Parsing Java Micro-benchmarking Harness data using dplyr – Part 1

This is about the venerable JMH and Hadley Wickham’s dplyr and pipes package. dplyr enables you to have too much fun with data. Its pipes are so powerful and makes short shrift of even messy data.

# VM invoker: D:\Java\bin\java.exe
# VM options: -XX:-TieredCompilation -Dbenchmark.n=10000
# Warmup: 5 iterations, 50 ms each
# Measurement: 20 iterations, 50 ms each
# Timeout: 10 min per iteration
# Threads: 1 thread, will synchronize iterations
# Benchmark mode: Average time, time/op
# Benchmark: oracle.stream.javaone.CollectionComparison.goldmansachscollections

# Run progress: 0.00% complete, ETA 00:00:02
# Fork: 1 of 1
# Warmup Iteration 1: 0.443 us/op
# Warmup Iteration 2: 0.290 us/op
# Warmup Iteration 3: 0.343 us/op
# Warmup Iteration 4: 0.350 us/op
# Warmup Iteration 5: 0.388 us/op
Iteration 1: 0.796 us/op
Iteration 2: 0.542 us/op
Iteration 3: 0.510 us/op
Iteration 4: 0.617 us/op
Iteration 5: 0.482 us/op
Iteration 6: 0.387 us/op
Iteration 7: 0.272 us/op
Iteration 8: 0.536 us/op
Iteration 9: 0.498 us/op
Iteration 10: 0.402 us/op
Iteration 11: 0.328 us/op
Iteration 12: 0.542 us/op
Iteration 13: 0.299 us/op
Iteration 14: 0.647 us/op
Iteration 15: 0.291 us/op
Iteration 16: 0.815 us/op
Iteration 17: 0.680 us/op
Iteration 18: 0.363 us/op
Iteration 19: 0.560 us/op
Iteration 20: 0.334 us/op

Result: 0.495 ¦(99.9%) 0.140 us/op [Average]
Statistics: (min, avg, max) = (0.272, 0.495, 0.815), stdev = 0.162
Confidence interval (99.9%): [0.355, 0.636]

# VM invoker: D:\Java\bin\java.exe
# VM options: -XX:-TieredCompilation -Dbenchmark.n=10000
# Warmup: 5 iterations, 50 ms each
# Measurement: 20 iterations, 50 ms each
# Timeout: 10 min per iteration
# Threads: 1 thread, will synchronize iterations
# Benchmark mode: Average time, time/op
# Benchmark: oracle.stream.javaone.CollectionComparison.javacollections

# Run progress: 50.00% complete, ETA 00:00:05
# Fork: 1 of 1
# Warmup Iteration 1: 0.475 us/op
# Warmup Iteration 2: 0.696 us/op
# Warmup Iteration 3: 0.816 us/op
# Warmup Iteration 4: 0.622 us/op
# Warmup Iteration 5: 0.574 us/op
Iteration 1: 0.987 us/op
Iteration 2: 0.585 us/op
Iteration 3: 0.770 us/op
Iteration 4: 0.711 us/op
Iteration 5: 0.546 us/op
Iteration 6: 0.553 us/op
Iteration 7: 1.164 us/op
Iteration 8: 1.096 us/op
Iteration 9: 1.477 us/op
Iteration 10: 0.824 us/op
Iteration 11: 1.002 us/op
Iteration 12: 0.504 us/op
Iteration 13: 1.019 us/op
Iteration 14: 0.834 us/op
Iteration 15: 0.589 us/op
Iteration 16: 0.557 us/op
Iteration 17: 1.338 us/op
Iteration 18: 0.906 us/op
Iteration 19: 0.486 us/op
Iteration 20: 0.587 us/op

Result: 0.827 ¦(99.9%) 0.252 us/op [Average]
Statistics: (min, avg, max) = (0.486, 0.827, 1.477), stdev = 0.291
Confidence interval (99.9%): [0.574, 1.079]

# Run complete. Total time: 00:00:10

Benchmark Mode Samples Score Scor
e error Units
o.s.j.CollectionComparison.goldmansachscollections avgt 20 0.495
0.140 us/op
o.s.j.CollectionComparison.javacollections avgt 20 0.827
0.252 us/op

library(stringr)
library(dplyr)

data <- read.table("D:\\jmh\\jmh.txt",sep="\t")

final <-data %>%
	    select(V1) %>%	
		filter(grepl("^Iteration", V1)) %>%  
        mutate(V1 = str_extract(V1, "\\d+\\.\\d*"))

print(final)

V1
1 0.796
2 0.542
3 0.510
4 0.617
5 0.482
6 0.387
7 0.272
8 0.536
9 0.498
10 0.402
11 0.328
12 0.542
13 0.299
14 0.647
15 0.291
16 0.815
17 0.680
18 0.363
19 0.560
20 0.334
21 0.987
22 0.585
23 0.770
24 0.711
25 0.546
26 0.553
27 1.164
28 1.096
29 1.477
30 0.824
31 1.002
32 0.504
33 1.019
34 0.834
35 0.589
36 0.557
37 1.338
38 0.906
39 0.486
40 0.587