def get_graph_data_from_livestatus(only_sites, host_name, service_description):
columns = ["perf_data", "metrics", "check_command"]
query = livestatus_lql([host_name], columns, service_description)
what = "host" if service_description == "_HOST_" else "service"
labels = ["site"] + ["%s_%s" % (what, col) for col in columns]
with sites.only_sites(only_sites), sites.prepend_site():
info = dict(zip(labels, sites.live().query_row(query)))
info["host_name"] = host_name
if what == "service":
info["service_description"] = service_description
return info
def get_graph_data_from_livestatus(only_sites, host_name, service_description):
columns = [u'perf_data', u'metrics', u'check_command']
query = livestatus_lql([host_name], columns, service_description)
what = 'host' if service_description == "_HOST_" else 'service'
labels = [u"site"] + [u"%s_%s" % (what, col) for col in columns]
with sites.only_sites(only_sites), sites.prepend_site():
info = dict(zip(labels, sites.live().query_row(query)))
info['host_name'] = host_name
if what == 'service':
info['service_description'] = service_description
return info
def fetch_rrd_data(site, host_name, service_description, entries, graph_recipe, graph_data_range):
start_time, end_time = graph_data_range["time_range"]
step: Union[int, float, str] = graph_data_range["step"]
# assumes str step is well formatted, colon separated step length & rrd point count
if not isinstance(step, str):
step = max(1, step)
point_range = ":".join(map(str, (start_time, end_time, step)))
lql_columns = list(rrd_columns(entries, graph_recipe["consolidation_function"], point_range))
query = livestatus_lql([host_name], lql_columns, service_description)
with sites.only_sites(site):
return list(zip(entries, sites.live().query_row(query)))
def livestatus_query_for_rrd_data(host_name, service_description, metric_cols,
default_cf, point_range):
lql_columns = []
for nr, (perfvar, cf, scale) in enumerate(metric_cols):
if default_cf:
cf = default_cf
else:
cf = cf or u"max"
rpn = u"%s.%s" % (perfvar, cf)
if scale != 1.0:
rpn += u",%f,*" % scale
lql_columns.append(u"rrddata:m%d:%s:%s" % (nr, rpn, point_range))
return livestatus_lql([host_name], lql_columns, service_description)
def fetch_rrd_data(
site: SiteId,
host_name: HostName,
service_description: ServiceName,
metrics: set[MetricProperties],
graph_recipe: GraphRecipe,
graph_data_range: GraphDataRange,
) -> list[tuple[MetricProperties, TimeSeriesValues]]:
start_time, end_time = graph_data_range["time_range"]
step = graph_data_range["step"]
# assumes str step is well formatted, colon separated step length & rrd point count
if not isinstance(step, str):
step = max(1, step)
point_range = ":".join(map(str, (start_time, end_time, step)))
lql_columns = list(
rrd_columns(metrics, graph_recipe["consolidation_function"],
point_range))
query = livestatus_lql([host_name], lql_columns, service_description)
with sites.only_sites(site):
return list(zip(metrics, sites.live().query_row(query)))
def test_livestatus_lql(args, result):
assert prediction.livestatus_lql(*args) == result
