For special remote read endpoints which have only data for specific
queries, it is desired to limit the number of queries sent to the
configured remote read endpoint to reduce latency and performance
overhead.
* Decouple remote client from ReadRecent feature.
* Separate remote read filter into a small, testable function.
* Use storage.Queryable interface to compose independent
functionalities.
The labelsets returned from remote read are mutated in higher levels
(like seriesFilter.Labels()) and since the concreteSeriesSet didn't
return a copy, the external mutation affected the labelset in the
concreteSeries itself. This resulted in bizarre bugs where local and
remote series would show with identical label sets in the UI, but not be
deduplicated, since internally, a series might come to look like:
{__name__="node_load5", instance="192.168.1.202:12090", job="node_exporter", node="odroid", node="odroid"}
(note the repetition of the last label)
staticcheck fails with:
storage/remote/read_test.go:199:27: do not pass a nil Context, even if a function permits it; pass context.TODO if you are unsure about which Context to use (SA1012)
Currently all read queries are simply pushed to remote read clients.
This is fine, except for remote storage for wich it unefficient and
make query slower even if remote read is unnecessary.
So we need instead to compare the oldest timestamp in primary/local
storage with the query range lower boundary. If the oldest timestamp
is older than the mint parameter, then there is no need for remote read.
This is an optionnal behavior per remote read client.
Signed-off-by: Thibault Chataigner <t.chataigner@criteo.com>
* Re-add contexts to storage.Storage.Querier()
These are needed when replacing the storage by a multi-tenant
implementation where the tenant is stored in the context.
The 1.x query interfaces already had contexts, but they got lost in 2.x.
* Convert promql.Engine to use native contexts
This can happen in the situation where the system scales up the number of shards massively (to deal with some backlog), then scales it down again as the number of samples sent during the time period is less than the number received.
