- 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>
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
This option can be useful when samples for the same time series are ingested with distinct order of labels.
For example, metric{k1="v1",k2="v2"} and metric{k2="v2",k1="v1"}.
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`).
This should reduce disk space usage when scraping targets containing metrics with identical names
such as `node_cpu_seconds_total`, histograms, quantiles, etc.
Expose `vm_timestamps_blocks_merged_total` and `vm_timestamps_bytes_saved_total` metrics for monitoring
the effectiveness of timestamp blocks merging.
Previously the `vm_slow_row_inserts_total` metric may be incremented multiple times for different data points per a single time series,
while only a single increment is needed when inserting the first data point for this time series.
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 condition may occur after the following sequence of events:
1) A goroutine enters the loop body when len(addRowsConcurrencyCh) == cap(addRowsConcurrencyCh) inside Storage.searchTSIDs.
2) All the goroutines return from Storage.AddRows.
3) The goroutine from step 1 blocks on searchTSIDsCond.Wait() inside the loop body.
The goroutine remains blocked until the next call to Storage.AddRows, which calls searchTSIDsCond.Signal().
This may take indefinite time.
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.
Previously the concurrency has been limited to GOMAXPROCS*2. This had little sense,
since every call to Storage.AddRows is bound to CPU, so the maximum ingestion bandwidth
is achieved when the number of concurrent calls to Storage.AddRows is limited to the number of CPUs,
i.e. to GOMAXPROCS.
Heavy queries could result in the lack of CPU resources for processing the current data ingestion stream.
Prevent this by delaying queries' execution until free resources are available for data ingestion.
Expose `vm_search_delays_total` metric, which may be used in for alerting when there is no enough CPU resources
for data ingestion and/or for executing heavy queries.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
This guarantees that the snapshot contains all the recently added data
from inmemory buffers when multiple concurrent calls to Storage.CreateSnapshot are performed.
Production workload shows that the index requires ~4Kb of RAM per active time series.
This is too much for high number of active time series, so let's delete this index.
Now the queries should fall back to the index for the current day instead of the index
for the recent hour. The query performance for the current day index should be good enough
given the 100M rows/sec scan speed per CPU core.
Issues fixed:
- Slow startup times. Now the index is loaded from cache during start.
- High memory usage related to superflouos index copies every 10 seconds.
This should improve inverted index search performance for filters matching big number of time series,
since `lib/uint64set.Set` is faster than `map[uint64]struct{}` for both `Add` and `Has` calls.
See the corresponding benchmarks in `lib/uint64set`.
This should reduce the amount of RAM required for processing time series
with non-zero churn rate.
The previous cache behavior can be restored with `-cache.oldBehavior` command-line flag.
Track also the number of dropped rows due to the exceeded timeout
on concurrency limit for Storage.AddRows. This number is tracked in `vm_concurrent_addrows_dropped_rows_total`
This should improve performance after restart when the db contains a lot of time series
with high time series churn (i.e. metrics from Kubernetes with many pods and frequent deployments)
- Increase update iterval from 1s to 10s. This should reduce CPU usage
for large amounts of metric ids with constant churn.
- Reduce pendingTodayMetricIDsLock lock duration during the update.
