class MetricHelper:
def __init__(self, test):
self.test = test
self.test_config = test.test_config
self.cluster_spec = test.cluster_spec
self.store = PerfStore(CBMONITOR_HOST)
@property
def _title(self) -> str:
return self.test_config.showfast.title
@property
def _order_by(self) -> str:
return self.test_config.showfast.order_by
@property
def _snapshots(self) -> List[str]:
return self.test.cbmonitor_snapshots
@property
def _num_nodes(self):
return self.test_config.cluster.initial_nodes[0]
def _metric_info(self,
metric_id: str = None,
title: str = None,
order_by: str = None) -> Dict[str, str]:
return {
'id': metric_id or self.test_config.name,
'title': title or self._title,
'orderBy': order_by or self._order_by,
}
def ycsb_queries(self, value: float, name: str, title: str) -> Metric:
metric_id = '{}_{}'.format(self.test_config.name, name)
title = '{}, {}'.format(title, self.test_config.showfast.title)
metric_info = self._metric_info(metric_id, title)
return value, self._snapshots, metric_info
@property
def query_id(self) -> str:
if 'views' in self._title:
return ''
query_id = self._title.split(',')[0]
query_id = query_id.split()[0]
for prefix in 'CI', 'Q', 'UP', 'DL', 'AG', 'PI', 'BF', 'WF':
query_id = query_id.replace(prefix, '')
return '{:05d}'.format(int(query_id))
def avg_n1ql_throughput(self) -> Metric:
metric_id = '{}_avg_query_requests'.format(self.test_config.name)
title = 'Avg. Query Throughput (queries/sec), {}'.format(self._title)
metric_info = self._metric_info(metric_id, title,
order_by=self.query_id)
throughput = self._avg_n1ql_throughput()
return throughput, self._snapshots, metric_info
def _avg_n1ql_throughput(self) -> int:
test_time = self.test_config.access_settings.time
query_node = self.cluster_spec.servers_by_role('n1ql')[0]
vitals = self.test.rest.get_query_stats(query_node)
total_requests = vitals['requests.count']
throughput = total_requests / test_time
return round(throughput, throughput < 1 and 1 or 0)
def bulk_n1ql_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
items = self.test_config.load_settings.items / 4
throughput = round(items / time_elapsed)
return throughput, self._snapshots, metric_info
def fts_index(self, elapsed_time: float) -> Metric:
metric_id = self.test_config.name
title = 'Index build time(sec), {}'.format(self._title)
metric_info = self._metric_info(metric_id, title)
index_time = round(elapsed_time, 1)
return index_time, self._snapshots, metric_info
def fts_index_size(self, index_size_raw: int) -> Metric:
metric_id = "{}_indexsize".format(self.test_config.name)
title = 'Index size (MB), {}'.format(self._title)
metric_info = self._metric_info(metric_id, title)
index_size_mb = int(index_size_raw / (1024 ** 2))
return index_size_mb, self._snapshots, metric_info
def jts_throughput(self) -> Metric:
metric_id = '{}_{}'.format(self.test_config.name, "jts_throughput")
metric_id = metric_id.replace('.', '')
title = "Average Throughput (q/sec), {}".format(self._title)
metric_info = self._metric_info(metric_id, title)
timings = self._jts_metric(collector="jts_stats", metric="jts_throughput")
thr = round(np.average(timings), 2)
if thr > 100:
thr = round(thr)
return thr, self._snapshots, metric_info
def jts_latency(self, percentile=50) -> Metric:
prefix = "Average latency (ms)"
if percentile != 50:
prefix = "{}th percentile latency (ms)".format(percentile)
metric_id = '{}_{}_{}th'.format(self.test_config.name, "jts_latency", percentile)
metric_id = metric_id.replace('.', '')
title = "{}, {}".format(prefix, self._title)
metric_info = self._metric_info(metric_id, title)
timings = self._jts_metric(collector="jts_stats", metric="jts_latency")
lat = round(np.percentile(timings, percentile), 2)
if lat > 100:
lat = round(lat)
return lat, self._snapshots, metric_info
def _jts_metric(self, collector, metric):
timings = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector=collector,
bucket=bucket)
timings += self.store.get_values(db, metric=metric)
return timings
def avg_ops(self) -> Metric:
metric_info = self._metric_info()
throughput = self._avg_ops()
return throughput, self._snapshots, metric_info
def _avg_ops(self) -> int:
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='ops')
return int(np.average(values))
def max_ops(self) -> Metric:
metric_info = self._metric_info()
throughput = self._max_ops()
return throughput, self._snapshots, metric_info
def _max_ops(self) -> int:
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='ops')
return int(np.percentile(values, 90))
def get_percentile_value_of_node_metric(self, collector, metric, server, percentile):
values = []
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector=collector,
server=server)
values += self.store.get_values(db, metric=metric)
return int(np.percentile(values, percentile))
def get_collector_values(self, collector):
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector=collector,
bucket=bucket)
values += self.store.get_values(db, metric=collector)
return values
def count_overthreshold_value_of_collector(self, collector, threshold):
values = self.get_collector_values(collector)
return sum(v >= threshold for v in values)
def get_percentile_value_of_collector(self, collector, percentile):
values = self.get_collector_values(collector)
return np.percentile(values, percentile)
def xdcr_lag(self, percentile: Number = 95) -> Metric:
metric_id = '{}_{}th_xdcr_lag'.format(self.test_config.name, percentile)
title = '{}th percentile replication lag (ms), {}'.format(percentile,
self._title)
metric_info = self._metric_info(metric_id, title)
xdcr_lag = self.get_percentile_value_of_collector('xdcr_lag', percentile)
return round(xdcr_lag, 1), self._snapshots, metric_info
def avg_replication_rate(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
rate = self._avg_replication_rate(time_elapsed)
return rate, self._snapshots, metric_info
def avg_replication_throughput(self, throughput: float, xdcr_link: str) -> Metric:
metric_id = self.test_config.name + '_' + xdcr_link
title = self.test_config.showfast.title + ', ' + xdcr_link
metric_info = self._metric_info(metric_id=metric_id, title=title)
return round(throughput), self._snapshots, metric_info
def avg_replication_multilink(self, time_elapsed: float, xdcr_link: str) -> Metric:
metric_id = self.test_config.name + '_' + xdcr_link
title = self.test_config.showfast.title + ', ' + xdcr_link
metric_info = self._metric_info(metric_id=metric_id, title=title)
rate = self._avg_replication_rate(time_elapsed)
return rate, self._snapshots, metric_info
def _avg_replication_rate(self, time_elapsed: float) -> float:
initial_items = self.test_config.load_settings.ops or \
self.test_config.load_settings.items
num_buckets = self.test_config.cluster.num_buckets
avg_replication_rate = num_buckets * initial_items / time_elapsed
return round(avg_replication_rate)
def max_drain_rate(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
items_per_node = self.test_config.load_settings.items / self._num_nodes
drain_rate = round(items_per_node / time_elapsed)
return drain_rate, self._snapshots, metric_info
def avg_disk_write_queue(self) -> Metric:
metric_info = self._metric_info()
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='disk_write_queue')
disk_write_queue = int(np.average(values))
return disk_write_queue, self._snapshots, metric_info
def avg_total_queue_age(self) -> Metric:
metric_info = self._metric_info()
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='vb_avg_total_queue_age')
avg_total_queue_age = int(np.average(values))
return avg_total_queue_age, self._snapshots, metric_info
def avg_couch_views_ops(self) -> Metric:
metric_info = self._metric_info()
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='couch_views_ops')
couch_views_ops = int(np.average(values))
return couch_views_ops, self._snapshots, metric_info
def query_latency(self, percentile: Number) -> Metric:
metric_id = self.test_config.name
title = '{}th percentile query latency (ms), {}'.format(percentile,
self._title)
metric_info = self._metric_info(metric_id, title,
order_by=self.query_id)
latency = self._query_latency(percentile)
return latency, self._snapshots, metric_info
def _query_latency(self, percentile: Number) -> float:
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='spring_query_latency',
bucket=bucket)
values += self.store.get_values(db, metric='latency_query')
query_latency = np.percentile(values, percentile)
if query_latency < 100:
return round(query_latency, 1)
return int(query_latency)
def secondary_scan_latency(self, percentile: Number) -> Metric:
metric_id = "{}_{}th".format(self.test_config.name, percentile)
title = '{}th percentile secondary scan latency (ms), {}'.format(percentile,
self._title)
metric_info = self._metric_info(metric_id, title)
cluster = ""
for cid in self.test.cbmonitor_clusters:
if "apply_scanworkload" in cid:
cluster = cid
break
db = self.store.build_dbname(cluster=cluster,
collector='secondaryscan_latency')
timings = self.store.get_values(db, metric='Nth-latency')
timings = list(map(int, timings))
logger.info("Number of samples are {}".format(len(timings)))
scan_latency = np.percentile(timings, percentile) / 1e6
scan_latency = round(scan_latency, 2)
return scan_latency, self._snapshots, metric_info
def kv_latency(self,
operation: str,
percentile: Number = 99.9,
collector: str = 'spring_latency') -> Metric:
metric_id = '{}_{}_{}th'.format(self.test_config.name,
operation,
percentile)
metric_id = metric_id.replace('.', '')
title = '{}th percentile {} {}'.format(percentile,
operation.upper(),
self._title)
metric_info = self._metric_info(metric_id, title)
latency = self._kv_latency(operation, percentile, collector)
return latency, self._snapshots, metric_info
def _kv_latency(self,
operation: str,
percentile: Number,
collector: str) -> float:
timings = []
metric = 'latency_{}'.format(operation)
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector=collector,
bucket=bucket)
timings += self.store.get_values(db, metric=metric)
latency = np.percentile(timings, percentile)
if latency > 100:
return round(latency)
return round(latency, 2)
def observe_latency(self, percentile: Number) -> Metric:
metric_id = '{}_{}th'.format(self.test_config.name, percentile)
title = '{}th percentile {}'.format(percentile, self._title)
metric_info = self._metric_info(metric_id, title)
timings = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='observe',
bucket=bucket)
timings += self.store.get_values(db, metric='latency_observe')
latency = round(np.percentile(timings, percentile), 2)
return latency, self._snapshots, metric_info
def cpu_utilization(self) -> Metric:
metric_id = '{}_avg_cpu'.format(self.test_config.name)
title = 'Avg. CPU utilization (%)'
title = '{}, {}'.format(title, self._title)
metric_info = self._metric_info(metric_id, title)
cluster = self.test.cbmonitor_clusters[0]
bucket = self.test_config.buckets[0]
db = self.store.build_dbname(cluster=cluster,
collector='ns_server',
bucket=bucket)
values = self.store.get_values(db, metric='cpu_utilization_rate')
cpu_utilization = int(np.average(values))
return cpu_utilization, self._snapshots, metric_info
def max_memcached_rss(self) -> Metric:
metric_id = '{}_memcached_rss'.format(self.test_config.name)
title = 'Max. memcached RSS (MB),{}'.format(
self._title.split(',')[-1]
)
metric_info = self._metric_info(metric_id, title)
max_rss = 0
for (cluster_name, servers), initial_nodes in zip(
self.cluster_spec.clusters,
self.test_config.cluster.initial_nodes,
):
cluster = list(filter(lambda name: name.startswith(cluster_name),
self.test.cbmonitor_clusters))[0]
for server in servers[:initial_nodes]:
hostname = server.replace('.', '')
db = self.store.build_dbname(cluster=cluster,
collector='atop',
server=hostname)
values = self.store.get_values(db, metric='memcached_rss')
rss = round(max(values) / 1024 ** 2)
max_rss = max(max_rss, rss)
return max_rss, self._snapshots, metric_info
def avg_memcached_rss(self) -> Metric:
metric_id = '{}_avg_memcached_rss'.format(self.test_config.name)
title = 'Avg. memcached RSS (MB),{}'.format(
self._title.split(',')[-1]
)
metric_info = self._metric_info(metric_id, title)
rss = []
for (cluster_name, servers), initial_nodes in zip(
self.cluster_spec.clusters,
self.test_config.cluster.initial_nodes,
):
cluster = list(filter(lambda name: name.startswith(cluster_name),
self.test.cbmonitor_clusters))[0]
for server in servers[:initial_nodes]:
hostname = server.replace('.', '')
db = self.store.build_dbname(cluster=cluster,
collector='atop',
server=hostname)
rss += self.store.get_values(db, metric='memcached_rss')
avg_rss = int(np.average(rss) / 1024 ** 2)
return avg_rss, self._snapshots, metric_info
def memory_overhead(self, key_size: int = 20) -> Metric:
metric_info = self._metric_info()
item_size = key_size + self.test_config.load_settings.size
user_data = self.test_config.load_settings.items * item_size
user_data *= self.test_config.bucket.replica_number + 1
user_data /= 2 ** 20
mem_used, _, _ = self.avg_memcached_rss()
mem_used *= self._num_nodes
overhead = int(100 * (mem_used / user_data - 1))
return overhead, self._snapshots, metric_info
def get_indexing_meta(self,
value: float,
index_type: str,
unit: str = "min") -> Metric:
metric_id = '{}_{}'.format(self.test_config.name, index_type.lower())
title = '{} index ({}), {}'.format(index_type, unit, self._title)
metric_info = self._metric_info(metric_id, title)
metric_info['category'] = index_type.lower()
value = s2m(value)
return value, self._snapshots, metric_info
def get_memory_meta(self,
value: float,
memory_type: str) -> Metric:
metric_id = '{}_{}'.format(self.test_config.name,
memory_type.replace(" ", "").lower())
title = '{} (GB), {}'.format(memory_type, self._title)
metric_info = self._metric_info(metric_id, title)
return value, self._snapshots, metric_info
def bnr_throughput(self,
time_elapsed: float,
edition: str,
tool: str) -> Metric:
metric_id = '{}_{}_thr_{}'.format(self.test_config.name, tool, edition)
title = '{} {} throughput (Avg. MB/sec), {}'.format(
edition, tool, self._title)
metric_info = self._metric_info(metric_id, title)
data_size = self.test_config.load_settings.items * \
self.test_config.load_settings.size / 2 ** 20 # MB
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def tool_time(self,
time_elapsed: float,
edition: str,
tool: str) -> Metric:
metric_id = '{}_{}_time_{}'.format(
self.test_config.name, tool, edition)
title = '{} {} time elapsed (seconds), {}'.format(
edition, tool, self._title)
metric_info = self._metric_info(metric_id, title)
return round(time_elapsed), self._snapshots, metric_info
def backup_size(self, size: float,
edition: str,
tool: str = None) -> Metric:
# To avoid duplicating existing metrics, only label with tool if some
# new parameter has been used. In future should look to purge all
# existing and replace.
if tool:
prefix = '{}_{}'.format(self.test_config.name, tool)
else:
prefix = self.test_config.name
metric_id = '{}_size_{}'.format(prefix, edition)
title = '{} {} backup size (GB), {}'.format(edition,
tool if tool else '',
self._title)
metric_info = self._metric_info(metric_id, title)
return size, self._snapshots, metric_info
def merge_throughput(self,
time_elapsed: float,
edition: str,
tool: str = None) -> Metric:
metric_id = '{}_{}_thr_{}'.format(self.test_config.name, tool, edition)
title = '{} {} throughput (Avg. MB/sec), {}'.format(
edition, tool, self._title)
metric_info = self._metric_info(metric_id, title)
data_size = 2 * self.test_config.load_settings.items * \
self.test_config.load_settings.size / 2 ** 20 # MB
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def compact_size_diff(self, size_diff: float,
edition: str,
tool: str) -> Metric:
prefix = '{}_{}'.format(self.test_config.name, tool)
metric_id = '{}_size_diff_{}'.format(prefix, edition)
title = '{} {} compact size difference (GB), {}'.format(edition,
tool,
self._title)
return size_diff, self._snapshots, self._metric_info(metric_id, title)
def import_and_export_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
data_size = self.test_config.load_settings.items * \
self.test_config.load_settings.size / 2 ** 20 # MB
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def import_file_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
import_file = self.test_config.export_settings.import_file
data_size = os.path.getsize(import_file) / 2.0 ** 20
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def verify_series_in_limits(self, expected_number: int) -> bool:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='secondary_debugstats')
values = self.store.get_values(db, metric='num_connections')
values = list(map(float, values))
logger.info("Number of samples: {}".format(len(values)))
logger.info("Sample values: {}".format(values))
if any(value > expected_number for value in values):
return False
return True
def _parse_ycsb_throughput(self) -> int:
throughput = 0
ycsb_log_files = [filename
for filename in glob.glob("YCSB/ycsb_run_*.log")
if "stderr" not in filename]
for filename in ycsb_log_files:
with open(filename) as fh:
for line in fh.readlines():
if line.startswith('[OVERALL], Throughput(ops/sec)'):
throughput += int(float(line.split()[-1]))
break
return throughput
def _ycsb_perc_calc(self, _temp: [], io_type: str, percentile: str, lat_dic: {}, _fc: int):
pio_type = '{}th Percentile {}'.format(percentile, io_type)
p_lat = round(np.percentile(_temp, percentile) / 1000, 3)
if _fc > 1:
p_lat = round((((lat_dic[pio_type] * (_fc - 1)) + p_lat) / _fc), 3)
lat_dic.update({pio_type: p_lat})
return lat_dic
def _ycsb_avg_calc(self, _temp: [], io_type: str, lat_dic: {}, _fc: int):
aio_type = 'Average {}'.format(io_type)
a_lat = round((sum(_temp) / len(_temp)) / 1000, 3)
if _fc > 1:
a_lat = round((((lat_dic[aio_type] * (_fc - 1)) + a_lat) / _fc), 3)
lat_dic.update({aio_type: a_lat})
return lat_dic
def _parse_ycsb_latency(self, percentile: str) -> int:
lat_dic = {}
_temp = []
_fc = 1
ycsb_log_files = [filename
for filename in glob.glob("YCSB/ycsb_run_*.log")
if "stderr" not in filename]
for filename in ycsb_log_files:
fh2 = open(filename)
_l1 = fh2.readlines()
_l1_len = len(_l1)
fh = open(filename)
_c = 0
for x in range(_l1_len - 1):
line = fh.readline()
if line.find('], 1000,') >= 1:
io_type = line.split('[')[1].split(']')[0]
_n = 0
while (line.startswith('[{}]'.format(io_type))):
lat = float(line.split()[-1])
_temp.append(lat)
line = fh.readline()
_n += 1
_temp.sort()
lat_dic = self._ycsb_perc_calc(_temp=_temp,
io_type=io_type,
lat_dic=lat_dic,
_fc=_fc,
percentile=percentile)
lat_dic = self._ycsb_avg_calc(_temp=_temp,
io_type=io_type,
lat_dic=lat_dic,
_fc=_fc)
_temp.clear()
_c += _n
_c += 1
x += _c
_fc += 1
if 'CLEANUP' in lat_dic:
del lat_dic['CLEANUP']
return lat_dic
def _parse_ycsb_latency_cbcollect(self, percentile: str):
lat_dic = {}
_temp = []
_fc = 1
_cbtime = int(self.test.cb_time)
_cbstart = int(self.test.cb_start * 1000)
ycsb_log_files = [filename
for filename in glob.glob("YCSB/ycsb_run_*.log")
if "stderr" not in filename]
for filename in ycsb_log_files:
fh2 = open(filename)
list1 = fh2.readlines()
list1_length = len(list1)
fh = open(filename)
_c = 0
for x in range(list1_length-1):
line = fh.readline()
if line.find('], {},'.format(_cbstart)) >= 1:
io_type = line.split('[')[1].split(']')[0]
for y in range(_cbtime):
lat = float(line.split()[-1])
_temp.append(lat)
if y < _cbtime-1:
line = fh.readline()
_temp.sort()
lat_dic = self._ycsb_perc_calc(_temp=_temp,
io_type=io_type,
lat_dic=lat_dic,
_fc=_fc,
percentile=percentile)
lat_dic = self._ycsb_avg_calc(_temp=_temp,
io_type=io_type,
lat_dic=lat_dic,
_fc=_fc)
_temp.clear()
_c += _cbtime
_c += 1
x += _c
_fc += 1
if 'CLEANUP' in lat_dic:
del lat_dic['CLEANUP']
return lat_dic
def dcp_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
throughput = round(self.test_config.load_settings.items / time_elapsed)
return throughput, self._snapshots, metric_info
def fragmentation_ratio(self, ratio: float) -> Metric:
metric_info = self._metric_info()
return ratio, self._snapshots, metric_info
def elapsed_time(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
time_elapsed = s2m(time_elapsed)
return time_elapsed, self._snapshots, metric_info
def kv_throughput(self, total_ops: int) -> Metric:
metric_info = self._metric_info()
throughput = total_ops // self.test_config.access_settings.time
return throughput, self._snapshots, metric_info
def ycsb_throughput(self) -> Metric:
metric_info = self._metric_info()
throughput = self._parse_ycsb_throughput()
return throughput, self._snapshots, metric_info
def ycsb_latency(self,
io_type: str,
latency: int,
) -> Metric:
title = '{} {}'.format(io_type, self._title)
metric_id = '{}_{}'.format(self.test_config.name, io_type.replace(' ', '_').casefold())
metric_info = self._metric_info(title=title, metric_id=metric_id)
return latency, self._snapshots, metric_info
def ycsb_get_latency(self,
percentile: str,
) -> Metric:
if self.test_config.access_settings.cbcollect:
latency_dic = self._parse_ycsb_latency_cbcollect(percentile)
else:
latency_dic = self._parse_ycsb_latency(percentile)
return latency_dic
def indexing_time(self, indexing_time: float) -> Metric:
return self.elapsed_time(indexing_time)
@property
def rebalance_order_by(self) -> str:
order_by = ''
for num_nodes in self.test_config.cluster.initial_nodes:
order_by += '{:03d}'.format(num_nodes)
order_by += '{:018d}'.format(self.test_config.load_settings.items)
return order_by
def rebalance_time(self, rebalance_time: float) -> Metric:
metric = self.elapsed_time(rebalance_time)
metric[-1]['orderBy'] = self.rebalance_order_by
return metric
def failover_time(self, delta: float) -> Metric:
metric_info = self._metric_info()
return delta, self._snapshots, metric_info
def gsi_connections(self, num_connections: int) -> Metric:
metric_info = self._metric_info()
return num_connections, self._snapshots, metric_info
def scan_throughput(self, throughput: float) -> Metric:
metric_info = self._metric_info()
throughput = round(throughput, 1)
return throughput, self._snapshots, metric_info
def multi_scan_diff(self, time_diff: float):
metric_info = self._metric_info()
time_diff = round(time_diff, 2)
return time_diff, self._snapshots, metric_info
def get_functions_throughput(self, time: int, event_name: str, events_processed: int) -> float:
throughput = 0
if event_name:
for name, file in self.test.functions.items():
for node in self.test.eventing_nodes:
throughput += self.test.rest.get_num_events_processed(
event=event_name, node=node, name=name)
else:
throughput = events_processed
throughput /= len(self.test.functions)
throughput /= time
return round(throughput, 0)
def function_throughput(self, time: int, event_name: str, events_processed: int) -> Metric:
metric_info = self._metric_info()
throughput = self.get_functions_throughput(time, event_name, events_processed)
return throughput, self._snapshots, metric_info
def eventing_rebalance_time(self, time: int) -> Metric:
title_split = self._title.split(sep=",", maxsplit=1)
title = "Rebalance Time(sec)," + title_split[1]
metric_id = '{}_rebalance_time'.format(self.test_config.name)
metric_info = self._metric_info(metric_id=metric_id, title=title)
return time, self._snapshots, metric_info
@staticmethod
def eventing_get_percentile_latency(percentile: float, stats: dict) -> float:
"""Calculate percentile latency.
We get latency stats in format of- time:number of events processed in that time(samples)
In this method we get latency stats then calculate percentile latency using-
Calculate total number of samples.
Keep adding sample until we get required percentile number.
For now it calculates for one function only
"""
latency = 0
total_samples = sum([sample for time, sample in stats])
latency_samples = 0
for time, samples in stats:
latency_samples += samples
if latency_samples >= (total_samples * percentile / 100):
latency = float(time) / 1000
break
latency = round(latency, 1)
return latency
def function_latency(self, percentile: float, latency_stats: dict) -> Metric:
"""Calculate eventing function latency from stats."""
metric_info = self._metric_info()
latency = 0
curl_latency = 0
for name, stats in latency_stats.items():
if name.startswith("curl_latency_"):
curl_latency = self.eventing_get_percentile_latency(percentile, stats)
logger.info("Curl percentile latency is {}ms".format(curl_latency))
else:
latency = self.eventing_get_percentile_latency(percentile, stats)
logger.info("On update percentile latency is {}ms".format(latency))
latency -= curl_latency
latency = round(latency, 1)
return latency, self._snapshots, metric_info
def function_time(self, time: int, time_type: str, initials: str) -> Metric:
title = initials + ", " + self._title
metric_id = '{}_{}'.format(self.test_config.name, time_type.lower())
metric_info = self._metric_info(metric_id=metric_id, title=title)
time = s2m(seconds=time)
return time, self._snapshots, metric_info
def analytics_latency(self, query: Query, latency: int) -> Metric:
metric_id = self.test_config.name + strip(query.description)
title = 'Avg. query latency (ms), {} {}, {}'.format(query.id,
query.description,
self._title)
order_by = '{:05d}_{}'.format(int(query.id[2:]), self._order_by)
metric_info = self._metric_info(metric_id,
title,
order_by)
return latency, self._snapshots, metric_info
def get_max_rss_values(self, function_name: str, server: str):
ratio = 1024 * 1024
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='eventing_consumer_stats',
bucket=function_name, server=server)
rss_list = self.store.get_values(db, metric='eventing_consumer_rss')
max_consumer_rss = round(max(rss_list) / ratio, 2)
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='atop', server=server)
rss_list = self.store.get_values(db, metric='eventing-produc_rss')
max_producer_rss = round(max(rss_list) / ratio, 2)
return max_consumer_rss, max_producer_rss
def avg_ingestion_rate(self, num_items: int, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
rate = round(num_items / time_elapsed)
return rate, self._snapshots, metric_info
def compression_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
throughput = round(self.test_config.load_settings.items / time_elapsed)
return throughput, self._snapshots, metric_info
class MetricHelper:
def __init__(self, test):
self.test = test
self.test_config = test.test_config
self.cluster_spec = test.cluster_spec
self.store = PerfStore(CBMONITOR_HOST)
@property
def _title(self) -> str:
return self.test_config.showfast.title
@property
def _order_by(self) -> str:
return self.test_config.showfast.order_by
@property
def _snapshots(self) -> List[str]:
return self.test.cbmonitor_snapshots
@property
def _num_nodes(self):
return self.test_config.cluster.initial_nodes[0]
def _metric_info(self,
metric_id: str = None,
title: str = None,
order_by: str = None) -> Dict[str, str]:
return {
'id': metric_id or self.test_config.name,
'title': title or self._title,
'orderBy': order_by or self._order_by,
}
def ycsb_queries(self, value: float, name: str, title: str) -> Metric:
metric_id = '{}_{}'.format(self.test_config.name, name)
title = '{}, {}'.format(title, self.test_config.showfast.title)
metric_info = self._metric_info(metric_id, title)
return value, self._snapshots, metric_info
@property
def query_id(self) -> str:
if 'views' in self._title:
return ''
query_id = self._title.split(',')[0]
query_id = query_id.split()[0]
for prefix in 'CI', 'Q', 'UP', 'DL', 'AG', 'PI', 'BF':
query_id = query_id.replace(prefix, '')
return '{:05d}'.format(int(query_id))
def avg_n1ql_throughput(self) -> Metric:
metric_id = '{}_avg_query_requests'.format(self.test_config.name)
title = 'Avg. Query Throughput (queries/sec), {}'.format(self._title)
metric_info = self._metric_info(metric_id, title,
order_by=self.query_id)
throughput = self._avg_n1ql_throughput()
return throughput, self._snapshots, metric_info
def _avg_n1ql_throughput(self) -> int:
test_time = self.test_config.access_settings.time
query_node = self.cluster_spec.servers_by_role('n1ql')[0]
vitals = self.test.rest.get_query_stats(query_node)
total_requests = vitals['requests.count']
throughput = total_requests / test_time
return round(throughput, throughput < 1 and 1 or 0)
def bulk_n1ql_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
items = self.test_config.load_settings.items / 4
throughput = round(items / time_elapsed)
return throughput, self._snapshots, metric_info
def fts_index(self, elapsed_time: float) -> Metric:
metric_id = self.test_config.name
title = 'Index build time(sec), {}'.format(self._title)
metric_info = self._metric_info(metric_id, title)
index_time = round(elapsed_time, 1)
return index_time, self._snapshots, metric_info
def fts_index_size(self, index_size_raw: int) -> Metric:
metric_id = "{}_indexsize".format(self.test_config.name)
title = 'Index size (MB), {}'.format(self._title)
metric_info = self._metric_info(metric_id, title)
index_size_mb = int(index_size_raw / (1024 ** 2))
return index_size_mb, self._snapshots, metric_info
def jts_throughput(self) -> Metric:
metric_id = '{}_{}'.format(self.test_config.name, "jts_throughput")
metric_id = metric_id.replace('.', '')
title = "Average Throughput (q/sec), {}".format(self._title)
metric_info = self._metric_info(metric_id, title)
timings = self._jts_metric(collector="jts_stats", metric="jts_throughput")
thr = round(np.average(timings), 2)
if thr > 100:
thr = round(thr)
return thr, self._snapshots, metric_info
def jts_latency(self, percentile=50) -> Metric:
prefix = "Average latency (ms)"
if percentile != 50:
prefix = "{}th percentile latency (ms)".format(percentile)
metric_id = '{}_{}'.format(self.test_config.name, "jts_latency")
metric_id = metric_id.replace('.', '')
title = "{}, {}".format(prefix, self._title)
metric_info = self._metric_info(metric_id, title)
timings = self._jts_metric(collector="jts_stats", metric="jts_latency")
lat = round(np.percentile(timings, percentile), 2)
if lat > 100:
lat = round(lat)
return lat, self._snapshots, metric_info
def _jts_metric(self, collector, metric):
timings = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector=collector,
bucket=bucket)
timings += self.store.get_values(db, metric=metric)
return timings
def max_ops(self) -> Metric:
metric_info = self._metric_info()
throughput = self._max_ops()
return throughput, self._snapshots, metric_info
def _max_ops(self) -> int:
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='ops')
return int(np.percentile(values, 90))
def get_collector_values(self, collector):
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector=collector,
bucket=bucket)
values += self.store.get_values(db, metric=collector)
return values
def count_overthreshold_value_of_collector(self, collector, threshold):
values = self.get_collector_values(collector)
return sum(v >= threshold for v in values)
def get_percentile_value_of_collector(self, collector, percentile):
values = self.get_collector_values(collector)
return np.percentile(values, percentile)
def xdcr_lag(self, percentile: Number = 95) -> Metric:
metric_id = '{}_{}th_xdcr_lag'.format(self.test_config.name, percentile)
title = '{}th percentile replication lag (ms), {}'.format(percentile,
self._title)
metric_info = self._metric_info(metric_id, title)
xdcr_lag = self.get_percentile_value_of_collector('xdcr_lag', percentile)
return round(xdcr_lag, 1), self._snapshots, metric_info
def avg_replication_rate(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
rate = self._avg_replication_rate(time_elapsed)
return rate, self._snapshots, metric_info
def _avg_replication_rate(self, time_elapsed: float) -> float:
initial_items = self.test_config.load_settings.ops or \
self.test_config.load_settings.items
num_buckets = self.test_config.cluster.num_buckets
avg_replication_rate = num_buckets * initial_items / time_elapsed
return round(avg_replication_rate)
def max_drain_rate(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
items_per_node = self.test_config.load_settings.items / self._num_nodes
drain_rate = round(items_per_node / time_elapsed)
return drain_rate, self._snapshots, metric_info
def avg_disk_write_queue(self) -> Metric:
metric_info = self._metric_info()
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='disk_write_queue')
disk_write_queue = int(np.average(values))
return disk_write_queue, self._snapshots, metric_info
def avg_total_queue_age(self) -> Metric:
metric_info = self._metric_info()
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='vb_avg_total_queue_age')
avg_total_queue_age = int(np.average(values))
return avg_total_queue_age, self._snapshots, metric_info
def avg_couch_views_ops(self) -> Metric:
metric_info = self._metric_info()
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='couch_views_ops')
couch_views_ops = int(np.average(values))
return couch_views_ops, self._snapshots, metric_info
def query_latency(self, percentile: Number) -> Metric:
metric_id = self.test_config.name
title = '{}th percentile query latency (ms), {}'.format(percentile,
self._title)
metric_info = self._metric_info(metric_id, title,
order_by=self.query_id)
latency = self._query_latency(percentile)
return latency, self._snapshots, metric_info
def _query_latency(self, percentile: Number) -> float:
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='spring_query_latency',
bucket=bucket)
values += self.store.get_values(db, metric='latency_query')
query_latency = np.percentile(values, percentile)
if query_latency < 100:
return round(query_latency, 1)
return int(query_latency)
def secondary_scan_latency(self, percentile: Number) -> Metric:
metric_id = self.test_config.name
title = '{}th percentile secondary scan latency (ms), {}'.format(percentile,
self._title)
metric_info = self._metric_info(metric_id, title)
cluster = ""
for cid in self.test.cbmonitor_clusters:
if "apply_scanworkload" in cid:
cluster = cid
break
db = self.store.build_dbname(cluster=cluster,
collector='secondaryscan_latency')
timings = self.store.get_values(db, metric='Nth-latency')
timings = list(map(int, timings))
logger.info("Number of samples are {}".format(len(timings)))
scan_latency = np.percentile(timings, percentile) / 1e6
scan_latency = round(scan_latency, 2)
return scan_latency, self._snapshots, metric_info
def kv_latency(self,
operation: str,
percentile: Number = 99.9,
collector: str = 'spring_latency') -> Metric:
metric_id = '{}_{}_{}th'.format(self.test_config.name,
operation,
percentile)
metric_id = metric_id.replace('.', '')
title = '{}th percentile {} {}'.format(percentile,
operation.upper(),
self._title)
metric_info = self._metric_info(metric_id, title)
latency = self._kv_latency(operation, percentile, collector)
return latency, self._snapshots, metric_info
def _kv_latency(self,
operation: str,
percentile: Number,
collector: str) -> float:
timings = []
metric = 'latency_{}'.format(operation)
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector=collector,
bucket=bucket)
timings += self.store.get_values(db, metric=metric)
latency = np.percentile(timings, percentile)
if latency > 100:
return round(latency)
return round(latency, 2)
def observe_latency(self, percentile: Number) -> Metric:
metric_id = '{}_{}th'.format(self.test_config.name, percentile)
title = '{}th percentile {}'.format(percentile, self._title)
metric_info = self._metric_info(metric_id, title)
timings = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='observe',
bucket=bucket)
timings += self.store.get_values(db, metric='latency_observe')
latency = round(np.percentile(timings, percentile), 2)
return latency, self._snapshots, metric_info
def cpu_utilization(self) -> Metric:
metric_id = '{}_avg_cpu'.format(self.test_config.name)
title = 'Avg. CPU utilization (%)'
title = '{}, {}'.format(title, self._title)
metric_info = self._metric_info(metric_id, title)
cluster = self.test.cbmonitor_clusters[0]
bucket = self.test_config.buckets[0]
db = self.store.build_dbname(cluster=cluster,
collector='ns_server',
bucket=bucket)
values = self.store.get_values(db, metric='cpu_utilization_rate')
cpu_utilization = int(np.average(values))
return cpu_utilization, self._snapshots, metric_info
def max_memcached_rss(self) -> Metric:
metric_id = '{}_memcached_rss'.format(self.test_config.name)
title = 'Max. memcached RSS (MB),{}'.format(
self._title.split(',')[-1]
)
metric_info = self._metric_info(metric_id, title)
max_rss = 0
for (cluster_name, servers), initial_nodes in zip(
self.cluster_spec.clusters,
self.test_config.cluster.initial_nodes,
):
cluster = list(filter(lambda name: name.startswith(cluster_name),
self.test.cbmonitor_clusters))[0]
for server in servers[:initial_nodes]:
hostname = server.replace('.', '')
db = self.store.build_dbname(cluster=cluster,
collector='atop',
server=hostname)
values = self.store.get_values(db, metric='memcached_rss')
rss = round(max(values) / 1024 ** 2)
max_rss = max(max_rss, rss)
return max_rss, self._snapshots, metric_info
def avg_memcached_rss(self) -> Metric:
metric_id = '{}_avg_memcached_rss'.format(self.test_config.name)
title = 'Avg. memcached RSS (MB),{}'.format(
self._title.split(',')[-1]
)
metric_info = self._metric_info(metric_id, title)
rss = []
for (cluster_name, servers), initial_nodes in zip(
self.cluster_spec.clusters,
self.test_config.cluster.initial_nodes,
):
cluster = list(filter(lambda name: name.startswith(cluster_name),
self.test.cbmonitor_clusters))[0]
for server in servers[:initial_nodes]:
hostname = server.replace('.', '')
db = self.store.build_dbname(cluster=cluster,
collector='atop',
server=hostname)
rss += self.store.get_values(db, metric='memcached_rss')
avg_rss = int(np.average(rss) / 1024 ** 2)
return avg_rss, self._snapshots, metric_info
def memory_overhead(self, key_size: int = 20) -> Metric:
metric_info = self._metric_info()
item_size = key_size + self.test_config.load_settings.size
user_data = self.test_config.load_settings.items * item_size
user_data *= self.test_config.bucket.replica_number + 1
user_data /= 2 ** 20
mem_used, _, _ = self.avg_memcached_rss()
mem_used *= self._num_nodes
overhead = int(100 * (mem_used / user_data - 1))
return overhead, self._snapshots, metric_info
def get_indexing_meta(self,
value: float,
index_type: str,
unit: str = "min") -> Metric:
metric_id = '{}_{}'.format(self.test_config.name, index_type.lower())
title = '{} index ({}), {}'.format(index_type, unit, self._title)
metric_info = self._metric_info(metric_id, title)
metric_info['category'] = index_type.lower()
value = s2m(value)
return value, self._snapshots, metric_info
def get_memory_meta(self,
value: float,
memory_type: str) -> Metric:
metric_id = '{}_{}'.format(self.test_config.name,
memory_type.replace(" ", "").lower())
title = '{} (GB), {}'.format(memory_type, self._title)
metric_info = self._metric_info(metric_id, title)
return value, self._snapshots, metric_info
def bnr_throughput(self,
time_elapsed: float,
edition: str,
tool: str) -> Metric:
metric_id = '{}_{}_thr_{}'.format(self.test_config.name, tool, edition)
title = '{} {} throughput (Avg. MB/sec), {}'.format(
edition, tool, self._title)
metric_info = self._metric_info(metric_id, title)
data_size = self.test_config.load_settings.items * \
self.test_config.load_settings.size / 2 ** 20 # MB
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def backup_size(self, size: float, edition: str) -> Metric:
metric_id = '{}_size_{}'.format(self.test_config.name, edition)
title = '{} backup size (GB), {}'.format(edition, self._title)
metric_info = self._metric_info(metric_id, title)
return size, self._snapshots, metric_info
def merge_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
data_size = 2 * self.test_config.load_settings.items * \
self.test_config.load_settings.size / 2 ** 20 # MB
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def import_and_export_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
data_size = self.test_config.load_settings.items * \
self.test_config.load_settings.size / 2 ** 20 # MB
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def import_file_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
import_file = self.test_config.export_settings.import_file
data_size = os.path.getsize(import_file) / 2.0 ** 20
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def verify_series_in_limits(self, expected_number: int) -> bool:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='secondary_debugstats')
values = self.store.get_values(db, metric='num_connections')
values = list(map(float, values))
logger.info("Number of samples: {}".format(len(values)))
logger.info("Sample values: {}".format(values))
if any(value > expected_number for value in values):
return False
return True
def _parse_ycsb_throughput(self) -> int:
throughput = 0
for filename in glob.glob("YCSB/ycsb_run_*.log"):
with open(filename) as fh:
for line in fh.readlines():
if line.startswith('[OVERALL], Throughput(ops/sec)'):
throughput += int(float(line.split()[-1]))
break
return throughput
def _ycsb_perc_calc(self, _temp: [], io_type: str, percentile: str, lat_dic: {}, _fc: int):
pio_type = '{}th Percentile {}'.format(percentile, io_type)
p_lat = round(np.percentile(_temp, percentile) / 1000, 3)
if _fc > 1:
p_lat = round((((lat_dic[pio_type] * (_fc - 1)) + p_lat) / _fc), 3)
lat_dic.update({pio_type: p_lat})
return lat_dic
def _ycsb_avg_calc(self, _temp: [], io_type: str, lat_dic: {}, _fc: int):
aio_type = 'Average {}'.format(io_type)
a_lat = round((sum(_temp) / len(_temp)) / 1000, 3)
if _fc > 1:
a_lat = round((((lat_dic[aio_type] * (_fc - 1)) + a_lat) / _fc), 3)
lat_dic.update({aio_type: a_lat})
return lat_dic
def _parse_ycsb_latency(self, percentile: str) -> int:
lat_dic = {}
_temp = []
_fc = 1
for filename in glob.glob("YCSB/ycsb_run_*.log"):
fh2 = open(filename)
_l1 = fh2.readlines()
_l1_len = len(_l1)
fh = open(filename)
_c = 0
for x in range(_l1_len - 1):
line = fh.readline()
if line.find('], 1000,') >= 1:
io_type = line.split('[')[1].split(']')[0]
_n = 0
while (line.startswith('[{}]'.format(io_type))):
lat = float(line.split()[-1])
_temp.append(lat)
line = fh.readline()
_n += 1
_temp.sort()
lat_dic = self._ycsb_perc_calc(_temp=_temp,
io_type=io_type,
lat_dic=lat_dic,
_fc=_fc,
percentile=percentile)
lat_dic = self._ycsb_avg_calc(_temp=_temp,
io_type=io_type,
lat_dic=lat_dic,
_fc=_fc)
_temp.clear()
_c += _n
_c += 1
x += _c
_fc += 1
if 'CLEANUP' in lat_dic:
del lat_dic['CLEANUP']
return lat_dic
def _parse_ycsb_latency_cbcollect(self, percentile: str):
lat_dic = {}
_temp = []
_fc = 1
_cbtime = int(self.test.cb_time)
_cbstart = int(self.test.cb_start * 1000)
for filename in glob.glob("YCSB/ycsb_run_*.log"):
fh2 = open(filename)
list1 = fh2.readlines()
list1_length = len(list1)
fh = open(filename)
_c = 0
for x in range(list1_length-1):
line = fh.readline()
if line.find('], {},'.format(_cbstart)) >= 1:
io_type = line.split('[')[1].split(']')[0]
for y in range(_cbtime):
lat = float(line.split()[-1])
_temp.append(lat)
if y < _cbtime-1:
line = fh.readline()
_temp.sort()
lat_dic = self._ycsb_perc_calc(_temp=_temp,
io_type=io_type,
lat_dic=lat_dic,
_fc=_fc,
percentile=percentile)
lat_dic = self._ycsb_avg_calc(_temp=_temp,
io_type=io_type,
lat_dic=lat_dic,
_fc=_fc)
_temp.clear()
_c += _cbtime
_c += 1
x += _c
_fc += 1
if 'CLEANUP' in lat_dic:
del lat_dic['CLEANUP']
return lat_dic
def dcp_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
throughput = round(self.test_config.load_settings.items / time_elapsed)
return throughput, self._snapshots, metric_info
def fragmentation_ratio(self, ratio: float) -> Metric:
metric_info = self._metric_info()
return ratio, self._snapshots, metric_info
def elapsed_time(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
time_elapsed = s2m(time_elapsed)
return time_elapsed, self._snapshots, metric_info
def kv_throughput(self, total_ops: int) -> Metric:
metric_info = self._metric_info()
throughput = total_ops // self.test_config.access_settings.time
return throughput, self._snapshots, metric_info
def ycsb_throughput(self) -> Metric:
metric_info = self._metric_info()
throughput = self._parse_ycsb_throughput()
return throughput, self._snapshots, metric_info
def ycsb_latency(self,
io_type: str,
latency: int,
) -> Metric:
title = '{} {}'.format(io_type, self._title)
metric_id = '{}_{}'.format(self.test_config.name, io_type.replace(' ', '_').casefold())
metric_info = self._metric_info(title=title, metric_id=metric_id)
return latency, self._snapshots, metric_info
def ycsb_get_latency(self,
percentile: str,
) -> Metric:
if self.test_config.access_settings.cbcollect:
latency_dic = self._parse_ycsb_latency_cbcollect(percentile)
else:
latency_dic = self._parse_ycsb_latency(percentile)
return latency_dic
def indexing_time(self, indexing_time: float) -> Metric:
return self.elapsed_time(indexing_time)
@property
def rebalance_order_by(self) -> str:
order_by = ''
for num_nodes in self.test_config.cluster.initial_nodes:
order_by += '{:03d}'.format(num_nodes)
order_by += '{:018d}'.format(self.test_config.load_settings.items)
return order_by
def rebalance_time(self, rebalance_time: float) -> Metric:
metric = self.elapsed_time(rebalance_time)
metric[-1]['orderBy'] = self.rebalance_order_by
return metric
def failover_time(self, delta: float) -> Metric:
metric_info = self._metric_info()
return delta, self._snapshots, metric_info
def gsi_connections(self, num_connections: int) -> Metric:
metric_info = self._metric_info()
return num_connections, self._snapshots, metric_info
def scan_throughput(self, throughput: float) -> Metric:
metric_info = self._metric_info()
throughput = round(throughput, 1)
return throughput, self._snapshots, metric_info
def multi_scan_diff(self, time_diff: float):
metric_info = self._metric_info()
time_diff = round(time_diff, 2)
return time_diff, self._snapshots, metric_info
def get_functions_throughput(self, time: int, event_name: str, events_processed: int) -> float:
throughput = 0
if event_name:
for name, file in self.test.functions.items():
throughput += self.test.rest.get_num_events_processed(
event=event_name, node=self.test.eventing_nodes[0], name=name)
else:
throughput = events_processed
throughput /= len(self.test.functions)
throughput /= time
return round(throughput, 0)
def function_throughput(self, time: int, event_name: str, events_processed: int) -> Metric:
metric_info = self._metric_info()
throughput = self.get_functions_throughput(time, event_name, events_processed)
return throughput, self._snapshots, metric_info
def eventing_rebalance_time(self, time: int) -> Metric:
title_split = self._title.split(sep=",", maxsplit=2)
title = "Rebalance Time(sec)," + title_split[1]
metric_id = '{}_rebalance_time'.format(self.test_config.name)
metric_info = self._metric_info(metric_id=metric_id, title=title)
return time, self._snapshots, metric_info
def function_latency(self, percentile: float, latency_stats: dict) -> Metric:
"""Calculate eventing function latency stats.
We get latency stats in format of- time:number of events processed in that time(samples)
In this method we get latency stats then calculate percentile latency using-
Calculate total number of samples.
Keep adding sample until we get required percentile number.
For now it calculates for one function only
"""
metric_info = self._metric_info()
latency = 0
for name, stats in latency_stats.items():
total_samples = sum([sample for time, sample in stats])
latency_samples = 0
for time, samples in stats:
latency_samples += samples
if latency_samples >= (total_samples * percentile / 100):
latency = float(time) / 1000
break
latency = round(latency, 1)
return latency, self._snapshots, metric_info
def function_time(self, time: int, time_type: str, initials: str) -> Metric:
title = initials + ", " + self._title
metric_id = '{}_{}'.format(self.test_config.name, time_type.lower())
metric_info = self._metric_info(metric_id=metric_id, title=title)
time = s2m(seconds=time)
return time, self._snapshots, metric_info
def analytics_latency(self, query: Query, latency: int) -> Metric:
metric_id = self.test_config.name + strip(query.description)
title = 'Avg. query latency (ms), {} {}, {}'.format(query.id,
query.description,
self._title)
order_by = '{:05d}_{}'.format(int(query.id[2:]), self._order_by)
metric_info = self._metric_info(metric_id,
title,
order_by)
return latency, self._snapshots, metric_info
def get_max_rss_values(self, function_name: str, server: str):
ratio = 1024 * 1024
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='eventing_consumer_stats',
bucket=function_name, server=server)
rss_list = self.store.get_values(db, metric='eventing_consumer_rss')
max_consumer_rss = round(max(rss_list) / ratio, 2)
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='atop', server=server)
rss_list = self.store.get_values(db, metric='eventing-produc_rss')
max_producer_rss = round(max(rss_list) / ratio, 2)
return max_consumer_rss, max_producer_rss
def avg_ingestion_rate(self, num_items: int, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
rate = round(num_items / time_elapsed)
return rate, self._snapshots, metric_info
def compression_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
throughput = round(self.test_config.load_settings.items / time_elapsed)
return throughput, self._snapshots, metric_info
class MetricHelper:
def __init__(self, test):
self.test = test
self.test_config = test.test_config
self.cluster_spec = test.cluster_spec
self.store = PerfStore(StatsSettings.CBMONITOR)
@property
def _title(self) -> str:
return self.test_config.test_case.title
@property
def _snapshots(self) -> List[str]:
return self.test.cbmonitor_snapshots
@property
def _num_nodes(self):
return self.test_config.cluster.initial_nodes[0]
def _metric_info(self,
metric_id: str = None,
title: str = None,
order_by: str = '') -> Dict[str, str]:
return {
'id': metric_id or self.test_config.name,
'title': title or self._title,
'orderBy': order_by,
}
def cbas_sync_latency(self, value_sec: float, name: str,
title: str) -> Metric:
metric_id = '{}_{}'.format(self.test_config.name, name)
title = '{}, {}'.format(title, self.test_config.test_case.title)
metric_info = self._metric_info(metric_id, title)
return value_sec, self._snapshots, metric_info
def cbas_query_latency(self, value_ms: float, name: str,
title: str) -> Metric:
metric_id = '{}_{}'.format(self.test_config.name, name)
title = '{}, {}'.format(title, self.test_config.test_case.title)
metric_info = self._metric_info(metric_id, title)
return value_ms, self._snapshots, metric_info
def ycsb_queries(self, value: float, name: str, title: str) -> Metric:
metric_id = '{}_{}'.format(self.test_config.name, name)
title = '{}, {}'.format(title, self.test_config.test_case.title)
metric_info = self._metric_info(metric_id, title)
return value, self._snapshots, metric_info
def avg_n1ql_throughput(self) -> Metric:
metric_id = '{}_avg_query_requests'.format(self.test_config.name)
title = 'Avg. Query Throughput (queries/sec), {}'.format(self._title)
metric_info = self._metric_info(metric_id, title)
throughput = self._avg_n1ql_throughput()
return throughput, self._snapshots, metric_info
def _avg_n1ql_throughput(self) -> int:
test_time = self.test_config.access_settings.time
query_node = self.cluster_spec.servers_by_role('n1ql')[0]
vitals = self.test.rest.get_query_stats(query_node)
total_requests = vitals['requests.count']
return int(total_requests / test_time)
def bulk_n1ql_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
items = self.test_config.load_settings.items / 4
throughput = round(items / time_elapsed)
return throughput, self._snapshots, metric_info
def avg_fts_throughput(self, order_by: str, name: str = 'FTS') -> Metric:
metric_id = '{}_avg_query_requests'.format(self.test_config.name)
title = 'Query Throughput (queries/sec), {}, {} node, {}'.\
format(self._title, self._num_nodes, name)
metric_info = self._metric_info(metric_id, title, order_by)
if name == 'FTS':
total_queries = 0
for host in self.test.active_fts_hosts:
all_stats = self.test.rest.get_fts_stats(host)
key = "{}:{}:{}".format(self.test_config.buckets[0],
self.test.index_name, "total_queries")
if key in all_stats:
total_queries += all_stats[key]
else:
all_stats = self.test.rest.get_elastic_stats(self.test.master_host)
total_queries = all_stats["_all"]["total"]["search"]["query_total"]
time_taken = self.test_config.access_settings.time
throughput = total_queries / float(time_taken)
if throughput < 100:
throughput = round(throughput, 2)
else:
throughput = round(throughput)
return throughput, self._snapshots, metric_info
def latency_fts_queries(self,
percentile: Number,
dbname: str,
metric: str,
order_by: str,
name='FTS') -> Metric:
if percentile == 0:
metric_id = '{}_average'.format(self.test_config.name)
title = 'Average query latency (ms), {}, {} node, {}'.\
format(self._title, self._num_nodes, name)
else:
metric_id = self.test_config.name
title = '{}th percentile query latency (ms), {}, {} node, {}'. \
format(percentile, self._title, self._num_nodes, name)
metric_info = self._metric_info(metric_id, title, order_by)
db = '{}{}'.format(dbname, self.test.cbmonitor_clusters[0])
timings = self.store.get_values(db, metric)
if percentile == 0:
latency = np.average(timings)
else:
latency = np.percentile(timings, percentile)
latency = round(latency)
return latency, self._snapshots, metric_info
def fts_index(self,
elapsed_time: float,
order_by: str,
name: str = 'FTS') -> Metric:
metric_id = self.test_config.name
title = 'Index build time(sec), {}, {} node, {}'.\
format(self._title, self._num_nodes, name)
metric_info = self._metric_info(metric_id, title, order_by)
index_time = round(elapsed_time, 1)
return index_time, self._snapshots, metric_info
def fts_index_size(self,
index_size_raw: int,
order_by: str,
name: str = 'FTS') -> Metric:
metric_id = "{}_indexsize".format(self.test_config.name)
title = 'Index size (MB), {}, {} node, {}'.\
format(self._title, self._num_nodes, name)
metric_info = self._metric_info(metric_id, title, order_by)
index_size_mb = int(index_size_raw / (1024**2))
return index_size_mb, self._snapshots, metric_info
def fts_rebalance_time(self,
reb_time: float,
order_by: str,
name: str = 'FTS') -> Metric:
metric_id = "{}_reb".format(self.test_config.name)
title = 'Rebalance time (min), {}, {}'.format(self._title, name)
metric_info = self._metric_info(metric_id, title, order_by)
reb_time = s2m(reb_time)
return reb_time, self._snapshots, metric_info
def max_ops(self) -> Metric:
metric_info = self._metric_info()
throughput = self._max_ops()
return throughput, self._snapshots, metric_info
def _max_ops(self) -> int:
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(
cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='ops')
return int(np.percentile(values, 90))
def lag_collector_metric(self,
collector,
percentile: Number = 95) -> Metric:
metric_id = '{}_{}th_{}'.format(self.test_config.name, percentile,
collector)
title = '{}th percentile {} (ms), {}'.format(percentile, collector,
self._title)
metric_info = self._metric_info(metric_id, title)
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(
cluster=self.test.cbmonitor_clusters[0],
collector=collector,
bucket=bucket)
values += self.store.get_values(db, metric=collector)
lag = round(np.percentile(values, percentile), 1)
return lag, self._snapshots, metric_info
def xdcr_lag(self, percentile: Number = 95) -> Metric:
return self.lag_collector_metric('xdcr_lag', percentile)
def cbas_lag(self, percentile: Number = 95) -> Metric:
return self.lag_collector_metric('cbas_lag', percentile)
def avg_replication_rate(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
rate = self._avg_replication_rate(time_elapsed)
return rate, self._snapshots, metric_info
def _avg_replication_rate(self, time_elapsed: float) -> float:
initial_items = self.test_config.load_settings.ops or \
self.test_config.load_settings.items
num_buckets = self.test_config.cluster.num_buckets
avg_replication_rate = num_buckets * initial_items / time_elapsed
return round(avg_replication_rate)
def max_drain_rate(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
items_per_node = self.test_config.load_settings.items / self._num_nodes
drain_rate = round(items_per_node / time_elapsed)
return drain_rate, self._snapshots, metric_info
def avg_disk_write_queue(self) -> Metric:
metric_info = self._metric_info()
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(
cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='disk_write_queue')
disk_write_queue = int(np.average(values))
return disk_write_queue, self._snapshots, metric_info
def avg_bg_wait_time(self) -> Metric:
metric_info = self._metric_info()
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(
cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='avg_bg_wait_time')
avg_bg_wait_time = np.mean(values) / 10**3 # us -> ms
avg_bg_wait_time = round(avg_bg_wait_time, 2)
return avg_bg_wait_time, self._snapshots, metric_info
def avg_couch_views_ops(self) -> Metric:
metric_info = self._metric_info()
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(
cluster=self.test.cbmonitor_clusters[0],
collector='ns_server',
bucket=bucket)
values += self.store.get_values(db, metric='couch_views_ops')
couch_views_ops = int(np.average(values))
return couch_views_ops, self._snapshots, metric_info
def query_latency(self, percentile: Number) -> Metric:
metric_id = self.test_config.name
title = '{}th percentile query latency (ms), {}'.format(
percentile, self._title)
metric_info = self._metric_info(metric_id, title)
latency = self._query_latency(percentile)
return latency, self._snapshots, metric_info
def _query_latency(self, percentile: Number) -> float:
values = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(
cluster=self.test.cbmonitor_clusters[0],
collector='spring_query_latency',
bucket=bucket)
values += self.store.get_values(db, metric='latency_query')
query_latency = np.percentile(values, percentile)
if query_latency < 100:
return round(query_latency, 1)
return int(query_latency)
def secondary_scan_latency(self, percentile: Number) -> Metric:
metric_id = self.test_config.name
title = '{}th percentile secondary scan latency (ms), {}'.format(
percentile, self._title)
metric_info = self._metric_info(metric_id, title)
cluster = ""
for cid in self.test.cbmonitor_clusters:
if "apply_scanworkload" in cid:
cluster = cid
break
db = self.store.build_dbname(cluster=cluster,
collector='secondaryscan_latency')
timings = self.store.get_values(db, metric='Nth-latency')
timings = list(map(int, timings))
logger.info("Number of samples are {}".format(len(timings)))
scan_latency = np.percentile(timings, percentile) / 1e6
scan_latency = round(scan_latency, 2)
return scan_latency, self._snapshots, metric_info
def kv_latency(self,
operation: str,
percentile: Number = 99.9,
collector: str = 'spring_latency') -> Metric:
metric_id = '{}_{}_{}th'.format(self.test_config.name, operation,
percentile)
metric_id = metric_id.replace('.', '')
title = '{}th percentile {} {}'.format(percentile, operation.upper(),
self._title)
metric_info = self._metric_info(metric_id, title)
latency = self._kv_latency(operation, percentile, collector)
return latency, self._snapshots, metric_info
def _kv_latency(self, operation: str, percentile: Number,
collector: str) -> float:
timings = []
metric = 'latency_{}'.format(operation)
for bucket in self.test_config.buckets:
db = self.store.build_dbname(
cluster=self.test.cbmonitor_clusters[0],
collector=collector,
bucket=bucket)
timings += self.store.get_values(db, metric=metric)
latency = np.percentile(timings, percentile)
if latency > 100:
return round(latency)
return round(latency, 2)
def observe_latency(self, percentile: Number) -> Metric:
metric_id = '{}_{}th'.format(self.test_config.name, percentile)
title = '{}th percentile {}'.format(percentile, self._title)
metric_info = self._metric_info(metric_id, title)
timings = []
for bucket in self.test_config.buckets:
db = self.store.build_dbname(
cluster=self.test.cbmonitor_clusters[0],
collector='observe',
bucket=bucket)
timings += self.store.get_values(db, metric='latency_observe')
latency = round(np.percentile(timings, percentile), 2)
return latency, self._snapshots, metric_info
def cpu_utilization(self) -> Metric:
metric_id = '{}_avg_cpu'.format(self.test_config.name)
title = 'Avg. CPU utilization (%)'
title = '{}, {}'.format(title, self._title)
metric_info = self._metric_info(metric_id, title)
cluster = self.test.cbmonitor_clusters[0]
bucket = self.test_config.buckets[0]
db = self.store.build_dbname(cluster=cluster,
collector='ns_server',
bucket=bucket)
values = self.store.get_values(db, metric='cpu_utilization_rate')
cpu_utilization = int(np.average(values))
return cpu_utilization, self._snapshots, metric_info
def max_beam_rss(self) -> Metric:
metric_id = 'beam_rss_max_{}'.format(self.test_config.name)
title = 'Max. beam.smp RSS (MB), {}'.format(self._title)
metric_info = self._metric_info(metric_id, title)
max_rss = 0
for cluster_name, servers in self.cluster_spec.clusters:
cluster = list(
filter(lambda name: name.startswith(cluster_name),
self.test.cbmonitor_clusters))[0]
for server in servers:
hostname = server.replace('.', '')
db = self.store.build_dbname(cluster=cluster,
collector='atop',
server=hostname)
values = self.store.get_values(db, metric='beam.smp_rss')
rss = round(max(values) / 1024**2)
max_rss = max(max_rss, rss)
return max_rss, self._snapshots, metric_info
def max_memcached_rss(self) -> Metric:
metric_id = '{}_memcached_rss'.format(self.test_config.name)
title = 'Max. memcached RSS (MB),{}'.format(self._title.split(',')[-1])
metric_info = self._metric_info(metric_id, title)
max_rss = 0
for (cluster_name, servers), initial_nodes in zip(
self.cluster_spec.clusters,
self.test_config.cluster.initial_nodes,
):
cluster = list(
filter(lambda name: name.startswith(cluster_name),
self.test.cbmonitor_clusters))[0]
for server in servers[:initial_nodes]:
hostname = server.replace('.', '')
db = self.store.build_dbname(cluster=cluster,
collector='atop',
server=hostname)
values = self.store.get_values(db, metric='memcached_rss')
rss = round(max(values) / 1024**2)
max_rss = max(max_rss, rss)
return max_rss, self._snapshots, metric_info
def avg_memcached_rss(self) -> Metric:
metric_id = '{}_avg_memcached_rss'.format(self.test_config.name)
title = 'Avg. memcached RSS (MB),{}'.format(self._title.split(',')[-1])
metric_info = self._metric_info(metric_id, title)
rss = []
for (cluster_name, servers), initial_nodes in zip(
self.cluster_spec.clusters,
self.test_config.cluster.initial_nodes,
):
cluster = list(
filter(lambda name: name.startswith(cluster_name),
self.test.cbmonitor_clusters))[0]
for server in servers[:initial_nodes]:
hostname = server.replace('.', '')
db = self.store.build_dbname(cluster=cluster,
collector='atop',
server=hostname)
rss += self.store.get_values(db, metric='memcached_rss')
avg_rss = int(np.average(rss) / 1024**2)
return avg_rss, self._snapshots, metric_info
def memory_overhead(self, key_size: int = 20) -> Metric:
metric_info = self._metric_info()
item_size = key_size + self.test_config.load_settings.size
user_data = self.test_config.load_settings.items * item_size
user_data *= self.test_config.bucket.replica_number + 1
user_data /= 2**20
mem_used, _, _ = self.avg_memcached_rss()
mem_used *= self._num_nodes
overhead = int(100 * (mem_used / user_data - 1))
return overhead, self._snapshots, metric_info
def get_indexing_meta(self,
value: float,
index_type: str,
unit: str = "min") -> Metric:
metric_id = '{}_{}'.format(self.test_config.name, index_type.lower())
title = '{} index ({}), {}'.format(index_type, unit, self._title)
metric_info = self._metric_info(metric_id, title)
metric_info['category'] = index_type.lower()
value = s2m(value)
return value, self._snapshots, metric_info
def get_memory_meta(self, value: float, memory_type: str) -> Metric:
metric_id = '{}_{}'.format(self.test_config.name,
memory_type.replace(" ", "").lower())
title = '{} (GB), {}'.format(memory_type, self._title)
metric_info = self._metric_info(metric_id, title)
return value, self._snapshots, metric_info
def bnr_throughput(self, time_elapsed: float, edition: str,
tool: str) -> Metric:
metric_id = '{}_{}_thr_{}'.format(self.test_config.name, tool, edition)
title = '{} {} throughput (Avg. MB/sec), {}'.format(
edition, tool, self._title)
metric_info = self._metric_info(metric_id, title)
data_size = self.test_config.load_settings.items * \
self.test_config.load_settings.size / 2 ** 20 # MB
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def backup_size(self, size: float, edition: str) -> Metric:
metric_id = '{}_size_{}'.format(self.test_config.name, edition)
title = '{} backup size (GB), {}'.format(edition, self._title)
metric_info = self._metric_info(metric_id, title)
return size, self._snapshots, metric_info
def merge_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
data_size = 2 * self.test_config.load_settings.items * \
self.test_config.load_settings.size / 2 ** 20 # MB
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def import_and_export_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
data_size = self.test_config.load_settings.items * \
self.test_config.load_settings.size / 2 ** 20 # MB
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def import_file_throughput(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
import_file = self.test_config.export_settings.import_file
data_size = os.path.getsize(import_file) / 2.0**20
avg_throughput = round(data_size / time_elapsed)
return avg_throughput, self._snapshots, metric_info
def verify_series_in_limits(self, expected_number: int) -> bool:
db = self.store.build_dbname(cluster=self.test.cbmonitor_clusters[0],
collector='secondary_debugstats')
values = self.store.get_values(db, metric='num_connections')
values = list(map(float, values))
logger.info("Number of samples: {}".format(len(values)))
logger.info("Sample values: {}".format(values))
if any(value > expected_number for value in values):
return False
return True
def _parse_ycsb_throughput(self) -> int:
throughput = 0
for filename in glob.glob("YCSB/ycsb_run_*.log"):
with open(filename) as fh:
for line in fh.readlines():
if line.startswith('[OVERALL], Throughput(ops/sec)'):
throughput += int(float(line.split()[-1]))
return throughput
def parse_cbas_query_latencies(self) -> Dict[str, float]:
query_count_dict = {}
query_exe_time_dict = {}
pattern = re.compile("[^\t]+")
for filename in glob.glob("loader/*.query.result"):
with open(filename) as fh:
for line in fh.readlines():
if line.startswith('CBAS success query'):
line = line.replace("\n", "")
parts = pattern.findall(line)
if len(parts) >= 10:
query = parts[1]
exe_time = 0.0
exe_time_str = parts[9]
if exe_time_str.endswith("ms"):
exe_time = float(exe_time_str.replace(
"ms", ""))
elif exe_time_str.endswith("s"):
exe_time = float(exe_time_str.replace(
"s", "")) * 1000
if query in query_count_dict:
query_count_dict[
query] = query_count_dict[query] + 1
query_exe_time_dict[
query] = query_exe_time_dict[
query] + exe_time
else:
query_count_dict[query] = 1
query_exe_time_dict[query] = exe_time
for query in query_exe_time_dict:
query_exe_time_dict[
query] = query_exe_time_dict[query] / query_count_dict[query]
return query_exe_time_dict
def _parse_dcp_throughput(self, output_file: str = 'dcpstatsfile') -> int:
# Get throughput from OUTPUT_FILE for posting to showfast
with open(output_file) as fh:
output_text = fh.read()
groups = re.search(r"Throughput = [^\d]*(\d*).*?", output_text)
return int(groups.group(1))
def dcp_throughput(self) -> Metric:
metric_info = self._metric_info()
throughput = self._parse_dcp_throughput()
return throughput, self._snapshots, metric_info
def fragmentation_ratio(self, ratio: float) -> Metric:
metric_info = self._metric_info()
return ratio, self._snapshots, metric_info
def elapsed_time(self, time_elapsed: float) -> Metric:
metric_info = self._metric_info()
time_elapsed = s2m(time_elapsed)
return time_elapsed, self._snapshots, metric_info
def kv_throughput(self, total_ops: int) -> Metric:
metric_info = self._metric_info()
throughput = total_ops // self.test_config.access_settings.time
return throughput, self._snapshots, metric_info
def ycsb_throughput(self) -> Metric:
metric_info = self._metric_info()
throughput = self._parse_ycsb_throughput()
return throughput, self._snapshots, metric_info
def indexing_time(self, indexing_time: float) -> Metric:
return self.elapsed_time(indexing_time)
def rebalance_time(self, rebalance_time: float) -> Metric:
return self.elapsed_time(rebalance_time)
def failover_time(self, delta: float) -> Metric:
metric_info = self._metric_info()
return delta, self._snapshots, metric_info
def gsi_connections(self, num_connections: int) -> Metric:
metric_info = self._metric_info()
return num_connections, self._snapshots, metric_info
def scan_throughput(self, throughput: float) -> Metric:
metric_info = self._metric_info()
throughput = round(throughput, 1)
return throughput, self._snapshots, metric_info
def multi_scan_diff(self, time_diff: float):
metric_info = self._metric_info()
time_diff = round(time_diff, 2)
return time_diff, self._snapshots, metric_info
def function_throughput(self, time: int, event_name: str) -> Metric:
metric_info = self._metric_info()
throughput = 0
for name, file in self.test.functions.items():
throughput += self.test.rest.get_num_events_processed(
event=event_name, node=self.test.eventing_nodes[0], name=name)
throughput /= len(self.test.functions)
throughput /= time
throughput = round(throughput, 1)
return throughput, self._snapshots, metric_info