`Storage.AddRows()` returns an error only in one case: when
`Storage.updatePerDateData()` fails to unmarshal a `metricNameRaw`. But
the same error is treated as a warning when it happens inside
`Storage.add()` or returned by `Storage.prefillNextIndexDB()`.
This commit fixes this inconsistency by treating the error returned by
`Storage.updatePerDateData()` as a warning as well. As a result
`Storage.add()` does not need a return value anymore and so doesn't
`Storage.AddRows()`.
Additionally, this commit adds a unit test that checks all cases that
result in a row not being added to the storage.
---------
Signed-off-by: Artem Fetishev <wwctrsrx@gmail.com>
Co-authored-by: Nikolay <nik@victoriametrics.com>
Pending rows and items unconditionally remain in memory for up to pending{Items,Rows}FlushInterval,
so there is no any sense in setting dataFlushInterval (the interval for guaranteed flush of in-memory data to disk)
to values smaller than pending{Items,Rows}FlushInterval, since this doesn't affect the interval
for flushing pending rows and items from memory to disk.
This is a follow-up for 4c80b17027
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6221
'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{}'.
### Describe Your Changes
Fix Date metricid cache consistency under concurrent use.
When one goroutine calls Has() and does not find the cache entry in the
immutable map it will acquire a lock and check the mutable map. And it
is possible that before that lock is acquired, the entry is moved from
the mutable map to the immutable map by another goroutine causing a
cache miss.
The fix is to check the immutable map again once the lock is acquired.
### 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: Nikolay <nik@victoriametrics.com>
* It must reduce memory usage for misbehaving clients. Since
VictoriaMetrics stores sparse index inmemory.
* Reduce disk space usage for indexdb.
* Prevent possible indexDB items drops.
* It may trigger slow insert and new timeseries registration due to
default value for flag change
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6176
---------
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
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.
It is incorrect applying the limit on the number of values to search without applying filters,
since the returned subset of label values may miss the label values matching the given filters.
This is a follow-up for 66630c7960
This speeds up auto-suggestion for metric names in VMUI and Grafana, which use the following query in this case:
/api/v1/label/__name__/values?match[]={__name__=~"*.some_value.*"}
When the user types `some_value` in the query input field.
This should improve debuggability of unexpected deletion of directories inside partitions.
While at it, log the proper path to parts.json when the directory for big part is missing in the partition.
parts.json is located inside directory with small parts, and there is no parts.json file inside directory with big parts.
* lib/storage: add ability to use downsampling for the given series filter
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* docs: add information about downsampling filters
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* docs: fix MetricsQL filter
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* lib/storage/downsampling: treat missing downsampling filter as a bug
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* lib/storage/part_header: verify correctness of downsampling filters when opening partition
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* lib/storage/downsampling: save only appliable rules in part metadata
Filter and save only rules which are appliable to partition based on MinTimestamp of stored data.
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* lib/storage/downsampling: update log messages for final dedup
Properly specify a reason of re-running deduplication for partition.
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* lib/storage: consistently use MaxTimestamp to determine deduplication/downsampling rules
Using MinTimestamp leads to applying downsampling to parts which are only partially covered by downsampling rule.
For example, partition covers range [1000-2000]. At t=2100 and rule offset 500 data with t=2100-500 => 1600 must be downsampled. The range check against MinTimestamp evaluates to true even though partition contains range which must not be downsampled - [1600:2000].
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* Follow-up
- Apply the first matching downsampling period if multiple filters match the given time series.
This allows fine-tuning the downsampling config for the specific needs.
- Take into account downsampling filters during search queries.
- Reduce the difference between community and enterprise branches. This should simplify further maintenance of these branches.
- Properly parse series filters with colons inside them.
- Document the feature at docs/CHANGELOG.md.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4960
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
* lib/storage: adds metrics for downsampling
vm_downsampling_partitions_scheduled - shows the number of parts, that must be downsampled
vm_downsampling_partitions_scheduled_size_bytes - shows total size in bytes for parts, the must be donwsampled
These two metrics answer the questions - is downsampling running? how many parts scheduled for downsampling and how many of them currently downsampled? Storage space that it occupies.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2612
* wip
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
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
* lib/storage/table: properly wait for force merges to be completed during shutdown
Properly keep track of running background merges and wait for merges completion when closing the table.
Previously, force merge was not in sync with overall storage shutdown which could lead to holding ptw ref.
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* docs: add changelog entry
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
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 should simplify code maintenance by gradually converting to atomic.* types instead of calling atomic.* functions
on int and bool types.
See ea9e2b19a5
The issue has been introduced in bace9a2501
The improper fix was in the d4c0615dcd ,
since it fixed the issue just by an accident, because Go comiler aligned the rawRowsShards field
by 4-byte boundary inside partition struct.
The proper fix is to use atomic.Int64 field - this guarantees that the access to this field
won't result in unaligned 64-bit atomic operation. See https://github.com/golang/go/issues/50860
and https://github.com/golang/go/issues/19057
Previously the (date, metricID) entries for dates older than the last 2 days were removed.
This could lead to slow check for the (date, metricID) entry in the indexdb during ingesting historical data (aka backfilling).
The issue has been introduced in 431aa16c8d
This commit returns back limits for these endpoints, which have been removed at 5d66ee88bd ,
since it has been appeared that missing limits result in high CPU usage, while the introduced concurrency limiter
results in failed lightweight requests to these endpoints because of timeout when heavyweight requests are executed.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5055
Do not convert shard items to part when a shard becomes full. Instead, collect multiple
full shards and then convert them to a searchable part at once. This reduces
the number of searchable parts, which, in turn, should increase query performance,
since queries need to scan smaller number of parts.
Previously the interval between item addition and its conversion to searchable in-memory part
could vary significantly because of too coarse per-second precision. Switch from fasttime.UnixTimestamp()
to time.Now().UnixMilli() for millisecond precision. It is OK to use time.Now() for tracking
the time when buffered items must be converted to searchable in-memory parts, since time.Now()
calls aren't located in hot paths.
Increase the flush interval for converting buffered samples to searchable in-memory parts
from one second to two seconds. This should reduce the number of blocks, which are needed
to be processed during high-frequency alerting queries. This, in turn, should reduce CPU usage.
While at it, hardcode the maximum size of rawRows shard to 8Mb, since this size gives the optimal
data ingestion pefromance according to load tests. This reduces memory usage and CPU usage on systems
with big amounts of RAM under high data ingestion rate.
The pooled rawRowsBlock objects occupies big amounts of memory between flushes,
and the flushes are relatively rare. So it is better to don't use the pool
and to allocate rawRow blocks on demand. This should reduce the average
memory usage between flushes.
The buffer can be quite big under high ingestion rate (e.g. more than 100MB).
This leads to increased memory usage between buffer flushes.
So it is better to re-create the buffer on every flush in order to reduce memory usage
between buffer flushes.
