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This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,86 @@ import os import sys import time from timeit import default_timer as timer from datetime import timedelta from os import path base_dir = "data" dummy_dir_small = path.join(base_dir, "small") dummy_dir_large = path.join(base_dir, "large") def create_dummpy_data(dir_path, count): os.makedirs(dir_path) print("create dummy data in {} starting".format(dir_path)) time_start_all = timer() time_start_last = timer() interval = 1000 for i in range(count): if (i + 1) % 1000 == 0: time_curr = timer() print( "[{}:{}] create dummy data elapsed: {}(avg: {}), in total: {}(avg: {})" .format( dir_path, i + 1, timedelta(seconds=(time_curr - time_start_last)), timedelta(seconds=((time_curr - time_start_last) / interval)), timedelta(seconds=(time_curr - time_start_all)), timedelta(seconds=((time_curr - time_start_all) / (i + 1))))) sys.stdout.flush() with open(path.join(dir_path, str(i)), 'w') as f: msg = "{:010d}".format(i) f.write(msg) print("[{}:{}] create dummy elapsed: {} avg: {}, all done".format( dir_path, count, timedelta(seconds=(time_curr - time_start_all)), timedelta(seconds=((time_curr - time_start_all) / count)))) sys.stdout.flush() def is_dir(dir_path): return os.path.exists(dir_path) def ensure_dir(dir_path, amount): if not is_dir(dir_path): create_dummpy_data(dir_path, amount) def test_reading(dir_path, count): print("test reading data in {} starting".format(dir_path)) time_start_all = timer() time_start_last = timer() interval = 1000 for i in range(count): if (i + 1) % interval == 0: time_curr = timer() print("[{}:{}] elapsed: {}(avg: {}), in total: {}(avg: {})".format( dir_path, i + 1, timedelta(seconds=(time_curr - time_start_last)), timedelta(seconds=((time_curr - time_start_last) / interval)), timedelta(seconds=(time_curr - time_start_all)), timedelta(seconds=((time_curr - time_start_all) / (i + 1))))) sys.stdout.flush() time_start_last = time_curr with open(path.join(dir_path, str(i)), 'r') as f: msg = "{:010d}".format(i) assert (f.readline() == msg) time_curr = timer() print("[{}:{}] elapsed: {} avg: {}".format( dir_path, count, timedelta(seconds=(time_curr - time_start_all)), timedelta(seconds=((time_curr - time_start_all) / count)))) sys.stdout.flush() def main(): ensure_dir(dummy_dir_small, 5000) ensure_dir(dummy_dir_large, 500000) test_reading(dummy_dir_small, 5000) test_reading(dummy_dir_large, 5000) if __name__ == "__main__": main() -
yuikns created this gist
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This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,523 @@ create dummy data in data/small starting [data/small:1000] create dummy data elapsed: 0:00:00.118295(avg: 0:00:00.000118), in total: 0:00:00.118297(avg: 0:00:00.000118) [data/small:2000] create dummy data elapsed: 0:00:00.241233(avg: 0:00:00.000241), in total: 0:00:00.241235(avg: 0:00:00.000121) [data/small:3000] create dummy data elapsed: 0:00:00.357166(avg: 0:00:00.000357), in total: 0:00:00.357168(avg: 0:00:00.000119) [data/small:4000] create dummy data elapsed: 0:00:00.476689(avg: 0:00:00.000477), in total: 0:00:00.476690(avg: 0:00:00.000119) [data/small:5000] create dummy data elapsed: 0:00:00.592847(avg: 0:00:00.000593), in total: 0:00:00.592848(avg: 0:00:00.000119) [data/small:5000] create dummy elapsed: 0:00:00.592848 avg: 0:00:00.000119, all done create dummy data in data/large starting [data/large:1000] create dummy data elapsed: 0:00:00.119876(avg: 0:00:00.000120), in total: 0:00:00.119876(avg: 0:00:00.000120) [data/large:2000] create dummy data elapsed: 0:00:00.257207(avg: 0:00:00.000257), in total: 0:00:00.257207(avg: 0:00:00.000129) [data/large:3000] create dummy data elapsed: 0:00:00.405286(avg: 0:00:00.000405), in total: 0:00:00.405287(avg: 0:00:00.000135) [data/large:4000] create dummy data elapsed: 0:00:00.536713(avg: 0:00:00.000537), in total: 0:00:00.536713(avg: 0:00:00.000134) [data/large:5000] create dummy data elapsed: 0:00:00.700402(avg: 0:00:00.000700), in total: 0:00:00.700402(avg: 0:00:00.000140) [data/large:6000] create dummy data elapsed: 0:00:00.851999(avg: 0:00:00.000852), in total: 0:00:00.851999(avg: 0:00:00.000142) [data/large:7000] create dummy data elapsed: 0:00:00.981476(avg: 0:00:00.000981), in total: 0:00:00.981476(avg: 0:00:00.000140) [data/large:8000] create dummy data elapsed: 0:00:01.096823(avg: 0:00:00.001097), in total: 0:00:01.096824(avg: 0:00:00.000137) [data/large:9000] create dummy data 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[data/large:477000] create dummy data elapsed: 0:01:15.630340(avg: 0:00:00.075630), in total: 0:01:15.630340(avg: 0:00:00.000159) [data/large:478000] create dummy data elapsed: 0:01:15.785136(avg: 0:00:00.075785), in total: 0:01:15.785136(avg: 0:00:00.000159) [data/large:479000] create dummy data elapsed: 0:01:15.944948(avg: 0:00:00.075945), in total: 0:01:15.944948(avg: 0:00:00.000159) [data/large:480000] create dummy data elapsed: 0:01:16.108207(avg: 0:00:00.076108), in total: 0:01:16.108207(avg: 0:00:00.000159) [data/large:481000] create dummy data elapsed: 0:01:16.271048(avg: 0:00:00.076271), in total: 0:01:16.271049(avg: 0:00:00.000159) [data/large:482000] create dummy data elapsed: 0:01:16.439072(avg: 0:00:00.076439), in total: 0:01:16.439072(avg: 0:00:00.000159) [data/large:483000] create dummy data elapsed: 0:01:16.607290(avg: 0:00:00.076607), in total: 0:01:16.607290(avg: 0:00:00.000159) [data/large:484000] create dummy data elapsed: 0:01:16.777778(avg: 0:00:00.076778), in total: 0:01:16.777778(avg: 0:00:00.000159) [data/large:485000] create dummy data elapsed: 0:01:16.952406(avg: 0:00:00.076952), in total: 0:01:16.952406(avg: 0:00:00.000159) [data/large:486000] create dummy data elapsed: 0:01:17.142495(avg: 0:00:00.077142), in total: 0:01:17.142495(avg: 0:00:00.000159) [data/large:487000] create dummy data elapsed: 0:01:17.307334(avg: 0:00:00.077307), in total: 0:01:17.307334(avg: 0:00:00.000159) [data/large:488000] create dummy data elapsed: 0:01:17.476143(avg: 0:00:00.077476), in total: 0:01:17.476143(avg: 0:00:00.000159) [data/large:489000] create dummy data elapsed: 0:01:17.641827(avg: 0:00:00.077642), in total: 0:01:17.641827(avg: 0:00:00.000159) [data/large:490000] create dummy data elapsed: 0:01:17.807092(avg: 0:00:00.077807), in total: 0:01:17.807092(avg: 0:00:00.000159) [data/large:491000] create dummy data elapsed: 0:01:17.974156(avg: 0:00:00.077974), in total: 0:01:17.974156(avg: 0:00:00.000159) [data/large:492000] create dummy data elapsed: 0:01:18.119202(avg: 0:00:00.078119), in total: 0:01:18.119202(avg: 0:00:00.000159) [data/large:493000] create dummy data elapsed: 0:01:18.270630(avg: 0:00:00.078271), in total: 0:01:18.270630(avg: 0:00:00.000159) [data/large:494000] create dummy data elapsed: 0:01:18.419652(avg: 0:00:00.078420), in total: 0:01:18.419652(avg: 0:00:00.000159) [data/large:495000] create dummy data elapsed: 0:01:18.576462(avg: 0:00:00.078576), in total: 0:01:18.576463(avg: 0:00:00.000159) [data/large:496000] create dummy data elapsed: 0:01:18.738918(avg: 0:00:00.078739), in total: 0:01:18.738918(avg: 0:00:00.000159) [data/large:497000] create dummy data elapsed: 0:01:18.916052(avg: 0:00:00.078916), in total: 0:01:18.916052(avg: 0:00:00.000159) [data/large:498000] create dummy data elapsed: 0:01:19.085928(avg: 0:00:00.079086), in total: 0:01:19.085928(avg: 0:00:00.000159) [data/large:499000] create dummy data elapsed: 0:01:19.251707(avg: 0:00:00.079252), in total: 0:01:19.251707(avg: 0:00:00.000159) [data/large:500000] create dummy data elapsed: 0:01:19.416390(avg: 0:00:00.079416), in total: 0:01:19.416390(avg: 0:00:00.000159) [data/large:500000] create dummy elapsed: 0:01:19.416390 avg: 0:00:00.000159, all done test reading data in data/small starting [data/small:1000] elapsed: 0:00:00.153062(avg: 0:00:00.000153), in total: 0:00:00.153062(avg: 0:00:00.000153) [data/small:2000] elapsed: 0:00:00.130151(avg: 0:00:00.000130), in total: 0:00:00.283214(avg: 0:00:00.000142) [data/small:3000] elapsed: 0:00:00.121274(avg: 0:00:00.000121), in total: 0:00:00.404488(avg: 0:00:00.000135) [data/small:4000] elapsed: 0:00:00.117216(avg: 0:00:00.000117), in total: 0:00:00.521704(avg: 0:00:00.000130) [data/small:5000] elapsed: 0:00:00.122165(avg: 0:00:00.000122), in total: 0:00:00.643869(avg: 0:00:00.000129) [data/small:5000] elapsed: 0:00:00.644100 avg: 0:00:00.000129 test reading data in data/large starting [data/large:1000] elapsed: 0:00:00.165082(avg: 0:00:00.000165), in total: 0:00:00.165082(avg: 0:00:00.000165) [data/large:2000] elapsed: 0:00:00.140481(avg: 0:00:00.000140), in total: 0:00:00.305562(avg: 0:00:00.000153) [data/large:3000] elapsed: 0:00:00.141707(avg: 0:00:00.000142), in total: 0:00:00.447269(avg: 0:00:00.000149) [data/large:4000] elapsed: 0:00:00.194968(avg: 0:00:00.000195), in total: 0:00:00.642237(avg: 0:00:00.000161) [data/large:5000] elapsed: 0:00:00.128898(avg: 0:00:00.000129), in total: 0:00:00.771135(avg: 0:00:00.000154) [data/large:5000] elapsed: 0:00:00.771358 avg: 0:00:00.000154 This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,523 @@ create dummy data in data/small starting [data/small:1000] create dummy data elapsed: 0:00:00.036681(avg: 0:00:00.000037), in total: 0:00:00.036681(avg: 0:00:00.000037) [data/small:2000] create dummy data elapsed: 0:00:00.184024(avg: 0:00:00.000184), in total: 0:00:00.184024(avg: 0:00:00.000092) [data/small:3000] create dummy data elapsed: 0:00:00.233445(avg: 0:00:00.000233), in total: 0:00:00.233445(avg: 0:00:00.000078) [data/small:4000] create dummy data elapsed: 0:00:00.314359(avg: 0:00:00.000314), in total: 0:00:00.314359(avg: 0:00:00.000079) [data/small:5000] create dummy data elapsed: 0:00:00.351601(avg: 0:00:00.000352), in total: 0:00:00.351601(avg: 0:00:00.000070) [data/small:5000] create dummy elapsed: 0:00:00.351601 avg: 0:00:00.000070, all done create dummy data in data/large starting [data/large:1000] create dummy data elapsed: 0:00:00.034126(avg: 0:00:00.000034), in total: 0:00:00.034126(avg: 0:00:00.000034) [data/large:2000] create dummy data elapsed: 0:00:00.072506(avg: 0:00:00.000073), in total: 0:00:00.072506(avg: 0:00:00.000036) [data/large:3000] create dummy data elapsed: 0:00:00.111110(avg: 0:00:00.000111), in total: 0:00:00.111110(avg: 0:00:00.000037) [data/large:4000] create dummy data elapsed: 0:00:00.149772(avg: 0:00:00.000150), in total: 0:00:00.149772(avg: 0:00:00.000037) [data/large:5000] create dummy data elapsed: 0:00:00.188803(avg: 0:00:00.000189), in total: 0:00:00.188803(avg: 0:00:00.000038) [data/large:6000] create dummy data elapsed: 0:00:00.227672(avg: 0:00:00.000228), in total: 0:00:00.227672(avg: 0:00:00.000038) [data/large:7000] create dummy data elapsed: 0:00:00.266520(avg: 0:00:00.000267), in total: 0:00:00.266520(avg: 0:00:00.000038) [data/large:8000] create dummy data elapsed: 0:00:00.305397(avg: 0:00:00.000305), in total: 0:00:00.305398(avg: 0:00:00.000038) [data/large:9000] create dummy data elapsed: 0:00:00.342445(avg: 0:00:00.000342), in total: 0:00:00.342445(avg: 0:00:00.000038) [data/large:10000] create dummy data elapsed: 0:00:00.380018(avg: 0:00:00.000380), in total: 0:00:00.380018(avg: 0:00:00.000038) [data/large:11000] create dummy data elapsed: 0:00:00.417818(avg: 0:00:00.000418), in total: 0:00:00.417818(avg: 0:00:00.000038) [data/large:12000] create dummy data elapsed: 0:00:00.455580(avg: 0:00:00.000456), in total: 0:00:00.455580(avg: 0:00:00.000038) [data/large:13000] create dummy data elapsed: 0:00:00.493272(avg: 0:00:00.000493), in total: 0:00:00.493273(avg: 0:00:00.000038) [data/large:14000] create dummy data elapsed: 0:00:00.531164(avg: 0:00:00.000531), in total: 0:00:00.531164(avg: 0:00:00.000038) [data/large:15000] create dummy data elapsed: 0:00:00.569206(avg: 0:00:00.000569), in total: 0:00:00.569206(avg: 0:00:00.000038) [data/large:16000] create dummy data elapsed: 0:00:00.607333(avg: 0:00:00.000607), in total: 0:00:00.607333(avg: 0:00:00.000038) [data/large:17000] create dummy data elapsed: 0:00:00.647495(avg: 0:00:00.000647), in total: 0:00:00.647495(avg: 0:00:00.000038) [data/large:18000] create dummy data elapsed: 0:00:00.685814(avg: 0:00:00.000686), in total: 0:00:00.685814(avg: 0:00:00.000038) [data/large:19000] create dummy data elapsed: 0:00:00.724375(avg: 0:00:00.000724), in total: 0:00:00.724375(avg: 0:00:00.000038) [data/large:20000] create dummy data elapsed: 0:00:00.762961(avg: 0:00:00.000763), in total: 0:00:00.762961(avg: 0:00:00.000038) [data/large:21000] create dummy data elapsed: 0:00:00.801665(avg: 0:00:00.000802), in total: 0:00:00.801665(avg: 0:00:00.000038) [data/large:22000] create dummy data elapsed: 0:00:00.842664(avg: 0:00:00.000843), in total: 0:00:00.842664(avg: 0:00:00.000038) [data/large:23000] create dummy data elapsed: 0:00:00.883977(avg: 0:00:00.000884), in total: 0:00:00.883977(avg: 0:00:00.000038) [data/large:24000] create dummy data elapsed: 0:00:00.923601(avg: 0:00:00.000924), in total: 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[data/large:40000] create dummy data elapsed: 0:00:01.589355(avg: 0:00:00.001589), in total: 0:00:01.589355(avg: 0:00:00.000040) [data/large:41000] create dummy data elapsed: 0:00:01.630784(avg: 0:00:00.001631), in total: 0:00:01.630784(avg: 0:00:00.000040) [data/large:42000] create dummy data elapsed: 0:00:01.672212(avg: 0:00:00.001672), in total: 0:00:01.672212(avg: 0:00:00.000040) [data/large:43000] create dummy data elapsed: 0:00:01.714179(avg: 0:00:00.001714), in total: 0:00:01.714179(avg: 0:00:00.000040) [data/large:44000] create dummy data elapsed: 0:00:01.756260(avg: 0:00:00.001756), in total: 0:00:01.756260(avg: 0:00:00.000040) [data/large:45000] create dummy data elapsed: 0:00:01.798232(avg: 0:00:00.001798), in total: 0:00:01.798232(avg: 0:00:00.000040) [data/large:46000] create dummy data elapsed: 0:00:01.840652(avg: 0:00:00.001841), in total: 0:00:01.840652(avg: 0:00:00.000040) [data/large:47000] create dummy data elapsed: 0:00:01.883404(avg: 0:00:00.001883), in total: 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[data/large:63000] create dummy data elapsed: 0:00:02.585549(avg: 0:00:00.002586), in total: 0:00:02.585549(avg: 0:00:00.000041) [data/large:64000] create dummy data elapsed: 0:00:02.630129(avg: 0:00:00.002630), in total: 0:00:02.630129(avg: 0:00:00.000041) [data/large:65000] create dummy data elapsed: 0:00:02.677784(avg: 0:00:00.002678), in total: 0:00:02.677784(avg: 0:00:00.000041) [data/large:66000] create dummy data elapsed: 0:00:02.722904(avg: 0:00:00.002723), in total: 0:00:02.722904(avg: 0:00:00.000041) [data/large:67000] create dummy data elapsed: 0:00:02.770480(avg: 0:00:00.002770), in total: 0:00:02.770480(avg: 0:00:00.000041) [data/large:68000] create dummy data elapsed: 0:00:02.815922(avg: 0:00:00.002816), in total: 0:00:02.815922(avg: 0:00:00.000041) [data/large:69000] create dummy data elapsed: 0:00:02.861454(avg: 0:00:00.002861), in total: 0:00:02.861454(avg: 0:00:00.000041) [data/large:70000] create dummy data elapsed: 0:00:02.907187(avg: 0:00:00.002907), in total: 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[data/large:454000] create dummy data elapsed: 0:01:26.119706(avg: 0:00:00.086120), in total: 0:01:26.119706(avg: 0:00:00.000190) [data/large:455000] create dummy data elapsed: 0:01:27.307797(avg: 0:00:00.087308), in total: 0:01:27.307797(avg: 0:00:00.000192) [data/large:456000] create dummy data elapsed: 0:01:28.958442(avg: 0:00:00.088958), in total: 0:01:28.958442(avg: 0:00:00.000195) [data/large:457000] create dummy data elapsed: 0:01:30.429200(avg: 0:00:00.090429), in total: 0:01:30.429201(avg: 0:00:00.000198) [data/large:458000] create dummy data elapsed: 0:01:31.784455(avg: 0:00:00.091784), in total: 0:01:31.784455(avg: 0:00:00.000200) [data/large:459000] create dummy data elapsed: 0:01:31.887309(avg: 0:00:00.091887), in total: 0:01:31.887309(avg: 0:00:00.000200) [data/large:460000] create dummy data elapsed: 0:01:31.991037(avg: 0:00:00.091991), in total: 0:01:31.991037(avg: 0:00:00.000200) [data/large:461000] create dummy data elapsed: 0:01:32.098824(avg: 0:00:00.092099), in 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0:01:32.930822(avg: 0:00:00.092931), in total: 0:01:32.930822(avg: 0:00:00.000198) [data/large:470000] create dummy data elapsed: 0:01:33.035687(avg: 0:00:00.093036), in total: 0:01:33.035687(avg: 0:00:00.000198) [data/large:471000] create dummy data elapsed: 0:01:33.140130(avg: 0:00:00.093140), in total: 0:01:33.140130(avg: 0:00:00.000198) [data/large:472000] create dummy data elapsed: 0:01:33.244556(avg: 0:00:00.093245), in total: 0:01:33.244556(avg: 0:00:00.000198) [data/large:473000] create dummy data elapsed: 0:01:33.349499(avg: 0:00:00.093349), in total: 0:01:33.349499(avg: 0:00:00.000197) [data/large:474000] create dummy data elapsed: 0:01:33.454579(avg: 0:00:00.093455), in total: 0:01:33.454580(avg: 0:00:00.000197) [data/large:475000] create dummy data elapsed: 0:01:33.559866(avg: 0:00:00.093560), in total: 0:01:33.559866(avg: 0:00:00.000197) [data/large:476000] create dummy data elapsed: 0:01:33.665069(avg: 0:00:00.093665), in total: 0:01:33.665070(avg: 0:00:00.000197) [data/large:477000] create dummy data elapsed: 0:01:33.770286(avg: 0:00:00.093770), in total: 0:01:33.770286(avg: 0:00:00.000197) [data/large:478000] create dummy data elapsed: 0:01:33.876158(avg: 0:00:00.093876), in total: 0:01:33.876158(avg: 0:00:00.000196) [data/large:479000] create dummy data elapsed: 0:01:33.983597(avg: 0:00:00.093984), in total: 0:01:33.983597(avg: 0:00:00.000196) [data/large:480000] create dummy data elapsed: 0:01:34.089136(avg: 0:00:00.094089), in total: 0:01:34.089136(avg: 0:00:00.000196) [data/large:481000] create dummy data elapsed: 0:01:34.195563(avg: 0:00:00.094196), in total: 0:01:34.195563(avg: 0:00:00.000196) [data/large:482000] create dummy data elapsed: 0:01:34.302550(avg: 0:00:00.094303), in total: 0:01:34.302550(avg: 0:00:00.000196) [data/large:483000] create dummy data elapsed: 0:01:34.409231(avg: 0:00:00.094409), in total: 0:01:34.409231(avg: 0:00:00.000195) [data/large:484000] create dummy data elapsed: 0:01:34.515793(avg: 0:00:00.094516), in total: 0:01:34.515793(avg: 0:00:00.000195) [data/large:485000] create dummy data elapsed: 0:01:34.622554(avg: 0:00:00.094623), in total: 0:01:34.622554(avg: 0:00:00.000195) [data/large:486000] create dummy data elapsed: 0:01:34.729335(avg: 0:00:00.094729), in total: 0:01:34.729335(avg: 0:00:00.000195) [data/large:487000] create dummy data elapsed: 0:01:34.836163(avg: 0:00:00.094836), in total: 0:01:34.836163(avg: 0:00:00.000195) [data/large:488000] create dummy data elapsed: 0:01:34.942985(avg: 0:00:00.094943), in total: 0:01:34.942985(avg: 0:00:00.000195) [data/large:489000] create dummy data elapsed: 0:01:35.049789(avg: 0:00:00.095050), in total: 0:01:35.049789(avg: 0:00:00.000194) [data/large:490000] create dummy data elapsed: 0:01:35.161149(avg: 0:00:00.095161), in total: 0:01:35.161149(avg: 0:00:00.000194) [data/large:491000] create dummy data elapsed: 0:01:35.268842(avg: 0:00:00.095269), in total: 0:01:35.268842(avg: 0:00:00.000194) [data/large:492000] create dummy data elapsed: 0:01:35.377144(avg: 0:00:00.095377), in total: 0:01:35.377144(avg: 0:00:00.000194) [data/large:493000] create dummy data elapsed: 0:01:35.485110(avg: 0:00:00.095485), in total: 0:01:35.485110(avg: 0:00:00.000194) [data/large:494000] create dummy data elapsed: 0:01:35.593280(avg: 0:00:00.095593), in total: 0:01:35.593280(avg: 0:00:00.000194) [data/large:495000] create dummy data elapsed: 0:01:35.701555(avg: 0:00:00.095702), in total: 0:01:35.701555(avg: 0:00:00.000193) [data/large:496000] create dummy data elapsed: 0:01:35.810289(avg: 0:00:00.095810), in total: 0:01:35.810289(avg: 0:00:00.000193) [data/large:497000] create dummy data elapsed: 0:01:35.918707(avg: 0:00:00.095919), in total: 0:01:35.918707(avg: 0:00:00.000193) [data/large:498000] create dummy data elapsed: 0:01:36.027510(avg: 0:00:00.096028), in total: 0:01:36.027511(avg: 0:00:00.000193) [data/large:499000] create dummy data elapsed: 0:02:25.620174(avg: 0:00:00.145620), in total: 0:02:25.620174(avg: 0:00:00.000292) [data/large:500000] create dummy data elapsed: 0:02:25.737173(avg: 0:00:00.145737), in total: 0:02:25.737173(avg: 0:00:00.000291) [data/large:500000] create dummy elapsed: 0:02:25.737173 avg: 0:00:00.000291, all done test reading data in data/small starting [data/small:1000] elapsed: 0:00:00.019693(avg: 0:00:00.000020), in total: 0:00:00.019693(avg: 0:00:00.000020) [data/small:2000] elapsed: 0:00:00.019776(avg: 0:00:00.000020), in total: 0:00:00.039470(avg: 0:00:00.000020) [data/small:3000] elapsed: 0:00:00.019675(avg: 0:00:00.000020), in total: 0:00:00.059145(avg: 0:00:00.000020) [data/small:4000] elapsed: 0:00:00.019751(avg: 0:00:00.000020), in total: 0:00:00.078895(avg: 0:00:00.000020) [data/small:5000] elapsed: 0:00:00.019847(avg: 0:00:00.000020), in total: 0:00:00.098742(avg: 0:00:00.000020) [data/small:5000] elapsed: 0:00:00.098801 avg: 0:00:00.000020 test reading data in data/large starting [data/large:1000] elapsed: 0:00:00.020725(avg: 0:00:00.000021), in total: 0:00:00.020725(avg: 0:00:00.000021) [data/large:2000] elapsed: 0:00:00.020510(avg: 0:00:00.000021), in total: 0:00:00.041234(avg: 0:00:00.000021) [data/large:3000] elapsed: 0:00:00.020382(avg: 0:00:00.000020), in total: 0:00:00.061616(avg: 0:00:00.000021) [data/large:4000] elapsed: 0:00:00.019515(avg: 0:00:00.000020), in total: 0:00:00.081131(avg: 0:00:00.000020) [data/large:5000] elapsed: 0:00:00.019756(avg: 0:00:00.000020), in total: 0:00:00.100886(avg: 0:00:00.000020) [data/large:5000] elapsed: 0:00:00.100943 avg: 0:00:00.000020