* feature: Add multitenant for vmagent
* Minor fix
* Fix rcs index out of range
* Minor fix
* Fix multi Init
* Fix multi Init
* Fix multi Init
* Add default multi
* Adjust naming
* Add TenantInserted metrics
* Add TenantInserted metrics
* fix: remove unused metrics for vmagent
* fix: remove unused metrics for vmagent
Co-authored-by: mghader <marc.ghader@ubisoft.com>
Co-authored-by: Sebastian YEPES <syepes@gmail.com>
* Rename -search.maxMetricsPointSearch to -search.maxSamplesPerQuery, so it is more consistent with the existing -search.maxSamplesPerSeries
* Move the -search.maxSamplesPerQuery from vmstorage to vmselect, so it could effectively limit the number of raw samples obtained from all the vmstorage nodes
* Document the -search.maxSamplesPerQuery in docs/CHANGELOG.md
Previously the switch occurred when the cache size becomes 100% of its capacity. The cache size could never reach 100% capacity.
This could prevent from switching from the split cache to full cache, thus reducing the cache effectiveness.
Previously needsDedup() could return true if the de-duplication wasn't needed for the following case:
d < interval
/ \
| v | v |
interval interval
Now it properly returns false for this case
This reverts commit 7c6d3981bf.
Reason for revert: high contention at bucket16Pool on systems with big number of CPU cores.
This slows down query processing significantly.
This should reduce memory usage on systems with big number of CPU cores,
since every inmemoryPart object occupies at least 64KB of memory and sync.Pool maintains
a separate pool inmemoryPart objects per each CPU core.
Though the new scheme for the pool worsens per-cpu cache locality, this should be amortized
by big sizes of inmemoryPart objects.
CPU and memory profiles show that the pool capacity for inmemoryBlock objects is too small.
This results in the increased load on memory allocation code in Go runtime.
Increase the pool capacity in order to reduce the load on Go runtime.
This should improve hit ratio for tagFiltersCache when big number of new time series are constantly registered
(aka high churn rate). This, in turn, should reduce CPU usage for queries over such time series.
Previously the stats for cache misses could be improperly counted, because it had inflated cache misses
if the entry was missing in the curr cache, but was existing in the prev cache.
The same applies to cache requests - they were inflated if the entry was missing in the curr cache.
Previously the switch from `split` to `whole` mode had been performed too early,
e.g. when the current cache size became bigger than 1/4 of the allowed cache size.
Now it is performed when the current cache size becomes bigger than 1/2 of the allowed cache size.
This change can reduce memory usage for data ingestion path when big number of active time series are ingested.
One minute cache timeout result in slower queries in some production workloads where the interval
between query execution is in the range 1 minute - 2 minutes.
This should reduce memory usage on a system with high number of active time series and a high churn rate.
One minute is enough for caching the blocks needed for repeated queries (e.g. alerting rules, recording rules and dashboard refreshes).
This should reduce memory usage for the pool on systems with big number of CPU cores.
The sync.Pool maintains per-CPU pools, so the total number of objects in the pool
is proportional to the number of available CPU cores. The channel limits the number
of pooled objects by its own capacity. This means smaller number of pooled objects on average.
