Sets

caution

These benchmarks should not be used as a definitive answer to which collections are best. It's typical for different implementations to perform better for specific use cases. You are strongly encouraged to verify any results.

Also bear in mind that these numbers are in microseconds.

N = 10	add	addAll (different)	addAll (same)	contains	remove
Dart Set	0.22	3.36	2.99	0.32	0.23
Dartz Set	2.34	25.57	12.81	0.79	1.79
FIC ISet	0.46	14.16	5.52	0.43	6.81
Ribs ISet	7.74	16.52	0.49	0.56	7.27
Ribs MSet	0.26	3.34	3.06	0.46	0.23

N = 100	add	addAll (different)	addAll (same)	contains	remove
Dart Set	0.21	32.19	24.47	0.32	0.18
Dartz Set	3.47	419.93	266.39	1.16	2.60
FIC ISet	0.45	142.47	28.48	0.43	47.22
Ribs ISet	1.61	246.64	0.49	1.14	0.85
Ribs MSet	0.26	35.14	35.23	0.45	0.23

N = 1000	add	addAll (different)	addAll (same)	contains	remove
Dart Set	0.21	359.39	236.85	0.31	0.22
Dartz Set	4.54	5445.82	3904.19	1.45	3.66
FIC ISet	0.46	1282.51	269.65	0.42	392.05
Ribs ISet	1.22	2294.61	0.48	1.12	0.86
Ribs MSet	0.28	447.29	383.99	0.50	0.24

N = 10000	add	addAll (different)	addAll (same)	contains	remove
Dart Set	0.21	2895.47	2384.16	0.31	0.18
Dartz Set	6.12	71305.97	52743.82	1.71	4.69
FIC ISet	0.45	14703.83	2689.55	0.42	5321.72
Ribs ISet	1.23	24720.56	0.49	1.12	1.06
Ribs MSet	0.26	5274.93	3806.19	0.45	0.24

N = 100000	add	addAll (different)	addAll (same)	contains	remove
Dart Set	0.23	43079.06	27273.68	0.32	0.18
Dartz Set	7.61	1066987.50	802796.00	2.07	7.20
FIC ISet	0.45	458190.20	54272.68	0.43	103934.64
Ribs ISet	1.64	355395.50	0.49	1.75	1.61
Ribs MSet	0.26	79470.23	50065.31	0.45	0.23

N = 1000000	add	addAll (different)	addAll (same)	contains	remove
Dart Set	0.22	900809.00	461075.40	0.33	0.18
Dartz Set	8.98	12626918.50	10597567.50	2.40	8.29
FIC ISet	0.45	5994375.50	1597626.00	0.43	1454784.50
Ribs ISet	2.01	3651381.50	0.49	1.74	1.61
Ribs MSet	0.27	1362908.50	773740.00	0.46	0.25