2023-06-20 05:55:12 +00:00
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package logstorage
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import (
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"fmt"
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2024-10-30 13:13:56 +00:00
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"slices"
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2024-07-04 23:17:03 +00:00
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"github.com/valyala/quicktemplate"
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2023-06-20 05:55:12 +00:00
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/bytesutil"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/encoding"
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)
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// Field is a single field for the log entry.
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type Field struct {
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// Name is the name of the field
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Name string
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// Value is the value of the field
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Value string
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}
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// Reset resets f for future re-use.
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func (f *Field) Reset() {
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f.Name = ""
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f.Value = ""
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}
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// String returns string representation of f.
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func (f *Field) String() string {
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2024-05-22 19:01:20 +00:00
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x := f.marshalToJSON(nil)
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return string(x)
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2023-06-20 05:55:12 +00:00
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}
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lib/logstorage: refactor storage format to be more efficient for querying wide events
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 .
2024-10-16 14:18:28 +00:00
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func (f *Field) marshal(dst []byte, marshalFieldName bool) []byte {
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if marshalFieldName {
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dst = encoding.MarshalBytes(dst, bytesutil.ToUnsafeBytes(f.Name))
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}
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2023-06-20 05:55:12 +00:00
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dst = encoding.MarshalBytes(dst, bytesutil.ToUnsafeBytes(f.Value))
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return dst
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}
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lib/logstorage: refactor storage format to be more efficient for querying wide events
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 .
2024-10-16 14:18:28 +00:00
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func (f *Field) unmarshalNoArena(src []byte, unmarshalFieldName bool) ([]byte, error) {
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2023-06-20 05:55:12 +00:00
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srcOrig := src
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// Unmarshal field name
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lib/logstorage: refactor storage format to be more efficient for querying wide events
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 .
2024-10-16 14:18:28 +00:00
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if unmarshalFieldName {
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name, nSize := encoding.UnmarshalBytes(src)
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if nSize <= 0 {
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return srcOrig, fmt.Errorf("cannot unmarshal field name")
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}
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src = src[nSize:]
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f.Name = bytesutil.ToUnsafeString(name)
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2023-06-20 05:55:12 +00:00
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}
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// Unmarshal field value
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lib/logstorage: refactor storage format to be more efficient for querying wide events
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 .
2024-10-16 14:18:28 +00:00
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value, nSize := encoding.UnmarshalBytes(src)
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2024-05-13 23:23:44 +00:00
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if nSize <= 0 {
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return srcOrig, fmt.Errorf("cannot unmarshal field value")
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2023-06-20 05:55:12 +00:00
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}
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2024-05-13 23:23:44 +00:00
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src = src[nSize:]
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lib/logstorage: refactor storage format to be more efficient for querying wide events
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 .
2024-10-16 14:18:28 +00:00
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f.Value = bytesutil.ToUnsafeString(value)
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2023-06-20 05:55:12 +00:00
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return src, nil
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}
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2024-05-22 19:01:20 +00:00
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func (f *Field) marshalToJSON(dst []byte) []byte {
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2024-06-17 10:13:18 +00:00
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name := f.Name
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if name == "" {
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name = "_msg"
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}
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2024-07-04 23:17:03 +00:00
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dst = quicktemplate.AppendJSONString(dst, name, true)
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2024-05-22 19:01:20 +00:00
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dst = append(dst, ':')
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2024-07-04 23:17:03 +00:00
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dst = quicktemplate.AppendJSONString(dst, f.Value, true)
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2024-05-22 19:01:20 +00:00
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return dst
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}
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2024-06-05 01:18:12 +00:00
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func (f *Field) marshalToLogfmt(dst []byte) []byte {
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2024-10-14 21:39:29 +00:00
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name := f.Name
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if name == "" {
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name = "_msg"
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}
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dst = append(dst, name...)
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2024-06-05 01:18:12 +00:00
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dst = append(dst, '=')
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if needLogfmtQuoting(f.Value) {
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2024-07-04 23:17:03 +00:00
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dst = quicktemplate.AppendJSONString(dst, f.Value, true)
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2024-06-05 01:18:12 +00:00
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} else {
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dst = append(dst, f.Value...)
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}
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return dst
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}
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2024-07-01 00:27:13 +00:00
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func getFieldValue(fields []Field, name string) string {
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for _, f := range fields {
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if f.Name == name {
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return f.Value
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2024-06-30 22:49:31 +00:00
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}
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}
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2024-07-01 00:27:13 +00:00
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return ""
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2024-06-30 22:49:31 +00:00
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}
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2024-06-05 01:18:12 +00:00
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func needLogfmtQuoting(s string) bool {
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for _, c := range s {
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2024-10-07 12:31:16 +00:00
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if isLogfmtSpecialChar(c) {
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2024-06-05 01:18:12 +00:00
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return true
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}
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}
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return false
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}
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2024-10-07 12:31:16 +00:00
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func isLogfmtSpecialChar(c rune) bool {
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if c <= 0x20 {
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return true
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}
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switch c {
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case '"', '\\':
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return true
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default:
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return false
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}
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}
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2024-10-30 13:13:56 +00:00
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// RenameField renames the first non-empty field with the name from oldNames list to newName in Fields
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func RenameField(fields []Field, oldNames []string, newName string) {
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if len(oldNames) == 0 {
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// Nothing to rename
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2024-06-17 10:13:18 +00:00
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return
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}
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for i := range fields {
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f := &fields[i]
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2024-10-30 13:13:56 +00:00
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if f.Value != "" && slices.Contains(oldNames, f.Name) {
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2024-06-17 10:13:18 +00:00
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f.Name = newName
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return
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}
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}
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}
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2024-06-05 01:18:12 +00:00
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// MarshalFieldsToJSON appends JSON-marshaled fields to dst and returns the result.
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2024-06-03 22:59:25 +00:00
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func MarshalFieldsToJSON(dst []byte, fields []Field) []byte {
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2024-10-14 21:39:29 +00:00
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fields = SkipLeadingFieldsWithoutValues(fields)
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2024-05-22 19:01:20 +00:00
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dst = append(dst, '{')
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if len(fields) > 0 {
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dst = fields[0].marshalToJSON(dst)
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fields = fields[1:]
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for i := range fields {
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2024-10-14 21:39:29 +00:00
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f := &fields[i]
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if f.Value == "" {
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// Skip fields without values
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continue
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}
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2024-05-22 19:01:20 +00:00
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dst = append(dst, ',')
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2024-10-14 21:39:29 +00:00
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dst = f.marshalToJSON(dst)
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2024-05-22 19:01:20 +00:00
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}
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}
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dst = append(dst, '}')
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return dst
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}
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2024-06-05 01:18:12 +00:00
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// MarshalFieldsToLogfmt appends logfmt-marshaled fields to dst and returns the result.
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func MarshalFieldsToLogfmt(dst []byte, fields []Field) []byte {
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if len(fields) == 0 {
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return dst
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}
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dst = fields[0].marshalToLogfmt(dst)
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fields = fields[1:]
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for i := range fields {
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dst = append(dst, ' ')
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dst = fields[i].marshalToLogfmt(dst)
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}
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return dst
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}
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2024-10-14 21:39:29 +00:00
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// SkipLeadingFieldsWithoutValues skips leading fields without values.
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func SkipLeadingFieldsWithoutValues(fields []Field) []Field {
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i := 0
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for i < len(fields) && fields[i].Value == "" {
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i++
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}
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return fields[i:]
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}
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2024-05-12 14:33:29 +00:00
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func appendFields(a *arena, dst, src []Field) []Field {
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for _, f := range src {
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dst = append(dst, Field{
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Name: a.copyString(f.Name),
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Value: a.copyString(f.Value),
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})
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}
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return dst
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}
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2023-06-20 05:55:12 +00:00
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// rows is an aux structure used during rows merge
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type rows struct {
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fieldsBuf []Field
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timestamps []int64
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rows [][]Field
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}
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// reset resets rs
|
|
|
|
func (rs *rows) reset() {
|
|
|
|
fb := rs.fieldsBuf
|
|
|
|
for i := range fb {
|
|
|
|
fb[i].Reset()
|
|
|
|
}
|
|
|
|
rs.fieldsBuf = fb[:0]
|
|
|
|
|
|
|
|
rs.timestamps = rs.timestamps[:0]
|
|
|
|
|
|
|
|
rows := rs.rows
|
|
|
|
for i := range rows {
|
|
|
|
rows[i] = nil
|
|
|
|
}
|
|
|
|
rs.rows = rows[:0]
|
|
|
|
}
|
|
|
|
|
|
|
|
// appendRows appends rows with the given timestamps to rs.
|
|
|
|
func (rs *rows) appendRows(timestamps []int64, rows [][]Field) {
|
|
|
|
rs.timestamps = append(rs.timestamps, timestamps...)
|
|
|
|
|
|
|
|
fieldsBuf := rs.fieldsBuf
|
|
|
|
for _, fields := range rows {
|
|
|
|
fieldsLen := len(fieldsBuf)
|
|
|
|
fieldsBuf = append(fieldsBuf, fields...)
|
|
|
|
rs.rows = append(rs.rows, fieldsBuf[fieldsLen:])
|
|
|
|
}
|
|
|
|
rs.fieldsBuf = fieldsBuf
|
|
|
|
}
|
|
|
|
|
|
|
|
// mergeRows merges the args and appends them to rs.
|
|
|
|
func (rs *rows) mergeRows(timestampsA, timestampsB []int64, fieldsA, fieldsB [][]Field) {
|
|
|
|
for len(timestampsA) > 0 && len(timestampsB) > 0 {
|
|
|
|
i := 0
|
|
|
|
minTimestamp := timestampsB[0]
|
|
|
|
for i < len(timestampsA) && timestampsA[i] <= minTimestamp {
|
|
|
|
i++
|
|
|
|
}
|
|
|
|
rs.appendRows(timestampsA[:i], fieldsA[:i])
|
|
|
|
fieldsA = fieldsA[i:]
|
|
|
|
timestampsA = timestampsA[i:]
|
|
|
|
|
|
|
|
fieldsA, fieldsB = fieldsB, fieldsA
|
|
|
|
timestampsA, timestampsB = timestampsB, timestampsA
|
|
|
|
}
|
|
|
|
if len(timestampsA) == 0 {
|
|
|
|
rs.appendRows(timestampsB, fieldsB)
|
|
|
|
} else {
|
|
|
|
rs.appendRows(timestampsA, fieldsA)
|
|
|
|
}
|
|
|
|
}
|