_time)
# Clean up
if location:
shutil.rmtree(location)
if __name__ == '__main__':
main()
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