-
Notifications
You must be signed in to change notification settings - Fork 0
/
ycsb.py
403 lines (401 loc) · 19.3 KB
/
ycsb.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
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
"""
:Date: 2022-11-28
:Version: 0.1
:Authors: Patrick Erdelt
"""
from bexhoma import *
from dbmsbenchmarker import *
#import experiments
import logging
import urllib3
import logging
import argparse
import time
from timeit import default_timer
import datetime
import pandas as pd
urllib3.disable_warnings()
logging.basicConfig(level=logging.ERROR)
if __name__ == '__main__':
description = """Perform YCSB benchmarks in a Kubernetes cluster.
Number of rows and operations is SF*1,000,000.
This installs a clean copy for each target and split of the driver.
Optionally monitoring is activated.
"""
# argparse
parser = argparse.ArgumentParser(description=description)
parser.add_argument('mode', help='import YCSB data or run YCSB queries', choices=['run', 'start', 'load', 'summary'], default='run')
parser.add_argument('-aws', '--aws', help='fix components to node groups at AWS', action='store_true', default=False)
parser.add_argument('-dbms', help='DBMS to load the data', choices=['PostgreSQL', 'MySQL'], default=[])
parser.add_argument('-workload', help='YCSB default workload', choices=['a', 'b', 'c', 'd', 'e', 'f'], default='a')
parser.add_argument('-db', '--debug', help='dump debug informations', action='store_true')
parser.add_argument('-cx', '--context', help='context of Kubernetes (for a multi cluster environment), default is current context', default=None)
parser.add_argument('-e', '--experiment', help='sets experiment code for continuing started experiment', default=None)
parser.add_argument('-d', '--detached', help='puts most of the experiment workflow inside the cluster', action='store_true')
parser.add_argument('-m', '--monitoring', help='activates monitoring for sut', action='store_true')
parser.add_argument('-mc', '--monitoring-cluster', help='activates monitoring for all nodes of cluster', action='store_true', default=False)
parser.add_argument('-ms', '--max-sut', help='maximum number of parallel DBMS configurations, default is no limit', default=None)
parser.add_argument('-dt', '--datatransfer', help='activates datatransfer', action='store_true', default=False)
parser.add_argument('-md', '--monitoring-delay', help='time to wait [s] before execution of the runs of a query', default=10)
parser.add_argument('-nr', '--num-run', help='number of runs per query', default=1)
parser.add_argument('-nc', '--num-config', help='number of runs per configuration', default=1)
parser.add_argument('-ne', '--num-query-executors', help='comma separated list of number of parallel clients', default="")
parser.add_argument('-nl', '--num-loading', help='number of parallel loaders per configuration', default=1)
parser.add_argument('-nlp', '--num-loading-pods', help='total number of loaders per configuration', default="1,8")
parser.add_argument('-sf', '--scaling-factor', help='scaling factor (SF) = number of rows in millions', default=1)
parser.add_argument('-sfo', '--scaling-factor-operations', help='scaling factor (SF) = number of operations in millions (=SF if not set)', default=None)
parser.add_argument('-su', '--scaling-users', help='scaling factor = number of total threads', default=64)
parser.add_argument('-sbs', '--scaling-batchsize', help='batch size', default="")
parser.add_argument('-ltf', '--list-target-factors', help='comma separated list of factors of 16384 ops as target - default range(1,9)', default="1,2,3,4,5,6,7,8")
parser.add_argument('-tb', '--target-base', help='ops as target, base for factors - default 16384 = 2**14', default="16384")
parser.add_argument('-t', '--timeout', help='timeout for a run of a query', default=180)
parser.add_argument('-rr', '--request-ram', help='request ram', default='16Gi')
parser.add_argument('-rc', '--request-cpu', help='request cpus', default='4')
parser.add_argument('-rct', '--request-cpu-type', help='request node having node label cpu=', default='')
parser.add_argument('-rg', '--request-gpu', help='request number of gpus', default=1)
parser.add_argument('-rgt', '--request-gpu-type', help='request node having node label gpu=', default='a100')
parser.add_argument('-rst', '--request-storage-type', help='request persistent storage of certain type', default=None, choices=[None, '', 'local-hdd', 'shared'])
parser.add_argument('-rss', '--request-storage-size', help='request persistent storage of certain size', default='10Gi')
parser.add_argument('-rnn', '--request-node-name', help='request a specific node', default=None)
parser.add_argument('-rnl', '--request-node-loading', help='request a specific node', default=None)
parser.add_argument('-rnb', '--request-node-benchmarking', help='request a specific node', default=None)
parser.add_argument('-tr', '--test-result', help='test if result fulfills some basic requirements', action='store_true', default=False)
# evaluate args
args = parser.parse_args()
if args.debug:
logging.basicConfig(level=logging.DEBUG)
#logging.basicConfig(level=logging.DEBUG)
if args.debug:
logger_bexhoma = logging.getLogger('bexhoma')
logger_bexhoma.setLevel(logging.DEBUG)
logger_loader = logging.getLogger('load_data_asynch')
logger_loader.setLevel(logging.DEBUG)
# set parameter
monitoring = args.monitoring
monitoring_cluster = args.monitoring_cluster
mode = str(args.mode)
SF = str(args.scaling_factor)
SFO = str(args.scaling_factor_operations)
if SFO == 'None':
SFO = SF
SU = int(args.scaling_users)
target_base = int(args.target_base)
list_target_factors = args.list_target_factors
if len(list_target_factors) > 0:
list_target_factors = list_target_factors.split(",")
list_target_factors = [int(x) for x in list_target_factors]
batchsize = args.scaling_batchsize
timeout = int(args.timeout)
numRun = int(args.num_run)
num_experiment_to_apply = int(args.num_config)
num_loading = int(args.num_loading)
#num_loading_pods = int(args.num_loading_pods)
num_loading_pods = args.num_loading_pods
if len(num_loading_pods) > 0:
num_loading_pods = num_loading_pods.split(",")
num_loading_pods = [int(x) for x in num_loading_pods]
#num_virtual_users = args.num_virtual_users
cpu = str(args.request_cpu)
memory = str(args.request_ram)
cpu_type = str(args.request_cpu_type)
gpu_type = str(args.request_gpu_type)
gpus = str(args.request_gpu)
request_storage_type = args.request_storage_type
request_storage_size = args.request_storage_size
request_node_name = args.request_node_name
request_node_loading = args.request_node_loading
request_node_benchmarking = args.request_node_benchmarking
datatransfer = args.datatransfer
test_result = args.test_result
code = args.experiment
# set cluster
aws = args.aws
if aws:
cluster = clusters.aws(context=args.context)
# scale up
node_sizes = {
'auxiliary': 1,
'sut-mid': 1,
'benchmarker': 1
}
#cluster.scale_nodegroups(node_sizes)
else:
cluster = clusters.kubernetes(context=args.context)
cluster_name = cluster.contextdata['clustername']
if args.max_sut is not None:
cluster.max_sut = int(args.max_sut)
# set experiment
if code is None:
code = cluster.code
experiment = experiments.ycsb(cluster=cluster, SF=SF, timeout=timeout, code=code, num_experiment_to_apply=num_experiment_to_apply)
experiment.prometheus_interval = "30s"
experiment.prometheus_timeout = "30s"
# remove running dbms
#experiment.clean()
if mode == 'run':
# we want all YCSB queries
#experiment.set_queries_full()
experiment.set_workload(
name = 'YCSB SF='+str(SF),
info = 'This experiment compares run time and resource consumption of YCSB queries.',
defaultParameters = {'SF': SF}
)
else:
# we want to profile the import
#experiment.set_queries_profiling()
experiment.set_workload(
name = 'YCSB Data Loading SF='+str(SF),
info = 'This imports YCSB data sets.',
defaultParameters = {'SF': SF}
)
if monitoring:
# we want to monitor resource consumption
experiment.monitoring_active = True
else:
# we want to just run the queries
experiment.monitoring_active = False
if monitoring_cluster:
# monitor all nodes of cluster (for not missing any component)
cluster.start_monitoring_cluster()
#experiment.set_queryfile('queries-tpcds-profiling-tables.config')
# set resources for dbms
#experiment.connectionmanagement['timeout'] = 180
experiment.set_resources(
requests = {
'cpu': cpu,
'memory': memory,
'gpu': 0
},
limits = {
'cpu': 0,
'memory': 0
},
nodeSelector = {
'cpu': cpu_type,
'gpu': '',
#'kubernetes.io/hostname': 'cl-worker13'
})
if request_node_name is not None:
experiment.set_resources(
nodeSelector = {
'cpu': cpu_type,
'gpu': '',
'kubernetes.io/hostname': request_node_name
})
# persistent storage
#print(request_storage_type)
#if not request_storage_type is None:# and (request_storage_type == 'shared' or request_storage_type == 'local-hdd'):
experiment.set_storage(
storageClassName = request_storage_type,
storageSize = request_storage_size,#'100Gi',
keep = True,
#storageConfiguration = 'mysql-bht'
)
# set node labes for components
"""
experiment.set_nodes(
#maintaining = 'auxiliary',
loading = 'loading',
sut = 'sut',
#benchmarking = 'benchmarker',
)
"""
cluster.start_datadir()
cluster.start_resultdir()
cluster.start_dashboard()
cluster.start_messagequeue()
if aws:
# set node labes for components
experiment.set_nodes(
sut = 'sut',
loading = 'sut',
monitoring = 'auxiliary',
benchmarking = 'auxiliary',
)
# note more infos about experiment in workload description
experiment.workload['info'] = experiment.workload['info']+" YCSB is performed using several threads and processes."
if len(args.dbms):
# import is limited to single DBMS
experiment.workload['info'] = experiment.workload['info']+" Benchmark is limited to DBMS {}.".format(args.dbms)
#if len(list_loading_split):
# # import uses several processes in pods
# experiment.workload['info'] = experiment.workload['info']+" Import is handled by {} processes.".format(num_loading_split)
# add configs
experiment.loading_active = True
experiment.jobtemplate_loading = "jobtemplate-loading-ycsb.yml"
#experiment.name_format = '{dbms}-{threads}-{pods}-{target}'
experiment.set_experiment(script='Schema')
ycsb_rows = int(SF)*1000000 # 1kb each, that is SF is size in GB
ycsb_operations = int(SFO)*1000000
# note more infos about experiment in workload description
experiment.workload['info'] = experiment.workload['info']+" YCSB data is loaded using several processes."
if len(args.dbms):
# import is limited to single DBMS
experiment.workload['info'] = experiment.workload['info']+" Benchmark is limited to DBMS {}.".format(args.dbms)
# fix loading
if not request_node_loading is None:
experiment.patch_loading(patch="""
spec:
template:
spec:
nodeSelector:
kubernetes.io/hostname: {node}
""".format(node=request_node_loading))
experiment.workload['info'] = experiment.workload['info']+" Loading is fixed to {}.".format(request_node_loading)
# fix benchmarking
if not request_node_benchmarking is None:
experiment.patch_benchmarking(patch="""
spec:
template:
spec:
nodeSelector:
kubernetes.io/hostname: {node}
""".format(node=request_node_benchmarking))
experiment.workload['info'] = experiment.workload['info']+" Benchmarking is fixed to {}.".format(request_node_benchmarking)
# add labels about the use case
experiment.set_additional_labels(
usecase="ycsb",
experiment_design="compare-scaleout",
ROWS=ycsb_rows,
OPERATIONS=ycsb_operations,
workload=args.workload,
)
# configure number of clients per config
list_clients = args.num_query_executors.split(",")
if len(list_clients) > 0:
list_clients = [int(x) for x in list_clients if len(x) > 0]
else:
list_clients = []
#experiment.add_benchmark_list(list_clients)
for threads in [SU]:#[8]:#[64]:
for pods in num_loading_pods:#[1,2]:#[1,8]:#range(2,5):
#pods = 2**p
#for t in range(1, 15):#range(1, 2):#range(1, 15):
for t in list_target_factors:#range(1, 9):#range(1, 2):#range(1, 15):
target = target_base*t#4*4096*t
threads_per_pod = int(threads/pods)
ycsb_operations_per_pod = int(ycsb_operations/pods)
target_per_pod = int(target/pods)
benchmarking_pods = [pods]
if len(list_clients) > 0:
# we want several benchmarking instances per installation
benchmarking_pods = list_clients
if (args.dbms == "PostgreSQL" or len(args.dbms) == 0):
# PostgreSQL
#name_format = 'PostgreSQL-{}-{}-{}-{}'.format(cluster_name, pods, worker, target)
name_format = 'PostgreSQL-{threads}-{pods}-{target}'
config = configurations.ycsb(experiment=experiment, docker='PostgreSQL', configuration=name_format.format(threads=threads, pods=pods, target=target), alias='DBMS D')
config.set_storage(
storageConfiguration = 'postgresql'
)
config.set_loading_parameters(
PARALLEL = str(pods),
SF = SF,
BEXHOMA_SYNCH_LOAD = 1,
YCSB_THREADCOUNT = threads_per_pod,
YCSB_TARGET = target_per_pod,
YCSB_STATUS = 1,
YCSB_WORKLOAD = args.workload,
YCSB_ROWS = ycsb_rows,
YCSB_OPERATIONS = ycsb_operations_per_pod,
YCSB_BATCHSIZE = batchsize,
)
config.set_loading(parallel=pods, num_pods=pods)
#config.set_loading(parallel=num_loading, num_pods=num_loading_pods)
config.set_benchmarking_parameters(
#PARALLEL = str(pods),
SF = SF,
BEXHOMA_SYNCH_LOAD = 1,
YCSB_THREADCOUNT = threads_per_pod,
YCSB_TARGET = target_per_pod,
YCSB_STATUS = 1,
YCSB_WORKLOAD = args.workload,
YCSB_ROWS = ycsb_rows,
YCSB_OPERATIONS = ycsb_operations_per_pod,
YCSB_BATCHSIZE = batchsize,
)
config.add_benchmark_list(benchmarking_pods)
if (args.dbms == "MySQL" or len(args.dbms) == 0):
# MySQL
#name_format = 'PostgreSQL-{}-{}-{}-{}'.format(cluster_name, pods, worker, target)
name_format = 'MySQL-{threads}-{pods}-{target}'
config = configurations.ycsb(experiment=experiment, docker='MySQL', configuration=name_format.format(threads=threads, pods=pods, target=target), alias='DBMS D')
config.set_storage(
storageConfiguration = 'mysql'
)
config.set_loading_parameters(
PARALLEL = str(pods),
SF = SF,
BEXHOMA_SYNCH_LOAD = 1,
YCSB_THREADCOUNT = threads_per_pod,
YCSB_TARGET = target_per_pod,
YCSB_STATUS = 1,
YCSB_WORKLOAD = args.workload,
YCSB_ROWS = ycsb_rows,
YCSB_OPERATIONS = ycsb_operations_per_pod,
YCSB_BATCHSIZE = batchsize,
)
config.set_loading(parallel=pods, num_pods=pods)
#config.set_loading(parallel=num_loading, num_pods=num_loading_pods)
config.set_benchmarking_parameters(
#PARALLEL = str(pods),
SF = SF,
BEXHOMA_SYNCH_LOAD = 1,
YCSB_THREADCOUNT = threads_per_pod,
YCSB_TARGET = target_per_pod,
YCSB_STATUS = 1,
YCSB_WORKLOAD = args.workload,
YCSB_ROWS = ycsb_rows,
YCSB_OPERATIONS = ycsb_operations_per_pod,
YCSB_BATCHSIZE = batchsize,
)
config.add_benchmark_list(benchmarking_pods)
# wait for necessary nodegroups to have planned size
if aws:
#cluster.wait_for_nodegroups(node_sizes)
pass
if args.mode == 'start':
experiment.start_sut()
elif args.mode == 'load':
# start all DBMS
experiment.start_sut()
# configure number of clients per config = 0
list_clients = []
# total time of experiment
experiment.add_benchmark_list(list_clients)
start = default_timer()
start_datetime = str(datetime.datetime.now())
# run workflow
experiment.work_benchmark_list()
# total time of experiment
end = default_timer()
end_datetime = str(datetime.datetime.now())
duration_experiment = end - start
elif args.mode == 'summary':
experiment.show_summary()
else:
# total time of experiment
start = default_timer()
start_datetime = str(datetime.datetime.now())
#print("Experiment starts at {} ({})".format(start_datetime, start))
print("{:30s}: starts at {} ({})".format("Experiment",start_datetime, start))
# run workflow
experiment.work_benchmark_list()
# total time of experiment
end = default_timer()
end_datetime = str(datetime.datetime.now())
duration_experiment = end - start
#print("Experiment ends at {} ({}): {}s total".format(end_datetime, end, duration_experiment))
print("{:30s}: ends at {} ({}) - {:.2f}s total".format("Experiment",end_datetime, end, duration_experiment))
##################
experiment.evaluate_results()
experiment.stop_benchmarker()
experiment.stop_sut()
#experiment.zip() # OOM? exit code 137
if test_result:
test_result_code = experiment.test_results()
if test_result_code == 0:
print("Test successful!")
#cluster.restart_dashboard() # only for dbmsbenchmarker because of dashboard. Jupyter server does not need to restart
experiment.show_summary()
exit()