### Describe Your Changes
Added an ability to query data across multiple tenants. See:
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1434
Currently, the following endpoints work with multi-tenancy:
- /prometheus/api/v1/query
- /prometheus/api/v1/query_range
- /prometheus/api/v1/series
- /prometheus/api/v1/labels
- /prometheus/api/v1/label/<label_name>/values
- /prometheus/api/v1/status/active_queries
- /prometheus/api/v1/status/top_queries
- /prometheus/api/v1/status/tsdb
- /prometheus/api/v1/export
- /prometheus/api/v1/export/csv
- /vmui
A note regarding VMUI: endpoints such as `active_queries` and
`top_queries` have been updated to indicate whether query was a
single-tenant or multi-tenant, but UI needs to be updated to display
this info.
cc: @Loori-R
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Signed-off-by: f41gh7 <nik@victoriametrics.com>
Co-authored-by: f41gh7 <nik@victoriametrics.com>
evalRollupFuncNoCache() may return time series with identical labels (aka duplicate series)
when performing queries satisfying all the following conditions:
- It must select time series with multiple metric names. For example, {__name__=~"foo|bar"}
- The series selector must be wrapped into rollup function, which drops metric names. For example, rate({__name__=~"foo|bar"})
- The rollup function must be wrapped into aggregate function, which has no streaming optimization.
For example, quantile(0.9, rate({__name__=~"foo|bar"})
In this case VictoriaMetrics shouldn't return `cannot merge series: duplicate series found` error.
Instead, it should fall back to query execution with disabled cache.
Also properly store the merged results. Previously they were incorrectly stored because of a typo
introduced in the commit 41a0fdaf39
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5332
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5337
Repeated instant queries with long lookbehind windows, which contain one of the following rollup functions,
are optimized via partial result caching:
- sum_over_time()
- count_over_time()
- avg_over_time()
- increase()
- rate()
The basic idea of optimization is to calculate
rf(m[d] @ t)
as
rf(m[offset] @ t) + rf(m[d] @ (t-offset)) - rf(m[offset] @ (t-d))
where rf(m[d] @ (t-offset)) is cached query result, which was calculated previously
The offset may be in the range of up to 1 hour.
Calculate incremental aggregates for `aggr(metric_selector)` function instead of
keeping all the time series matching the given `metric_selector` in memory.
