Related issue:
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/7182
- add a separate index cache for searches which might read through large
amounts of random entries. Primary use-case for this is retention and
downsampling filters, when applying filters background merge needs to
fetch large amount of random entries which pollutes an index cache.
Using different caches allows to reduce effect on memory usage and cache
efficiency of the main cache while still having high cache hit rate. A
separate cache size is 5% of allowed memory.
- reduce size of indexdb/dataBlocks cache in order to free memory for
new sparse cache. Reduced size by 5% and moved this to a separate cache.
- add a separate metricName search which does not cache metric names -
this is needed in order to allow disabling metric name caching when
applying downsampling/retention filters. Applying filters during
background merge accesses random entries, this fills up cache and does
not provide an actual improvement due to random access nature.
Merge performance and memory usage stats before and after the change:
- before
- after
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
### Describe Your Changes
Add support for
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6930
Calculate `-search.maxUniqueTimeseries` by
`-search.maxConcurrentRequests` and remaining memory if it's **not set**
or **less equal than 0**.
The remaining memory is affected by `-memory.allowedPercent`,
`-memory.allowedBytes` and cgroup memory limit.
### Checklist
The following checks are **mandatory**:
- [x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
(cherry picked from commit 85f60237e2)
Signed-off-by: hagen1778 <roman@victoriametrics.com>
### Describe Your Changes
Introduce the `-search.maxDeleteSeries` flag that limits the number of
time series that can be deleted with a single
`/api/v1/admin/tsdb/delete_series` call.
Currently, any number can be deleted and if the number is big (millions)
then the operation may result in unaccounted CPU and memory usage spikes
which in some cases may result in OOM kill (see #7027). The flag limits
the number to 30k by default and the users may override it if needed at
the vmstorage start time.
---------
Signed-off-by: Artem Fetishev <rtm@victoriametrics.com>
Co-authored-by: Nikolay <nik@victoriametrics.com>
### Describe Your Changes
Currently it the metricID list is empty it won't be mashalled and as the
result won't be put into the tagFiltersToMetricIDsCache which causes the
cache misses for the corresponding tagFilters. In some setups this
causes severe search speed detradation (see #7009).
The empty metric IDs was covered before but then was accidentally
removed in 6c21439.
This PR restores the coverage of this case.
A new unit test can be used as a proof that empty metricID lists are not
added to the cache (just remove the fix in index_db.go and run the test
to see the result)
Also a benchmark has been added to see the implications of the
compression.
```
user@laptop:~/p/github.com/rtm0/VictoriaMetrics/01/src$ go test ./lib/storage/ -run=NONE -bench BenchmarkMarshalUnmarshalMetricIDs --loggerLevel=ERROR
goos: linux
goarch: amd64
pkg: github.com/VictoriaMetrics/VictoriaMetrics/lib/storage
cpu: 13th Gen Intel(R) Core(TM) i7-1355U
BenchmarkMarshalUnmarshalMetricIDs/numMetricIDs-0-12 3237240 363.5 ns/op 0 compression-rate
BenchmarkMarshalUnmarshalMetricIDs/numMetricIDs-1-12 2831049 451.8 ns/op 0.4706 compression-rate
BenchmarkMarshalUnmarshalMetricIDs/numMetricIDs-10-12 1152764 1009 ns/op 1.667 compression-rate
BenchmarkMarshalUnmarshalMetricIDs/numMetricIDs-100-12 297055 3998 ns/op 5.755 compression-rate
BenchmarkMarshalUnmarshalMetricIDs/numMetricIDs-1000-12 31172 34566 ns/op 8.484 compression-rate
BenchmarkMarshalUnmarshalMetricIDs/numMetricIDs-10000-12 4900 289659 ns/op 9.416 compression-rate
BenchmarkMarshalUnmarshalMetricIDs/numMetricIDs-100000-12 447 2341173 ns/op 9.456 compression-rate
BenchmarkMarshalUnmarshalMetricIDs/numMetricIDs-1000000-12 42 24926928 ns/op 9.468 compression-rate
BenchmarkMarshalUnmarshalMetricIDs/numMetricIDs-10000000-12 5 204098872 ns/op 9.467 compression-rate
PASS
ok github.com/VictoriaMetrics/VictoriaMetrics/lib/storage 15.018s
```
### Checklist
The following checks are **mandatory**:
- [x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
---------
Signed-off-by: Artem Fetishev <wwctrsrx@gmail.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
The indexSearch.containsTimeRange() function is called for the current indexDB and the previous indexDB
every time when searching for metricIDs by label filters. This function consumes a lot of additional CPU time
for cases when queries with lightweight label filters are sent to VictoriaMetrics at high rate (e.g. thousands of RPS),
like in the issue https://github.com/VictoriaMetrics/VictoriaMetrics/issues/7009 .
Optimize indexSearch.containsTimeRange() function in the following ways:
- Unconditionally return true if this function is called for the current indexDB, since there are very high
chances that the current indexDB contains the data with timestamps in the requested time range.
- Cache the minimum timestamp, which is missing in the indexed data for the previous indexDB.
This is safe to do, since the previous indexDB is readonly.
This optimization eliminates potentially slow lookup in the previous indexDB for typical
use cases when the requested time range is close to the current time.
Previously indexDB.doExtDB() was returning boolean value, which was indicating whether f callback was called.
There is no need in returning this boolean value, since the f callback can determine on itself whether it was called.
This simplifies the code a bit.
While at it, document indexDB.doExtDB().
Make function name and comments more clear.
d8f8822fa5
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Nikolay <nik@victoriametrics.com>
* Previously, only metricID->metricName missing index records were
tracked with deadline But it was possible a case for missing
metricID->TSID index records. IndexDB metrics fix exposed misleading
metric for such missing records.
* This commit adds check for metricID->TSID missing index records. And
delete missing metricID entry if it hit 60 second deadline.
Related issue
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6931
Signed-off-by: f41gh7 <nik@victoriametrics.com>
follow up
4ecc370acb
### Describe Your Changes
Please provide a brief description of the changes you made. Be as
specific as possible to help others understand the purpose and impact of
your modifications.
### Checklist
The following checks are **mandatory**:
- [ ] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
Previously, some extIndexDB metrics were not registered. It resulted
into missing metrics, if metric value was added to the extIndexDB. It's
a usual case for search requests at both indexes.
Current commit updates all metrics from extIndexDB according to the
current IndexDB. It must fix such cases
Related issue:
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6868
### Describe Your Changes
Please provide a brief description of the changes you made. Be as
specific as possible to help others understand the purpose and impact of
your modifications.
### Checklist
The following checks are **mandatory**:
- [ ] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
`TL;DR` This PR improves the metric IDs search in IndexDB:
- Avoid seaching for metric IDs twice when `maxMetrics` limit is
exceeded
- Use correct error type for indicating that the `maxMetrics` limit is
exceded
- Simplify the logic of deciding between per-day and global index search
A unit test has been added to ensure that this refactoring does not
break anything.
---
Function calls before the fix:
```
idb.searchMetricIDs
|__ is.searchMetricIDs
|__ is.searchMetricIDsInternal
|__ is.updateMetricIDsForTagFilters
|__ is.tryUpdatingMetricIDsForDateRange
| |
|__ is.getMetricIDsForDateAndFilters
```
- `searchMetricIDsInternal` searches metric IDs for each filter set. It
maintains a metric ID set variable which is updated every time the
`updateMetricIDsForTagFilters` function is called. After each successful
call, the function checks the length of the updated metric ID set and if
it is greater than `maxMetrics`, the function returns `too many
timeseries` error.
- `updateMetricIDsForTagFilters` uses either per-day or global index to
search metric IDs for the given filter set. The decision of which index
to use is made is made within the `tryUpdatingMetricIDsForDateRange`
function and if it returns `fallback to global search` error then the
function uses global index by calling `getMetricIDsForDateAndFilters`
with zero date.
- `tryUpdatingMetricIDsForDateRange` first checks if the given time
range is larger than 40 days and if so returns `fallback to global
search` error. Otherwise it proceeds to searching for metric IDs within
that time range by calling `getMetricIDsForDateAndFilters` for each
date.
- `getMetricIDsForDateAndFilters` searches for metric IDs for the given
date and returns `fallback to global search` error if the number of
found metric IDs is greater than `maxMetrics`.
Problems with this solution:
1. The `fallback to global search` error returned by
`getMetricIDsForDateAndFilters` in case when maxMetrics is exceeded is
misleading.
2. If `tryUpdatingMetricIDsForDateRange` proceeds to date range search
and returns `fallback to global search` error (because
`getMetricIDsForDateAndFilters` returns it) then this will trigger
global search in `updateMetricIDsForTagFilters`. However the global
search uses the same maxMetrics value which means this search is
destined to fail too. I.e. the same search is performed twice and fails
twice.
3. `too many timeseries` error is already handled in
`searchMetricIDsInternal` and therefore handing this error in
`updateMetricIDsForTagFilters` is redundant
4. updateMetricIDsForTagFilters is a better place to make a decision on
whether to use per-day or global index.
Solution:
1. Use a dedicated error for `too many timeseries` case
2. Handle `too many timeseries` error in `searchMetricIDsInternal` only
3. Move the per-day or global search decision from
`tryUpdatingMetricIDsForDateRange` to `updateMetricIDsForTagFilters` and
remove `fallback to global search` error.
---------
Signed-off-by: Artem Fetishev <wwctrsrx@gmail.com>
Co-authored-by: Nikolay <nik@victoriametrics.com>
'any' type is supported starting from Go1.18. Let's consistently use it
instead of 'interface{}' type across the code base, since `any` is easier to read than 'interface{}'.
Change the return values for these functions - now they return the unmarshaled result plus
the size of the unmarshaled result in bytes, so the caller could re-slice the src for further unmarshaling.
This improves performance of these functions in hot loops of VictoriaLogs a bit.
Store the deadline when the metricID entries must be deleted from indexdb
if metricID->metricName entry isn't found after the deadline. This should
make the code more clear comparing the the previous version, where the timestamp
of the first metricID->metricName lookup miss was stored in missingMetricIDs.
Remove the misleading comment about the importance of the order for creating entries
in the inverted index when registering new time series. The order doesn't matter,
since any subset of the created entries can become visible for search
before any other subset after registering in indexdb.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5948
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5959
This makes the code less fragile - it is harder to skip the convertToCompositeTagFilterss() call now.
While at it, call indexSearch.containsTimeRange() inside indexSearch.searchMetricIDsInternal()
in order to quickly terminate search of time series in the old indexdb for new time ranges.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5055
This is a follow-up for 2d31fd7855
This allows reducing the indexdb/tagFiltersToMetricIDs cache size by 8 on average.
The cache size can be checked via vm_cache_size_bytes{type="indexdb/tagFiltersToMetricIDs"} metric exposed at /metrics page.
* lib/storage: pre-create timeseries before indexDB rotation
during an hour before indexDB rotation start creating records at the next indexDB
it must improve performance during switch for the next indexDB and remove ingestion issues.
Since there is no need for creation new index records for timeseries already ingested into current indexDB
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4563
* lib/storage: further work on indexdb rotation optimization
- Document the change at docs/CHAGNELOG.md
- Move back various caches from indexDB to Storage. This makes the change less intrusive.
The dateMetricIDCache now takes into account indexDB generation, so it stores (date, metricID)
entries for both the current and the next indexDB.
- Consolidate the code responsible for idbNext pre-filling into prefillNextIndexDB() function.
This improves code readability and maintainability a bit.
- Rewrite and simplify the code responsible for calculating the next retention timestamp.
Add various tests for corner cases of this code.
- Remove indexdb pre-filling from RegisterMetricNames() function, since this function is rarely called.
It is OK to add indexdb entries on demand in this function. This simplifies the code.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401
* docs/CHANGELOG.md: refer to https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4563
---------
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
Previously all the newly ingested time series were registered in global `MetricName -> TSID` index.
This index was used during data ingestion for locating the TSID (internal series id)
for the given canonical metric name (the canonical metric name consists of metric name plus all its labels sorted by label names).
The `MetricName -> TSID` index is stored on disk in order to make sure that the data
isn't lost on VictoriaMetrics restart or unclean shutdown.
The lookup in this index is relatively slow, since VictoriaMetrics needs to read the corresponding
data block from disk, unpack it, put the unpacked block into `indexdb/dataBlocks` cache,
and then search for the given `MetricName -> TSID` entry there. So VictoriaMetrics
uses in-memory cache for speeding up the lookup for active time series.
This cache is named `storage/tsid`. If this cache capacity is enough for all the currently ingested
active time series, then VictoriaMetrics works fast, since it doesn't need to read the data from disk.
VictoriaMetrics starts reading data from `MetricName -> TSID` on-disk index in the following cases:
- If `storage/tsid` cache capacity isn't enough for active time series.
Then just increase available memory for VictoriaMetrics or reduce the number of active time series
ingested into VictoriaMetrics.
- If new time series is ingested into VictoriaMetrics. In this case it cannot find
the needed entry in the `storage/tsid` cache, so it needs to consult on-disk `MetricName -> TSID` index,
since it doesn't know that the index has no the corresponding entry too.
This is a typical event under high churn rate, when old time series are constantly substituted
with new time series.
Reading the data from `MetricName -> TSID` index is slow, so inserts, which lead to reading this index,
are counted as slow inserts, and they can be monitored via `vm_slow_row_inserts_total` metric exposed by VictoriaMetrics.
Prior to this commit the `MetricName -> TSID` index was global, e.g. it contained entries sorted by `MetricName`
for all the time series ever ingested into VictoriaMetrics during the configured -retentionPeriod.
This index can become very large under high churn rate and long retention. VictoriaMetrics
caches data from this index in `indexdb/dataBlocks` in-memory cache for speeding up index lookups.
The `indexdb/dataBlocks` cache may occupy significant share of available memory for storing
recently accessed blocks at `MetricName -> TSID` index when searching for newly ingested time series.
This commit switches from global `MetricName -> TSID` index to per-day index. This allows significantly
reducing the amounts of data, which needs to be cached in `indexdb/dataBlocks`, since now VictoriaMetrics
consults only the index for the current day when new time series is ingested into it.
The downside of this change is increased indexdb size on disk for workloads without high churn rate,
e.g. with static time series, which do no change over time, since now VictoriaMetrics needs to store
identical `MetricName -> TSID` entries for static time series for every day.
This change removes an optimization for reducing CPU and disk IO spikes at indexdb rotation,
since it didn't work correctly - see https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 .
At the same time the change fixes the issue, which could result in lost access to time series,
which stop receving new samples during the first hour after indexdb rotation - see https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698
The issue with the increased CPU and disk IO usage during indexdb rotation will be addressed
in a separate commit according to https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401#issuecomment-1553488685
This is a follow-up for 1f28b46ae9
This reverts the following commits:
- e0e16a2d36
- 2ce02a7fe6
The reason for revert: the updated logic breaks assumptions made
when fixing https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 .
For example, if a time series stop receiving new samples during the first
day after the indexdb rotation, there are chances that the time series
won't be registered in the new indexdb. This is OK until the next indexdb
rotation, since the time series is registered in the previous indexdb,
so it can be found during queries. But the time series will become invisible
for search after the next indexdb rotation, while its data is still there.
There is also incompletely solved issue with the increased CPU and disk IO resource
usage just after the indexdb rotation. There was an attempt to fix it, but it didn't fix
it in full, while introducing the issue mentioned above. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401
TODO: to find out the solution, which simultaneously solves the following issues:
- increased memory usage for setups high churn rate and long retention (e.g. what the reverted commit does)
- increased CPU and disk IO usage during indexdb rotation ( https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 )
- https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698
Possible solution - to create the new indexdb in one hour before the indexdb rotation
and to gradually pre-populate it with the needed index data during the last hour before indexdb rotation.
Then the new indexdb will contain all the needed data just after the rotation,
so it won't trigger increased CPU and disk IO.
- Document the change at docs/CHANGELOG.md
- Clarify comments for non-trivial code touched by the commit
- Improve the logic behind maybeCreateIndexes():
- Correctly create per-day indexes if the indexdb rotation is performed during
the first hour or the last hour of the day by UTC.
Previously there was a possibility of missing index entries on that day.
- Increase the duration for creating new indexes in the current indexdb for up to 22 hours
after indexdb rotation. This should reduce the increased resource usage
after indexdb rotation.
It is safe to postpone index creation for the current day until the last hour
of the current day after indexdb rotation by UTC, since the corresponding (date, ...)
entries exist in the previous indexdb.
- Search for TSID by (date, MetricName) in both the current and the previous indexdb.
Previously the search was performed only in the current indexdb. This could lead
to excess creation of per-day indexes for the current day just after indexdb rotation.
- Search for (date, metricID) entries in both the current and the previous indexdb.
Previously the search was performed only in the current indexdb. This could lead
to excess creation of per-day indexes for the current day just after indexdb rotation.
The new index substitutes global MetricName=>TSID index
used for locating TSIDs on ingestion path.
For installations with high ingestion and churn rate, global
MetricName=>TSID index can grow enormously making
index lookups too expensive. This also results into bigger
than expected cache growth for indexdb blocks.
New per-day index supposed to be much smaller and more efficient.
This should improve ingestion speed and reliability during
re-routings in cluster.
The negative outcome could be occupied disk size, since
per-day index is more expensive comparing to global index.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Use fs.MustReadDir() instead of os.ReadDir() across the code in order to reduce the code verbosity.
The fs.MustReadDir() logs the error with the directory name and the call stack on error
before exit. This information should be enough for debugging the cause of the error.
The issue triggers after the indexdb rotation for time series, which stop receiving new samples.
This results in missing data for such time series in query responses.
This commit should address the https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3502
The issue has been introduced in 2dd93449d8
