- Postpone the pre-poulation to the last hour of the current day. This should reduce the number
of useless entries in the next per-day index, which shouldn't be created there,
when the corresponding time series are stopped to be pushed during the current day.
- Make the pre-population more smooth in time by using the hash of MetricID instead of MetricID itself
when calculating the need for for the given MetricID pre-population.
- Sync the logic for pre-population of the next day inverted index with the logic of pre-populating tsid cache
after indexdb rotation. This should improve code maintainability.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/430
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401
* lib/index: reduce read/write load after indexDB rotation
IndexDB in VM is responsible for storing TSID - ID's used for identifying
time series. The index is stored on disk and used by both ingestion and read path.
IndexDB is stored separately to data parts and is global for all stored data.
It can't be deleted partially as VM deletes data parts. Instead, indexDB is
rotated once in `retention` interval.
The rotation procedure means that `current` indexDB becomes `previous`,
and new freshly created indexDB struct becomes `current`. So in any time,
VM holds indexDB for current and previous retention periods.
When time series is ingested or queried, VM checks if its TSID is present
in `current` indexDB. If it is missing, it checks the `previous` indexDB.
If TSID was found, it gets copied to the `current` indexDB. In this way
`current` indexDB stores only series which were active during the retention
period.
To improve indexDB lookups, VM uses a cache layer called `tsidCache`. Both
write and read path consult `tsidCache` and on miss the relad lookup happens.
When rotation happens, VM resets the `tsidCache`. This is needed for ingestion
path to trigger `current` indexDB re-population. Since index re-population
requires additional resources, every index rotation event may cause some extra
load on CPU and disk. While it may be unnoticeable for most of the cases,
for systems with very high number of unique series each rotation may lead
to performance degradation for some period of time.
This PR makes an attempt to smooth out resource usage after the rotation.
The changes are following:
1. `tsidCache` is no longer reset after the rotation;
2. Instead, each entry in `tsidCache` gains a notion of indexDB to which
they belong;
3. On ingestion path after the rotation we check if requested TSID was
found in `tsidCache`. Then we have 3 branches:
3.1 Fast path. It was found, and belongs to the `current` indexDB. Return TSID.
3.2 Slow path. It wasn't found, so we generate it from scratch,
add to `current` indexDB, add it to `tsidCache`.
3.3 Smooth path. It was found but does not belong to the `current` indexDB.
In this case, we add it to the `current` indexDB with some probability.
The probability is based on time passed since the last rotation with some threshold.
The more time has passed since rotation the higher is chance to re-populate `current` indexDB.
The default re-population interval in this PR is set to `1h`, during which entries from
`previous` index supposed to slowly re-populate `current` index.
The new metric `vm_timeseries_repopulated_total` was added to identify how many TSIDs
were moved from `previous` indexDB to the `current` indexDB. This metric supposed to
grow only during the first `1h` after the last rotation.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* wip
* wip
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
* lib/promscrape: support prometheus-like duration in scrape configs
The change allows to specify duration values like `1d`, `1w`
for fields `scrape_interval`, `scrape_timeout`, etc.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/817#issuecomment-1033384766
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* lib/blockcache: make linter happy
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* lib/promscrape: support prometheus-like duration in scrape configs
* add support for extra fields `scrape_align_interval` and `scrape_offset`;
* support Prometheus duration parsing for `__scrape_interval__`
and `__scrape_duration__` labels;
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* wip
* wip
* docs/CHANGELOG.md: document the feature
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
* fixes service discovery for kubernetes
now it must take in account all pods that belong to the discovered endpoint and endpointslice
adds simple test for endpoints
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2134
* wip
* docs/CHANGELOG.md: document the change
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
This should improve data ingestion speed if time series samples are ingested with interval bigger than 2 minutes.
The actual interval could exceed 2 minutes if the original interval between samples doesn't exceed 2 minutes
in the case of slow inserts. Slow inserts may appear in the following cases:
* Big number of new time series are pushed to VictoriaMetrics, so they couldn't be registered in 2 minutes.
* MetricName->tsid cache reset on indexdb rotation or due to unclean shutdown.
In this case VictoriaMetrics needs to load MetricName->tsid entries for all the incoming series from IndexDB.
IndexDB uses the block cache for increasing lookup performance. If the cache has no the needed block,
then IndexDB reads and unpacks the block from disk. This requires an extra disk read IO and CPU.
See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2007
This also should increase performance for periodically executed queries with intervals from 2 minutes to 5 minutes.
See the previous similar commit - 43103be011
It is possible that the timeout can be increased further. Let's collect production numbers for this change
so the timeout could be adjusted further.
Previously limits for new caches were taken from cache stats.
These limits could mismatch the original limits. This could result in failed cache load
if the stored cache has been created with the limits obtained from cache stats.
This metric shows the number of CPU cores available to the process.
This allows creating alerting rules on CPU saturation with the following query:
rate(process_cpu_seconds_total[5m]) / process_cpu_cores_available > 0.9
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2107
* optimized code ,because only the first error,so no need var errors []error
* optimized code ,because only the first error,so no need var errors []error
Co-authored-by: lirenzuo <lirenzuo@shein.com>
Previously bytesutil.Resize() was copying the original byte slice contents to a newly allocated slice.
This wasted CPU cycles and memory bandwidth in some places, where the original slice contents wasn't needed
after slize resizing. Switch such places to bytesutil.ResizeNoCopy().
Rename the original bytesutil.Resize() function to bytesutil.ResizeWithCopy() for the sake of improved readability.
Additionally, allocate new slice with `make()` instead of `append()`. This guarantees that the capacity of the allocated slice
exactly matches the requested size. The `append()` could return a slice with bigger capacity as an optimization for further `append()` calls.
This could result in excess memory usage when the returned byte slice was cached (for instance, in lib/blockcache).
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2007
- Optimize Cache.RemoveBlocksFromPart(), so it doesn't need to iterate over all the cached blocks.
- Cache blocks if there were no cache misses during the last 2 minutes.
This may be the case when new blocks are added simultaneously to the storage and to the cache.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2007
Previously these caches could exceed limits set via `-memory.allowedPercent` and/or `-memory.allowedBytes`,
since limits were set independently per each data part. If the number of data parts was big, then limits could be exceeded,
which could result to out of memory errors.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2007
