Use fs.MustReadDir() instead of os.ReadDir() across the code in order to reduce the code verbosity.
The fs.MustReadDir() logs the error with the directory name and the call stack on error
before exit. This information should be enough for debugging the cause of the error.
The commit 5fb45173ae takes into account only newly registered series
when applying cardinality limits. This means that the cardinality limit could be exceeded with already registered series.
This commit returns back accounting for already registered series when applying cardinality limits.
Previously the creation of per-day indexes and global indexes
for the newly registered time series was decoupled.
Now global indexes and per-day indexes for the current day are created toghether for new time series.
This should speed up registering new time series a bit.
* 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>
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 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.
See the corresponding benchmark in Prometheus - 23c0299d85/tsdb/head_bench_test.go (L52)
The benchmark allows performing apples-to-apples comparison of time series search
in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness -
contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this.
Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots:
- Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers
- VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers
Benchmark results:
benchmark old ns/op new ns/op delta
BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87%
BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14%
BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15%
BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41%
BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89%
BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84%
BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59%
BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88%
BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46%
BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36%
BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62%
BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40%
BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32%
BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68%
BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27%
BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41%
The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics.
As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark.
Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
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.
