Delete series for all metrics for the specified entity with names starting with the specified prefix.
'''
# Connect to ATSD server
# connection = connect('/path/to/connection.properties')
connection = connect_url('https://atsd_hostname:8443', 'username', 'password')
# Set query
entity = "entity"
metric_expr = "name LIKE 'me*'"
# Initialize services
entities_service = EntitiesService(connection)
series_service = SeriesService(connection)
# Query all metrics for entity
metrics = entities_service.metrics(entity=entity, expression=metric_expr)
if not metrics:
print("No metrics are found for entity " + entity)
else:
# Delete series for each metric
for metric in metrics:
query = SeriesDeleteQuery(entity=entity,
metric=metric.name,
exact_match=False)
print("deleting ", entity, metric.name)
# Uncomment next line to delete series
# response = series_service.delete(query)
# print(response)
from prettytable import PrettyTable
from atsd_client import connect, connect_url
from atsd_client.services import MetricsService, EntitiesService
# Connect to ATSD server
#connection = connect('/path/to/connection.properties')
connection = connect_url('https://atsd_hostname:8443', 'username', 'password')
entity = 'my-entity'
entities_service = EntitiesService(connection)
metrics_service = MetricsService(connection)
# query all metrics for entity
metrics = entities_service.metrics(entity, tags='frequency,seasonal_adjustment,observation_start,observation_end,category,parent_category', limit=5)
t = PrettyTable(['Top Category', 'Category', 'Name', 'Label', 'Frequency', 'Adjustment', 'Observation Start', 'Observation End'])
# iterate over metrics and add their fields/tags as rows into a PrettyTable
for metric in metrics:
t.add_row([metric.tags['category'], metric.tags['parent_category'], metric.name, metric.label, metric.tags['frequency'], metric.tags['seasonal_adjustment'], metric.tags['observation_start'], metric.tags['observation_end']])
# Sort metrics by name
t.sortby = "Name"
# Print metrics as ASCII table
#print(t)
# Print metrics as HTML table with header
print(t.get_html_string(title="Available Metrics"))
# Connect to an ATSD server
connection = connect_url('https://atsd_hostname:8443', 'user', 'password')
# set grace interval in hours
grace_interval_hours = 1
entities_service = EntitiesService(connection)
metrics_service = MetricsService(connection)
# query all entities that have last_insert_date, i.e. series
entities = entities_service.list(expression="name LIKE '06*'", min_insert_date="1970-01-01T00:00:00.000Z")
print('metric,entity,tags,last_insert_date')
for entity in entities:
# query all metrics for each entity
metrics = entities_service.metrics(entity.name)
for m in metrics:
# query series list for each metric and entity
series_list = metrics_service.series(m.name, entity.name)
# for each list with more than 1 series
if len(series_list) > 1:
# calculate maximum of all last_insert_date's in list and subtract 1 hour
# it will be lower limit date to compare
lower_limit_date = max(s.last_insert_date for s in series_list) - timedelta(
seconds=grace_interval_hours * 3600)
for s in series_list:
# check actual data existence
if s.last_insert_date < lower_limit_date:
print("%s,%s,%s,%s" % (s.metric, s.entity, s.tags, s.last_insert_date))
'''
Locate series that have no data during the interval for one day before entity last_insert_date.
'''
# Connect to ATSD server
#connection = connect('/path/to/connection.properties')
connection = connect_url('https://atsd_hostname:8443', 'username', 'password')
# set grace interval in hours
grace_interval_hours = 1
# list of the agents (entities)
agents = ['nurswgvml007', 'nurswgvml010']
entities_service = EntitiesService(connection)
metrics_service = MetricsService(connection)
for agent in agents:
# query agent meta information
entity = entities_service.get(agent)
if entity is None:
print('Agent %s not found' % agent)
continue
date = entity.last_insert_date
# query all metrics collecting by agent
metrics = entities_service.metrics(entity, use_entity_insert_time=True)
for metric in metrics:
# check actual data existence
if date - metric.last_insert_date > timedelta(seconds=grace_interval_hours * 3600):
print("%s, %s" % (metric.name, agent))
title = '\nentity: %s, last hours: %s, minimal score: %s, aggregate period: %s min, interpolate period: %s min, ' \
'data interval: %s days' % (
args.entity, args.last_hours, args.min_score, args.aggregate_period, args.interpolate_period, args.data_interval)
if args.metric_filter is None:
metric_expression = None
else:
metric_expression = "name like '%s'" % args.metric_filter
title = '%s, metric filter: %s' % (title, args.metric_filter)
message = [title]
now = datetime.now()
metrics = entities_service.metrics(args.entity, expression=metric_expression,
min_insert_date=now - timedelta(seconds=args.last_hours * 3600),
use_entity_insert_time=True)
log('Processing: ')
for metric in metrics:
sf = SeriesFilter(metric=metric.name)
ef = EntityFilter(entity=args.entity)
df = DateFilter(start_date=datetime(now.year, now.month, now.day) - timedelta(days=args.data_interval),
end_date='now')
tf = TransformationFilter()
query = SeriesQuery(series_filter=sf, entity_filter=ef, date_filter=df)
if args.aggregate_period > 0:
tf.set_aggregate(
Aggregate(period={'count': args.aggregate_period, 'unit': TimeUnit.MINUTE}, types=[AggregateType.MEDIAN]))
if args.interpolate_period > 0:
'''
Delete series for all metrics for the specified entity with names starting with the specified prefix.
'''
# Connect to ATSD server
# connection = connect('/path/to/connection.properties')
connection = connect_url('https://atsd_hostname:8443', 'username', 'password')
# Set query
entity = "entity"
metric_expr = "name LIKE 'me*'"
# Initialize services
entities_service = EntitiesService(connection)
series_service = SeriesService(connection)
# Query all metrics for entity
metrics = entities_service.metrics(entity=entity, expression=metric_expr)
if not metrics:
print("No metrics are found for entity " + entity)
else:
# Delete series for each metric
for metric in metrics:
query = SeriesDeleteQuery(entity=entity, metric=metric.name, exact_match=False)
print("deleting ", entity, metric.name)
# Uncomment next line to delete series
# response = series_service.delete(query)
# print(response)
'''
Locate series that have no data during the actual time interval (grace_interval) using an expression filter for entity.
'''
# Connect to ATSD server
#connection = connect('/path/to/connection.properties')
connection = connect_url('https://atsd_hostname:8443', 'username', 'password')
# set grace_interval to one day
grace_interval_minutes = 24 * 60
# query entities with last_insert_date
min_insert_date = "1970-01-01T00:00:00.000Z"
# calculate the upper boundary for the allowed last_insert_date values excluding grace_interval
max_insert_date = datetime.now() - timedelta(seconds=grace_interval_minutes * 60)
entities_service = EntitiesService(connection)
metrics_service = MetricsService(connection)
# query entities that have name started with 06
entities = entities_service.list(expression="name LIKE '06*'")
print('metric, entity, tags, last_insert_date')
for entity in entities:
# query all metrics for each entity
metrics = entities_service.metrics(entity)
for metric in metrics:
# query all series for each metric and entity
series = metrics_service.series(metric, entity, min_insert_date=min_insert_date, max_insert_date=max_insert_date)
for s in series:
print("%s,%s,%s,%s" % (s.metric, s.entity, print_tags(s.tags), s.last_insert_date))
title = '\nentity: %s, last hours: %s, minimal score: %s, aggregate period: %s min, interpolate period: %s min, ' \
'data interval: %s days' % (
args.entity, args.last_hours, args.min_score, args.aggregate_period, args.interpolate_period, args.data_interval)
if args.metric_filter is None:
metric_expression = None
else:
metric_expression = "name like '%s'" % args.metric_filter
title = '%s, metric filter: %s' % (title, args.metric_filter)
message = [title]
now = datetime.now()
metrics = entities_service.metrics(args.entity, expression=metric_expression,
min_insert_date=now - timedelta(seconds=args.last_hours * 3600),
use_entity_insert_time=True)
log('Processing: ')
for metric in metrics:
sf = SeriesFilter(metric=metric.name)
ef = EntityFilter(entity=args.entity)
df = DateFilter(start_date=datetime(now.year, now.month, now.day) - timedelta(days=args.data_interval),
end_date='now')
tf = TransformationFilter()
query = SeriesQuery(series_filter=sf, entity_filter=ef, date_filter=df)
if args.aggregate_period > 0:
tf.set_aggregate(
Aggregate(period={'count': args.aggregate_period, 'unit': TimeUnit.MINUTE}, types=[AggregateType.MEDIAN]))
if args.interpolate_period > 0: