Split unique values (groups) into shards according to the configured concurrency
during processing of the matching rows if the number of unique values exceeds the hardcoded threshold.
Previously this splitting was performed unconditionally at the merge stage when merging independently
calculated per-CPU states into a single state. It is faster to perform the split during rows processing
if the number of unique values is big.
This gives up to 30% perfromance improvements when these pipes are applied to big number of unique values (groups).
The number of worker shards per each pipe processor is created during query initialization.
This number equals to the `options(concurrency=N)` if this option is set or to the number of available CPU cores.
This means that all the pipes must adhere the given concurrency when passing data blocks
to the next pipe.
See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/8201
The bug has been introduced in 0214aa328e
The purpose of extra filters ( https://docs.victoriametrics.com/victorialogs/querying/#extra-filters )
is to limit the subset of logs, which can be queried. For example, it is expected that all the queries
with `extra_filters={tenant=123}` can access only logs, which contain `123` value for the `tenant` field.
Previously this wasn't the case, since the provided extra filters weren't applied to subqueries.
For example, the following query could be used to select all the logs outside `tenant=123`, for any `extra_filters` arg:
* | union({tenant!=123})
This commit fixes this by propagating extra filters to all the subqueries.
While at it, this commit also properly propagates [start, end] time range filter from HTTP querying APIs
into all the subqueries, since this is what most users expect. This behaviour can be overriden on per-subquery
basis with the `options(ignore_global_time_filter=true)` option - see https://docs.victoriametrics.com/victorialogs/logsql/#query-options
Also properly apply apply optimizations across all the subqueries. Previously the optimizations at Query.optimize()
function were applied only to the top-level query.
While at it, reduce memory allocations at Storage.getFieldValuesNoHits and make it more scalable on multi-CPU systems.
This improves performance of in(<query>) filter when the <query> returns big number of values.
Use chunked allocator in order to reduce memory allocations. It allocates objects from slices of up to 64Kb size.
This improves performance for `stats` and `top` pipes by up to 2x when they are applied to big number of `by (...)` groups.
Also parallelize execution of `count_uniq`, `count_uniq_hash` and `uniq_values` stats functions,
so they are executed faster on hosts with many CPU cores when applied to fields with big number
of unique values.
This eliminates possible bugs related to forgotten Query.Optimize() calls.
This also allows removing optimize() function from pipe interface.
While at it, drop filterNoop inside filterAnd.
- Use parallel merge of per-CPU shard results. This improves merge performance on multi-CPU systems.
- Use topN heap sort of per-shard results. This improves performance when results contain millions of entries.
Create blockResultColumn.forEachDictValue* helper functions for visiting matching
dictionary values. These helper functions should prevent from counting dictionary values
without matching logs in the future.
This is a follow-up for 0c0f013a60
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/7152
encoding.GetUint64s() returns uninitialized slice, which may contain arbitrary values.
So values in this slice must be reset to zero before using it for counting hits in `uniq` and `top` pipes.
This simplifies pipeProcessor initialization logic a bit.
This also doesn't mangle the original maxStateSize value, which is used in error messages when the state size exceeds maxStateSize.
