Prometheus

What is it?

Prometheus is a free software ecosystem for monitoring and alerting, with focus on reliability and simplicity. See also prometheus overview and prometheus FAQ.

There's a few interesting features that are missing from what we have now, among others:

multi-dimensional data model

Metrics have a name and several key=value pairs to better model what the metric is about. e.g. to measure varnish requests in the upload cache in eqiad we'd have a metric like http_requests_total{cache="upload",site="eqiad"}.

a powerful query language

Makes it able to ask complex questions, e.g. when debugging problems or drilling down for root cause during outages. From the example above, the query topk(3, sum(http_requests_total{status~="^5"}) by (cache)) would return the top 3 caches (text/upload/misc) with the most errors (status matches the regexp "^5")

pull metrics from targets

Prometheus is primarily based on a pull model, in which the prometheus server has a list of targets it should scrape metrics from. The pull protocol is HTTP based and simply put, the target returns a list of "<metric> <value>". Pushing metrics is supported too, see also http://prometheus.io/docs/instrumenting/pushing/.

After the Prometheus POC (as per User:Filippo_Giunchedi/Prometheus_POC) has been running in Labs for some time, during FQ1 2016-2017 the Prometheus deployment has been extended to production, as outlined in the Technical Operations goals .

Architecture

Each prometheus server is configured to scrape a list of targets (i.e. HTTP endpoints) at a certain frequency, in our case starting at 60s. All metrics are stored on the local disk with a per-server retention period (minimum of 4 months for the initial goal).

All targets to be scraped are grouped into jobs, depending on the purpose that those targets serve. For example the job to scrape all host-level data for a given location using node-exporter will be called node and each target will be listed as hostname:9100. Similarly there could be jobs for varnish, mysql, etc.

Each prometheus server is meant to be stand-alone and polling targets in the same failure domain as the server itself as appropriate (e.g. the same datacenter, the same vlan and so on). For example this allows to keep the monitoring local to the datacenter and not have spotty metrics upon cross-datacenter connectivity blips. (See also Federation)

Exporters

The endpoint being polled by the prometheus server and answering the GET requests is typically called exporter, e.g. the host-level metrics exporter is node-exporter.

Each exporter serves the current snapshot of metrics when polled by the prometheus server, there is no metric history kept by the exporter itself. Further, the exporter usually runs on the same host as the service or host it is monitoring.

Storage

Why just stand-alone prometheus servers with local storage and not clustered storage? The idea behind a single prometheus server is one of reliability: a monitoring system must be more reliabile than the systems it is monitoring. It is certainly easier to get local storage right and reliable than clustered storage, especially important when collecting operational metrics.

See also prometheus storage documentation for a more in-depth explanation and storage space requirements.

High availability

With local storage being the basic building block we can still achieve high-availability by running more than one server in parallel, each configured the same and polling the same set of targets. Queries for data can be routed via LVS in an active/standby fashion.

Backups

For efficiency reasons, prometheus spools chunks of datapoints in memory for each metric before flushing them to disk. This makes it harder to perform backups online by simply copying the files on disk. The issue of having consistent backups is also discussed in prometheus #651.

Notwithstanding the above, it should be possible to backup the prometheus local storage files as-is by archiving its storage directory with tar before regular (bacula) backups. Since the backup is being done online it will result in some inconsistencies, upon restoring the backup Prometheus will crash-recovery its storage at startup.

To perform backups of consistent/clean state, at the moment prometheus needs to be shutdown gracefully, therefore when running an active/standby configuration backup can be taken on the standby prometheus to minimize its impact. Note that the shutdown will result in gaps in the standby prometheus server for the duration of the shutdown.

Failure recovery

In the event of a prometheus server having an unusable local storage (disk failed, FS failed, corruption, etc) failure recovery can take the form of:

• start with empty storage: of course it is a complete loss of metric history for the local server and will obviously fully recover once the metric retention period has passed.

• recover from backups: restore the storage directory to the last good backup

• copy data from a similar server: when deployed in pairs it is possible to copy/rsync the storage directory onto the failed server, this will likely result in gaps in the recent history though (see also Backups)

Federation and multiple DCs

Each prometheus server is able to act as a target to another prometheus server by means of Prometheus federation. Our use case for this feature is primarily hierarchical federation, namely to have a 'global' prometheus that aggregates datacenter-level metrics from prometheus in each datacenter.

The global instance is what we would normally use in grafana as the "datasource" for dashboards to get an overview of all sites and aggregated metrics. To drilldown further and get more details it is possible to use the datacenter-local datasource and dashboard.

Server location

In the diagram above the various Prometheus servers are logically separated, though physically they can share one/multiple machines. As of Nov 2016 Prometheus dc-local runs in two VMs for each of eqiad/codfw (instance named "ops") and we're in process of provisioning real hardware.

An open question at this time is where to host the dc-local Prometheus servers for caching centers, essentially two options:

1. Local to the site
2. Remote, e.g. codfw polling ulsfo and eqiad polling esams

The local option offers some advantages since all sites are logically the same and all polling for monitoring purposes is kept local to the site and reflects our current Ganglia deployment. Only the global instance would reach out to remote sites and thus could be affected by cross-DC network unavailability.

This is significant especially during outages: the global instance would show a drop in global aggregates while the dc-local instance can keep collecting high-resolution data from site-local machines.

Disadvantages of the local option include (as of Nov 2016) running Prometheus on the bastion for sites where we lack internal dedicated machines (e.g. ulsfo) alongside other services like tftp/installserver. Also the fact that running Prometheus on a single bastion would provide no redundancy when the bastion is down.

Service Discovery

Prometheus supports different kinds of discovery through its configuration. For example, in role::prometheus::labs_project implements auto-discovery of all instances for a given labs project. file_sd_config is used to continuously monitor a set of configuration files for changes and the script prometheus-labs-targets is run periodically to write the list of instances to the relative configuration file. The file_sd files are reloaded automatically by prometheus, so new instances will be auto-discovered and have their instance-level metrics collected.

While file-based service discovery works, Prometheus also supports higher-level discovery for example for Kubernetes (see also role::prometheus::tools).

Adding new metrics

In general Prometheus' model is pull-based. In practical terms that means that once metrics are available over HTTP somewhere on the network with the methods described below, Prometheus itself should be instructed to poll for metrics via its configuration (more specifically, a job as described in https://prometheus.io/docs/concepts/jobs_instances/). Within WMF's Puppet the Prometheus configuration lives inside its respective instance profile, for example modules/profile/manifests/prometheus/ops.pp is often the right place to add new jobs.

Direct service instrumentation

The most benefits from service metrics are obtained when services are directly instrumented with one of Prometheus clients, e.g. Python client. Metrics are then exposed via HTTP, commonly at /metrics, on the service's HTTP port (in the common case) or a separate port if the service isn't HTTP to begin with.

Service exporters

For cases where services can't be directly instrumented (aka whitebox monitoring), a sidekick application exporter can be run alongside the service that will query the service using whatever mechanism and expose prometheus metrics via the client. This is the case for example for varnish_exporter parsing varnishstat -j or apache_exporter parsing apache's mod_status page.

Machine-level metrics

Another class of metrics is all those related to the machine itself rather than a particular service. Those involve calling a subprocess and parsing the result, often in a cronjob. In these cases the simplest thing to do is drop plaintext files on the machine's filesystem for node-exporter to pick up and expose the metrics on HTTP. This mechanism is named textfile and for example the python client has support for it, e.g. sample textfile collector usage. This is most likely the mechanism we could use to replace most of the custom collectors we have for Diamond.

Ephemeral jobs

Yet another case involves service-level ephemeral jobs that are not quite long-lived enough to be queried via HTTP. For those jobs there's a push mechanism to be used: metrics are pushed to Prometheus' pushgateway via HTTP and subsequently scraped by Prometheus from the gateway itself. This method appears similar to what statsd for its semplicity but it should be used with care, see also best practices on when to use the pushgateway. Good use cases could be MW's maintenance jobs: tracking how long the job took and when it last succeeded; if the job isn't tied to a machine in particular it is usually a good candidate.

HOWTO

Global view (Thanos) web interface

As of Jul 2020 the Thanos web interface is available at https://thanos.wikimedia.org. This interface offers a global view over Prometheus data and should be preferred for new use cases. Please consult the Thanos page to find out more.

Access Prometheus web interface

Prometheus web interface can be accessed on each prometheus server on port 80 (eg: prometheus2003.codfw.wmnet). SSH port forwarding can be used to access the web UI as follows:

ssh -L 8000:prometheus.svc.eqiad.wmnet:80 prometheus1003.eqiad.wmnet

or if you don't have shell on prometheus1003 just:

ssh -L 8000:prometheus.svc.eqiad.wmnet:80 bast4002.wikimedia.org

and then pointing your browser to http://localhost:8000/ops/. This method as of May 2018 works only if you have access to Prometheus machines (i.e. ops unix group). Access to Prometheus web interface is planned to be replaced by LDAP authentication, see also bug T151009

To access the prometheus web interface in beta (deployment-prep) you can simply use https://beta-prometheus.wmflabs.org/beta/graph

To access the prometheus web interface for Cloud Services hardware that are using the cloudmetrics monitoring setup, please follow the instructions at Portal:Cloud_VPS/Admin/Monitoring#Accessing_"labs"_prometheus

List metrics with curl

One easy way to check what metrics are being collected by prometheus on a given machine is to request the metrics via HTTP like prometheus server does at scrape time, e.g. for node-exporter on port 9100:

 curl -s localhost:9100/metrics

Query cheatsheet

Filter for a specific instance

Given values such as

varnish_mgt_child_stop{instance="cp2001:9131",job="varnish-text",layer="backend"}

and a template variable called $server, containing the server hostname, one can filter for the selected instance as follows:

varnish_mgt_child_start{instance=~"$server:.*",layer="backend"}

Filter by label using multi-values template variables

Given the following two metrics:

varnish_version{job="varnish-upload", ...}
node_uname_info{cluster="cache_upload", ...}

and a multi-value template variable called $cache_type, with the following values: text,upload,misc,canary, it is possible to write a prometheus query filtering the selected cache_types:

node_uname_info{cluster=~"cache_($cache_type)"}
varnish_version{job=~"varnish-($cache_type)"}

Dynamic, query-based template variables

Grafana's templating allows to define template variables based on Prometheus queries.

Given the following metric:

node_uname_info{release="4.9.0-0.bpo.4-amd64", ...}
node_uname_info{release="4.9.0-0.bpo.3-amd64", ...}

Choose Query as the variable Type, the desired Data Source, and specify a query such as the following to extract the values:

 label_values(node_uname_info, release)

Aggregate metrics from multiple sites

Sometimes it is useful to have an overall view of all sites from where metrics are collected. That's the use case for our 'global' instance of Prometheus, namely to pull metrics from site-local Prometheus instances.

Prometheus' name for this feature is federation, as described in https://prometheus.io/docs/prometheus/latest/federation/ and https://www.robustperception.io/federation-what-is-it-good-for/.

Adding new aggregated metrics to the global instance is composed of two parts:

1. Instruct the site-local Prometheus to calculate new aggregated metrics, for example the ops instance uses modules/role/files/prometheus/rules_ops.conf in Puppet. The format of the file and its best practices are described at https://prometheus.io/docs/practices/rules/
2. Instruct the global instance to pick up the newly-created aggregated metrics, via the global instance configuration at modules/role/manifests/prometheus/global.pp

Sync data from an existing Prometheus host

When replacing existing Prometheus hosts it is possible to keep existing data by rsync'ing the metrics directory from the old host into the new. It is important to make sure first that the new host has puppet run successfully (thus Prometheus is configured) and can Prometheus can reach its targets successfully (i.e. the new host is part of prometheus_nodes for its site. Once all of that is done the rsync can happen, on the new host:

 puppet agent --disable "copying prometheus data"
 export old_host=<hostname>
 export instance_name=ops
 systemctl stop prometheus@${instance_name}
 su -s /bin/bash prometheus
 rsync -vd ${old_host}::prometheus-${instance_name}/ /srv/prometheus/${instance_name}/metrics/
 # do a first rsync pass in parallel for each subdirectory
 /usr/bin/time parallel -j10 -i rsync -a ${old_host}::prometheus-${instance_name}/{}/ {}/ -- /srv/prometheus/${instance_name}/metrics/*
 # once this is completed stop puppet and prometheus on $old_host as well, and repeat the rsync for a final pass.
 rsync -vd ${old_host}::prometheus-${instance_name}/ /srv/prometheus/${instance_name}/metrics/
 /usr/bin/time parallel -j10 -i rsync -a ${old_host}::prometheus-${instance_name}/{}/ {}/ -- /srv/prometheus/${instance_name}/metrics/*
 # once this is completed you can restart prometheus and puppet on both hosts

Prometheus host running out of space

It might happen that Prometheus hosts get close to running out of space on one of their per-instance filesystems. Assuming the underlying volume group has space available (lvs to check what LVs are present and on which VGs, then vgs to check VGs themselves) then it is possible to extend the filesystem online with (e.g. +25G to the prometheus-foo LV on vg-hdd VG, remove --test once happy).

 lvextend --test --resizefs --size +25G vg-hdd/prometheus-foo

Make sure to: leave some space available on the VG, to handle cases like this in the future if possible. Also extend the filesystem on all prometheus hosts in the same site.

No space available on the volume group

At some point the space on volume group might be fully allocated (e.g. like on bastions). In this case the emergency remedy is to decrease Prometheus retention time via prometheus::server::storage_retention in Puppet, and restart Prometheus with the new settings.

In the unfortunate case that the filesystem is 100% utilized is also possible to manually remove storage "blocks" (i.e. directories) from the metrics directory under /srv/prometheus/INSTANCE. The filenames are sortable, which each directory representing maximum 24h of data.

Add metrics from a new service

Most services which export metrics to Prometheus do so via an HTTP endpoint, running on its own port. This HTTP endpoint can be served by the daemon itself, or by a separate "exporter" process.

Prometheus needs to be told to scrape the HTTP endpoint, which it calls a "target." (A logical grouping of targets is called a "job.") In addition to adding the new job to the Prometheus server, you will need to add a firewall rule exposing the HTTP endpoint.

For an example Puppet changes to add new jobs, see https://gerrit.wikimedia.org/r/c/operations/puppet/+/504360 or https://gerrit.wikimedia.org/r/#/c/operations/puppet/+/572141.

Use cases

MySQL

MySQL monitoring is performed by running prometheus-mysqld-exporter on the database machine to be monitored. Metrics are exported via http on port 9104 and fetched by prometheus server(s), to preview what metrics are being collected a fetch can be simulated with:

curl -s localhost:9104/metrics | grep -v '^#'

Dashboards

Per group / shard / role overview

https://grafana.wikimedia.org/dashboard/db/mysql-aggregated

Per server drilldown

https://grafana.wikimedia.org/dashboard/db/mysql

Ganglia

One of the initial use cases for Prometheus is to provide at least as good service as Ganglia. For host-level metrics we're using prometheus-node-exporter and grouping hosts based on $cluster puppet variable.

Dashboards

Per cluster overview

https://grafana.wikimedia.org/dashboard/db/prometheus-by-ganglia-cluster

Replacing Ganglia

As of Aug 2016 Prometheus is deployed in WMF's main locations: codfw and eqiad. To achieve feature-parity with Ganglia we'd need to expand Prometheus deployment to more locations, more machines and more metrics.

more locations

To fully replace Ganglia we'd need to deploy one (or two) prometheus servers in caching DCs too, similar to what we're doing with the ganglia aggregators. In practice this would mean running the server on ulsfo and esams bastions, as of Aug 2016 resources on both seem available (i.e. disk space and memory). To have aggregated stats available it is also possible to deploy one (in eqiad/codfw) "global" Prometheus servers that federates from each DC-local Prometheus.

more machines

Increase the number of machines from which we collect host metrics to 100% for each location Prometheus is deployed to, for jessie and trusty distributions.

more metrics

The current Ganglia deployment includes other metrics other than machine-level, namely the gmond plugins listed below and committed to puppet.git. Some of those can be replaced by existing exporters listed at https://prometheus.io/docs/instrumenting/exporters/ while others will require some porting to prometheus' python client (packaged as python-prometheus-client). Each prometheus exporter will require some deployment/packaging work, namely creating packages (preferably using Debian native go packaging, or fpm as outlined at Prometheus/Exporters) plus puppet integration and instruct prometheus to poll the additional exporters.

Ganglia plugins

apache_status.py

Parses apache's status page, similar to https://github.com/neezgee/apache_exporter

gdnsd.py

Parses gdnsd JSON stats from localhost:3506/json, will require porting to prometheus python client

varnish.py

Parses varnish's JSON, similar to https://github.com/jonnenauha/prometheus_varnish_exporter

vhtcpd.py

Parses metrics from /tmp/vhtcpd.stats and will require porting

mysql.py

Already replaced by prometheus-mysqld-exporter

elasticsearch_monitoring.py

Parse metrics from localhost:9200, replacement could be based off something like https://github.com/justwatchcom/elasticsearch_exporter

hhvm_mem.py

Parse json from localhost:9002/memory.json, will require porting to prometheus python client

hhvm_health.py

Ditto, for localhost:9002/check-health

gmond_memcached.py

Similar to https://github.com/prometheus/memcached_exporter

ocg.py

Parses stats from http://localhost:8000/?command=health, OCG is on its way out though

osm.py

Parse stats from /srv/osmosis/state.txt, from OSM's ganglia.py

postgresql.py

Similar to https://github.com/wrouesnel/postgres_exporter

gmond_jenkins.py

Similar to https://github.com/lovoo/jenkins_exporter

udp2log_socket.py

Counts sockets from udp2log, still used/useful?

varnishkafka_ganglia.py

Parse json from /var/cache/varnishkafka/varnishkafka.stats.json

kafkatee_ganglia.py

Similar to varnishkafka_ganglia.py, parses json stats from /var/cache/kafkatee/kafkatee.stats.json

exim-to-gmetric

Parse exim log files and state files and queue to extract messages stats

Replacing Graphite

Another use case imaginable for Prometheus is to replace the current Graphite deployment. This task is less "standalone" than replacing Ganglia and therefore more difficult: Graphite is more powerful and used by more people/services/dashboards. Nevertheless it should be possible to keep Prometheus and Graphite alongside each other and progressively put more data into Prometheus without affecting Graphite users. The top contributors to data that flows into Graphite as of Aug 2016 are Diamond, Statsd and Cassandra.

Diamond

Diamond runs on each machine in the fleet, collecting local data and send the resulting metrics via TCP using carbon line-oriented protocol. See also add Prometheus support to Diamond though this might not be trivial as there needs to be a mapping from flat metric names to key => value pairs.

Similarly to Ganglia, there are custom collectors in use that would need an equivalent functionality using Prometheus clients/exporters. For some simple results (e.g. exit code / single output from commands) it is easier to write metrics in a text file for node_exporter to pick up and present it together with machine-level metrics.

extendedexim.py

Parse exim's paniclog and queue stats by calling exim -bpr

localcrontab.py

Report the number of users' crontabs, mainly used in tools

minimalpuppetagent.py

Report puppet stats from last_run_summary.yaml

nagios.py

Execute nagios commands locally and report the exit code

nginx.py

Collect nginx basic metrics from nginx's status page

blazegraph.py

Parse XML from localhost:9999

cherry-pick-counter-collector.py

Report the number of cherry-pick patches in a given git repo

etherpad.py

Parse localhost:9001 and report stats

hhvm_apc.py

Parse localhost:9002/dump-apc-info and report stats

ircd_stats.py

Parse MOTD from local irc server

libvirtkvm.py

Parse libvirt local KVM stats and expose per-instance stats

memcached

See memcached in ganglia above

nf_conntrack_counter.py

Report sysctl net.netfilter.nf_conntrack_count

nfsd.py

Parse and report stats from /proc/net/rpc/nfsd and /proc/fs/nfsd/pool_stats

nfsiostat.py

Emulate iostat for NFS mount points using /proc/self/mountstats

nutcracker.py

Parse json from nutcracker stats, though nutcracker might be on its way out and replaced by mcrouter

openldap.py

Parse openldap metrics from local ldap server

powerdns.py / powerdns_recursor.py

Parse metrics from rec_control

pybal_state.py

Parse PyBal's pools info from localhost:9090

rcstream diamond_collector.py

Parse RCStream stats from localhost:10080 (Rcstream will be deprecated in July 2017)

rabbitmq.py

Collect rabbitmq queue stats, for openstack

redisstat.py

Collect redis stats from multiple instances

sge.py

Collect metrics from gridengine

sshsessions.py

Collect number of lines from who

varnishstatus.py

Collect varnish stats from varnishtop, used in beta only ?

wdqs_updater.py

Collect jmx stats exported by jolokia at http://localhost:8778

wmfelastic.py

Paired down collector for elasticsearch, exports basic stats and not per-index

Statsd

Statsd traffic for the most part flows from machines to statsd.eqiad.wmnet over UDP on port 8125 for aggregation. There are some exceptions (e.g. swift) where statsd aggregation is performed on localhost and then pushed via graphite line-oriented protocol.

Prometheus provides statsd_exporter to receive statsd metrics and turn those into key => value prometheus metrics according to a user-supplied mapping. The resulting metrics are then exposed via HTTP for prometheus server to scrape.

One idea to integrate statsd_exporter into our statsd traffic is to put it "inline" between the application and statsd.eqiad.wmnet. In other words we would need to:

1. Modify statsd_exporter to mirror received udp packets to statsd.eqiad.wmnet and install it on end hosts
2. Opt-in applications by changing their statsd host from statsd.eqiad.wmnet to localhost
3. Extend the statsd_exporter mapping file to include mappings for our statsd metrics.

This method works well for applications/languages that are request-scoped (e.g. php) since there isn't necessarily a server process to keep and aggregate metrics in. For services that qualify, the recommended way is to switch to Prometheus client for instrumentation.

If you are migrating your service that uses statsd to k8s, see also Prometheus/statsd_k8s

Cassandra

Cassandra is hosted on separate Graphite machines due to the number and size of metrics it pushes, particularly in conjunction with Restbase. It should be evaluated separatedly too if e.g. a separate prometheus instance makes sense. WRT implementation there are two viable options:

• Scrape JMX cassandra metrics with https://github.com/prometheus/jmx_exporter either externally with a JMX connection or as a "java agent" on the side to cassandra
• Add Prometheus java client (https://github.com/prometheus/client_java) to cassandra-metrics-collector (https://github.com/wikimedia/cassandra-metrics-collector) alongside Graphite support.
• Add Prometheus java client to creole (https://github.com/eevans/creole)

JMX

Prometheus jmx_exporter can be used to collect metrics through JMX.

A few notes:

• Some standard JVM metrics are always collected as DefaultsExports, those cannot be ignored in the jmx_exporter configuration. The same metrics could be collected explicitly from their respective MBeans, but we chose to standardize on the default exports.
• Without whitelist / blacklist, jmx_exporter will iterate through all MBeans and read all their attributes. This can be expensive, or even dangerous depending on the MBeans exposed by the application.
• The whitelist / blacklist work as:
  • load all mbeans corresponding to the whitelist query,
  • load all mbeans corresponding to the black list query,
  • remove all blacklisted mbeans from the list of whitelisted mbeans,
  • iterate over the remaining mbeans, including reading all their attributes.

This implies that an overly broad blacklist query can still have a non trivial cost.

List/inspect existing mbeans

Scenario: you want to check JMX MBeans available or generic JVM data in Production from your laptop:

ssh -ND 9099 $some_hostname$
jconsole -J-DsocksProxyHost=localhost -J-DsocksProxyPort=9099

Then Jconsole will be opened and you'll need to select Remote Process, adding the following: $hostname$:port (don't use localhost, it will not work!)

Dashboards

Grafana dashboards will need porting from Graphite to Prometheus metrics; this is likely to be the most labor-intensive part since most (all?) dashboards are hand-curated. While it should be possible to programmatically change statsd metric names into prometheus metric names, the query language is different enough to make this impractical except for very basic cases.

Runbooks

Stop queries on problematic instances

If a single Prometheus instance is misbehaving (e.g. overloaded) it is possible to temporarily stop queries from reaching that instance, by stopping Puppet commenting the relevant ProxyPass entry in /etc/apache2/prometheus.d/ and issue apache2ctl graceful. See also bug T217715.

Prometheus was restarted

The alert on Prometheus uptime exists to notify opsen of the possibility of strange monitoring artifacts occurring, as has happened in the past. If it was just a single restart, and not a crashloop, no action is strictly necessary (but investigating what happened isn't a bad idea; Prometheus isn't supposed to crash or restart).

If this alert is firing for a 'global' Prometheus, it can mean that either the global instance restarted, or that one of the Prometheis scraped by the global instance restarted.

Configuration reload failure

Check for recent changes in Puppet, particular modifications to monitoring::check_prometheus invocations or to the underlying module/prometheus templates themselves. Hopefully the error message from Prometheus gives you some idea.

k8s cache not updating

As discovered in bug T227478 the Prometheus kubernetes cache can stop updating (reasons TBD). In this case systemctl restart prometheus@k8s "fixes" the issue.

Prometheus job unavailable

As part of bug T187708 there's alerting in place for unavailable Prometheus jobs. This means that Prometheus was unable to fetch metrics from most of the job's targets, e.g. because the target is down, unreachable or fetching metrics timed out. See also https://grafana.wikimedia.org/d/NEJu05xZz/prometheus-targets for dashboard and logs.

Prometheus exporters "up" metrics unavailable

Some services don't have native Prometheus metrics support, thus an "exporter" is used that runs alongside the service and converts metrics from the service into Prometheus metrics. It might happen that the exporter itself is up (thus the job is available, see above) but the exporter is unable to contact the service for some reason. Such conditions are reported in metrics such as mysql_up for example by the mysql exporter. See also https://grafana.wikimedia.org/d/NEJu05xZz/prometheus-targets for dashboard and logs.