1. Verify if field in [fields
pipe](https://docs.victoriametrics.com/victorialogs/logsql/#fields-pipe)
exists. If not, it generates a metric with illegal float value "" for
prometheus metrics protocol.
2. check if multiple time range filters produce conflicted query time
range, for instance:
```
query: _time: 5m | stats count(),
start:2024-10-08T10:00:00.806Z,
end: 2024-10-08T12:00:00.806Z,
time: 2024-10-10T10:02:59.806Z
```
must give no result due to invalid final time range.
---------
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
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 .
Empty fields are treated as non-existing fields by VictoriaLogs data model.
So there is no sense in returning empty fields in query results, since they may mislead and confuse users.
s.partitions can be changed when new partition is registered or when old partition is dropped.
This could lead to data races and panics when s.partitions slice is accessed by concurrently executed queries.
The fix is to make a copy of the selected partitions under s.partitionsLock before performing the query.
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().
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.
Improperly written pipes could be silently parsed as filter pipe.
For example, the following query:
* | by (x)
was silently parsed to:
* | filter "by" x
It is better to return error, so the user could identify and fix invalid pipe
instead of silently executing invalid query with `filter` pipe.
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
Previously the phrase filter with `!` was treated unexpectedly.
For example, `foo!bar` filter was treated at `foo AND NOT bar`,
while most users expect that it matches "foo!bar" phrase.
This commit aligns with users' expectations.
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.
This allows executing queries with `stats` pipe, which calculate multiple results with the same functions,
but with different `if (...)` conditions. For example:
_time:5m | count(), count() if (error)
Previously such queries couldn't be executed becasue automatically generated name for the second result
didn't include `if (error)`, so names for both results were identical - `count(*)`.
Now the following queries are equivalents:
_time:5s | sort by (_time)
_time:5s | order by (_time)
This is needed for convenience, since `order by` is commonly used in other query languages such as SQL.
Previously only logs inside the selected time range could be returned by stream_context pipe.
For example, the following query could return up to 10 surrounding logs only for the last 5 minutes,
while most users expect this query should return up to 10 surrounding logs without restrictions on the time range.
_time:5m panic | stream_context before 10
This enables the ability to implement stream context feature at VictoriaLogs web UI: https://github.com/VictoriaMetrics/VictoriaMetrics/issues/7063 .
Reduce memory usage when returning stream context over big log streams with millions of entries.
The new logic scans over all the log messages for the selected log stream, while keeping in memory only
the given number of surrounding logs. Previously all the logs for the given log stream on the selected time range
were loaded in memory before selecting the needed surrounding logs.
This should help https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6730 .
Reduce the scan performance for big log streams by fetching only the requested fields. For example, the following
query should be executed much faster than before if logs contain many fields other than _stream, _msg and _time:
panic | stream_context after 30 | fields _stream, _msg, _time
Use local timezone of the host server in this case. The timezone can be overridden
with TZ environment variable if needed.
While at it, allow using whitespace instead of T as a delimiter between data and time
in the ingested _time field. For example, '2024-09-20 10:20:30' is now accepted
during data ingestion. This is valid ISO8601 format, which is used by some log shippers,
so it should be supported. This format is also known as SQL datetime format.
Also assume local time zone when time without timezone information is passed to querying APIs.
Previously such a time was parsed in UTC timezone. Add `Z` to the end of the time string
if the old behaviour is preferred.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6721
Previously the original timestamp was used in the copied query, so _time:duration filters
were applied to the original time range: (timestamp-duration ... timestamp]. This resulted
in stopped live tailing, since new logs have timestamps bigger than the original time range.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/7028
This pipe is useful for debugging purposes when the number of processed blocks must be calculated for the given query:
<query> | blocks_count
This helps detecting the root cause of query performance slowdown in cases like https://github.com/VictoriaMetrics/VictoriaMetrics/issues/7070
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.
The streamID.marshalString() is executed in hot path if the query selects _stream_id field.
Command to run the benchmark:
go test ./lib/logstorage/ -run=NONE -bench=BenchmarkStreamIDMarshalString -benchtime=5s
Results before the commit:
BenchmarkStreamIDMarshalString-16 438480714 14.04 ns/op 71.23 MB/s 0 B/op 0 allocs/op
Results after the commit:
BenchmarkStreamIDMarshalString-16 982459660 6.049 ns/op 165.30 MB/s 0 B/op 0 allocs/op
This fixes flaky test TestGetCommonTokensForOrFilters:
filter_or_test.go:143: unexpected tokens for field "_msg"; got ["foo" "bar"]; want ["bar" "foo"]
Previously the query could return incorrect results, since the query timestamp was updated with every Query.Clone() call
during iterative search for the time range with up to limit=N rows.
While at it, optimize queries, which find low number of matching logs, while spend a lot of CPU time for searching
across big number of logs. The optimization reduces the upper bound of the time range to search if the current time range
contains zero matching rows.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6785
Previously tokens from AND filters were extracted in random order. This could slow down
checking them agains bloom filters if the most specific tokens go at the beginning of the AND filters.
Preserve the original order of tokens when matching them against bloom filters,
so the user could control the performance of the query by putting the most specific AND filters
at the beginning of the query.
While at it, add tests for getCommonTokensForAndFilters() and getCommonTokensForOrFilters().
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6554
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6556
Previously the following query could miss rows matching !bar if these rows do not contain foo:
foo OR !bar
This is because of incorrect detection of common tokens for OR filters - all the unsupported filters
were skipped (including the NOT filter (aka `!`)), while in this case zero common tokens must be returned.
While at it, move repetiteve code in TestFilterAnd and TestFilterOr into f function.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6554
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6556
This is needed for avoiding confusion between the `|` operator at `math` pipe and `|` pipe delimiter.
For example, the following query was parsed unexpectedly:
* | math foo / bar | fields x
as
* | math foo / (bar | fields) as x
Substituting `|` with `or` inside `math` pipe fixes this ambiguity.
Previously per-token hashes for per-block bloom filters were re-calculated on every scanned block.
This could be slow when the number of tokens is big or when the number of blocks to scan is big.
Pre-calculate hashes for bloom filters and then use them for searching in bloom filters.
This improves performance by 2.5x for in(...) filters with many values to search inside `in()`.
