Get(...) should return []Exemplar, multiple exemplars could match l if not many labels are passed.

So the idea was it has to be a full equality match on labels. The labels here describe the exact metric including all the labels and the name, so there should only be one. Otherwise the lookup is not that useful. We need to be able to find the exemplar corresponding to the metric.

I can't speak for everyone else but I think we should follow the same pattern as existing queries; return all matches for the labels provided. That doesn't exclude users from passing labels that would match only a single exemplar, but is better than error; query for exemplar would return more than one result in the case of the current functions IMO.

Ok.. But in that case the return value will have to change.. It will additionally have to include information of the actual series matched as the exemplar is not useful without the series itself. So returns something like

type SeriesExemplar struct {
    l labels.Labels
    e Exemplar
}

and the method becomes Get(l labels.Labels, t int64) ([]SeriesExemplar, bool, error)

I assume @shreyassrivatsan 's original idea was to keep things really as simple as possible. With exact matches, we can just have all the exemplars in a simple hash map. With label matching, we'll find ourselves once more in a situation where we have to implement a database (or do full scans of the exemplars in memory). Is that part of the plan?

Could we perhaps quickly think through the typical query pattern for exemplars? Will we commonly need label matching at all?

I think a Grafana dashboard is going to be a pretty common usecase. If a dashboard panel has a single query for something like http_request_duration_seconds, without enough labels to narrow the query down to a single series, I don't think it's reasonable for Grafana to have to do the work to split all the results into individual label sets and make a query per unique series.

There's also the issue that many histogram queries aggregate away labels that we'd need to uniquely identify which exemplar to select.

@shreyassrivatsan can you elaborate on your use case and what M3 needs from the API, and hopefully we can get some more details from @davkal as well.

Ah. So I did not mean that grafana makes a query and then retrieves the exemplars. I meant a prometheus query will first query the series, get the exemplars and then apply functions.. I am simplifying a lot here, but essentially /api/v1/query and /api/v1/query_range will do

func query(query string) series {
  ls := querySeries() // returns []labels.Labels
  if exemplarsRequested {
    for _, l := range ls {
      queryExemplar(l)
    }
  }
  // apply functions
}

The resulting response will probably contain something maybe like the below

{
   "status" : "success",
   "data" : {
      "resultType" : "vector",
      "result" : [
         {
            "metric" : {
               "__name__" : "up",
               "job" : "prometheus",
               "instance" : "localhost:9090"
            },
            "value": [ 1435781451.781, "1", {<exemplar>} ]
         }
      ]
  }
}

Eventually for full exemplar support , functions would have to be updated to handle exemplars. For example, aggregation functions can have a pick one policy.

I think this approach makes a lot of sense. However, it requires to intertwine the existing API with exemplar support. My impression from the Prometheus side of things was that the plan is to implement something experimental that is a completely separate API so that consumers of that API can play with it and prove or disprove its usefulness. We had a few chats now with @juliusv and @davkal, wherein we realized that it is actually very hard for a visualization frontend like Grafana to craft dedicated queries for exemplars (for example in case the user clicks on a peak of a latency graph or on a tile of a histogram heatmap).

Returning exemplars alongside normal queries, if so requested, is solving that problem, but it's quite invasive (as the exemplar support has to be piped through the whole query engine, including smart decisions what to pick for aggregations). If we went down that alley, the exact-match method as suggested here would indeed make sense as illustrated by your pseudo code. (We might want to use a time range and not just a single timestamp in the signature then.)

While I like the idea, I'm pretty sure amending everything in the query engine is not feasible for an experimental feature. I'm still thinking about ways how to do something for the MVP that leaves the current query API and engine as is…

Ok, that makes sense. And yes, this is going more towards what will be the right solution if we decide to pipe this through the query engine. I still do think the exact timestamp makes for a faster implementation as we will have access to that as a part of the results for a time series query.

For an experimental implementation using a time range and partial label match is probably a better approach, but implementing the backing store will be some more work. I made some changes to the interface. Get still does exact matches for a given timestamp. Query does partial label matches, takes a time range and returns a list of SeriesExemplars.

My idea for how the (MVP) query flow and UI in Grafana could look like:

The Grafana user creating a dashboard would be required to pair each graphing query with an additional "query", which would be required to just be a PromQL label selector without any aggregations or functions, e.g. request_latency_bucket{code=~"2..",job="foo"}. The exemplar API would take exactly that query string (i.e. a naked label matcher in PromQL syntax) and a time range to return exemplars, possibly sampled to avoid too many return values. Grafana could then use the provided string to query for exemplars upon a click event (could include the le label when in a histogram heatmap, but would also work without it when clicking on a latency graph). Further server-side filtering by exemplar or specific downsampling value would not be in the MVP but could be done in the browser for now.

A selection of exemplars with links to logs or traces could then be shown in a pop-up, or exemplars could show up as clickable "stars" in a latency graph.

@davkal does that make sense?