Performance Results Report

Summary

transactions: 3692446
errors: 0
run time: 1800 secs
rampup: 1800 secs

test start: 2010-07-26 14:42:19
test finish: 2010-07-26 15:12:19

time-series interval: 100 secs

workload configuration:

group name	threads	script name
user_group-2	140	mem_set.py
user_group-1	140	mem_get.py

Notes:

Membase Server 1.6 Beta 2
1 Membase Node
Node Specs: Ubuntu 10.04 64-bit, 4 CPU, 10 GB RAM, 1 Gbit NIC, VMWare on HP G6
1 Client Load Generator (PFTOOLS205)
Client Specs: Ubuntu 10.04 64-bit, 4 CPU, 10 GB RAM, 1 Gbit NIC, VMWare on HP G6
Client Lib: python-memcached 1.44
VU script doing Sets (random key range 1-1000), 30kb string values
VU script doing Gets (random key range 1-1000)
.05 sec (50 ms) sleep inside each script/transaction
Load scales by increasing connections/threads over time (rampup)

All Transactions

Transaction Response Summary (secs)

count	min	avg	80pct	90pct	95pct	max	stdev
3692446	0.050	0.063	0.058	0.065	0.076	3.062	0.048

Interval Details (secs)

interval	count	rate	min	avg	80pct	90pct	95pct	max	stdev
1	16996	169.96	0.051	0.052	0.052	0.053	0.053	0.065	0.001
2	46678	466.78	0.051	0.052	0.052	0.053	0.054	0.090	0.001
3	75690	756.90	0.051	0.052	0.053	0.054	0.056	0.316	0.004
4	102259	1022.59	0.051	0.054	0.054	0.056	0.058	0.763	0.014
5	126894	1268.94	0.051	0.055	0.055	0.058	0.061	0.745	0.020
6	149384	1493.84	0.051	0.057	0.056	0.060	0.065	0.624	0.025
7	175532	1755.32	0.051	0.057	0.056	0.059	0.064	1.068	0.027
8	184503	1845.03	0.051	0.062	0.058	0.064	0.076	1.275	0.040
9	210036	2100.36	0.051	0.060	0.059	0.066	0.076	3.062	0.034
10	222000	2220.00	0.051	0.063	0.059	0.069	0.084	1.115	0.043
11	247942	2479.42	0.051	0.062	0.058	0.065	0.078	1.243	0.043
12	254466	2544.66	0.051	0.065	0.061	0.071	0.087	2.037	0.050
13	278695	2786.95	0.051	0.065	0.060	0.068	0.084	1.859	0.049
14	312915	3129.15	0.051	0.063	0.058	0.063	0.072	2.019	0.049
15	315024	3150.24	0.051	0.066	0.059	0.065	0.076	2.020	0.055
16	326897	3268.97	0.050	0.066	0.059	0.064	0.076	3.055	0.063
17	321649	3216.49	0.050	0.068	0.061	0.070	0.090	2.048	0.059
18	324605	3246.05	0.051	0.069	0.061	0.069	0.090	2.101	0.068

Graphs

Response Time: 100 sec time-series

Response Time: raw data (all points)

Throughput: 5 sec time-series

Custom Timer: GET

Timer Summary (secs)

count	min	avg	80pct	90pct	95pct	max	stdev
1759922	0.000	0.008	0.007	0.011	0.017	3.003	0.032

Interval Details (secs)

interval	count	rate	min	avg	80pct	90pct	95pct	max	stdev
1	8491	84.91	0.001	0.001	0.002	0.002	0.003	0.014	0.001
2	23280	232.80	0.001	0.002	0.002	0.003	0.004	0.038	0.001
3	37695	376.95	0.001	0.002	0.003	0.004	0.005	0.214	0.003
4	50890	508.90	0.001	0.003	0.003	0.005	0.006	0.713	0.012
5	63165	631.65	0.001	0.004	0.004	0.006	0.008	0.670	0.017
6	74450	744.50	0.001	0.005	0.005	0.007	0.010	0.562	0.020
7	87572	875.72	0.001	0.005	0.005	0.007	0.010	0.722	0.022
8	91472	914.72	0.001	0.008	0.006	0.009	0.015	0.886	0.032
9	102732	1027.32	0.001	0.007	0.006	0.010	0.016	3.003	0.027
10	108853	1088.53	0.001	0.008	0.006	0.010	0.019	1.027	0.031
11	120879	1208.79	0.001	0.008	0.007	0.010	0.017	1.192	0.030
12	122825	1228.25	0.001	0.010	0.007	0.012	0.022	0.848	0.034
13	134123	1341.23	0.001	0.009	0.007	0.011	0.018	0.957	0.032
14	149427	1494.27	0.001	0.008	0.007	0.010	0.015	0.791	0.030
15	146780	1467.80	0.001	0.009	0.008	0.012	0.017	0.821	0.030
16	149290	1492.90	0.000	0.010	0.008	0.012	0.019	1.897	0.041
17	144629	1446.29	0.000	0.011	0.009	0.015	0.023	1.771	0.038
18	143369	1433.69	0.001	0.013	0.010	0.016	0.025	1.693	0.044

Graphs

Response Time: 100 sec time-series

Response Time: raw data (all points)

Throughput: 5 sec time-series

Custom Timer: SET

Timer Summary (secs)

count	min	avg	80pct	90pct	95pct	max	stdev
1932243	0.000	0.007	0.004	0.007	0.012	3.011	0.036

Interval Details (secs)

interval	count	rate	min	avg	80pct	90pct	95pct	max	stdev
1	8505	85.05	0.001	0.001	0.002	0.002	0.003	0.014	0.001
2	23402	234.02	0.001	0.002	0.002	0.002	0.003	0.030	0.001
3	37991	379.91	0.001	0.002	0.002	0.003	0.004	0.261	0.005
4	51373	513.73	0.001	0.003	0.002	0.003	0.005	0.633	0.013
5	63726	637.26	0.001	0.004	0.003	0.004	0.007	0.543	0.021
6	74934	749.34	0.001	0.005	0.003	0.005	0.009	0.327	0.025
7	87959	879.59	0.001	0.005	0.003	0.005	0.009	1.018	0.028
8	93031	930.31	0.001	0.008	0.004	0.007	0.013	1.193	0.035
9	107304	1073.04	0.001	0.006	0.004	0.006	0.012	3.011	0.028
10	113152	1131.52	0.001	0.007	0.004	0.007	0.015	1.051	0.035
11	127059	1270.59	0.001	0.007	0.004	0.007	0.013	1.087	0.035
12	131645	1316.45	0.001	0.008	0.004	0.008	0.016	1.986	0.042
13	144575	1445.75	0.001	0.008	0.004	0.007	0.013	1.808	0.036
14	163482	1634.82	0.001	0.007	0.004	0.006	0.011	1.969	0.039
15	168244	1682.44	0.001	0.007	0.004	0.007	0.011	1.504	0.035
16	177606	1776.06	0.000	0.008	0.005	0.007	0.011	2.065	0.048
17	177022	1770.22	0.000	0.008	0.005	0.009	0.016	1.996	0.039
18	181233	1812.33	0.001	0.009	0.006	0.010	0.017	1.971	0.043

Graphs

Response Time: 100 sec time-series

Response Time: raw data (all points)

Throughput: 5 sec time-series

VU Script: mem_get.py

#!/usr/bin/env python

import random
import time
import memcache

NODES = ['192.168.12.171:11211',]
WAIT_TIME = .05
KEY_RANGE = (1, 1000)

class Transaction(object):
    def __init__(self):
        self.mc = memcache.Client(NODES)
        self.custom_timers = {}

    def run(self):
        key = str(random.randint(*KEY_RANGE))

        start_timer = time.time()
        self.mc.get(key)
        stop_timer = time.time()

        self.custom_timers['GET'] = stop_timer - start_timer

        time.sleep(WAIT_TIME)

VU Script: mem_set.py

#!/usr/bin/env python

import random
import time
import memcache

NODES = ['192.168.12.171:11211',]
DATA_SIZE = 30000
WAIT_TIME = .05
KEY_RANGE = (1, 1000)

class Transaction(object):
    def __init__(self):
        self.mc = memcache.Client(NODES)
        self.custom_timers = {}

    def run(self):
        key = str(random.randint(*KEY_RANGE))
        data = '*' * DATA_SIZE

        start_timer = time.time()
        self.mc.set(key, data)
        stop_timer = time.time()

        self.custom_timers['SET'] = stop_timer - start_timer

        time.sleep(WAIT_TIME)