def _vs_elements(cls):
return [
("title", TextUnicode(default_value="", title=_("Figure title"))),
("metric",
MetricName()), # MetricChoice would be nicer, but is CEE
("rrd_consolidation",
DropdownChoice(
choices=[
("average", _("Average")),
("min", _("Minimum")),
("max", _("Maximum")),
],
default_value="average",
title="RRD consolidation",
help=
_("Consolidation function for the [cms_graphing#rrds|RRD] data column"
),
)),
(
"time_range",
CascadingDropdown(
title=_("Timerange"),
orientation="horizontal",
choices=[
("current", _("Only show current value")),
(
"range",
_("Show historic values"),
# TODO: add RRD consolidation, here and in _get_data below
Timerange(title=_("Time range to consider"),
default_value="d0",
allow_empty=True)),
],
default_value="current"))
]
def _vs_elements() -> DictionaryElements:
return [
("metric", MetricName()),
("time_range", Timerange(
title=_("Time range"),
default_value='d0',
)),
(
"metric_color",
ColorWithThemeOrMetricDefault(
title=_("Color of the scattered dots"),
default_value="default",
),
),
(
"avg_color",
ColorWithThemeOrMetricDefault(
title=_("Color of the average"),
default_value="default",
),
),
(
"median_color",
ColorWithThemeOrMetricDefault(
title=_("Color of the median"),
default_value="default",
),
),
]
def _vs_elements(cls):
return [
("metric", MetricName()
), # MetricChoice would be nicer, but we use the context filters
("time_range",
CascadingDropdown(
title=_("Timerange"),
orientation="horizontal",
choices=[
("current", _("Only show current value")),
("range", _("Show historic values"),
Dictionary(
optional_keys=False,
elements=[
('window',
Timerange(title=_("Time range to consider"),
default_value="d0",
allow_empty=True)),
("rrd_consolidation",
DropdownChoice(
choices=[
("average", _("Average")),
("min", _("Minimum")),
("max", _("Maximum")),
],
default_value="max",
title="RRD consolidation",
help=
_("Consolidation function for the [cms_graphing#rrds|RRD] data column"
),
)),
])),
],
default_value="current"))
]
def vs_parameters(cls):
return Dictionary(title=_("Properties"),
render="form",
optional_keys=[],
elements=[
("metric", MetricName()),
("time_range", Timerange(
title=_("Time range"),
default_value='d0',
)),
])
def _vs_elements() -> DictionaryElements:
return [
("metric", MetricName()),
("time_range",
Timerange(
title=_("Time range"),
default_value='d0',
)),
("metric_color",
GraphColor(title=_("Color for the main metric scattered dots"),
default_value="default")),
("avg_color",
GraphColor(title=_("Color for the average line"),
default_value="default")),
("median_color",
GraphColor(title=_("Color for the median line"),
default_value="default")),
]
def vs_parameters(cls):
return Dictionary(
title=_("Properties"),
render="form",
optional_keys=[],
elements=[
("metric", MetricName()),
("time_range",
Timerange(
title=_("Time range"),
default_value='d0',
)),
("metric_color",
GraphColor(
title=_("Color for the main metric scattered dots"),
default_value="default")),
("avg_color",
GraphColor(title=_("Color for the average line"),
default_value="default")),
("median_color",
GraphColor(title=_("Color for the median line"),
default_value="default")),
])
def vs_parameters(cls):
return Dictionary(title=_("Properties"),
render="form",
optional_keys=[],
elements=cls._scatterplot_vs_components() +
[("metric", MetricName())])
def _vs_elements(cls):
return [
("metric", MetricName()
), # MetricChoice would be nicer, but we use the context filters
("time_range",
CascadingDropdown(
title=_("Timerange"),
orientation="horizontal",
choices=[
("current", _("Only show current value")),
("range", _("Show historic values"),
Dictionary(
optional_keys=False,
elements=[
('window',
Timerange(title=_("Time range to consider"),
default_value="d0",
allow_empty=True)),
("rrd_consolidation",
DropdownChoice(
choices=[
("average", _("Average")),
("min", _("Minimum")),
("max", _("Maximum")),
],
default_value="max",
title="RRD consolidation",
help=
_("Consolidation function for the [cms_graphing#rrds|RRD] data column"
),
)),
])),
],
default_value="current")),
("display_range",
CascadingDropdown(
title=_("Display range"),
choices=[
("infer",
_("Infer range from available data and check output")),
("fixed", _("Fixed range"),
Tuple(
title=_("Fixed range"),
help=_(
"Use a fixed range for the Data displayed. This "
"value has no scaling. If your metric is in the "
"range of MB, you'll need to place 9 zeros for "
"display to make sense."),
elements=[
Float(title=_("Minimum")),
Float(title=_("Maximum")),
])),
])),
("status_border",
DropdownChoice(
title=_("Status border"),
choices=[
(False, _("Do not show any service status border")),
("not_ok",
_("Draw a status border when service is not OK")),
("always",
_("Always draw the service status on the border")),
],
default_value="not_ok")),
]
def _vs_elements(with_elements) -> DictionaryElements:
yield ("metric", MetricName()
) # MetricChoice would be nicer, but we use the context filters
if "time_range" in with_elements:
yield "time_range", CascadingDropdown(
title=_("Timerange"),
orientation="horizontal",
choices=[
(
"current",
_("Only show current value"),
),
(
"range",
_("Show historic values"),
Dictionary(
optional_keys=False,
elements=_time_range_historic_dict_elements(
with_elements),
),
),
],
default_value="current",
)
if "display_range" in with_elements:
def validate_range(value, varprefix):
_min, _max = value
if _min >= _max:
raise MKUserError(
varprefix,
_("Display range: Minimum must be strictly less than maximum"
))
fix_range: CascadingDropdownChoice = (
"fixed", _("Fixed range"),
ValuesWithUnits(vs_name="display_range",
metric_vs_name="metric",
help=_(
"Set the range in which data is displayed. "
"Having selected a metric before auto selects "
"here the matching unit of the metric."),
elements=[_("Minimum"), _("Maximum")],
validate_value_elemets=validate_range))
auto_range: CascadingDropdownChoice = (
"automatic", _("Automatically adjusted to available data"))
choices = [fix_range] + ([auto_range]
if "automatic_range" in with_elements else [])
yield "display_range", CascadingDropdown(
title=_("Data range"),
choices=choices,
default_value="automatic"
if "automatic_range" in with_elements else "fixed")
if "toggle_range_display" in with_elements:
yield "toggle_range_display", DropdownChoice(
title=_("Show range limits"),
choices=[(True, _("Show the limits of values displayed")),
(False, _("Don't show information of limits"))])
if "inner_state_display" in with_elements:
yield "inner_state_display", DropdownChoice(
title=_("Metric color"),
choices=[
(None, _("Follow theme default")),
("service", _("Color value after SERVICE state")),
("metric",
_("Color value following it's METRIC THRESHOLDS if available")
)
],
)
if "status_border" in with_elements:
yield "status_border", DropdownChoice(
title=_("Status border"),
choices=[
(None, _("Do not show any status border")),
("service", _("Draw a status border when SERVICE is not OK")),
("metric",
_("Draw a status border when METRICS THRESHOLDS are crossed")
),
],
default_value="service")
def _valuespec_generic_metrics_prometheus():
namespace_element = (
"prepend_namespaces",
DropdownChoice(
title=_("Prepend namespace prefix for hosts"),
help=_(
"If a cluster uses multiple namespaces you need to activate this option. "
"Hosts for namespaced Kubernetes objects will then be prefixed with the "
"name of their namespace. This makes Kubernetes resources in different "
"namespaces that have the same name distinguishable, but results in "
"longer hostnames."
),
choices=[
("use_namespace", _("Use a namespace prefix")),
("omit_namespace", _("Don't use a namespace prefix")),
],
),
)
return Transform(
valuespec=Dictionary(
elements=[
(
"connection",
CascadingDropdown(
choices=[
(
"ip_address",
_("IP Address"),
Dictionary(
elements=api_request_connection_elements(
help_text=_(
"Specifies a URL path prefix, which is prepended to API calls "
"to the Prometheus API. If this option is not relevant for "
"your installation, please leave it unchecked."
),
default_port=6443,
),
),
),
(
"host_name",
_("Host name"),
Dictionary(
elements=api_request_connection_elements(
help_text=_(
"Specifies a URL path prefix, which is prepended to API calls "
"to the Prometheus API. If this option is not relevant for "
"your installation, please leave it unchecked."
),
default_port=6443,
),
),
),
(
"url_custom",
_("Custom URL"),
Dictionary(
elements=[
(
"url_address",
TextInput(
title=_("Custom URL server address"),
help=_(
"Specify a custom URL to connect to "
"your server. Do not include the "
"protocol. This option overwrites "
"all available options such as port and "
"other URL prefixes."
),
allow_empty=False,
),
)
],
optional_keys=[],
),
),
],
title=_("Prometheus connection option"),
),
),
ssl_verification(),
api_request_authentication(),
(
"protocol",
DropdownChoice(
title=_("Protocol"),
choices=[
("http", "HTTP"),
("https", "HTTPS"),
],
),
),
(
"exporter",
ListOf(
valuespec=CascadingDropdown(
choices=[
(
"node_exporter",
_("Node Exporter"),
Dictionary(
elements=[
(
"host_mapping",
Hostname(
title=_("Explicitly map Node Exporter host"),
allow_empty=True,
help=_(
"Per default, Checkmk tries to map the underlying Checkmk host "
"to the Node Exporter host which contains either the Checkmk "
'hostname, host address or "localhost" in its endpoint address. '
"The created services of the mapped Node Exporter will "
"be assigned to the Checkmk host. A piggyback host for each "
"Node Exporter host will be created if none of the options are "
"valid. "
"This option allows you to explicitly map one of your Node "
"Exporter hosts to the underlying Checkmk host. This can be "
"used if the default options do not apply to your setup."
),
),
),
(
"entities",
ListChoice(
choices=[
("df", _("Filesystems")),
("diskstat", _("Disk IO")),
("mem", _("Memory")),
(
"kernel",
_(
"CPU utilization & Kernel performance"
),
),
],
default_value=[
"df",
"diskstat",
"mem",
"kernel",
],
allow_empty=False,
title=_("Retrieve information about..."),
help=_(
"For your respective kernel select the hardware or OS entity "
"you would like to retrieve information about."
),
),
),
],
title=_("Node Exporter metrics"),
optional_keys=["host_mapping"],
),
),
(
"kube_state",
_("Kube-state-metrics"),
Dictionary(
elements=[
(
"cluster_name",
Hostname(
title=_("Cluster name"),
allow_empty=False,
help=_(
"You must specify a name for your Kubernetes cluster. The provided name"
" will be used to create a piggyback host for the cluster related services."
),
),
),
namespace_element,
filter_kubernetes_namespace_element(),
(
"entities",
ListChoice(
choices=[
("cluster", _("Cluster")),
("nodes", _("Nodes")),
("services", _("Services")),
("pods", _("Pods")),
("daemon_sets", _("Daemon sets")),
],
default_value=[
"cluster",
"nodes",
"services",
"pods",
"daemon_sets",
],
allow_empty=False,
title=_("Retrieve information about..."),
help=_(
"For your Kubernetes cluster select for which entity levels "
"you would like to retrieve information about. Piggyback hosts "
"for the respective entities will be created."
),
),
),
],
title=_("Kube state metrics"),
optional_keys=["namespace_include_patterns"],
),
),
(
"cadvisor",
_("cAdvisor"),
Dictionary(
elements=[
(
"entity_level",
CascadingDropdown(
title=_(
"Entity level used to create Checkmk piggyback hosts"
),
help=_(
"The retrieved information from the cAdvisor will be aggregated according"
" to the selected entity level. Resulting services will be allocated to the created"
" Checkmk piggyback hosts."
),
choices=[
(
"container",
_(
"Container - Display the information on container level"
),
Dictionary(
elements=[
(
"container_id",
DropdownChoice(
title=_(
"Host name used for containers"
),
help=_(
"For Containers - Choose which identifier is used for the monitored containers."
" This will affect the name used for the piggyback host"
" corresponding to the container, as well as items for"
" services created on the node for each container."
),
choices=[
(
"short",
_(
"Short - Use the first 12 characters of the docker container ID"
),
),
(
"long",
_(
"Long - Use the full docker container ID"
),
),
(
"name",
_(
"Name - Use the name of the container"
),
),
],
),
)
],
optional_keys=[],
),
),
(
"pod",
_(
"Pod - Display the information for pod level"
),
Dictionary(
elements=[namespace_element],
optional_keys=[],
),
),
(
"both",
_(
"Both - Display the information for both, pod and container, levels"
),
Dictionary(
elements=[
(
"container_id",
DropdownChoice(
title=_(
"Host name used for containers"
),
help=_(
"For Containers - Choose which identifier is used for the monitored containers."
" This will affect the name used for the piggyback host"
" corresponding to the container, as well as items for"
" services created on the node for each container."
),
choices=[
(
"short",
_(
"Short - Use the first 12 characters of the docker container ID"
),
),
(
"long",
_(
"Long - Use the full docker container ID"
),
),
(
"name",
_(
"Name - Use the name of the container"
),
),
],
),
),
namespace_element,
],
optional_keys=[],
),
),
],
),
),
filter_kubernetes_namespace_element(),
(
"entities",
ListChoice(
choices=[
("diskio", _("Disk IO")),
("cpu", _("CPU utilization")),
("df", _("Filesystem")),
("if", _("Network")),
("memory", _("Memory")),
],
default_value=[
"diskio",
"cpu",
"df",
"if",
"memory",
],
allow_empty=False,
title=_("Retrieve information about..."),
help=_(
"For your respective kernel select the hardware or OS entity "
"you would like to retrieve information about."
),
),
),
],
title=_("CAdvisor"),
validate=_check_not_empty_exporter_dict,
optional_keys=[
"diskio",
"cpu",
"df",
"if",
"memory",
"namespace_include_patterns",
],
),
),
]
),
add_label=_("Add new Scrape Target"),
title=_(
"Prometheus Scrape Targets (include Prometheus Exporters) to fetch information from"
),
help=_(
"You can specify which Scrape Targets including Exporters "
"are connected to your Prometheus instance. The Prometheus "
"Special Agent will automatically generate services for the "
"selected monitoring information. You can create your own "
"defined services with the custom PromQL query option below "
"if one of the Scrape Target types are not listed here."
),
),
),
(
"promql_checks",
ListOf(
valuespec=Dictionary(
elements=[
(
"service_description",
TextInput(
title=_("Service name"),
allow_empty=False,
),
),
(
"host_name",
Hostname(
title=_("Assign service to following host"),
allow_empty=False,
help=_(
"Specify the host to which the resulting "
"service will be assigned to. The host "
"should be configured to allow Piggyback "
"data."
),
),
),
(
"metric_components",
ListOf(
valuespec=Dictionary(
title=_("PromQL query"),
elements=[
(
"metric_label",
TextInput(
title=_("Metric label"),
allow_empty=False,
help=_(
"The metric label is displayed alongside the "
"queried value in the status detail the resulting service. "
"The metric name will be taken as label if "
"nothing was specified."
),
),
),
("metric_name", MetricName()),
(
"promql_query",
TextInput(
title=_(
"PromQL query (only single return value permitted)"
),
allow_empty=False,
size=80,
help=_(
'Example PromQL query: up{job="node_exporter"}'
),
),
),
(
"levels",
Dictionary(
elements=[
(
"lower_levels",
Tuple(
title=_("Lower levels"),
elements=[
Float(
title=_("Warning below")
),
Float(
title=_(
"Critical below"
)
),
],
),
),
(
"upper_levels",
Tuple(
title=_("Upper levels"),
elements=[
Float(
title=_("Warning at")
),
Float(
title=_("Critical at")
),
],
),
),
],
title="Metric levels",
validate=_verify_prometheus_empty,
help=_(
"Specify upper and/or lower levels for the queried PromQL value. This option "
"should be used for simple cases where levels are only required once. You "
"should use the Prometheus custom services monitoring rule if you want to "
"specify a rule which applies to multiple Prometheus custom services at once. "
"The custom rule always has priority over the rule specified here "
"if the two overlap."
),
),
),
],
optional_keys=["metric_name", "levels"],
),
title=_("PromQL queries for Service"),
add_label=_("Add new PromQL query"),
allow_empty=False,
magic="@;@",
validate=_validate_prometheus_service_metrics,
),
),
],
optional_keys=["host_name"],
),
title=_("Service creation using PromQL queries"),
add_label=_("Add new Service"),
),
),
],
title=_("Prometheus"),
optional_keys=["auth_basic"],
),
forth=_transform_agent_prometheus,
)
def _vs_elements(cls):
return [
("metric", MetricName()
), # MetricChoice would be nicer, but we use the context filters
("time_range",
CascadingDropdown(
title=_("Timerange"),
orientation="horizontal",
choices=[
("current", _("Only show current value")),
("range", _("Show historic values"),
Dictionary(
optional_keys=False,
elements=[
('window',
Timerange(title=_("Time range to consider"),
default_value="d0",
allow_empty=True)),
("rrd_consolidation",
DropdownChoice(
choices=[
("average", _("Average")),
("min", _("Minimum")),
("max", _("Maximum")),
],
default_value="max",
title="RRD consolidation",
help=
_("Consolidation function for the [cms_graphing#rrds|RRD] data column"
),
)),
])),
],
default_value="current")),
("display_range",
CascadingDropdown(
title=_("Display range"),
choices=[
("infer", _("Automatic")),
("fixed", _("Fixed range"),
ValuesWithUnits(
vs_name="display_range",
metric_vs_name="metric",
help=_(
"Set the range in which data is displayed. "
"Having selected a metric before auto selects "
"here the matching unit of the metric."),
elements=[_("Minimum"), _("Maximum")])),
])),
("status_border",
DropdownChoice(
title=_("Status border"),
choices=[
(False, _("Do not show any service status border")),
("not_ok",
_("Draw a status border when service is not OK")),
("always",
_("Always draw the service status on the border")),
],
default_value="not_ok")),
]
def _vs_elements(with_elements) -> DictionaryElements:
yield ("metric", MetricName()
) # MetricChoice would be nicer, but we use the context filters
if "time_range" in with_elements:
yield "time_range", CascadingDropdown(
title=_("Timerange"),
orientation="horizontal",
choices=[
(
"current",
_("Only show current value"),
),
(
"range",
_("Show historic values"),
Dictionary(
optional_keys=False,
elements=_time_range_historic_dict_elements(
with_elements),
),
),
],
default_value="current",
)
if "display_range" in with_elements:
def validate_range(value, varprefix):
_min, _max = value
if _min >= _max:
raise MKUserError(
varprefix,
_("Display range: Minimum must be strictly less than maximum"
))
yield "display_range", CascadingDropdown(
title=_("Display range"),
choices=[
("fixed", _("Fixed range"),
ValuesWithUnits(
vs_name="display_range",
metric_vs_name="metric",
help=_("Set the range in which data is displayed. "
"Having selected a metric before auto selects "
"here the matching unit of the metric."),
elements=[_("Minimum"), _("Maximum")],
validate_value_elemets=validate_range)),
# ("infer", _("Automatic")), # For future logic
],
default_value="fixed")
if "metric_status_display" in with_elements:
yield "metric_status_display", DropdownChoice(
title=_("Metric Status"),
choices=[(None, _("Metric value is displayed in neutral color")),
("text", _("Metric state is colored in its value")),
("background",
_("Metric status is colored on the dashlet background"))
],
)
if "status_border" in with_elements:
yield "status_border", DropdownChoice(
title=_("Status border"),
choices=[
(False, _("Do not show any service status border")),
("not_ok", _("Draw a status border when service is not OK")),
("always", _("Always draw the service status on the border")),
],
default_value="not_ok")
