- 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>
* 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
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
For example, `{__graphite__=~"foo.(bar|baz)"}` is automatically converted to `{__graphite__=~"foo.{bar,baz}"}` before execution.
This allows using multi-value Grafana template variables such as `{__graphite__=~"foo.($app)"}`.
The vm_cache_size_max_bytes metric can be used for determining caches which reach their capacity via the following query:
vm_cache_size_bytes / vm_cache_size_max_bytes > 0.9
* adds read-only mode for vmstorage
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/269
* changes order a bit
* moves isFreeDiskLimitReached var to storage struct
renames functions to be consistent
change protoparser api - with optional storage limit check for given openned storage
* renames freeSpaceLimit to ReadOnly
The number of series per target can be limited with the following options:
* Global limit with `-promscrape.maxSeriesPerTarget` command-line option.
* Per-target limit with `max_series: N` option in `scrape_config` section.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1561
Previously needsDedup() could return true if the de-duplication wasn't needed for the following case:
d < interval
/ \
| v | v |
interval interval
Now it properly returns false for this case
This reverts commit 7c6d3981bf.
Reason for revert: high contention at bucket16Pool on systems with big number of CPU cores.
This slows down query processing significantly.
This should reduce memory usage on systems with big number of CPU cores,
since every inmemoryPart object occupies at least 64KB of memory and sync.Pool maintains
a separate pool inmemoryPart objects per each CPU core.
Though the new scheme for the pool worsens per-cpu cache locality, this should be amortized
by big sizes of inmemoryPart objects.
Previously the stats for cache misses could be improperly counted, because it had inflated cache misses
if the entry was missing in the curr cache, but was existing in the prev cache.
The same applies to cache requests - they were inflated if the entry was missing in the curr cache.
One minute cache timeout result in slower queries in some production workloads where the interval
between query execution is in the range 1 minute - 2 minutes.
This should reduce memory usage on a system with high number of active time series and a high churn rate.
One minute is enough for caching the blocks needed for repeated queries (e.g. alerting rules, recording rules and dashboard refreshes).
These functions are non-trivial, while their code has minimal differences.
It is better from maintainability PoV to merge these functions into a single function.
It must match all the time series on the given time range.
Previously it was matched to all the time series without the restriction on the given time range.
