forked from PyTables/PyTables
-
Notifications
You must be signed in to change notification settings - Fork 0
/
searchsorted-bench.py
340 lines (303 loc) · 11.7 KB
/
searchsorted-bench.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
#!/usr/bin/env python
from __future__ import print_function
import time
from tables import *
class Small(IsDescription):
var1 = StringCol(itemsize=4)
var2 = Int32Col()
var3 = Float64Col()
var4 = BoolCol()
# Define a user record to characterize some kind of particles
class Medium(IsDescription):
var1 = StringCol(itemsize=16) # 16-character String
#float1 = Float64Col(dflt=2.3)
#float2 = Float64Col(dflt=2.3)
# zADCcount = Int16Col() # signed short integer
var2 = Int32Col() # signed short integer
var3 = Float64Col()
grid_i = Int32Col() # integer
grid_j = Int32Col() # integer
pressure = Float32Col() # float (single-precision)
energy = Float64Col(shape=2) # double (double-precision)
def createFile(filename, nrows, filters, atom, recsize, index, verbose):
# Open a file in "w"rite mode
fileh = open_file(filename, mode="w", title="Searchsorted Benchmark",
filters=filters)
title = "This is the IndexArray title"
# Create an IndexArray instance
rowswritten = 0
# Create an entry
klass = {"small": Small, "medium": Medium}
table = fileh.create_table(fileh.root, 'table', klass[recsize], title,
None, nrows)
for i in range(nrows):
#table.row['var1'] = str(i)
#table.row['var2'] = random.randrange(nrows)
table.row['var2'] = i
table.row['var3'] = i
#table.row['var4'] = i % 2
#table.row['var4'] = i > 2
table.row.append()
rowswritten += nrows
table.flush()
rowsize = table.rowsize
indexrows = 0
# Index one entry:
if index:
if atom == "string":
indexrows = table.cols.var1.create_index()
elif atom == "bool":
indexrows = table.cols.var4.create_index()
elif atom == "int":
indexrows = table.cols.var2.create_index()
elif atom == "float":
indexrows = table.cols.var3.create_index()
else:
raise ValueError("Index type not supported yet")
if verbose:
print("Number of indexed rows:", indexrows)
# Close the file (eventually destroy the extended type)
fileh.close()
return (rowswritten, rowsize)
def readFile(filename, atom, niter, verbose):
# Open the HDF5 file in read-only mode
fileh = open_file(filename, mode="r")
table = fileh.root.table
print("reading", table)
if atom == "string":
idxcol = table.cols.var1.index
elif atom == "bool":
idxcol = table.cols.var4.index
elif atom == "int":
idxcol = table.cols.var2.index
else:
idxcol = table.cols.var3.index
if verbose:
print("Max rows in buf:", table.nrowsinbuf)
print("Rows in", table._v_pathname, ":", table.nrows)
print("Buffersize:", table.rowsize * table.nrowsinbuf)
print("MaxTuples:", table.nrowsinbuf)
print("Chunk size:", idxcol.sorted.chunksize)
print("Number of elements per slice:", idxcol.nelemslice)
print("Slice number in", table._v_pathname, ":", idxcol.nrows)
rowselected = 0
if atom == "string":
for i in range(niter):
#results = [table.row["var3"] for i in table.where(2+i<=table.cols.var2 < 10+i)]
#results = [table.row.nrow() for i in table.where(2<=table.cols.var2 < 10)]
results = [p["var1"] # p.nrow()
for p in table.where(table.cols.var1 == "1111")]
# for p in table.where("1000"<=table.cols.var1<="1010")]
rowselected += len(results)
elif atom == "bool":
for i in range(niter):
results = [p["var2"] # p.nrow()
for p in table.where(table.cols.var4 == 0)]
rowselected += len(results)
elif atom == "int":
for i in range(niter):
#results = [table.row["var3"] for i in table.where(2+i<=table.cols.var2 < 10+i)]
#results = [table.row.nrow() for i in table.where(2<=table.cols.var2 < 10)]
results = [p["var2"] # p.nrow()
# for p in table.where(110*i<=table.cols.var2<110*(i+1))]
# for p in table.where(1000-30<table.cols.var2<1000+60)]
for p in table.where(table.cols.var2 <= 400)]
rowselected += len(results)
elif atom == "float":
for i in range(niter):
# results = [(table.row.nrow(), table.row["var3"])
# for i in table.where(3<=table.cols.var3 < 5.)]
# results = [(p.nrow(), p["var3"])
# for p in table.where(1000.-i<=table.cols.var3<1000.+i)]
results = [
p["var3"] # (p.nrow(), p["var3"])
for p in table.where(
100 * i <= table.cols.var3 < 100 * (i + 1))
]
# for p in table
# if 100*i<=p["var3"]<100*(i+1)]
# results = [ (p.nrow(), p["var3"]) for p in table
# if (1000.-i <= p["var3"] < 1000.+i) ]
rowselected += len(results)
else:
raise ValueError("Unsuported atom value")
if verbose and 1:
print("Values that fullfill the conditions:")
print(results)
rowsread = table.nrows * niter
rowsize = table.rowsize
# Close the file (eventually destroy the extended type)
fileh.close()
return (rowsread, rowselected, rowsize)
def searchFile(filename, atom, verbose, item):
# Open the HDF5 file in read-only mode
fileh = open_file(filename, mode="r")
rowsread = 0
uncomprBytes = 0
table = fileh.root.table
if atom == "int":
idxcol = table.cols.var2.index
elif atom == "float":
idxcol = table.cols.var3.index
else:
raise ValueError("Unsuported atom value")
print("Searching", table, "...")
if verbose:
print("Chunk size:", idxcol.sorted.chunksize)
print("Number of elements per slice:", idxcol.sorted.nelemslice)
print("Slice number in", table._v_pathname, ":", idxcol.sorted.nrows)
(positions, niter) = idxcol.search(item)
if verbose:
print("Positions for item", item, "==>", positions)
print("Total iterations in search:", niter)
rowsread += table.nrows
uncomprBytes += idxcol.sorted.chunksize * niter * idxcol.sorted.itemsize
results = table.read(coords=positions)
print("results length:", len(results))
if verbose:
print("Values that fullfill the conditions:")
print(results)
# Close the file (eventually destroy the extended type)
fileh.close()
return (rowsread, uncomprBytes, niter)
if __name__ == "__main__":
import sys
import getopt
try:
import psyco
psyco_imported = 1
except:
psyco_imported = 0
usage = """usage: %s [-v] [-p] [-R range] [-r] [-w] [-s recsize ] [-a
atom] [-c level] [-l complib] [-S] [-F] [-i item] [-n nrows] [-x]
[-k niter] file
-v verbose
-p use "psyco" if available
-R select a range in a field in the form "start,stop,step"
-r only read test
-w only write test
-s record size
-a use [float], [int], [bool] or [string] atom
-c sets a compression level (do not set it or 0 for no compression)
-S activate shuffling filter
-F activate fletcher32 filter
-l sets the compression library to be used ("zlib", "lzo", "ucl", "bzip2")
-i item to search
-n set the number of rows in tables
-x don't make indexes
-k number of iterations for reading\n""" % sys.argv[0]
try:
opts, pargs = getopt.getopt(sys.argv[1:], 'vpSFR:rwxk:s:a:c:l:i:n:')
except:
sys.stderr.write(usage)
sys.exit(0)
# if we pass too much parameters, abort
if len(pargs) != 1:
sys.stderr.write(usage)
sys.exit(0)
# default options
verbose = 0
rng = None
item = None
atom = "int"
fieldName = None
testread = 1
testwrite = 1
usepsyco = 0
complevel = 0
shuffle = 0
fletcher32 = 0
complib = "zlib"
nrows = 100
recsize = "small"
index = 1
niter = 1
# Get the options
for option in opts:
if option[0] == '-v':
verbose = 1
if option[0] == '-p':
usepsyco = 1
if option[0] == '-S':
shuffle = 1
if option[0] == '-F':
fletcher32 = 1
elif option[0] == '-R':
rng = [int(i) for i in option[1].split(",")]
elif option[0] == '-r':
testwrite = 0
elif option[0] == '-w':
testread = 0
elif option[0] == '-x':
index = 0
elif option[0] == '-s':
recsize = option[1]
elif option[0] == '-a':
atom = option[1]
if atom not in ["float", "int", "bool", "string"]:
sys.stderr.write(usage)
sys.exit(0)
elif option[0] == '-c':
complevel = int(option[1])
elif option[0] == '-l':
complib = option[1]
elif option[0] == '-i':
item = eval(option[1])
elif option[0] == '-n':
nrows = int(option[1])
elif option[0] == '-k':
niter = int(option[1])
# Build the Filters instance
filters = Filters(complevel=complevel, complib=complib,
shuffle=shuffle, fletcher32=fletcher32)
# Catch the hdf5 file passed as the last argument
file = pargs[0]
if testwrite:
print("Compression level:", complevel)
if complevel > 0:
print("Compression library:", complib)
if shuffle:
print("Suffling...")
t1 = time.time()
cpu1 = time.clock()
if psyco_imported and usepsyco:
psyco.bind(createFile)
(rowsw, rowsz) = createFile(file, nrows, filters,
atom, recsize, index, verbose)
t2 = time.time()
cpu2 = time.clock()
tapprows = round(t2 - t1, 3)
cpuapprows = round(cpu2 - cpu1, 3)
tpercent = int(round(cpuapprows / tapprows, 2) * 100)
print("Rows written:", rowsw, " Row size:", rowsz)
print("Time writing rows: %s s (real) %s s (cpu) %s%%" %
(tapprows, cpuapprows, tpercent))
print("Write rows/sec: ", int(rowsw / float(tapprows)))
print("Write KB/s :", int(rowsw * rowsz / (tapprows * 1024)))
if testread:
if psyco_imported and usepsyco:
psyco.bind(readFile)
psyco.bind(searchFile)
t1 = time.time()
cpu1 = time.clock()
if rng or item:
(rowsr, uncomprB, niter) = searchFile(file, atom, verbose, item)
else:
for i in range(1):
(rowsr, rowsel, rowsz) = readFile(file, atom, niter, verbose)
t2 = time.time()
cpu2 = time.clock()
treadrows = round(t2 - t1, 3)
cpureadrows = round(cpu2 - cpu1, 3)
tpercent = int(round(cpureadrows / treadrows, 2) * 100)
tMrows = rowsr / (1000 * 1000.)
sKrows = rowsel / 1000.
print("Rows read:", rowsr, "Mread:", round(tMrows, 3), "Mrows")
print("Rows selected:", rowsel, "Ksel:", round(sKrows, 3), "Krows")
print("Time reading rows: %s s (real) %s s (cpu) %s%%" %
(treadrows, cpureadrows, tpercent))
print("Read Mrows/sec: ", round(tMrows / float(treadrows), 3))
# print "Read KB/s :", int(rowsr * rowsz / (treadrows * 1024))
# print "Uncompr MB :", int(uncomprB / (1024 * 1024))
# print "Uncompr MB/s :", int(uncomprB / (treadrows * 1024 * 1024))
# print "Total chunks uncompr :", int(niter)