This should improve the performance for items sorting inside inmemoryBlock.MarshalUnsortedData
if they have common prefix.
While at it, improve the performance for inmemoryBlock.updateCommonPrefix for sorted items.
This should improve performance for inmemoryBlock.MarshalSortedData during background merge.
The lifetime of storageBlock is much shorter comparing to the lifetime of inmemoryPart,
so sync.Pool usage should reduce overall memory usage and improve performance
because of better locality of reference when marshaling inmemoryBlock to inmemoryPart.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2247
There is no need to sort the underlying data according to sorted items there.
This should reduce cpu usage when registering new time series in `indexdb`.
Thanks to @ahfuzhang for the suggestion at https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2245
* optimized code ,because only the first error,so no need var errors []error
* optimized code ,because only the first error,so no need var errors []error
Co-authored-by: lirenzuo <lirenzuo@shein.com>
Previously bytesutil.Resize() was copying the original byte slice contents to a newly allocated slice.
This wasted CPU cycles and memory bandwidth in some places, where the original slice contents wasn't needed
after slize resizing. Switch such places to bytesutil.ResizeNoCopy().
Rename the original bytesutil.Resize() function to bytesutil.ResizeWithCopy() for the sake of improved readability.
Additionally, allocate new slice with `make()` instead of `append()`. This guarantees that the capacity of the allocated slice
exactly matches the requested size. The `append()` could return a slice with bigger capacity as an optimization for further `append()` calls.
This could result in excess memory usage when the returned byte slice was cached (for instance, in lib/blockcache).
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2007
Previously these caches could exceed limits set via `-memory.allowedPercent` and/or `-memory.allowedBytes`,
since limits were set independently per each data part. If the number of data parts was big, then limits could be exceeded,
which could result to out of memory errors.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2007
The vm_cache_size_max_bytes metric can be used for determining caches which reach their capacity via the following query:
vm_cache_size_bytes / vm_cache_size_max_bytes > 0.9
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.
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.
The pool for inmemoryBlock struct doesn't give any performance gains in production workloads,
while it may result in excess memory usage for inmemoryBlock structs inside the pool during
background merge of indexdb.
While this may increase CPU and disk IO usage needed for background merge,
this also recudes CPU usage during queries in production. This is because
such queries tend to read recently added data and it is better to have lower number
of parts for such data in order to reduce CPU usage.
This partially reverts ebf8da3730
Production workloads show that indexdb blocks must be cached unconditionally for reducing CPU usage.
This shouldn't increase memory usage too much, since unused blocks are removed from the cache every two minutes.
