- 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>
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
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.
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.
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.
It should be faster querying all the labels and/or all the values instead of querying per-day labels/values on time ranges exceeding maxDaysForPerDaySearch
Do not pass filter metric ids to getMetricIDsForTagFilter, since it has been appeared that this slows down
the function by multiple times when it finds big number of metricIDs (tens of millions).
The filter arg has been removed in the commit c7ee2fabb8
because it was preventing from caching the number of matching time series per each tf.
Now the cache contains duration for tf execution, so the filter shouldn't break such caching.
The `filter` arg breaks the logic for sorting tag filters by the matching metrics,
which may result in non-optimal performance during time series search.
Production workloads show that indexdb blocks must be cached unconditionally for reducing CPU usage.
This shouldn't increase memory usage too much, since unused blocks are removed from the cache every two minutes.
Remove the code that uses metricIDs caches for the current and the previous hour during metricIDs search,
since this code became unused after implementing per-day inverted index almost a year ago.
While at it, fix a bug, which could prevent from finding time series with names containing dots (aka Graphite-like names
such as `foo.bar.baz`).
Previously the maximum cache lifetime has been limited by 10 seconds. Now it is extended up to a day.
This should reduce CPU usage in the following cases:
* when querying recently added data with small churn rate for time series
* when querying historical data
Previously the time spent on inverted index search could exceed the configured `-search.maxQueryDuration`.
This commit stops searching in inverted index on query timeout.
This is a follow-up commit after 12b16077c4 ,
which didn't reset the `tsidCache` in all the required places.
This could result in indefinite errors like:
missing metricName by metricID ...; this could be the case after unclean shutdown; deleting the metricID, so it could be re-created next time
Fix this by resetting the cache inside deleteMetricIDs function.
Add index for reverse Graphite-like metric names with dots. Use this index during search for filters
like `__name__=~"foo\\.[^.]*\\.bar\\.baz"` which end with non-empty suffix with dots, i.e. `.bar.baz` in this case.
This change may "hide" historical time series during queries. The workaround is to add `[.]*` to the end of regexp label filter,
i.e. "foo\\.[^.]*\\.bar\\.baz" should be substituted with "foo\\.[^.]*\\.bar\\.baz[.]*".
Newly added index entries can be missing after unclean shutdown, since they didn't flush to persistent storage yet.
Log about this and delete the corresponding metricID, so it could be re-created next time.
This should reduce the frequency of the following errors:
cannot find tag filter matching less than N time series; either increase -search.maxUniqueTimeseries or use more specific tag filters
more than N time series found on the time range [...]; either increase -search.maxUniqueTimeseries or shrink the time range
This case is possible when the corresponding metricID->metricName entry didn't propagate to inverted index yet.
This should fix the following error:
error when searching tsids for tfss [...]: cannot find metricName by metricID 1582417212213420669: EOF
