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Agent check API

This page describes StackState version 4.3.
The StackState 4.3 version range is End of Life (EOL) and no longer supported. We encourage customers still running the 4.3 version range to upgrade to a more recent release.

Overview

The Agent check API can be used to create checks that run on the StackState Agent. This page explains how to work with the Agent check API to write checks that send topology, metrics, events and service status information to StackState.
Code examples for the open source StackState Agent checks can be found on GitHub at: https://github.com/StackVista/stackstate-agent-integrations.

Agent checks

An Agent Check is a Python class that inherits from AgentCheck and implements the check method:
from stackstate_checks.checks import AgentCheck
​
class MyCheck(AgentCheck):
def check(self, instance):
# Collect metrics and topologies, emit events, submit service checks
​
def get_instance_key(self, instance):
# Provide an identifier (TopologyInstance)
The Agent creates an object of type MyCheck for each element contained in the instances sequence of the corresponding Agent Check configuration file:
instances:
- host: localhost
port: 6379
​
- host: example.com
port: 6379
All mapping included in the instances section of the Agent Check configuration file is passed to the check method using the declared instance value.
The AgentCheck class provides the following methods and attributes:
  • self.name - a name of the check
  • self.init_config - init_config that corresponds in the check configuration
  • self.log - a Python logger (python.org)​

Scheduling

Multiple instances of the same check can run concurrently. If a check is already running, it is not necessary to schedule another one.

Send data

Topology

Topology elements can be sent to StackState with the following methods:
  • self.component - Create a component in StackState. See send components.
  • self.relation - Create a relation between two components in StackState. See send relations.
  • self.start_snapshot() - Start a topology snapshot for a specific topology instance source.
  • self.stop_snapshot() - Stop a topology snapshot for a specific topology instance source.

Send components

Components can be sent to StackState using the self.component(id, type, data) method.
Example - send a component
self.component(
"urn:example:/host:this_host", # the ID
"Host", # the type
data={
"name": "this-host",
"domain": "Webshop",
"layer": "Machines",
"identifiers": ["urn:host:/this-host-fqdn"],
"labels": ["host:this_host", "region:eu-west-1"],
"environment": "Production"
})
The method requires the following details:
  • id - string. A unique ID for this component. This has to be unique for this instance.
  • type - string. A named parameter for this type.
  • data - dictionary. A JSON blob of arbitrary data. The fields within this object can be referenced in the ComponentTemplateFunction and the RelationTemplateFunction within StackState.
See the example of creating a component in StackState in the StackState MySQL check (github.com).
All submitted topologies are collected by StackState and flushed together with all the other Agent metrics at the end of check function.

Send relations

Relations can be sent to StackState using the self.relation(source_id, target_id, type, data) method.
Example - send a relation
self.relation(
"nginx3.e5dda204-d1b2-11e6-a015-0242ac110005", # source ID
"nginx5.0df4bc1e-c695-4793-8aae-a30eba54c9d6", # target ID
"uses_service", # type
{}) # data
The method requires the following details:
  • source_id - string. The source component externalId.
  • target_id - string. The target component externalId.
  • type - string. The type of relation.
  • data - dictionary. A JSON blob of arbitrary data. The fields within this object can be referenced in the ComponentTemplateFunction and the RelationTemplateFunction within StackState.
See the example of creating a relation in StackState in the StackState SAP check (github.com).
All submitted topologies are collected by StackState and flushed together with all the other Agent metrics at the end of check function.

Metrics

Metrics can be sent to StackState with the following methods:
  • self.gauge - Sample a gauge metric.
  • self.count - Sample a raw count metric.
  • self.rate - Sample a point, with the rate calculated at the end of the check.
  • self.increment - Increment a counter metric.
  • self.decrement - Decrement a counter metric.
  • self.histogram - Sample a histogram metric.
  • self.historate - Sample a histogram based on rate metrics.
  • self.monotonic_count - Sample an increasing counter metric.
Example - send a gauge metric
self.gauge(
"test.metric", # the metric name
10.0, # value of the metric
"tags": [
"tag_key1:tag_value1",
"tag_key2:tag_value2"
],
"localdocker.test") # the hostname
Each method accepts the following metric details:
  • name - the name of the metric.
  • value - the value for the metric. Defaults to 1 on increment, -1 on decrement.
  • tags - optional. A list of tags to associate with this metric.
  • hostname - optional. A hostname to associate with this metric. Defaults to the current host.
All submitted metrics are collected and flushed with all the other Agent metrics at the end of check function.
Check the example to send metrics in the StackState MySQL check (github.com).

Events

Events can be sent to StackState with the self.event(event_dict) method.
Example - send an event
self.event(
{
"context": {
"category": "Changes",
"data": {
"data_key1":"data_value1",
"data_key2":"data_value2"
},
"element_identifiers": [
"element_identifier1",
"element_identifier2"
],
"source": "source_system",
"source_links": [
{
"title": "link_title",
"url": "link_url"
}
]
},
"event_type": "event_typeEvent",
"msg_title": "event_title",
"msg_text": "event_text",
"source_type_name": "source_event_type",
"tags": [
"tag_key1:tag_value1",
"tag_key2:tag_value2",
],
"timestamp": 1607432944
})
The event-dict is a valid event JSON dictionary.
Note that msg_title and msg_text are required fields from Agent v2.11.0.
All events will be collected and flushed with the rest of the Agent payload at the end of the check function.

Status

Reporting status of a service is handled by calling the service_check method:
self.service_check(name, status, tags=None, message="")
The method can accept the following arguments:
  • name - the name of the service check
  • status - a constant describing the service status defined in the AgentCheck class:
    • AgentCheck.OK for success status.
    • AgentCheck.WARNING for failure status.
    • AgentCheck.CRITICAL for failure status.
    • AgentCheck.UNKNOWN for indeterminate status.
  • tags - a list of tags to associate with the check. (optional)
  • message - additional information about the current status. (optional)
Check the usage in the following example.

Checks and streams

Streams and health checks can be sent to StackState together with a topology component. These can then be mapped together in StackState by a StackPack to give you telemetry streams and health states on your components.
All telemetry classes and methods can be imported from stackstate_checks.base. The following stream types can be added:
  • ​Metric stream - a metric stream and associated metric health checks.
  • ​Events stream - a log stream with events and associated event health checks.
  • ​Service check stream - a log stream with service check statuses for a specific integration and associated event health checks.
In the example below, a MetricStream is created on the metric system.cpu.usage with some conditions specific to a component. A health check (check) maximum_average is then created on this metric stream using this_host_cpu_usage.identifier. The stream and check are then added to the streams and checks list for the component this-host.
Example - metric stream with metric health check
this_host_cpu_usage = MetricStream(
"Host CPU Usage",
"system.cpu.usage",
conditions={
"tags.hostname": "this-host",
"tags.region": "eu-west-1"
},
unit_of_measure="Percentage",
aggregation="MEAN",
priority="HIGH")
​
cpu_max_average_check = MetricHealthChecks.maximum_average(
this_host_cpu_usage.identifier,
"Max CPU Usage (Average)",
75,
90,
remediation_hint="Too much activity")
​
self.component(
"urn:example:/host:this_host",
"Host",
data={
"name": "this-host",
"domain": "Webshop",
"layer": "Machines",
"identifiers": ["urn:host:/this-host-fqdn"],
"labels": ["host:this_host", "region:eu-west-1"],
"environment": "Production"
},
streams=[this_host_cpu_usage],
checks=[cpu_max_average_check])

Events stream

Log streams containing events can be added to a component using the EventStream class.
Example - events stream
EventStream(
"Host events stream", # name
conditions={
"key1": "value1",
"key2": "value2"
})
Each events stream has the following details:
  • name - The name for the stream in StackState.
  • conditions - A dictionary of key:value arguments that are used to filter the event values for the stream.
Event stream health check
Event stream health checks can optionally be mapped to an events stream using the stream identifier. The following event stream health checks are supported out of the box:
Event stream health check
Description
contains_key_value
Checks that the last event contains (at the top-level), the specified value for a key.
use_tag_as_health
Checks that returns the value of a tag in the event as the health state.
custom_health_check
This method provides the functionality to send in a custom event health check.
Example - event stream health check
EventHealthChecks.contains_key_value(
"this_host_events", # stream_id
"Events on this host", # name
75, # contains_key
90, # contains_value
"CRITICAL" # health state when key found
"CLEAR" # health state when key not found
remediation_hint="Bad event found!")
An event stream health check includes the details listed below. Note that a custom_health_check only requires a name and check_arguments:
  • stream_id - the identifier of the stream the check should run on.
  • name - the name the check will have in StackState.
  • description - the description for the check in StackState.
  • remediation_hint - the remediation hint to display when the check return a critical health state.
  • contains_key - for check contains_key_value only. The key that should be contained in the event.
  • contains_value - for check contains_key_value only. The value that should be contained in the event.
  • found_health_state - for check contains_key_value only. The health state to return when this tag and value is found.
  • missing_health_state - for check contains_key_value only. The health state to return when the tag/value is not found.
  • tag_name - for check use_tag_as_health only. The key of the tag that should be used as the health state.

Metric stream

Metric streams can be added to a component using the MetricStream class.
Example - metric stream
MetricStream(
"Host CPU Usage", # name
"system.cpu.usage", # metricField
conditions={
"tags.hostname": "this-host",
"tags.region": "eu-west-1"
},
unit_of_measure="Percentage",
aggregation="MEAN",
priority="HIGH")
Each metric stream has the following details:
  • name - The name for the stream in StackState.
  • metricField - The name of the metric to select.
  • conditions - A dictionary of key:value arguments that are used to filter the metric values for the stream.
  • unit_of_measure - Optional. The unit of measure for the metric points, it gets appended after the stream name: name (unit_of_measure)
  • aggregation - Optional. sets the aggregation function for the metrics in StackState. See aggregation methods.
  • priority - Optional. The stream priority in StackState, one of NONE, LOW, MEDIUM, HIGH. HIGH priority streams are used for anomaly detection in StackState.
Metric stream health check
Metric stream health checks can optionally be mapped to a metric stream using the stream identifier. Note that some metric health checks require multiple streams for ratio calculations.
The following metric stream health checks are supported out of the box:
Metric stream health check
Description
maximum_average
Calculates the health state by comparing the average of all metric points in the time window against the configured maximum values.
maximum_last
Calculates the health state only by comparing the last value in the time window against the configured maximum values.
maximum_percentile
Calculates the health state by comparing the specified percentile of all metric points in the time window against the configured maximum values. For the median specify 50 for the percentile. The percentile parameter must be a value > 0 and <= 100.
maximum_ratio
Calculates the ratio between the values of two streams and compares it against the critical and deviating value. If the ratio is larger than the specified critical or deviating value, the corresponding health state is returned.
minimum_average
Calculates the health state by comparing the average of all metric points in the time window against the configured minimum values.
minimum_last
Calculates the health state only by comparing the last value in the time window against the configured minimum values.
minimum_percentile
Calculates the health state by comparing the specified percentile of all metric points in the time window against the configured minimum values. For the median specify 50 for the percentile. The percentile must be a value > 0 and <= 100.
failed_ratio
Calculates the ratio between the last values of two streams (one is the normal metric stream and one is the failed metric stream). This ratio is compared against the deviating or critical value.
custom_health_check
Provides the functionality to send in a custom metric health check.
Example - metric health check
MetricHealthChecks.maximum_average(
this_host_cpu_usage.identifier, # stream_id
"Max CPU Usage (Average)", # name
75, # deviating value
90, # critical value
remediation_hint="Too much activity on host")
A metric stream health check has the details listed below. Note that a custom_health_check only requires a name and check_arguments:
  • name - the name the health check will have in StackState.
  • description - the description for the health check in StackState.
  • deviating_value - the threshold at which point the check will return a deviating health state.
  • critical_value - the threshold at which point the check will return a critical health state.
  • remediation_hint - the remediation hint to display when the check returns a critical health state.
  • max_window - the max window size for the metrics.
  • percentile - for maximum_percentile and minimum_percentile checks only. The percentile value to use for the calculation.
  • stream identifier(s):
    • stream_id - for maximum_percentile, maximum_last, maximum_average, minimum_average, minimum_last, minimum_percentile checks. The identifier of the stream the check should run on.
    • denominator_stream_id - for maximum_ratio checks only. The identifier of the denominator stream the check should run on.
    • numerator_stream_id - for maximum_ratio checks only. The identifier of the numerator stream the check should run on.
    • success_stream_id - for failed_ratio checks only. The identifier of the success stream this check should run on.
    • failed_stream_id - for failed_ratio checks only. The identifier of the failures stream this check should run on.

Service check stream

A Service Check stream can be added to a component using the ServiceCheckStream class. It expects a stream name and conditions for the metric telemetry query in StackState. Service Check Streams has one out of the box supported check which can be mapped using the stream identifier.
class ServiceCheckStream(TelemetryStream):
"""
creates a service check stream definition for the component that will bind service checks in StackState for the
conditions.
args: `name, conditions
`name` The name for the stream in StackState
`conditions` is a dictionary of key -> value arguments that are used to filter the event values for the stream.
"""
​
class ServiceCheckHealthChecks(object):
​
def service_check_health(stream_id, name, description=None, remediation_hint=None):
"""
Check that returns the service check status as a health status in StackState
args: `stream_id, name, description, remediation_hint`
`stream_id` the identifier of the stream this check should run on
`name` the name this check will have in StackState
`description` the description for this check in StackState
`remediation_hint` the remediation hint to display when this check return a critical health state
"""

Override base class methods

Depending on the configuration used, overriding the __init__ constructor may cause the Agent to create several check instances, each calling the method.
The best practice recommendation is not to override anything from the base class, except the check method. However, sometimes it might be useful for a check to have its own constructor. In such cases, the __init__ constructor can be overridden with __init__ method using the following convention:
from stackstate_checks.checks import AgentCheck
​
class MyCheck(AgentCheck):
def __init__(self, name, init_config, instances):
super(MyCheck, self).__init__(name, init_config, instances)
The following arguments are required to pass to super:
  • name - the name of the check.
  • init_config - the init_config section of the configuration files.
  • instances - a one-element list that contains the instance options from the configuration file.

Logging

The self.log field is a Python logger (python.org) instance that prints to the main Agent log file. The log level can be set in the Agent configuration file stackstate.yaml.
Example - logging
def _collect_type(self, key, mapping, the_type):
self.log.debug("Collecting data with %s" % key)
if key not in mapping:
self.log.debug("%s returned None" % key)
return None
self.log.debug("Collecting done, value %s" % mapping[key])
return the_type(mapping[key])

Error handling

A check should raise a significant exception when it cannot work correctly, for example due to a wrong configuration or runtime error. Exceptions are logged and shown in the Agent status page. The warning method can be used to log a warning message and display it on the Agent status page.
self.warning("This will be visible in the status page")
Example - warning message
if len(queries) > max_custom_queries:
self.warning("Max number (%s) of custom queries reached. Skipping the rest."
% max_custom_queries)

See also