Merge "Add support for instance metrics to prometheus datasource"
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Zuul
Gerrit Code Review
@@ -38,11 +38,11 @@ class PrometheusHelper(base.DataSourceBase):
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host_inlet_temp=None,
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host_airflow=None,
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host_power=None,
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instance_cpu_usage=None,
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instance_ram_usage=None,
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instance_ram_allocated=None,
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instance_cpu_usage='ceilometer_cpu',
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instance_ram_usage='ceilometer_memory_usage',
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instance_ram_allocated='instance.memory',
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instance_l3_cache_usage=None,
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instance_root_disk_size=None,
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instance_root_disk_size='instance.disk',
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)
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AGGREGATES_MAP = dict(mean='avg', max='max', min='min', count='avg')
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@@ -258,7 +258,7 @@ class PrometheusHelper(base.DataSourceBase):
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return promql_aggregate
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def _build_prometheus_query(self, aggregate, meter, instance_label,
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period):
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period, resource=None):
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"""Build and return the prometheus query string with the given args
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This function builds and returns the string query that will be sent
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@@ -286,12 +286,14 @@ class PrometheusHelper(base.DataSourceBase):
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:param meter: the name of the Prometheus meter to use
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:param instance_label: the Prometheus instance label (scrape target).
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:param period: the period in seconds for which to query
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:param resource: the resource object for which metrics are requested
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:return: a String containing the Prometheus query
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:raises watcher.common.exception.InvalidParameter if params are None
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:raises watcher.common.exception.InvalidParameter if meter is not
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known or currently supported (prometheus meter name).
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"""
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query_args = None
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uuid_label_key = CONF.prometheus_client.instance_uuid_label
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if (meter is None or aggregate is None or instance_label is None or
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period is None):
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raise exception.InvalidParameter(
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@@ -317,6 +319,30 @@ class PrometheusHelper(base.DataSourceBase):
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(instance_label, aggregate, meter,
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instance_label, period)
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)
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elif meter == 'ceilometer_memory_usage':
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query_args = (
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"%s_over_time(%s{%s='%s'}[%ss])" %
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(aggregate, meter, uuid_label_key, instance_label, period)
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)
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elif meter == 'ceilometer_cpu':
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# We are converting the total cumulative cpu time (ns) to cpu usage
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# percentage so we need to divide between the number of vcpus.
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# As this is a percentage metric, we set a max level of 100. It has
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# been observed in very high usage cases, prometheus reporting
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# values higher that 100 what can lead to unexpected behaviors.
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vcpus = resource.vcpus
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if not vcpus:
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LOG.warning(
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"instance vcpu count not set for instance %s, assuming 1",
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instance_label
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)
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vcpus = 1
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query_args = (
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"clamp_max((%s by (instance)(rate(%s{%s='%s'}[%ss]))/10e+8) "
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"*(100/%s), 100)" %
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(aggregate, meter, uuid_label_key, instance_label, period,
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vcpus)
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)
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else:
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raise exception.InvalidParameter(
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message=(_("Cannot process prometheus meter %s") % meter)
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@@ -365,9 +391,21 @@ class PrometheusHelper(base.DataSourceBase):
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query_args = ''
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instance_label = ''
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# For instance resource type, the datasource expects the uuid of the
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# instance to be assigned to a label in the prometheus metrics, with a
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# specific key value.
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if resource_type == 'compute_node':
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instance_label = self._resolve_prometheus_instance_label(
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resource.hostname)
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elif resource_type == 'instance':
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instance_label = resource.uuid
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# For ram_allocated and root_disk size metrics there are no valid
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# values in the prometheus backend store. We rely in the values
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# provided in the vms inventory.
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if meter == 'instance.memory':
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return float(resource.memory)
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elif meter == 'instance.disk':
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return float(resource.disk)
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else:
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LOG.warning(
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"Prometheus data source does not currently support "
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@@ -377,7 +415,7 @@ class PrometheusHelper(base.DataSourceBase):
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promql_aggregate = self._resolve_prometheus_aggregate(aggregate, meter)
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query_args = self._build_prometheus_query(
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promql_aggregate, meter, instance_label, period
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promql_aggregate, meter, instance_label, period, resource
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)
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if not query_args:
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LOG.error("Cannot proceed without valid prometheus query")
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@@ -440,3 +478,35 @@ class PrometheusHelper(base.DataSourceBase):
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'host_ram_usage', period=period,
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granularity=granularity, aggregate=aggregate)
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return float(ram_usage) if ram_usage else None
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def get_instance_ram_usage(self, resource, period=300,
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aggregate="mean", granularity=None):
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ram_usage = self.statistic_aggregation(
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resource, 'instance',
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'instance_ram_usage', period=period,
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granularity=granularity, aggregate=aggregate)
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return ram_usage
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def get_instance_cpu_usage(self, resource, period=300,
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aggregate="mean", granularity=None):
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cpu_usage = self.statistic_aggregation(
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resource, 'instance',
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'instance_cpu_usage', period=period,
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granularity=granularity, aggregate=aggregate)
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return cpu_usage
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def get_instance_ram_allocated(self, resource, period=300,
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aggregate="mean", granularity=None):
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ram_allocated = self.statistic_aggregation(
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resource, 'instance',
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'instance_ram_allocated', period=period,
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granularity=granularity, aggregate=aggregate)
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return ram_allocated
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def get_instance_root_disk_size(self, resource, period=300,
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aggregate="mean", granularity=None):
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root_disk_size = self.statistic_aggregation(
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resource, 'instance',
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'instance_root_disk_size', period=period,
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granularity=granularity, aggregate=aggregate)
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return root_disk_size
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