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Quick pass on the writing exporters docs (#935) 

Quick pass on the writing exporters docs to make them clearer and more consistent
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content/docs/instrumenting/writing_exporters.md
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# Writing exporters
When directly instrumenting your own code, the general rules of how to
instrument code with a Prometheus client library can be followed quite
directly. When taking metrics from another monitoring or instrumentation
system, things tend not to be so black and white.
If you are instrumenting your own code, the [general rules of how to
instrument code with a Prometheus client
library](/docs/practices/instrumentation/) should be followed. When
taking metrics from another monitoring or instrumentation system, things
tend not to be so black and white.
This document contains things you should consider when writing an exporter or
custom collector. The theory covered will also be of interest to those doing
direct instrumentation.
This document contains things you should consider when writing an
exporter or custom collector. The theory covered will also be of
interest to those doing direct instrumentation.
If you are writing an exporter and are unclear on anything here, contact us on
IRC (#prometheus on Freenode) or the [mailing list](/community).
If you are writing an exporter and are unclear on anything here, please
contact us on IRC (#prometheus on Freenode) or the [mailing
list](/community).
## Maintainability and purity
The main decision you need to make when writing an exporter is how much work
you’re willing to put in to get perfect metrics out of it.
The main decision you need to make when writing an exporter is how much
work you’re willing to put in to get perfect metrics out of it.
If the system in question has only a handful of metrics that rarely change,
then getting everything perfect is an easy choice (e.g. the [haproxy
exporter](https://github.com/prometheus/haproxy_exporter)).
If the system in question has only a handful of metrics that rarely
change, then getting everything perfect is an easy choice, a good
example of this is the [HAProxy
exporter](https://github.com/prometheus/haproxy_exporter).
If on the other hand the system has hundreds of metrics that change
continuously with new versions, if you try to get things perfect then you’ve
signed yourself up for a lot of ongoing work. The [mysql
exporter](https://github.com/prometheus/mysqld_exporter) is on this end of the
spectrum.
On the other hand, if you try to get things perfect when the system has
hundreds of metrics that change frequently with new versions, then
you’ve signed yourself up for a lot of ongoing work. The [MySQL
exporter](https://github.com/prometheus/mysqld_exporter) is on this end
of the spectrum.
The [node exporter](https://github.com/prometheus/node_exporter) is a mix,
varying by module. For mdadm we have to hand-parse a file and come up with our
own metrics, so we may as well get the metrics right while we’re at it. For
meminfo on the other hand, the results vary across kernel versions so we end up
doing just enough of a transform to create valid metrics.
The [node exporter](https://github.com/prometheus/node_exporter) is a
mix of these, with complexity varying by module. For example, the
`mdadm` collector hand-parses a file and exposes metrics created
specifically for that collector, so we may as well get the metrics
right. For the `meminfo` collector the results vary across kernel
versions so we end up doing just enough of a transform to create valid
metrics.
## Configuration
When working with applications, you should aim for an exporter that requires no
custom configuration by the user beyond telling it where the application is.
You may also need to offer the ability to filter out certain metrics if they
may be too granular and expensive on large setups (e.g. the haproxy exporter
allows filtering of per-server stats). Similarly there may be expensive metrics
When working with applications, you should aim for an exporter that
requires no custom configuration by the user beyond telling it where the
application is. You may also need to offer the ability to filter out
certain metrics if they may be too granular and expensive on large
setups, for example the [HAProxy
exporter](https://github.com/prometheus/haproxy_exporter) allows
filtering of per-server stats. Similarly, there may be expensive metrics
that are disabled by default.
When working with monitoring systems, frameworks and protocols things are not
so simple.
In the best case the system in question has a similar enough data model to
Prometheus that you can automatically determine how to transform metrics. This
is the case for Cloudwatch, SNMP and Collectd. At most we need the ability to
let the user select which metrics they want to pull out.
In the more common case metrics from the system are completely non-standard,
depending on how the user is using it and what the underlying application is.
In that case the user has to tell us how to transform the metrics. The JMX
exporter is the worst offender here, with the graphite and statsd exporters
also requiring configuration to extract labels.
Providing something that produces some output out of the box and a selection of
example configurations is advised. When writing configurations for such
exporters, this document should be kept in mind.
YAML is the standard Prometheus configuration format.
When working with other monitoring systems, frameworks and protocols you
will often need to provide additional configuration or customization to
generate metrics suitable for Prometheus. In the best case scenario, a
monitoring system has a similar enough data model to Prometheus that you
can automatically determine how to transform metrics. This is the case
for [Cloudwatch](https://github.com/prometheus/cloudwatch_exporter),
[SNMP](https://github.com/prometheus/snmp_exporter) and
[collectd](https://github.com/prometheus/collectd_exporter). At most, we
need the ability to let the user select which metrics they want to pull
out.
In other cases, metrics from the system are completely non-standard,
depending on the usage of the system and the underlying application. In
that case the user has to tell us how to transform the metrics. The [JMX
exporter](https://github.com/prometheus/jmx_exporter) is the worst
offender here, with the
[Graphite](https://github.com/prometheus/graphite_exporter) and
[StatsD](https://github.com/prometheus/statsd_exporter) exporters also
requiring configuration to extract labels.
Ensuring the exporter works out of the box without configuration, and
providing a selection of example configurations for transformation if
required, is advised.
YAML is the standard Prometheus configuration format, all configuration
should use YAML by default.
## Metrics
......@@ -73,111 +87,119 @@ YAML is the standard Prometheus configuration format.
Follow the [best practices on metric naming](/docs/practices/naming).
Generally metric names should allow someone who’s familiar with Prometheus but
not a particular system to make a good guess as to what a metric means. A
metric named `http_requests_total` is not extremely useful - are these being
measured as they come in, in some filter or when they get to the user’s code?
And `requests_total` is even worse, what type of requests?
Generally metric names should allow someone who is familiar with
Prometheus but not a particular system to make a good guess as to what a
metric means. A metric named `http_requests_total` is not extremely
useful - are these being measured as they come in, in some filter or
when they get to the user’s code? And `requests_total` is even worse,
what type of requests?
To put it another way with direct instrumentation, a given metric should exist
within exactly one file. Accordingly within exporters and collectors, a metric
should apply to exactly one subsystem and be named accordingly.
With direct instrumentation, a given metric should exist within exactly
one file. Accordingly, within exporters and collectors, a metric should
apply to exactly one subsystem and be named accordingly.
Metric names should never be procedurally generated, except when writing a
custom collector or exporter.
Metric names should never be procedurally generated, except when writing
a custom collector or exporter.
Metric names for applications should generally be prefixed by the exporter
name, e.g. `haproxy_up`.
Metric names for applications should generally be prefixed by the
exporter name, e.g. `haproxy_up`.
Metrics must use base units (e.g. seconds, bytes) and leave converting them to
something more readable to the graphing software. No matter what units you end
up using, the units in the metric name must match the units in use. Similarly
expose ratios, not percentages (though a counter for each of the two components
of the ratio is better).
Metrics must use base units (e.g. seconds, bytes) and leave converting
them to something more readable to graphing tools. No matter what units
you end up using, the units in the metric name must match the units in
use. Similarly, expose ratios, not percentages. Even better, specify a
counter for each of the two components of the ratio.
Metric names should not include the labels that they’re exported with (e.g.
`by_type`) as that won’t make sense if the label is aggregated away.
Metric names should not include the labels that they’re exported with,
e.g. `by_type`, as that won’t make sense if the label is aggregated
away.
The one exception is when you’re exporting the same data with different labels
via multiple metrics, in which case that’s usually the sanest way to
distinguish them. For direct instrumentation this should only come up when
exporting a single metric with all the labels would have too high a
cardinality.
The one exception is when you’re exporting the same data with different
labels via multiple metrics, in which case that’s usually the sanest way
to distinguish them. For direct instrumentation, this should only come
up when exporting a single metric with all the labels would have too
high a cardinality.
Prometheus metrics and label names are written in `snake_case`. Converting
`camelCase` to `snake_case` is desirable, though doing so automatically
doesn’t always produce nice results for things like `myTCPExample` or `isNaN`
so sometimes it’s best to leave them as-is.
Prometheus metrics and label names are written in `snake_case`.
Converting `camelCase` to `snake_case` is desirable, though doing so
automatically doesn’t always produce nice results for things like
`myTCPExample` or `isNaN` so sometimes it’s best to leave them as-is.
Exposed metrics should not contain colons, these are for users to use when
aggregating.
Exposed metrics should not contain colons, these are reserved for users
to use when aggregating.
Only `[a-zA-Z0-9:_]` are valid in metric names, any other characters should be
sanitized to an underscore.
Only `[a-zA-Z0-9:_]` are valid in metric names, any other characters
should be sanitized to an underscore.
The `_sum`, `_count`, `_bucket` and `_total` suffixes are used by Summaries,
Histograms and Counters. Unless you’re producing one of those, avoid these
suffixes.
The `_sum`, `_count`, `_bucket` and `_total` suffixes are used by
Summaries, Histograms and Counters. Unless you’re producing one of
those, avoid these suffixes.
`_total` is a convention for counters, you should use it if you’re using the
COUNTER type.
`_total` is a convention for counters, you should use it if you’re using
the COUNTER type.
The `process_` and `scrape_` prefixes are reserved. It’s okay to add your own
prefix on to these if they follow the [matching
The `process_` and `scrape_` prefixes are reserved. It’s okay to add
your own prefix on to these if they follow the [matching
semantics](https://docs.google.com/document/d/1Q0MXWdwp1mdXCzNRak6bW5LLVylVRXhdi7_21Sg15xQ/edit).
E.g. Prometheus has `scrape_duration_seconds` for how long a scrape took, it’s
good practice to have e.g. `jmx_scrape_duration_seconds` saying how long the
JMX collector took to do its thing. For process stats where you have access to
the pid, both Go and Python offer collectors that’ll handle this for you (see
the [haproxy exporter](https://github.com/prometheus/haproxy_exporter) for an
example).
When you have a successful request count and a failed request count, the best
way to expose this is as one metric for total requests and another metric for
failed requests. This makes it easy to calculate the failure ratio. Do not use
one metric with a failed/success label. Similarly with hit/miss for caches,
it’s better to have one metric for total and another for hits.
Consider the likelihood that someone using monitoring will do a code or web
search for the metric name. If the names are very well established and unlikely
to be used outside of the realm of people used to those names (e.g. SNMP and
network engineers) then leaving them as-is may be a good idea. This logic
doesn’t apply for e.g. MySQL as non-DBAs can be expected to be poking around
the metrics. A `HELP` string with the original name can provide most of the
same benefits as using the original names.
For example, Prometheus has `scrape_duration_seconds` for how long a
scrape took, it's good practice to also have an exporter-centric metric,
e.g. `jmx_scrape_duration_seconds`, saying how long the specific
exporter took to do its thing. For process stats where you have access
to the PID, both Go and Python offer collectors that’ll handle this for
you. A good example of this is the [HAProxy
exporter](https://github.com/prometheus/haproxy_exporter).
When you have a successful request count and a failed request count, the
best way to expose this is as one metric for total requests and another
metric for failed requests. This makes it easy to calculate the failure
ratio. Do not use one metric with a failed or success label. Similarly,
with hit or miss for caches, it’s better to have one metric for total and
another for hits.
Consider the likelihood that someone using monitoring will do a code or
web search for the metric name. If the names are very well-established
and unlikely to be used outside of the realm of people used to those
names, for example SNMP and network engineers, then leaving them as-is
may be a good idea. This logic doesn’t apply for all exporters, for
example the MySQL exporter metric's may be used by a variety of people,
not just DBAs. A `HELP` string with the original name can provide most
of the same benefits as using the original names.
### Labels
Read the [general
advice](/docs/practices/instrumentation/#things-to-watch-out-for) on labels.
advice](/docs/practices/instrumentation/#things-to-watch-out-for) on
labels.
Avoid `type` as a label name, it’s too generic and meaningless. You should also
try where possible to avoid names that are likely to clash with target labels,
such as `region`, `zone`, `cluster`, `availability_zone`, `az`, `datacenter`,
`dc`, `owner`, `customer`, `stage`, `service`, `environment` and `env` - though
if that’s what the application calls something it’s best not to cause confusion
by renaming it.
Avoid `type` as a label name, it’s too generic and often meaningless.
You should also try where possible to avoid names that are likely to
clash with target labels, such as `region`, `zone`, `cluster`,
`availability_zone`, `az`, `datacenter`, `dc`, `owner`, `customer`,
`stage`, `service`, `environment` and `env`. If, however, that’s what
the application calls some resource, it’s best not to cause confusion by
renaming it.
Avoid the temptation to put things into one metric just because they share a
prefix. Unless you’re sure something makes sense as one metric, multiple
metrics is safer.
Avoid the temptation to put things into one metric just because they
share a prefix. Unless you’re sure something makes sense as one metric,
multiple metrics is safer.
The label `le` has special meaning for Histograms, and `quantile` for
Summaries. Avoid these labels generally.
Read/write and send/receive are best as separate metrics, rather than as a
label. This is usually because you care about only one of them at a time, and
it’s easier to use them that way.
The rule of thumb is that one metric should make sense when summed or averaged.
There is one other case that comes up with exporters, and that’s where the data
is fundamentally tabular and doing otherwise would require users to do regexes
on metric names to be usable. Consider the voltage sensors on your
motherboard, while doing math across them is meaningless, it makes sense to
have them in one metric rather than having one metric per sensor. All values
within a metrics should (almost) always have the same unit (consider if fan
speeds were mixed in with the voltages, and you had no way to automatically
separate them).
Read/write and send/receive are best as separate metrics, rather than as
a label. This is usually because you care about only one of them at a
time, and it is easier to use them that way.
The rule of thumb is that one metric should make sense when summed or
averaged. There is one other case that comes up with exporters, and
that’s where the data is fundamentally tabular and doing otherwise would
require users to do regexes on metric names to be usable. Consider the
voltage sensors on your motherboard, while doing math across them is
meaningless, it makes sense to have them in one metric rather than
having one metric per sensor. All values within a metrics should
(almost) always have the same unit, for example consider if fan speeds
were mixed in with the voltages, and you had no way to automatically
separate them.
Don’t do this:
......@@ -195,32 +217,35 @@ my_metric{label=b} 6
<b>my_metric{} 7</b>
</pre>
The former breaks people who do a `sum()` over your metric, and the latter
breaks sum and also is quite difficult to work with. Some client libraries
(e.g. Go) will actively try to stop you doing the latter in a custom collector,
and all client libraries should stop you from doing the former with direct
instrumentation. Never do either of these, rely on Prometheus aggregation
instead.
If your monitoring exposes a total like this, drop the total. If you have to
keep it around for some reason (e.g. the total includes things not counted
individually), use different metric names.
Instrumentation labels should be minimal, every extra label is one more that
users need to consider when writing their PromQL. Accordingly, avoid having
instrumentation labels which could be removed without affecting uniqueness of
time series. Additional information around a metric can be added via an info
metric, see below around how to handle version numbers.
However there are cases where it is expected that virtually all users of a
metric will want the additional information a non-unique label could add, so an
info metric is not the right tradeoff. For example the mysqld_exporter's
`mysqld_perf_schema_events_statements_total`'s `digest` label is a hash of the
full query pattern and is sufficient for uniqueness. However it is of little
use without the human readable `digest_text` label, which for long queries will
contain only the start of the query pattern and is thus not unique. Thus we end
up with both the `digest_text` for humans and the `digest` label for
uniqueness.
The former breaks for people who do a `sum()` over your metric, and the
latter breaks sum and is quite difficult to work with. Some client
libraries, for example Go, will actively try to stop you doing the
latter in a custom collector, and all client libraries should stop you
from doing the latter with direct instrumentation. Never do either of
these, rely on Prometheus aggregation instead.
If your monitoring exposes a total like this, drop the total. If you
have to keep it around for some reason, for example the total includes
things not counted individually, use different metric names.
Instrumentation labels should be minimal, every extra label is one more
that users need to consider when writing their PromQL. Accordingly,
avoid having instrumentation labels which could be removed without
affecting the uniqueness of the time series. Additional information
around a metric can be added via an info metric, for an example see
below how to handle version numbers.
However, there are cases where it is expected that virtually all users of
a metric will want the additional information. If so, adding a
non-unique label, rather than an info metric, is the right solution. For
example the
[mysqld_exporter](https://github.com/prometheus/mysqld_exporter)'s
`mysqld_perf_schema_events_statements_total`'s `digest` label is a hash
of the full query pattern and is sufficient for uniqueness. However, it
is of little use without the human readable `digest_text` label, which
for long queries will contain only the start of the query pattern and is
thus not unique. Thus we end up with both the `digest_text` label for
humans and the `digest` label for uniqueness.
### Target labels, not static scraped labels
......@@ -229,256 +254,277 @@ metrics, stop.
There’s generally two cases where this comes up.
The first is some label it’d be useful to have on the metrics that are about,
such as the version number of the software. Use the approach described at
[https://www.robustperception.io/how-to-have-labels-for-machine-roles/](http://www.robustperception.io/how-to-have-labels-for-machine-roles/)
instead.
The first is for some label it would be useful to have on the metrics
such as the version number of the software. Instead, use the approach
described at
[https://www.robustperception.io/how-to-have-labels-for-machine-roles/](http://www.robustperception.io/how-to-have-labels-for-machine-roles/).
The other case are what are really target labels. These are things like region,
cluster names, and so on, that come from your infrastructure setup rather than
the application itself. It’s not for an application to say where it fits in
your label taxonomy, that’s for the person running the Prometheus server to
configure and different people monitoring the same application may give it
different names.
The second case is when a label is really a target label. These are
things like region, cluster names, and so on, that come from your
infrastructure setup rather than the application itself. It’s not for an
application to say where it fits in your label taxonomy, that’s for the
person running the Prometheus server to configure and different people
monitoring the same application may give it different names.
Accordingly these labels belong up in the scrape configs of Prometheus via
whatever service discovery you’re using. It’s okay to apply the concept of
machine roles here as well, as it’s likely useful information for at least some
of the people scraping it.
Accordingly, these labels belong up in the scrape configs of Prometheus
via whatever service discovery you’re using. It’s okay to apply the
concept of machine roles here as well, as it’s likely useful information
for at least some people scraping it.
### Types
You should try to match up the types of your metrics to Prometheus types. This
usually means counters and gauges. The `_count` and `_sum` of summaries are
also relatively common, and on occasion you’ll see quantiles. Histograms are
rare, if you come across one remember that the exposition format exposes
cumulative values.
You should try to match up the types of your metrics to Prometheus
types. This usually means counters and gauges. The `_count` and `_sum`
of summaries are also relatively common, and on occasion you’ll see
quantiles. Histograms are rare, if you come across one remember that the
exposition format exposes cumulative values.
Often it won’t be obvious what the type of a metric is (especially if you’re
automatically processing a set of metrics), use `UNTYPED` in that case. In
general `UNTYPED` is a safe default.
Often it won’t be obvious what the type of metric is, especially if
you’re automatically processing a set of metrics. In general `UNTYPED`
is a safe default.
Counters can’t go down, so if you’ve a counter type coming from another
instrumentation system that has a way to decrement it (e.g. Dropwizard metrics)
that’s not a counter - it’s a gauge. `UNTYPED` is probably the best type to use
there, as `GAUGE` would be misleading if it were being used as a counter.
Counters can’t go down, so if you have a counter type coming from
another instrumentation system that can be decremented, for example
Dropwizard metrics then it's not a counter, it's a gauge. `UNTYPED` is
probably the best type to use there, as `GAUGE` would be misleading if
it were being used as a counter.
### Help strings
When you’re transforming metrics it’s useful for users to be able to track back
to what the original was, and what rules were in play that caused that
transform. Putting in the name of the collector/exporter, the id of any rule
that was applied and the name/details of the original metric into the help
string will greatly aid users.
When you’re transforming metrics it’s useful for users to be able to
track back to what the original was, and what rules were in play that
caused that transformation. Putting in the name of the
collector or exporter, the ID of any rule that was applied and the
name and details of the original metric into the help string will greatly
aid users.
Prometheus doesn’t like one metric having different help strings. If you’re
making one metric from many others, choose one of them to put in the help
string.
Prometheus doesn’t like one metric having different help strings. If
you’re making one metric from many others, choose one of them to put in
the help string.
For examples of this, the SNMP exporter uses the OID and the JMX exporter puts
in a sample mBean name. The [haproxy
exporter](https://github.com/prometheus/haproxy_exporter) has hand-written
strings. The [node exporter](https://github.com/prometheus/node_exporter) has a
wide variety of examples.
For examples of this, the SNMP exporter uses the OID and the JMX
exporter puts in a sample mBean name. The [HAProxy
exporter](https://github.com/prometheus/haproxy_exporter) has
hand-written strings. The [node
exporter](https://github.com/prometheus/node_exporter) also has a wide
variety of examples.
### Drop less useful statistics
Some instrumentation systems expose 1m/5m/15m rates, average rates since
application start (called `mean` in dropwizard metrics for example), minimums,
maximums and standard deviations.
Some instrumentation systems expose 1m, 5m, 15m rates, average rates since
application start (these are called `mean` in Dropwizard metrics for
example) in addition to minimums, maximums and standard deviations.
These should all be dropped, as they’re not very useful and add clutter.
Prometheus can calculate rates itself, and usually more accurately (these are
usually exponentially decaying averages). You don’t know what time the min/max
were calculated over, and the stddev is statistically useless (expose sum of
squares, `_sum` and `_count` if you ever need to calculate it).
Prometheus can calculate rates itself, and usually more accurately as
the averages exposed are usually exponentially decaying. You don’t know
what time the min or max were calculated over, and the standard deviation
is statistically useless and you can always expose sum of squares,
`_sum` and `_count` if you ever need to calculate it.
Quantiles have related issues, you may choose to drop them or put them in a
Summary.
Quantiles have related issues, you may choose to drop them or put them
in a Summary.
### Dotted strings
Many monitoring systems don’t have labels, instead doing things like
`my.class.path.mymetric.labelvalue1.labelvalue2.labelvalue3`.
The graphite and statsd exporters share a way of doing this with a small
configuration language. Other exporters should implement the same. It’s
currently implemented only in Go, and would benefit from being factored out
into a separate library.
The [Graphite](https://github.com/prometheus/graphite_exporter) and
[StatsD](https://github.com/prometheus/statsd_exporter) exporters share
a way of transforming these with a small configuration language. Other
exporters should implement the same. The transformation is currently
implemented only in Go, and would benefit from being factored out into a
separate library.
## Collectors
When implementing the collector for your exporter, you should never use the
usual direct instrumentation approach and then update the metrics on each
scrape.
When implementing the collector for your exporter, you should never use
the usual direct instrumentation approach and then update the metrics on
each scrape.
Rather create new metrics each time. In Go this is done with
[MustNewConstMetric](https://godoc.org/github.com/prometheus/client_golang/prometheus#MustNewConstMetric)
in your `Update()` method. For Python see
[https://github.com/prometheus/client_python#custom-collectors](https://github.com/prometheus/client_python#custom-collectors)
and for Java generate a `List<MetricFamilySamples>` in your collect method -
see
and for Java generate a `List<MetricFamilySamples>` in your collect
method, see
[StandardExports.java](https://github.com/prometheus/client_java/blob/master/simpleclient_hotspot/src/main/java/io/prometheus/client/hotspot/StandardExports.java)
for an example.
The reason for this is firstly that two scrapes could happen at the same time,
and direct instrumentation uses what are effectively (file-level) global
variables so you’ll get race conditions. The second reason is that if a label
value disappears, it’ll still be exported.
The reason for this is two-fold. Firstly, two scrapes could happen at
the same time, and direct instrumentation uses what are effectively
file-level global variables, so you’ll get race conditions. Secondly, if
a label value disappears, it’ll still be exported.
Instrumenting your exporter itself via direct instrumentation is fine, e.g.
total bytes transferred or calls performed by the exporter across all scrapes.
For exporters such as the blackbox exporter and snmp exporter which aren’t tied
to a single target, these should only be exposed on a vanilla `/metrics` call -
not on a scrape of a particular target.
Instrumenting your exporter itself via direct instrumentation is fine,
e.g. total bytes transferred or calls performed by the exporter across
all scrapes. For exporters such as the [blackbox
exporter](https://github.com/prometheus/blackbox_exporter) and [SMNP
exporter](https://github.com/prometheus/snmp_exporter), which aren’t
tied to a single target, these should only be exposed on a vanilla
`/metrics` call, not on a scrape of a particular target.
### Metrics about the scrape itself
Sometimes you’d like to export metrics that are about the scrape, like how long
it took or how many records you processed.
Sometimes you’d like to export metrics that are about the scrape, like
how long it took or how many records you processed.
These should be exposed as gauges (as they’re about an event, the scrape) and
the metric name prefixed by the exporter name e.g.
`jmx_scrape_duration_seconds`. Usually the `_exporter` is excluded (and if the
exporter also makes sense to use as just a collector, definitely exclude it).
These should be exposed as gauges as they’re about an event, the scrape,
and the metric name prefixed by the exporter name, for example
`jmx_scrape_duration_seconds`. Usually the `_exporter` is excluded and
if the exporter also makes sense to use as just a collector, then
definitely exclude it.
### Machine and process metrics
Many systems (e.g. elasticsearch) expose machine metrics such a CPU, memory and
filesystem information. As the node exporter provides these in the Prometheus
ecosystem, such metrics should be dropped.
Many systems, for example Elasticsearch, expose machine metrics such a
CPU, memory and filesystem information. As the [node
exporter](https://github.com/prometheus/node_exporter) provides these in
the Prometheus ecosystem, such metrics should be dropped.
In the Java world, many instrumentation frameworks expose process-level and
JVM-level stats such as CPU and GC. The Java client and JMX exporter already
include these in the preferred form via
In the Java world, many instrumentation frameworks expose process-level
and JVM-level stats such as CPU and GC. The Java client and JMX exporter
already include these in the preferred form via
[DefaultExports.java](https://github.com/prometheus/client_java/blob/master/simpleclient_hotspot/src/main/java/io/prometheus/client/hotspot/DefaultExports.java),
so these should be dropped.
so these should also be dropped.
Similarly with other languages.
Similarly with other languages and frameworks.
## Deployment
Each exporter should monitor exactly one instance application, preferably
sitting right beside it on the same machine. That means for every haproxy you
run, you run a `haproxy_exporter` process. For every machine with a mesos
slave, you run the mesos exporter on it (and another one for the master if a
machine has both).
The theory behind this is that for direct instrumentation this is what you’d be
doing, and we’re trying to get as close to that as we can in other layouts.
This means that all service discovery is done in Prometheus, not in exporters.
This also has the benefit that Prometheus has the target information it needs
to allow users probe your service with the blackbox exporter.
Each exporter should monitor exactly one instance application,
preferably sitting right beside it on the same machine. That means for
every HAProxy you run, you run a `haproxy_exporter` process. For every
machine with a Mesos worker, you run the [Mesos
exporter](https://github.com/mesosphere/mesos_exporter) on it, and
another one for the master, if a machine has both.
The theory behind this is that for direct instrumentation this is what
you’d be doing, and we’re trying to get as close to that as we can in
other layouts. This means that all service discovery is done in
Prometheus, not in exporters. This also has the benefit that Prometheus
has the target information it needs to allow users probe your service
with the [blackbox
exporter](https://github.com/prometheus/blackbox_exporter).
There are two exceptions:
The first is where running beside the application your monitoring is completely
nonsensical. SNMP, blackbox and IPMI are the main examples of this. IPMI and
SNMP as the devices are effectively black boxes that it’s impossible to run
code on (though if you could run a node exporter on them instead that’d be
better), and blackbox as if you’re monitoring something like a DNS name there’s
nothing to run on. In this case Prometheus should still do service discovery,
and pass on the target to be scraped. See the blackbox and SNMP exporters for
examples.
Note that it is only currently possible to write this type of exporter with the
Go, Python and Java client libraries.
The other is where you’re pulling some stats out of a random instance of a
system and don’t care which one you’re talking to. Consider a set of MySQL
slaves you wanted to run some business queries against the data to then export.
Having an exporter that uses your usual load balancing approach to talk to one
slave is the sanest approach.
This doesn’t apply when you’re monitoring a system with master-election, in
that case you should monitor each instance individually and deal with the
masterness in Prometheus. This is as there isn’t always exactly one master,
and changing what a target is underneath Prometheus’s feet will cause oddities.
The first is where running beside the application your monitoring is
completely nonsensical. The SNMP, blackbox and IPMI exporters are the
main examples of this. The IPMI and SNMP exporters as the devices are
often black boxes that it’s impossible to run code on (though if you
could run a node exporter on them instead that’d be better), and the
blackbox exporter where you’re monitoring something like a DNS name,
where there’s also nothing to run on. In this case, Prometheus should
still do service discovery, and pass on the target to be scraped. See
the blackbox and SNMP exporters for examples.
Note that it is only currently possible to write this type of exporter
with the Go, Python and Java client libraries.
The second exception is where you’re pulling some stats out of a random
instance of a system and don’t care which one you’re talking to.
Consider a set of MySQL replicas you wanted to run some business queries
against the data to then export. Having an exporter that uses your usual
load balancing approach to talk to one replica is the sanest approach.
This doesn’t apply when you’re monitoring a system with master-election,
in that case you should monitor each instance individually and deal with
the "masterness" in Prometheus. This is as there isn’t always exactly
one master, and changing what a target is underneath Prometheus’s feet
will cause oddities.
### Scheduling
Metrics should only be pulled from the application when Prometheus scrapes
them, exporters should not perform scrapes based on their own timers. That is,
all scrapes should be synchronous.
Metrics should only be pulled from the application when Prometheus
scrapes them, exporters should not perform scrapes based on their own
timers. That is, all scrapes should be synchronous.
Accordingly you should not set timestamps on the metric you expose, let
Accordingly, you should not set timestamps on the metrics you expose, let
Prometheus take care of that. If you think you need timestamps, then you
probably need the pushgateway (without timestamps) instead.
probably need the
[Pushgateway](https://prometheus.io/docs/instrumenting/pushing/)
instead.
If a metric is particularly expensive to retrieve (i.e. takes more than a
minute), it is acceptable to cache it. This should be noted in the `HELP`
string.
If a metric is particularly expensive to retrieve, i.e. takes more than
a minute, it is acceptable to cache it. This should be noted in the
`HELP` string.
The default scrape timeout for Prometheus is 10 seconds. If your exporter can
be expected to exceed this, you should explicitly call this out in your user
docs.
The default scrape timeout for Prometheus is 10 seconds. If your
exporter can be expected to exceed this, you should explicitly call this
out in your user documentation.
### Pushes
Some applications and monitoring systems only push metrics e.g. statsd,
graphite and collectd.
Some applications and monitoring systems only push metrics, for example
StatsD, Graphite and collectd.
There’s two considerations here.
There are two considerations here.
Firstly, when do you expire metrics? Collected and things talking to Graphite
both export regularly, and when they stop we want to stop exposing the metrics.
Collected includes an expiry time so we use that, Graphite doesn’t so it’s a
flag on the exporter.
Firstly, when do you expire metrics? Collectd and things talking to
Graphite both export regularly, and when they stop we want to stop
exposing the metrics. Collectd includes an expiry time so we use that,
Graphite doesn’t so it is a flag on the exporter.
Statsd is a bit different, as it’s dealing with events rather than metrics. The
best model is to run one exporter beside each application and restart them when
the application restarts so that state is cleared.
StatsD is a bit different, as it is dealing with events rather than
metrics. The best model is to run one exporter beside each application
and restart them when the application restarts so that the state is
cleared.
The second is that these sort of systems tend to allow your users to send
either deltas or raw counters. You should rely on the raw counters as far as
Secondly, these sort of systems tend to allow your users to send either
deltas or raw counters. You should rely on the raw counters as far as
possible, as that’s the general Prometheus model.
For service-level metrics (e.g. service-level batch jobs) you should have your
exporter push into the push gateway and exit after the event rather than
handling the state yourself. For instance-level batch metrics, there is no
clear pattern yet - options are either to abuse the node exporter’s textfile
collector, rely on in-memory state (probably best if you don’t need to persist
over a reboot) or implement similar functionality to the textfile collector.
For service-level metrics, e.g. service-level batch jobs, you should
have your exporter push into the Pushgateway and exit after the event
rather than handling the state yourself. For instance-level batch
metrics, there is no clear pattern yet. The options are either to abuse
the node exporter’s textfile collector, rely on in-memory state
(probably best if you don’t need to persist over a reboot) or implement
similar functionality to the textfile collector.
### Failed scrapes
There are currently two patterns for failed scrapes where the application
you’re talking to doesn’t respond or has other problems.
There are currently two patterns for failed scrapes where the
application you’re talking to doesn’t respond or has other problems.
The first is to return a 5xx error.
The seconds is to have an `myexporter_up` (e.g. `haproxy_up`) variable that’s
0/1 depending on whether the scrape worked.
The second is to have a `myexporter_up`, e.g. `haproxy_up`, variable
that has a value of 0 or 1 depending on whether the scrape worked.
The latter is better where there’s still some useful metrics you can get even
with a failed scrape, such as the haproxy exporter providing process stats. The
former is a tad easier for users to deal with, as `up` works in the usual way
(though you can’t distinguish between the exporter being down and the
application being down).
The latter is better where there’s still some useful metrics you can get
even with a failed scrape, such as the HAProxy exporter providing
process stats. The former is a tad easier for users to deal with, as
`up` works in the usual way, although you can’t distinguish between the
exporter being down and the application being down.
### Landing page
It’s nicer for users if visiting `http://yourexporter/` has a simple html page
with the name of the exporter, and a link to the `/metrics`.
It’s nicer for users if visiting `http://yourexporter/` has a simple
HTML page with the name of the exporter, and a link to the `/metrics`
page.
### Port numbers
A user may have many exporters and Prometheus components on the same machine,
so to make that easier each has a unique port number.
A user may have many exporters and Prometheus components on the same
machine, so to make that easier each has a unique port number.
[https://github.com/prometheus/prometheus/wiki/Default-port-allocations](https://github.com/prometheus/prometheus/wiki/Default-port-allocations)
is where we track them, this is publicly editable.
Feel free to grab the next free port number when developing your exporter,
preferably before publicly announcing it. If you’re not ready to release yet,
putting your username and WIP is fine.
Feel free to grab the next free port number when developing your
exporter, preferably before publicly announcing it. If you’re not ready
to release yet, putting your username and WIP is fine.
This is a registry to make our users’ lives a little easier, not a commitment
to develop particular exporters. For exporters for internal applications we
recommend using ports outside of the range of default port allocations.
This is a registry to make our users’ lives a little easier, not a
commitment to develop particular exporters. For exporters for internal
applications we recommend using ports outside of the range of default
port allocations.
## Announcing
Once you’re ready to announce your exporter to the world, send an email to the
Once you’re ready to announce your exporter to the world, email the
mailing list and send a PR to add it to [the list of available
exporters](https://github.com/prometheus/docs/blob/master/content/docs/instrumenting/exporters.md).
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Technounit-GROUP 2023