prometheus/discovery
Ganesh Vernekar 095f572d4a
Sync sparsehistogram branch with main (#9189)
* Fix `kuma_sd` targetgroup reporting (#9157)

* Bundle all xDS targets into a single group

Signed-off-by: austin ce <austin.cawley@gmail.com>

* Snapshot in-memory chunks on shutdown for faster restarts (#7229)

Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com>

* Rename links

Signed-off-by: Levi Harrison <git@leviharrison.dev>

* Remove Individual Data Type Caps in Per-shard Buffering for Remote Write (#8921)

* Moved everything to nPending buffer

Signed-off-by: Levi Harrison <git@leviharrison.dev>

* Simplify exemplar capacity addition

Signed-off-by: Levi Harrison <git@leviharrison.dev>

* Added pre-allocation

Signed-off-by: Levi Harrison <git@leviharrison.dev>

* Don't allocate if not sending exemplars

Signed-off-by: Levi Harrison <git@leviharrison.dev>

* Avoid deadlock when processing duplicate series record (#9170)

* Avoid deadlock when processing duplicate series record

`processWALSamples()` needs to be able to send on its output channel
before it can read the input channel, so reads to allow this in case the
output channel is full.

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* processWALSamples: update comment

Previous text seems to relate to an earlier implementation.

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* Optimise WAL loading by removing extra map and caching min-time (#9160)

* BenchmarkLoadWAL: close WAL after use

So that goroutines are stopped and resources released

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* BenchmarkLoadWAL: make series IDs co-prime with #workers

Series are distributed across workers by taking the modulus of the
ID with the number of workers, so multiples of 100 are a poor choice.

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* BenchmarkLoadWAL: simulate mmapped chunks

Real Prometheus cuts chunks every 120 samples, then skips those samples
when re-reading the WAL. Simulate this by creating a single mapped chunk
for each series, since the max time is all the reader looks at.

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* Fix comment

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* Remove series map from processWALSamples()

The locks that is commented to reduce contention in are now sharded
32,000 ways, so won't be contended. Removing the map saves memory and
goes just as fast.

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* loadWAL: Cache the last mmapped chunk time

So we can skip calling append() for samples it will reject.

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* Improvements from code review

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* Full stops and capitals on comments

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* Cache max time in both places mmappedChunks is updated

Including refactor to extract function `setMMappedChunks`, to reduce
code duplication.

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* Update head min/max time when mmapped chunks added

This ensures we have the correct values if no WAL samples are added for
that series.

Note that `mSeries.maxTime()` was always `math.MinInt64` before, since
that function doesn't consider mmapped chunks.

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

* Split Go and React Tests (#8897)

* Added go-ci and react-ci

Co-authored-by: Julien Pivotto <roidelapluie@inuits.eu>
Signed-off-by: Levi Harrison <git@leviharrison.dev>

* Remove search keymap from new expression editor (#9184)

Signed-off-by: Julius Volz <julius.volz@gmail.com>

Co-authored-by: Austin Cawley-Edwards <austin.cawley@gmail.com>
Co-authored-by: Levi Harrison <git@leviharrison.dev>
Co-authored-by: Julien Pivotto <roidelapluie@inuits.eu>
Co-authored-by: Bryan Boreham <bjboreham@gmail.com>
Co-authored-by: Julius Volz <julius.volz@gmail.com>
2021-08-11 15:43:17 +05:30
..
aws Add AZ ID label to discovered EC2 targets (#8896) 2021-07-23 09:42:03 +02:00
azure Add nil checks 2021-07-28 19:23:17 +05:30
consul remove redundant type conversion (#9126) 2021-07-28 13:33:46 +05:30
digitalocean Switched to go-kit/log 2021-06-11 12:28:36 -04:00
dns Switched to go-kit/log 2021-06-11 12:28:36 -04:00
eureka Switched to go-kit/log 2021-06-11 12:28:36 -04:00
file fixes yamllint errors 2021-06-12 12:47:47 +02:00
gce Update discovery/gce/gce.go 2021-07-06 07:57:29 -04:00
hetzner hcloud discovery: Add new labelpresent label (#9028) 2021-07-03 01:51:50 +02:00
http Merge release-2.28 back into main (#9035) 2021-07-01 18:02:13 +02:00
install Add base xDS discovery and kuma SD implementation 2021-07-21 12:55:02 -04:00
kubernetes add kubeconfig support in discovery module (#8811) 2021-06-17 12:41:50 +02:00
linode optimize Linode SD by polling for event changes during refresh (#8980) 2021-08-04 12:05:49 +02:00
marathon Fix: Use json.Unmarshal() instead of json.Decoder (#9033) 2021-07-02 09:38:14 +01:00
moby HostNetworkHost -> HostNetworkingHost 2021-08-03 05:58:49 -06:00
openstack Update Go dependencies 2021-07-29 09:11:04 +02:00
refresh Switched to go-kit/log 2021-06-11 12:28:36 -04:00
scaleway Switched to go-kit/log 2021-06-11 12:28:36 -04:00
targetgroup Testify: move to require (#8122) 2020-10-29 09:43:23 +00:00
triton Switched to go-kit/log 2021-06-11 12:28:36 -04:00
xds Sync sparsehistogram branch with main (#9189) 2021-08-11 15:43:17 +05:30
zookeeper Switched to go-kit/log 2021-06-11 12:28:36 -04:00
discovery.go Switched to go-kit/log 2021-06-11 12:28:36 -04:00
manager.go Switched to go-kit/log 2021-06-11 12:28:36 -04:00
manager_test.go Switched to go-kit/log 2021-06-11 12:28:36 -04:00
README.md Replace godoc.org links 2021-06-17 07:18:51 -04:00
registry.go Move away from testutil, refactor imports (#8087) 2020-10-22 11:00:08 +02:00

Service Discovery

This directory contains the service discovery (SD) component of Prometheus.

Design of a Prometheus SD

There are many requests to add new SDs to Prometheus, this section looks at what makes a good SD and covers some of the common implementation issues.

Does this make sense as an SD?

The first question to be asked is does it make sense to add this particular SD? An SD mechanism should be reasonably well established, and at a minimum in use across multiple organizations. It should allow discovering of machines and/or services running somewhere. When exactly an SD is popular enough to justify being added to Prometheus natively is an open question.

Note: As part of lifting the past moratorium on new SD implementations it was agreed that, in addition to the existing requirements, new service discovery implementations will be required to have a committed maintainer with push access (i.e., on -team).

It should not be a brand new SD mechanism, or a variant of an established mechanism. We want to integrate Prometheus with the SD that's already there in your infrastructure, not invent yet more ways to do service discovery. We also do not add mechanisms to work around users lacking service discovery and/or configuration management infrastructure.

SDs that merely discover other applications running the same software (e.g. talk to one Kafka or Cassandra server to find the others) are not service discovery. In that case the SD you should be looking at is whatever decides that a machine is going to be a Kafka server, likely a machine database or configuration management system.

If something is particularly custom or unusual, file_sd is the generic mechanism provided for users to hook in. Generally with Prometheus we offer a single generic mechanism for things with infinite variations, rather than trying to support everything natively (see also, alertmanager webhook, remote read, remote write, node exporter textfile collector). For example anything that would involve talking to a relational database should use file_sd instead.

For configuration management systems like Chef, while they do have a database/API that'd in principle make sense to talk to for service discovery, the idiomatic approach is to use Chef's templating facilities to write out a file for use with file_sd.

Mapping from SD to Prometheus

The general principle with SD is to extract all the potentially useful information we can out of the SD, and let the user choose what they need of it using relabelling. This information is generally termed metadata.

Metadata is exposed as a set of key/value pairs (labels) per target. The keys are prefixed with __meta_<sdname>_<key>, and there should also be an __address__ label with the host:port of the target (preferably an IP address to avoid DNS lookups). No other labelnames should be exposed.

It is very common for initial pull requests for new SDs to include hardcoded assumptions that make sense for the author's setup. SD should be generic, any customisation should be handled via relabelling. There should be basically no business logic, filtering, or transformations of the data from the SD beyond that which is needed to fit it into the metadata data model.

Arrays (e.g. a list of tags) should be converted to a single label with the array values joined with a comma. Also prefix and suffix the value with a comma. So for example the array [a, b, c] would become ,a,b,c,. As relabelling regexes are fully anchored, this makes it easier to write correct regexes against (.*,a,.* works no matter where a appears in the list). The canonical example of this is __meta_consul_tags.

Maps, hashes and other forms of key/value pairs should be all prefixed and exposed as labels. For example for EC2 tags, there would be __meta_ec2_tag_Description=mydescription for the Description tag. Labelnames may only contain [_a-zA-Z0-9], sanitize by replacing with underscores as needed.

For targets with multiple potential ports, you can a) expose them as a list, b) if they're named expose them as a map or c) expose them each as their own target. Kubernetes SD takes the target per port approach. a) and b) can be combined.

For machine-like SDs (OpenStack, EC2, Kubernetes to some extent) there may be multiple network interfaces for a target. Thus far reporting the details of only the first/primary network interface has sufficed.

Other implementation considerations

SDs are intended to dump all possible targets. For example the optional use of EC2 service discovery would be to take the entire region's worth of EC2 instances it provides and do everything needed in one scrape_config. For large deployments where you are only interested in a small proportion of the returned targets, this may cause performance issues. If this occurs it is acceptable to also offer filtering via whatever mechanisms the SD exposes. For EC2 that would be the Filter option on DescribeInstances. Keep in mind that this is a performance optimisation, it should be possible to do the same filtering using relabelling alone. As with SD generally, we do not invent new ways to filter targets (that is what relabelling is for), merely offer up whatever functionality the SD itself offers.

It is a general rule with Prometheus that all configuration comes from the configuration file. While the libraries you use to talk to the SD may also offer other mechanisms for providing configuration/authentication under the covers (EC2's use of environment variables being a prime example), using your SD mechanism should not require this. Put another way, your SD implementation should not read environment variables or files to obtain configuration.

Some SD mechanisms have rate limits that make them challenging to use. As an example we have unfortunately had to reject Amazon ECS service discovery due to the rate limits being so low that it would not be usable for anything beyond small setups.

If a system offers multiple distinct types of SD, select which is in use with a configuration option rather than returning them all from one mega SD that requires relabelling to select just the one you want. So far we have only seen this with Kubernetes. When a single SD with a selector vs. multiple distinct SDs makes sense is an open question.

If there is a failure while processing talking to the SD, abort rather than returning partial data. It is better to work from stale targets than partial or incorrect metadata.

The information obtained from service discovery is not considered sensitive security wise. Do not return secrets in metadata, anyone with access to the Prometheus server will be able to see them.

Writing an SD mechanism

The SD interface

A Service Discovery (SD) mechanism has to discover targets and provide them to Prometheus. We expect similar targets to be grouped together, in the form of a target group. The SD mechanism sends the targets down to prometheus as list of target groups.

An SD mechanism has to implement the Discoverer Interface:

type Discoverer interface {
	Run(ctx context.Context, up chan<- []*targetgroup.Group)
}

Prometheus will call the Run() method on a provider to initialize the discovery mechanism. The mechanism will then send all the target groups into the channel. Now the mechanism will watch for changes. For each update it can send all target groups, or only changed and new target groups, down the channel. Manager will handle both cases.

For example if we had a discovery mechanism and it retrieves the following groups:

[]targetgroup.Group{
	{
		Targets: []model.LabelSet{
			{
				"__instance__": "10.11.150.1:7870",
				"hostname":     "demo-target-1",
				"test":         "simple-test",
			},
			{
				"__instance__": "10.11.150.4:7870",
				"hostname":     "demo-target-2",
				"test":         "simple-test",
			},
		},
		Labels: model.LabelSet{
			"job": "mysql",
		},
		"Source": "file1",
	},
	{
		Targets: []model.LabelSet{
			{
				"__instance__": "10.11.122.11:6001",
				"hostname":     "demo-postgres-1",
				"test":         "simple-test",
			},
			{
				"__instance__": "10.11.122.15:6001",
				"hostname":     "demo-postgres-2",
				"test":         "simple-test",
			},
		},
		Labels: model.LabelSet{
			"job": "postgres",
		},
		"Source": "file2",
	},
}

Here there are two target groups one group with source file1 and another with file2. The grouping is implementation specific and could even be one target per group. But, one has to make sure every target group sent by an SD instance should have a Source which is unique across all the target groups of that SD instance.

In this case, both the target groups are sent down the channel the first time Run() is called. Now, for an update, we need to send the whole changed target group down the channel. i.e, if the target with hostname: demo-postgres-2 goes away, we send:

&targetgroup.Group{
	Targets: []model.LabelSet{
		{
			"__instance__": "10.11.122.11:6001",
			"hostname":     "demo-postgres-1",
			"test":         "simple-test",
		},
	},
	Labels: model.LabelSet{
		"job": "postgres",
	},
	"Source": "file2",
}

down the channel.

If all the targets in a group go away, we need to send the target groups with empty Targets down the channel. i.e, if all targets with job: postgres go away, we send:

&targetgroup.Group{
	Targets:  nil,
	"Source": "file2",
}

down the channel.

The Config interface

Now that your service discovery mechanism is ready to discover targets, you must help Prometheus discover it. This is done by implementing the discovery.Config interface and registering it with discovery.RegisterConfig in an init function of your package.

type Config interface {
	// Name returns the name of the discovery mechanism.
	Name() string

	// NewDiscoverer returns a Discoverer for the Config
	// with the given DiscovererOptions.
	NewDiscoverer(DiscovererOptions) (Discoverer, error)
}

type DiscovererOptions struct {
	Logger log.Logger
}

The value returned by Name() should be short, descriptive, lowercase, and unique. It's used to tag the provided Logger and as the part of the YAML key for your SD mechanism's list of configs in scrape_config and alertmanager_config (e.g. ${NAME}_sd_configs).

New Service Discovery Check List

Here are some non-obvious parts of adding service discoveries that need to be verified:

  • Validate that discovery configs can be DeepEqualled by adding them to config/testdata/conf.good.yml and to the associated tests.

  • If the config contains file paths directly or indirectly (e.g. with a TLSConfig or HTTPClientConfig field), then it must implement config.DirectorySetter.

  • Import your SD package from prometheus/discovery/install. The install package is imported from main to register all builtin SD mechanisms.

  • List the service discovery in both <scrape_config> and <alertmanager_config> in docs/configuration/configuration.md.

Examples of Service Discovery pull requests

The examples given might become out of date but should give a good impression about the areas touched by a new service discovery.