This improves performance of `field_values` pipe when it is applied to large number of data blocks.
This also improves performance of /select/logsql/field_values HTTP API.
(cherry picked from commit 8d968acd0a)
Unpack the full columnsHeader block instead of unpacking meta-information per each individual column
when the query, which selects all the columns, is executed. This improves performance when scanning
logs with big number of fields.
(cherry picked from commit 2023f017b1)
It has been appeared that VictoriaLogs is frequently used for collecting logs with tens of fields.
For example, standard Kuberntes setup on top of Filebeat generates more than 20 fields per each log.
Such logs are also known as "wide events".
The previous storage format was optimized for logs with a few fields. When at least a single field
was referenced in the query, then the all the meta-information about all the log fields was unpacked
and parsed per each scanned block during the query. This could require a lot of additional disk IO
and CPU time when logs contain many fields. Resolve this issue by providing an (field -> metainfo_offset)
index per each field in every data block. This index allows reading and extracting only the needed
metainfo for fields used in the query. This index is stored in columnsHeaderIndexFilename ( columns_header_index.bin ).
This allows increasing performance for queries over wide events by 10x and more.
Another issue was that the data for bloom filters and field values across all the log fields except of _msg
was intermixed in two files - fieldBloomFilename ( field_bloom.bin ) and fieldValuesFilename ( field_values.bin ).
This could result in huge disk read IO overhead when some small field was referred in the query,
since the Operating System usually reads more data than requested. It reads the data from disk
in at least 4KiB blocks (usually the block size is much bigger in the range 64KiB - 512KiB).
So, if 512-byte bloom filter or values' block is read from the file, then the Operating System
reads up to 512KiB of data from disk, which results in 1000x disk read IO overhead. This overhead isn't visible
for recently accessed data, since this data is usually stored in RAM (aka Operating System page cache),
but this overhead may become very annoying when performing the query over large volumes of data
which isn't present in OS page cache.
The solution for this issue is to split bloom filters and field values across multiple shards.
This reduces the worst-case disk read IO overhead by at least Nx where N is the number of shards,
while the disk read IO overhead is completely removed in best case when the number of columns doesn't exceed N.
Currently the number of shards is 8 - see bloomValuesShardsCount . This solution increases
performance for queries over large volumes of newly ingested data by up to 1000x.
The new storage format is versioned as v1, while the old storage format is version as v0.
It is stored in the partHeader.FormatVersion.
Parts with the old storage format are converted into parts with the new storage format during background merge.
It is possible to force merge by querying /internal/force_merge HTTP endpoint - see https://docs.victoriametrics.com/victorialogs/#forced-merge .
This localizes blockSearch.getColumnsHeader() call at block_search.go .
This call is going to be optimized in the next commits in order to avoid
unmarshaling of header data for unneeded columns, which weren't requested
by getConstColumnValue() / getColumnHeader().
(cherry picked from commit 507b206a7d)
Refer the original byte slice with the marshaled columnsHeader for columns names and dictionary-encoded column values.
This improves query performance a bit when big number of blocks with big number of columns are scanned during the query.
(cherry picked from commit 279e25e7c8)
This should improve performance when blockSearch.getColumnsHeader() is called multiple times
from different places of the code.
(cherry picked from commit 9e48074b59)
This improves performance for analytical queries, which do not need column headers metadata.
For example, the following query doesn't need column headers metadata, since _stream and min(_time)
are stored in block header, which is read separately from colum headers metadata:
_time:1w | stats by (_stream) min(_time) min_time
This commit significantly improves the performance for this query.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/7070
Substitute global streamTagsCache with per-blockSearch cache for ((stream.id) -> (_stream value)) entries.
This improves scalability of obtaining _stream values on a machine with many CPU cores, since every CPU
has its own blockSearch instance.
This also should reduce memory usage when querying logs over big number of streams, since per-blockSearch
cache of ((stream.id) -> (_stream value)) entries is limited in size, and its lifetime is bounded by a single query.
