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Add proposal: Simplify joins with info metrics in PromQL #37

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Aug 9, 2024
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# Add 1st class feature to PromQL for handling info type metrics
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* **Owners:**
* Arve Knudsen [@aknuds1](https://github.com/aknuds1) [[email protected]](mailto:[email protected])

* **Implementation Status:** Partially implemented

* **Related Issues and PRs:**
* [WIP: Info PromQL function prototype](https://github.com/grafana/mimir-prometheus/pull/598)

* **Other docs or links:**
* [Proper support for OTEL resource attributes](https://docs.google.com/document/d/1FgHxOzCQ1Rom-PjHXsgujK8x5Xx3GTiwyG__U3Gd9Tw/edit#heading=h.unv3m5m27vuc)
* [Special treatment of info metrics in Prometheus](https://docs.google.com/document/d/1ebhGNLs3uhdeprJCullM-ywA9iMRDg_mmnuFAQCloqY/edit#heading=h.2rmzk7oo6tu8)
* [Scenarios scratch pad](https://docs.google.com/document/d/1nV6N3pDfvZhmG2658huNbFSkz2rsM6SpkHabp9VVpw0/edit#heading=h.luf3yapzr29e)

> This proposal collects the requirements and implementation proposals for adding a 1st class feature to PromQL for handling info type metrics.

## Why

Currently, enriching Prometheus query results with corresponding labels from info metrics is challenging.
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More specifically, it requires writing advanced PromQL to join with the info metric in question.
Take as an example querying HTTP request rates per K8s cluster and status code, while having to join with the `target_info` metric to obtain the `k8s_cluster_name` label:

```promql
sum by (k8s_cluster_name, http_status_code) (
rate(http_server_request_duration_seconds_count[2m])
* on (job, instance) group_left (k8s_cluster_name)
target_info
)
```

The `target_info` metric is in fact the motivation for this proposal, as it's how Prometheus encodes OpenTelemetry (OTel for short) [resource attributes](https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/resource/sdk.md).
As a result, it's a very important info metric for those using Prometheus as an OTel backend.
OTel resource attributes model metadata about the environment producing metrics received by the backend (e.g. Prometheus), and Prometheus persists them as labels of `target_info`.
Typically, OTel users want to include some of these attributes (as `target_info` labels) in their query results, to correlate them with entities of theirs (e.g. K8s pods).
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Based on user demand, it would be preferable if Prometheus were to have better UX for enriching query results with info metrics labels, especially with OTel in mind.
There are other problems with Prometheus' current method of including info metric labels in queries, beyond just the technical barrier:
* Explicit knowledge of each info metric's identifying labels must be embedded in join queries for when you wish to enrich queries with data (non-identifying) labels from info metrics.
* A certain pair of OTel resource attributes (`service.name` and `service.instance.id`) are currently assumed to be the identifying pair and mapped to `target_info`'s `job` and `instance` labels respectively, but this may become a dynamic property of the OTel model.
* Both attributes are in reality optional, so either of them might be empty (`service.name` is only mandatory for OTel SDK clients).
* If both identifying attributes are empty, `target_info` isn't generated (there being no identifying labels to join against).
* If an info metric's data (non-identifying) labels change (a situation that should become more frequent with OTel in the future, as the model will probably start allowing for non-identifying resource attribute mutations), join queries against the info metric (e.g. `target_info`) will temporarily fail due to resolving the join keys to two different metrics, until the old metric is marked stale (by default after five minutes).
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"By default" the old metric is marked as stale as soon as the scrape happens that contains the new metric. The problem is that this doesn't work if ingesting via OTLP, which is obviously common in an OTel use case. Only in that case, you run into the problem of five minutes of duplication. (Which is the reason behind a minimal proposal to "fix" the problem (discussed elsewhere): Somehow make staleness handling work with OTLP ingestion.)

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You may be interested in the current OTel Entities proposal, which is designed to carry non-identifying resource information: https://github.com/open-telemetry/oteps/blob/6d6febfaf05f130c703e2dd0fa91dfacada82a7d/text/entities/0256-entities-data-model.md. My hope would be that we can potentially map these to OM info metrics in the future.

edit: I forgot that I already left the same comment on the previous proposal docs.

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@dashpole yes, this point in fact refers to the proposed Entity model, since it will allow for non-identifying resource/entity attributes to change.


If Prometheus could persist info metrics' identifying labels (e.g. `job` and `instance` for `target_info`), human knowledge of the correct identifying labels may become unnecessary when "joining" with info metrics.
Information about info metric identifying labels is present in at least the OpenMetrics protobuf exposition format (the OpenMetrics text exposition format unfortunately lacks this capability).
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And metrics that are "info metrics in spirit only" (kube_pod_labels) aren't even covered at all in OM because it would be invalid in OM to mark a metric as info metric that doesn't end on _info. (Quote from OM spec: "The Sample MetricName for the value of a MetricPoint for a MetricFamily of type Info MUST have the suffix '_info'.") I believe to unleash the full potential of info metrics, we need to relax this requirement in OM.

Just saying this here for the record. We don't need to discuss it in this proposal (or maybe just as a footnote).

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I also worry about this being limited to OM, as most of the ecosystem seems to still be using prometheus text/protobuf formats today.

Did we consider to expanding this to also include gauge metrics with an _info suffix? Many OTel prometheus exporters still export target_info as a gauge due to lack of OM support, or because the Prometheus client doesn't support Info metrics.

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Even OM needs an amendment for its text version.

So the best outcome here is to "fix" OpenMetrics (including marking identifying labels in the text format and allowing info metrics that do not end on _info, but also a number of other things that have hampered adoption so far) so that it finally OM gets adopted everywhere.

The second best outcome is to also amend classic Prometheus exposition formats. (It's a bit weird because I'd rather see the effort of changing the exposition format to lead directly to OM everywhere rather than more versions of the many formats that we already have.)

In the meantime, I would go for scrape config options to mark metrics of whatever source and format as info metrics, including the option to mark certain labels as identifying labels. (The heuristic "target labels → identifying labels, other labels → date labels" should be fine in most cases.) A rule to say "all metrics ending on _info are info metrics" would be easy to configure. And then special cases could be configured to say "kube_pod_labels coming from kube-state-metrics should be considered an info metric with namespace and pod as the identifying labels. Etc.

It can also easily be deduced when ingesting metrics from OTLP (OTel Protocol).
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Maybe we could mention here that info metrics with additional identifying labels in the exposition format are rare. Usually, just the target labels added upon ingestion are the identifying labels, so this would be a good first guess. (Works for build_info, but doesn't work for kube_pod_labels.)

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I'm adding the following sentence, does it work for you @beorn7?

Most info metrics' identifying labels will be job and instance, but there are some exceptions (e.g. kube_pod_labels).

Intrinsic knowledge of info metrics' identifying labels could also help in solving temporary conflicts between old and new versions of info metrics, when data (non-identifying) labels change.
Another possible positive outcome might be dedicated support in UIs (e.g. Grafana) for visualizing the resource attributes of OTel metrics.

### Pitfalls of the current solution

Prometheus currently persists info metrics as if they were normal float samples.
This means that knowledge of info metrics' identifying labels are lost, and you have to base yourself on convention when querying them (for example that `target_info` should have `job` and `instance` as identifying labels).
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Well, the damage is much bigger -- we essentially use all labels as "identifying" causing massive churn and cardinality bomb. With this feature we essentially increase use of info metric, so that's perhaps relevant to fix.

There's also no particular support for enriching query results with info metric labels in PromQL.
The consequence is that you need relatively expert level PromQL knowledge to include info metric labels in your query results; as OTel grows in popularity, this becomes more and more of a problem as users will want to include certain labels from `target_info` (corresponding to OTel resource attributes).
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Without persisted info metric metadata, one can't build more user friendly abstractions (e.g. a PromQL function) for including OTel resource attributes (or other info metric labels) in query results.
Neither can you build dedicated UI for OTel resource attributes (or other info metric labels).
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Perhaps you could mention that there is room for a future iteration addressing these issues in various forms, one could think of info metrics with persistence or some other kind of metadata store.

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@jesusvazquez should that perhaps be mentioned in the Goals section? Before revising the proposal after @codesome's feedback, solving these problems in Prometheus was part of the goals. My understanding of the "Pitfalls of the current solution" section is that it should be referring to status quo in Prometheus, before the proposed solution.

To be honest I'm a bit unsure of what to put in the Pitfalls section currently, until @codesome reviews so the final form of the proposal is clearer.


## Goals

Goals and use cases for the solution as proposed in [How](#how):
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Is the goal to also select certain metrics based on certain target_info non-identifying label?

e.g. would I be able to do something like info(http_request{...}, {"go_version" = "1.22.3"}, so filtering http_requests by label which only exists on target e.g. version of the Go that target was compiled with?

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I added more specification about the new TSDB API and its use of label matchers. If I understand you correctly, the answer is yes:

If data label matchers are provided, time series are only included in the result if matching data labels from info metrics were found.

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Nice! Then I wonder if simpler solution would be worth to discuss as an alternative e.g. special prefixed labels: https://cloud.google.com/stackdriver/docs/managed-prometheus/promql#metadata-labels 🤔

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I will have a read, thanks.

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After reading the link about metadata labels, as I understand Bartek, the idea is to perhaps use metadata like syntax instead of introducing an info function. I think the underlying TSDB API would be the same. Alternative syntax to info is definitely on the table, also @jesusvazquez has thought about this.

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If I understand the Stackdriver documentation correctly, metadata labels are, from the outside API/PromQL perspective equivalent to adding all OTel resource attributes (or version number from build_info or pod labels from kube_pod_labels) to all metrics as label values (just with a prefix so that you have some hope (not a strategy!) that no label name collisions will happen, and that users can recognize which labels are coming from metadata).

Obviously, a backend can be implemented in a way to make that efficient, so that the efficiency concerns can be addressed. However, there are still concerns about UX, like every metric will now have a ginormous amount of labels, which needs additional tooling to get under control (like displaying metadata labels differently). It as a long-held Prometheus best practice to not add the same information (like the version number of the exposing binary) to every metric exposed, but bundle them in an info metric (like build_info), and the motivation for that was not just a concern about storage efficiency in the backend, but also UX.

My understanding is that this whole proposal is an attempt to solve the problem in the "classic" Prometheus way by making info metrics more powerful and more usable. The proposal to add all of the "metadata" to every metric in form of metadata labels is still on the table, but it's a different proposal.

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Thanks for sharing those insights @beorn7 :)

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Makes sense, thanks!


* Persist info metrics with labels categorized as either identifying or non-identifying.
* Track when info metrics' set of identifying labels changes. This shouldn't be a frequent occurrence, but it should be handled.
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Similar question as the data labels - if the identifying labels change in between let's say t1 (with t0 being a older time and t2 being a future time), do we use the old identifying labels for queries touching t0 to t1, and then new identifying labels for queries after t1? Let's document this.

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Maybe a change of identifying labels should really be seen as a completely independent new series, without any staleness marker insertion. As said, this should be rare (while the fallout of a change in the data labels in the absence of staleness markers, e.g. what you get when ingesting OTLP, triggered the whole effort). And it could actually mean the new info series is actually meant as a completely independent new series.

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do we use the old identifying labels for queries touching t0 to t1, and then new identifying labels for queries after t1?

@codesome yes, that is my current thinking at least. My thinking is to track through the info metric's samples, what is the identifying label set at any given point in time. Agree to document it properly, I'll give my proposal a re-read, to see whether I need to clarify further.

Maybe a change of identifying labels should really be seen as a completely independent new series, without any staleness marker insertion.

@beorn7 there shouldn't be any need for a staleness marker insertion, when the identifying label set changes, so long as the entire series' label set remains the same. My thinking is to just record a new sample for the info metric, that records the new set of identifying labels. Does that make sense to you as well?

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It probably works (but again, I would almost see this as an implementation detail).

Staleness marker will be inserted anyway if you scrape Prometheus-style, and the problems you have because of staleness markers not happening with OTLP ingestion will also happen anyway. It's not just for info metrics, and I think we don't need a bespoke staleness-marker ingestion solution here.

* Automatically treat the old version of an info metric as stale for query result enriching purposes, when its data labels change (producing a new time series, but with same identity).
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Let's say a time series starts at time t0, and if the data labels of the info metric changes at time t1, and t2 is a future time t2>t1. Then in this scenario, when I do a range query of info() function for time range t0 through t2, which data labels will be used? Will it use the old data labels between t0 and t1 and then new data labels between t1 to t2, or new data labels for t0 through t2? It will be good to document this because "stale" makes me feel new labels for everything but I think the right thing to do will be old labels for t0 to t1 (I think this is the plan, right? I forgot)

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Yes, I think it should be always the relevant label for the respective timestamp. I guess the term "stale" comes from looking at a possible implementation detail, which is to insert a staleness marker for the "old series". However, if the identity of the info series is only defined by the identifying labels, then there is actually no new series. Only the existing series changes its value (where the value are the data labels). (This is the "logical view", if you want. I can imagine it is hard to implement that way directly in Prometheus. And I haven't looked at @aknuds1's PoC to check how it is done there.)

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@aknuds1 aknuds1 Jun 14, 2024

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I'm not sure if the prototype currently works this way, since I did some additional thinking when drafting relevant scenarios for info, but my current thinking is indeed that the info function should pick the correct data labels on a sample by sample basis. Does that answer your question @codesome?

@beorn7 I haven't changed the fact that there will be a new time series when the data labels change. I just use a head/block index to associate info metrics with their identifying label sets, so you can find different time series (as data labels change) for the same identifying labels.

* Add TSDB API for, given a certain time series and a certain timestamp, getting data labels, potentially filtered by certain matchers, from info metrics with identifying labels in common with the time series in question.
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* Simplify enriching of query results with info metric labels in PromQL, e.g. via a new function.

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I would like to clarify whether ensure backward compatibility with current Prometheus usage and minimize potential conflicts with existing metric labels is part of the goal. especially when considering metadata labels as an alternative solution, this clarity will guide whether this approach is viable and if additional tools or documentation are needed to guide user managing such conflicts.

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Thanks, this is good feedback. It's good to explicitly call out an implicit motivation behind the info function design, i.e. backwards compatibility (improving the existing solution of info metrics).

minimize potential conflicts with existing metric labels

Do you mean here that adding e.g. OTel resource attributes as labels could lead to conflicts?

I've added the following two points to goals:

  • Ensure backwards compatibility with current Prometheus usage
  • Minimize potential conflicts with existing metric labels

### Audience

Prometheus maintainers.

## Non-Goals

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## How

* Introduce a new info metric sample type, to track the info metric's identifying label set over time (in case it changes).
* Augment the head and block indexes with indexes of info metrics, for easy finding of info metrics matching time series.
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Would be nice to know how exactly 🙈

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@aknuds1 aknuds1 May 23, 2024

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Yes, I'm trying to derive how to best model the info metric indexes in the scenarios scratch pad. I'm currently just focusing on the TSDB head, the block index is trickier to design due to being on disk.

I think I need some feedback on whether my idea of tracking changes to info metrics' identifying labels through a native sample type is a good idea. @beorn7 suggests that we might persist these info metrics completely outside of the TSDB, but I have the impression that would require reinventing the wheel wrt. querying the info metrics themselves (rather than just including their labels via the info function).

I'm on holidays until the 28th of May. When I come back, I'll have to seek feedback on how to solve this particular design problem.

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@beorn7 suggests that we might persist these info metrics completely outside of the TSDB

Scratch that, in retrospect I noticed a footnote in the doc, where @beorn7 does say we need to keep storing info metric samples in TSDB to support querying:

To query and display an info metric as a regular time series, we would still store the info metric as usual in the TSDB.

@beorn7 my question is: Does it make sense to you to use the identifying label set as the info metric sample value? I.e., it would be a new info metric sample type, that also pretends to have a float value of 1 (backwards compatible behaviour). From my prototyping, this is what would make sense, otherwise you would need to model changes over time in the info metric index. It seems better to me to model this via the usual TSDB time series mechanism instead.

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I don't have a detailed implementation idea here. You have probably most experience with this by now through your prototyping.

However, it feels like you wanted to say "Does it make sense to you to use the data label set as the info metric sample value?". The identifying labels are like "normal" labels, by which you want to find an info metric, and then you take the data labels from their sample value and add it to the metric you want to join with. Right?

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@aknuds1 aknuds1 May 31, 2024

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However, it feels like you wanted to say "Does it make sense to you to use the data label set as the info metric sample value?".

@beorn7 why do you think the data label set is better suited for the sample value? My reasoning is that for any given info metric, when you ingest a sample, the identifying label set may have changed, while the label set as a whole is unchanged. I.e., it's the same metric to Prometheus, but you need to track that the identifying label set has changed at that point in time. I should think you can instead use the data label set for the sample value, as it's just the inverse of the identifying label set. However, isn't it more natural to track that the identifying label set changes, as that's what declared over the ingestion protocol?

Maybe I should clarify that I currently just store the label indices in samples, not the strings, so the data label values would be extracted from the metric's label set. Not 100% sure if it's a good design choice, but that's how it's currently done. Make sense?

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Maybe I have understood the actual issue only now. Let me re-phrase: What you are discussing here is an info metric foo{a="aa", b="bb", c="cc"} where b and c used to be data labels (and a identifying labels), and then a change happens where b gets "promoted" to an identifying label, so nothing really changes from the point of view of the Prometheus data model is it exists right now.

That's a bit different from what I understood first.

How to implement this is again a question that you can probably answer better than anyone by now. It depends how "thoroughly" the new data model will be implemented in PromQL.

From a pure "new data model" perspective, the info metric after the change is a different metric (because it has different identifying labels). The info function might now join it with different other metrics, and it will often add other labels to its output.

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How to implement this is again a question that you can probably answer better than anyone by now. It depends how "thoroughly" the new data model will be implemented in PromQL.

I've thus far chosen the "simplistic" route of not changing Prometheus' core time series identity model (so that when the identifying label set changes, as you describe above, it's the same time series according to Prometheus). Then, for the purpose of implementing the info function, the info metric's identifying label set is determined on a per timestamp basis (via the info metric's samples). I haven't implemented the latter, it's just design at the moment, but I think it should work. I think I will return to work some more on the prototype soon.

You don't see any real problems with my proposed design, as I describe it above, I hope?

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I cannot say at the moment.

In any case, changing which labels are identifying should be a rare thing to happen, so even if it isn't solved in the ideal way, it shouldn't hurt much.

This is very much different from the current state of a change in a target_info label that then creates duplicates because staleness isn't handled properly when ingesting OTLP. The latter happens all the time in regular operation.

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Thanks! :) I'll try fixing up the prototype to match the design in the proposal, then.

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I've fixed up the prototype, so it matches the design in the proposal (info metric samples define the identifying label set).

* Add a method to the TSDB API for matching info metric data labels to a time series, given a certain timestamp and potentially data label matchers - the method will use the aforementioned head and block info metric indexes.
* Simplify the inclusion of info metric labels in PromQL through a new `info` function: `info(v instant-vector[, ls label-selector])`.
This function will be UI for the aforementioned TSDB API.

Using the `info` function, we can simplify the previously given PromQL join example as follows:

```
sum by (k8s_cluster_name, http_status_code) (
info(
rate(http_server_request_duration_seconds_count[2m]),
{k8s_cluster_name=~".+"}
)
)
Comment on lines +111 to +116
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Can you help me understand this example? I know the current rule for aggregating rates is Rate then sum, never sum then rate.

Now, will this turn into rate then info then sum, or could you also info then rate then sum?

Not sure if there is any specific reason to do rate() before info(), but sum() after info().

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Disclaimer: I just copied this example from Beorn's original document. I'll answer based on my knowledge of info though.

rate(http_server_request_duration_seconds_count[2m]) becomes a sub-query to the info function, which adds the k8s_cluster_name label from target_info to the http_server_request_duration_seconds_count time series resulting from the sub-query. The sum expression aggregates by the labels k8s_cluster_name and http_status_code. If you don't apply sum after info, the k8s_cluster_name label won't be available.

As for whether you could do info before rate; I haven't tested, but I think it would work. However, it looks less logical/comprehensible to me, as you really want the rate of http_server_request_duration_seconds_count[2m].

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@aknuds1 already explained nicely why you have to do the sum last.

In current PromQL, the order before the sum has to be range selectorrateinfo.

The range selector is a construct that only works directly with a metric, i.e. info(http_server_request_duration_seconds_count)[2m] doesn't even parse. (You could do a sub-query, i.e. info(http_server_request_duration_seconds_count)[2m:], but that's quite a different thing.)

Neither can you apply info to a range selector: info(http_server_request_duration_seconds_count[2m]) doesn't even parse again, because the grammar knows that info applies to an instant vector, not a range vector. (And the concept of overloaded function, i.e. functions with different type signature but the same name, doesn't exist in PromQL. Within the current framework, you could only implement a separate info_range function or something, but I hope we don't need that.)

```

TODO:
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* Make it concise and **simple**; put diagrams; be concrete, avoid using “really”, “amazing” and “great” (:
* How you will test and verify?
* How you will migrate users, without downtime. How we solve incompatibilities?
* What open questions are left? (“Known unknowns”)

## Alternatives
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This could describe what I alluded to above, i.e. "adding all resource attributes and build versions and pod labels and everything as prefixed labels" with its pros and cons.

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Good suggestion.

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I quickly drafted a corresponding alternative, it could be expanded upon. PTAL.

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@fpetkovski fpetkovski Jul 23, 2024

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This might have been discussed before, but I wonder if we are missing the simplest solution of query rewriting as a considered alternative. Given the example query on line 119, can we rewrite it into a join and let the engine execute it with the existing semantics of PromQL. Information needed for the join can either be implicitly deducted or specified by the user as arguments.

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@aknuds1 aknuds1 Jul 23, 2024

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The info function can't be simply reduced to syntactic sugar for joins though, for the following reasons at least:

  1. The info function automatically finds suitable info metrics to join with (the MVP only supports target_info, but we aim to lift that limitation in the future)
  2. The info function automatically knows which are info metrics' identifying labels, i.e. which to join on (the MVP assumes instance and job, but we aim to lift that limitation in the future)
  3. The info function solves the problem of temporary join query conflicts between info metrics due to non-identifying labels changing, until the old version goes stale

Information needed for the join can either be implicitly deducted or specified by the user as arguments.

Assuming we were to use query rewriting, how would you deduce which info metric(s) to join against (when info is no longer limited to just the target_info metric)? If the user, as in join queries, has to supply which info metric to join with and which labels to join on plus which labels to include, have we really gained much versus status quo?

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I can only see this working well for a static list of well known info metrics like.. target_info or by specifying which info metrics to use expicitely. If i have multiple info metrics that match by accident and dont specify matchers I probably get confusing labels added. Thats not possible if i specify the info metric explicitely or constrain it to a well known list like target_info. Prometheus is too flexible to make such convenience safe I think.

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I can only see this working well for a static list of well known info metrics like.. target_info or by specifying which info metrics to use expicitely.

@MichaHoffmann if that is your concern with the info function (and @fpetkovski's?), I think that should be an explicit criticism of the proposed design, rather than suggesting info as proposed here can be simply reduced to syntactic sugar. If you think the design should be explicit about picking info metrics rather than automatic (although that can be filtered down via the __name__ label matcher), I think there should be an alternative proposal. I'm not sure though if it's most feasible to list competing proposals in the same document, or whether they should be separate.

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Oh, Sorry I didnt want to propose something differently, I was just concerning out loud. What I want to say is that the name label matcher for info metric probably should be requirement for this to be generally least surprising for the user.

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@aknuds1 aknuds1 Jul 23, 2024

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Thanks @MichaHoffmann, I can note your wish about the name label matcher being a requirement, so a consensus can be reached.

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@beorn7 beorn7 Jul 23, 2024

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I should note that my original idea was indeed that there is generally a relatively low number of info metrics so that a default matching behavior of "all the info metrics known to the TSDB" is feasible, which is in fact one of the reasons that makes the "joining" easier.

Note that with a "general metadata store", you would probably also end up with "give me all the metadata you know for this metric", which is (more or less) equivalent to "give me all the info metrics that have an identifying labels match with this metric".


The section stating potential alternatives. Highlight the objections reader should have towards your proposal as they read it. Tell them why you still think you should take this path [[ref](https://twitter.com/whereistanya/status/1353853753439490049)]

1. This is why not solution Z...

## Action Plan

The tasks to do in order to migrate to the new idea.

* [ ] Task one <GH issue>
* [ ] Task two <GH issue> ...
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I believe we do have an action plan now right? Something along the lines of implementing the info function without TSDB modifications and then iterate further with a new proposal addressing the issues listed above.

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Thanks, I'll add some.

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@aknuds1 aknuds1 Jul 23, 2024

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I added two tasks, could you please take a look?

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