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#!/usr/bin/python
#
# 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 os
import random
import shutil
import sys
import time
import btree
def measure(keys, func):
start = time.clock()
for key in keys:
func(key)
end = time.clock()
return end - start
def main():
location = sys.argv[1]
n = int(sys.argv[2])
key_size = 8
value_size = 128
node_size = 4096
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)
# Measure inserts.
random.shuffle(keys)
value = 'x' * value_size
insert_time = measure(keys, lambda key: tree.insert(key, value)) - looptime
# Measure lookups.
random.shuffle(keys)
lookup_time = measure(keys, lambda key: tree.lookup(key)) - looptime
# Report
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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