This is an HPE exporter for Prometheus.
It supports both the regular /metrics
endpoint, exposing metrics from the
host that the exporter is running on, as well as an /ipmi
endpoint that
supports IPMI over RMCP - one exporter running on one host can be used to
monitor a large number of IPMI interfaces by passing the target
parameter to
a scrape.
The exporter relies on tools from the FreeIPMI suite for the actual IPMI implementation.
You need a Go development environment. Then, run the following to get the source code and build and install the binary:
go get github.com/pyguy/hpe-exporter
A minimal invocation looks like this:
./hpe_exporter
Supported parameters include:
web.listen-address
: the address/port to listen on (default:":9290"
)config.file
: path to the configuration file (default:hpe.yml
)path
: path to the FreeIPMI executables (default: rely on$PATH
)
Make sure you have the following tools from the FreeIPMI suite installed:
ipmimonitoring
ipmi-dcmi
bmc-info
Simply scraping the standard /metrics
endpoint will make the exporter emit
local IPMI metrics. No special configuration is required.
For remote metrics, the general configuration pattern is similar to that of the
blackbox exporter, i.e.
Prometheus scrapes a small number (possibly one) of IPMI exporters with a
target
URL parameter to tell the exporter which IPMI device it should use to
retrieve the IPMI metrics. We offer this approach as IPMI devices often provide
useful information even while the supervised host is turned off. If you are
running the exporter on a separate host anyway, it makes more sense to have
only a few of them, each probing many (possibly thousands of) IPMI devices,
rather than one exporter per IPMI device.
The exporter requires a configuration file called ipmi.yml
(can be
overridden, see above). To collect local metrics, an empty file is technically
sufficient. For remote metrics, it must contain user names and passwords for
IPMI access to all targets. It supports a “default” target, which is used as
fallback if the target is not explicitly listed in the file.
The configuration file also supports a blacklist of sensors, useful in case of OEM-specific sensors that FreeIPMI cannot deal with properly or otherwise misbehaving sensors. This applies to both local and remote metrics.
See the included ipmi.yml
file for an example.
Collecting local IPMI metrics is fairly straightforward. Simply configure your server to scrape the default metrics endpoint on the hosts running the exporter.
- job_name: ipmi
scrape_interval: 1m
scrape_timeout: 30s
metrics_path: /metrics
scheme: http
static_configs:
- targets:
- 10.1.2.23:9290
- 10.1.2.24:9290
- 10.1.2.25:9290
To add your IPMI targets to Prometheus, you can use any of the supported service discovery mechanism of your choice. The following example uses the file-based SD and should be easy to adjust to other scenarios.
Create a YAML file that contains a list of targets, e.g.:
---
- targets:
- 10.1.2.23
- 10.1.2.24
- 10.1.2.25
- 10.1.2.26
- 10.1.2.27
- 10.1.2.28
- 10.1.2.29
- 10.1.2.30
labels:
job: ipmi_exporter
This file needs to be stored on the Prometheus server host. Assuming that this
file is called /srv/ipmi_exporter/targets.yml
, and the IPMI exporter is
running on a host that has the DNS name ipmi-exporter.internal.example.com
,
add the following to your Prometheus config:
- job_name: ipmi
scrape_interval: 1m
scrape_timeout: 30s
metrics_path: /ipmi
scheme: http
file_sd_configs:
- files:
- /srv/ipmi_exporter/targets.yml
refresh_interval: 5m
relabel_configs:
- source_labels: [__address__]
separator: ;
regex: (.*)(:80)?
target_label: __param_target
replacement: ${1}
action: replace
- source_labels: [__param_target]
separator: ;
regex: (.*)
target_label: instance
replacement: ${1}
action: replace
- separator: ;
regex: .*
target_label: __address__
replacement: ipmi-exporter.internal.example.com:9290
action: replace
For more information, e.g. how to use mechanisms other than a file to discover the list of hosts to scrape, please refer to the Prometheus documentation.
These metrics provide data about the scrape itself:
ipmi_up{collector="<NAME>"}
is1
if the data for this collector could successfully be retrieved from the remote host,0
otherwise. The following collectors are available:ipmi
: collects IPMI sensor data. If it fails, sensor metrics (see below) will not be availabledcmi
: collects DCMI data, currently only power consumption. If it fails, power consumption metrics (see below) will not be availablebmc
: collects BMC details. If if fails, BMC info metrics (see below) will not be available
ipmi_scrape_duration_seconds
is the amount of time it took to retrieve the data
For some basic information, there is a constant metric ipmi_bmc_info
with
value 1
and labels providing the firmware revision and manufacturer as
returned from the BMC. Example:
ipmi_bmc_info{firmware_revision="2.52",manufacturer_id="Dell Inc. (674)"} 1
The metric ipmi_dcmi_power_consumption_current_watts
can be used to monitor
the live power consumption of the machine in Watts. If in doubt, this metric
should be used over any of the sensor data (see below), even if their name
might suggest that they measure the same thing. This metric has no labels.
IPMI sensors in general have one or two distinct pieces of information that are of interest: a value and/or a state. The exporter always exports both, even if the value is NaN or the state non-sensical. This is so one can still always find the metrics to avoid ending up in a situation where one is looking for e.g. the value of a sensor that is in a critical state, but can't find it and assume this to be a problem.
The state of a sensor can be one of nominal, warning, critical, or N/A,
reflected by the metric values 0
, 1
, 2
, and NaN
respectively. Think of
this as a kind of severity.
For sensors with known semantics (i.e. units), corresponding specific metrics are exported. For everything else, generic metrics are exported.
Temperature sensors measure a temperature in degrees Celsius and their state usually reflects the temperature going above the vendor-recommended value. For each temperature sensor, two metrics are exported (state and value), using the sensor ID and the sensor name as labels. Example:
ipmi_temperature_celsius{id="18",name="Inlet Temp"} 24
ipmi_temperature_state{id="18",name="Inlet Temp"} 0
Fan speed sensors measure fan speed in rotations per minute (RPM) and their state usually reflects the speed being to low, indicating the fan might be broken. For each fan speed sensor, two metrics are exported (state and value), using the sensor ID and the sensor name as labels. Example:
ipmi_fan_speed_rpm{id="12",name="Fan1A"} 4560
ipmi_fan_speed_state{id="12",name="Fan1A"} 0
Voltage sensors measure a voltage in Volts. For each voltage sensor, two metrics are exported (state and value), using the sensor ID and the sensor name as labels. Example:
ipmi_voltage_state{id="2416",name="12V"} 0
ipmi_voltage_volts{id="2416",name="12V"} 12
Current sensors measure a current in Amperes. For each current sensor, two metrics are exported (state and value), using the sensor ID and the sensor name as labels. Example:
ipmi_current_state{id="83",name="Current 1"} 0
ipmi_current_amperes{id="83",name="Current 1"} 0
Power sensors measure power in Watts. For each power sensor, two metrics are exported (state and value), using the sensor ID and the sensor name as labels. Example:
ipmi_power_state{id="90",name="Pwr Consumption"} 0
ipmi_power_watts{id="90",name="Pwr Consumption"} 70
Note that based on our observations, this may or may not be a reading reflecting the actual live power consumption. We recommend using the more explicit power consumption metrics for this.
For all sensors that can not be classified, two generic metrics are exported, the state and the value. However, to provide a little more context, the sensor type is added as label (in addition to name and ID). Example:
ipmi_sensor_state{id="139",name="Power Cable",type="Cable/Interconnect"} 0
ipmi_sensor_value{id="139",name="Power Cable",type="Cable/Interconnect"} NaN