mirror of
https://github.com/VictoriaMetrics/VictoriaMetrics.git
synced 2024-12-25 20:00:06 +01:00
edeb56d208
docs: mention cluster URL for exporting series Signed-off-by: hagen1778 <roman@victoriametrics.com>
324 lines
22 KiB
Markdown
324 lines
22 KiB
Markdown
---
|
|
sort: 23
|
|
---
|
|
|
|
# Troubleshooting
|
|
|
|
This document contains troubleshooting guides for most common issues when working with VictoriaMetrics:
|
|
|
|
- [Unexpected query results](#unexpected-query-results)
|
|
- [Slow data ingestion](#slow-data-ingestion)
|
|
- [Slow queries](#slow-queries)
|
|
- [Out of memory errors](#out-of-memory-errors)
|
|
- [Cluster instability](#cluster-instability)
|
|
- [Monitoring](#monitoring)
|
|
|
|
## Unexpected query results
|
|
|
|
If you see unexpected or unreliable query results from VictoriaMetrics, then try the following steps:
|
|
|
|
1. Check whether simplified queries return unexpected results. For example, if the query looks like
|
|
`sum(rate(http_requests_total[5m])) by (job)`, then check whether the following queries return
|
|
expected results:
|
|
|
|
- Remove the outer `sum` and execute `rate(http_requests_total[5m])`,
|
|
since aggregations could hide some missing series, gaps in data or anomalies in existing series.
|
|
If this query returns too many time series, then try adding more specific label filters to it.
|
|
For example, if you see that the original query returns unexpected results for the `job="foo"`,
|
|
then use `rate(http_requests_total{job="foo"}[5m])` query.
|
|
If this isn't enough, then continue adding more specific label filters, so the resulting query returns
|
|
manageable number of time series.
|
|
|
|
- Remove the outer `rate` and execute `http_requests_total`. Additional label filters may be added here in order
|
|
to reduce the number of returned series.
|
|
|
|
Sometimes the query may be improperly constructed, so it returns unexpected results.
|
|
It is recommended reading and understanding [MetricsQL docs](https://docs.victoriametrics.com/MetricsQL.html),
|
|
especially [subqueries](https://docs.victoriametrics.com/MetricsQL.html#subqueries)
|
|
and [rollup functions](https://docs.victoriametrics.com/MetricsQL.html#rollup-functions) sections.
|
|
|
|
2. If the simplest query continues returning unexpected / unreliable results, then try verifying correctness
|
|
of raw unprocessed samples for this query via [/api/v1/export](https://docs.victoriametrics.com/#how-to-export-data-in-json-line-format)
|
|
on the given `[start..end]` time range and check whether they are expected:
|
|
|
|
```console
|
|
single-node: curl http://victoriametrics:8428/api/v1/export -d 'match[]=http_requests_total' -d 'start=...' -d 'end=...'
|
|
|
|
cluster: curl http://<vmselect>:8481/select/<tenantID>/prometheus/api/v1/export -d 'match[]=http_requests_total' -d 'start=...' -d 'end=...'
|
|
```
|
|
Note that responses returned from [/api/v1/query](https://docs.victoriametrics.com/keyConcepts.html#instant-query)
|
|
and from [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) contain **evaluated** data
|
|
instead of raw samples stored in VictoriaMetrics. See [these docs](https://prometheus.io/docs/prometheus/latest/querying/basics/#staleness)
|
|
for details.
|
|
|
|
If you migrate from InfluxDB, then pass `-search.setLookbackToStep` command-line flag to single-node VictoriaMetrics
|
|
or to `vmselect` in VictoriaMetrics cluster. See also [how to migrate from InfluxDB to VictoriaMetrics](https://docs.victoriametrics.com/guides/migrate-from-influx.html).
|
|
|
|
3. Sometimes response caching may lead to unexpected results when samples with older timestamps
|
|
are ingested into VictoriaMetrics (aka [backfilling](https://docs.victoriametrics.com/#backfilling)).
|
|
Try disabling response cache and see whether this helps. This can be done in the following ways:
|
|
|
|
- By passing `-search.disableCache` command-line flag to a single-node VictoriaMetrics
|
|
or to all the `vmselect` components if cluster version of VictoriaMetrics is used.
|
|
|
|
- By passing `nocache=1` query arg to every request to `/api/v1/query` and `/api/v1/query_range`.
|
|
If you use Grafana, then this query arg can be specified in `Custom Query Parameters` field
|
|
at Prometheus datasource settings - see [these docs](https://grafana.com/docs/grafana/latest/datasources/prometheus/) for details.
|
|
|
|
4. If you use cluster version of VictoriaMetrics, then it may return partial responses by default
|
|
when some of `vmstorage` nodes are temporarily unavailable - see [cluster availability docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#cluster-availability)
|
|
for details. If you want prioritizing query consistency over cluster availability,
|
|
then you can pass `-search.denyPartialResponse` command-line flag to all the `vmselect` nodes.
|
|
In this case VictoriaMetrics returns an error during querying if at least a single `vmstorage` node is unavailable.
|
|
Another option is to pass `deny_partial_response=1` query arg to `/api/v1/query` and `/api/v1/query_range`.
|
|
If you use Grafana, then this query arg can be specified in `Custom Query Parameters` field
|
|
at Prometheus datasource settings - see [these docs](https://grafana.com/docs/grafana/latest/datasources/prometheus/) for details.
|
|
|
|
5. If you pass `-replicationFactor` command-line flag to `vmselect`, then it is recommended removing this flag from `vmselect`,
|
|
since it may lead to incomplete responses when `vmstorage` nodes contain less than `-replicationFactor`
|
|
copies of the requested data.
|
|
|
|
6. Try upgrading to the [latest available version of VictoriaMetrics](https://github.com/VictoriaMetrics/VictoriaMetrics/releases)
|
|
and verifying whether the issue is fixed there.
|
|
|
|
7. Try executing the query with `trace=1` query arg. This enables query tracing, which may contain
|
|
useful information on why the query returns unexpected data. See [query tracing docs](https://docs.victoriametrics.com/#query-tracing) for details.
|
|
|
|
8. Inspect command-line flags passed to VictoriaMetrics components. If you don't understand clearly the purpose
|
|
or the effect of some flags, then remove them from the list of flags passed to VictoriaMetrics components,
|
|
because some command-line flags may change query results in unexpected ways when set to improper values.
|
|
VictoriaMetrics is optimized for running with default flag values (e.g. when they aren't set explicitly).
|
|
|
|
9. If the steps above didn't help identifying the root cause of unexpected query results,
|
|
then [file a bugreport](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/new) with details on how to reproduce the issue.
|
|
|
|
|
|
## Slow data ingestion
|
|
|
|
There are the following most commons reasons for slow data ingestion in VictoriaMetrics:
|
|
|
|
1. Memory shortage for the given amounts of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series).
|
|
|
|
VictoriaMetrics (or `vmstorage` in cluster version of VictoriaMetrics) maintains an in-memory cache
|
|
for quick search for internal series ids per each incoming metric.
|
|
This cache is named `storage/tsid`. VictoriaMetrics automatically determines the maximum size for this cache
|
|
depending on the available memory on the host where VictoriaMetrics (or `vmstorage`) runs. If the cache size isn't enough
|
|
for holding all the entries for active time series, then VictoriaMetrics locates the needed data on disk,
|
|
unpacks it, re-constructs the missing entry and puts it into the cache. This takes additional CPU time and disk read IO.
|
|
|
|
The [official Grafana dashboards for VictoriaMetrics](https://docs.victoriametrics.com/#monitoring)
|
|
contain `Slow inserts` graph, which shows the cache miss percentage for `storage/tsid` cache
|
|
during data ingestion. If `slow inserts` graph shows values greater than 5% for more than 10 minutes,
|
|
then it is likely the current number of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series)
|
|
cannot fit the `storage/tsid` cache.
|
|
|
|
There are the following solutions exist for this issue:
|
|
|
|
- To increase the available memory on the host where VictoriaMetrics runs until `slow inserts` percentage
|
|
will become lower than 5%. If you run VictoriaMetrics cluster, then you need increasing total available
|
|
memory at `vmstorage` nodes. This can be done in two ways: either to increase the available memory
|
|
per each existing `vmstorage` node or to add more `vmstorage` nodes to the cluster.
|
|
|
|
- To reduce the number of active time series. The [official Grafana dashboards for VictoriaMetrics](https://docs.victoriametrics.com/#monitoring)
|
|
contain a graph showing the number of active time series. Recent versions of VictoriaMetrics
|
|
provide [cardinality explorer](https://docs.victoriametrics.com/#cardinality-explorer),
|
|
which can help determining and fixing the source of [high cardinality](https://docs.victoriametrics.com/FAQ.html#what-is-high-cardinality).
|
|
|
|
2. [High churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate),
|
|
e.g. when old time series are substituted with new time series at a high rate.
|
|
When VictoriaMetrics encounters a sample for new time series, it needs to register the time series
|
|
in the internal index (aka `indexdb`), so it can be quickly located on subsequent select queries.
|
|
The process of registering new time series in the internal index is an order of magnitude slower
|
|
than the process of adding new sample to already registered time series.
|
|
So VictoriaMetrics may work slower than expected under [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
|
|
|
|
The [official Grafana dashboards for VictoriaMetrics](https://docs.victoriametrics.com/#monitoring)
|
|
provides `Churn rate` graph, which shows the average number of new time series registered
|
|
during the last 24 hours. If this number exceeds the number of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series),
|
|
then you need to identify and fix the source of [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
|
|
The most commons source of high churn rate is a label, which frequently changes its value. Try avoiding such labels.
|
|
The [cardinality explorer](https://docs.victoriametrics.com/#cardinality-explorer) can help identifying
|
|
such labels.
|
|
|
|
3. Resource shortage. The [official Grafana dashboards for VictoriaMetrics](https://docs.victoriametrics.com/#monitoring)
|
|
contain `resource usage` graphs, which show memory usage, CPU usage, disk IO usage and free disk size.
|
|
Make sure VictoriaMetrics has enough free resources for graceful handling of potential spikes in workload
|
|
according to the following recommendations:
|
|
|
|
- 50% of free CPU
|
|
- 50% of free memory
|
|
- 20% of free disk space
|
|
|
|
If VictoriaMetrics components have lower amounts of free resources, then this may lead
|
|
to **significant** performance degradation after workload increases slightly.
|
|
For example:
|
|
|
|
- If the percentage of free CPU is close to 0, then VictoriaMetrics
|
|
may experience arbitrary long delays during data ingestion when it cannot keep up
|
|
with slightly increased data ingestion rate.
|
|
|
|
- If the percentage of free memory reaches 0, then the Operating System where VictoriaMetrics components run,
|
|
may have no enough memory for [page cache](https://en.wikipedia.org/wiki/Page_cache).
|
|
VictoriaMetrics relies on page cache for quick queries over recently ingested data.
|
|
If the operating system has no enough free memory for page cache, then it needs
|
|
to re-read the requested data from disk. This may **significantly** increase disk read IO
|
|
and slow down both queries and data ingestion.
|
|
|
|
- If free disk space is lower than 20%, then VictoriaMetrics is unable to perform optimal
|
|
background merge of the incoming data. This leads to increased number of data files on disk,
|
|
which, in turn, slows down both data ingestion and querying. See [these docs](https://docs.victoriametrics.com/#storage) for details.
|
|
|
|
4. If you run cluster version of VictoriaMetrics, then make sure `vminsert` and `vmstorage` components
|
|
are located in the same network with small network latency between them.
|
|
`vminsert` packs incoming data into batch packets and sends them to `vmstorage` on-by-one.
|
|
It waits until `vmstorage` returns back `ack` response before sending the next packet.
|
|
If the network latency between `vminsert` and `vmstorage` is high (for example, if they run in different datacenters),
|
|
then this may become limiting factor for data ingestion speed.
|
|
|
|
The [official Grafana dashboard for cluster version of VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#monitoring)
|
|
contain `connection saturation` graph for `vminsert` components. If this graph reaches 100% (1s),
|
|
then it is likely you have issues with network latency between `vminsert` and `vmstorage`.
|
|
Another possible issue for 100% connection saturation between `vminsert` and `vmstorage`
|
|
is resource shortage at `vmstorage` nodes. In this case you need to increase amounts
|
|
of available resources (CPU, RAM, disk IO) at `vmstorage` nodes or to add more `vmstorage` nodes to the cluster.
|
|
|
|
5. Noisy neighbor. Make sure VictoriaMetrics components run in an envirnoments without other resource-hungry apps.
|
|
Such apps may steal RAM, CPU, disk IO and network bandwidth, which is needed for VictoriaMetrics components.
|
|
Issues like this are very hard to catch via [official Grafana dashboard for cluster version of VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#monitoring)
|
|
and proper diagnosis would require checking resource usage on the instances where VictoriaMetrics runs.
|
|
|
|
6. If you see `TooHighSlowInsertsRate` [alert](https://docs.victoriametrics.com/#monitoring) when single-node VictoriaMetrics or `vmstorage` has enough
|
|
free CPU and RAM, then increase `-cacheExpireDuration` command-line flag at single-node VictoriaMetrics or at `vmstorage` to the value,
|
|
which exceeds the interval between ingested samples for the same time series (aka `scrape_interval`).
|
|
See [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3976#issuecomment-1476883183) for more details.
|
|
|
|
## Slow queries
|
|
|
|
Some queries may take more time and resources (CPU, RAM, network bandwidth) than others.
|
|
VictoriaMetrics logs slow queries if their execution time exceeds the duration passed
|
|
to `-search.logSlowQueryDuration` command-line flag (5s by default).
|
|
VictoriaMetrics also provides `/api/v1/status/top_queries` endpoint, which returns
|
|
queries that took the most time to execute.
|
|
See [these docs](https://docs.victoriametrics.com/#prometheus-querying-api-enhancements) for details.
|
|
|
|
There are the following solutions exist for slow queries:
|
|
|
|
- Adding more CPU and memory to VictoriaMetrics, so it may perform the slow query faster.
|
|
If you use cluster version of VictoriaMetrics, then migration of `vmselect` nodes to machines
|
|
with more CPU and RAM should help improving speed for slow queries. Query performance
|
|
is always limited by resources of one vmselect which processes the query. For example, if 2vCPU cores on `vmselect`
|
|
isn't enough to process query fast enough, then migrating `vmselect` to a machine with 4vCPU cores should increase heavy query performance by up to 2x.
|
|
If the line on `Concurrent select` graph form the [official Grafana dashboard for VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#monitoring)
|
|
is close to the limit, then prefer adding more `vmselect` nodes to the cluster.
|
|
Sometimes adding more `vmstorage` nodes also can help improving the speed for slow queries.
|
|
|
|
- Rewriting slow queries, so they become faster. Unfortunately it is hard determining
|
|
whether the given query is slow by just looking at it.
|
|
VictoriaMetrics provides [query tracing](https://docs.victoriametrics.com/#query-tracing) feature,
|
|
which can help determine the source of slow query.
|
|
See also [this article](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986),
|
|
which explains how to determine and optimize slow queries.
|
|
|
|
In practice many slow queries are generated because of improper use of [subqueries](https://docs.victoriametrics.com/MetricsQL.html#subqueries).
|
|
It is recommended avoiding subqueries if you don't understand clearly how they work.
|
|
It is easy to create a subquery without knowing about it.
|
|
For example, `rate(sum(some_metric))` is implicitly transformed into the following subquery
|
|
according to [implicit conversion rules for MetricsQL queries](https://docs.victoriametrics.com/MetricsQL.html#implicit-query-conversions):
|
|
|
|
```metricsql
|
|
rate(
|
|
sum(
|
|
default_rollup(some_metric[1i])
|
|
)[1i:1i]
|
|
)
|
|
```
|
|
|
|
It is likely this query won't return the expected results. Instead, `sum(rate(some_metric))` must be used instead.
|
|
See [this article](https://www.robustperception.io/rate-then-sum-never-sum-then-rate/) for more details.
|
|
|
|
|
|
## Out of memory errors
|
|
|
|
There are the following most common sources of out of memory (aka OOM) crashes in VictoriaMetrics:
|
|
|
|
1. Improper command-line flag values. Inspect command-line flags passed to VictoriaMetrics components.
|
|
If you don't understand clearly the purpose or the effect of some flags - remove them
|
|
from the list of flags passed to VictoriaMetrics components. Improper command-line flags values
|
|
may lead to increased memory and CPU usage. The increased memory usage increases chances for OOM crashes.
|
|
VictoriaMetrics is optimized for running with default flag values (e.g. when they aren't set explicitly).
|
|
|
|
For example, it isn't recommended tuning cache sizes in VictoriaMetrics, since it frequently leads to OOM exceptions.
|
|
[These docs](https://docs.victoriametrics.com/#cache-tuning) refer command-line flags, which aren't
|
|
recommended to tune. If you see that VictoriaMetrics needs increasing some cache sizes for the current workload,
|
|
then it is better migrating to a host with more memory instead of trying to tune cache sizes manually.
|
|
|
|
2. Unexpected heavy queries. The query is considered as heavy if it needs to select and process millions of unique time series.
|
|
Such query may lead to OOM exception, since VictoriaMetrics needs to keep some of per-series data in memory.
|
|
VictoriaMetrics provides [various settings](https://docs.victoriametrics.com/#resource-usage-limits),
|
|
which can help limit resource usage.
|
|
For more context, see [How to optimize PromQL and MetricsQL queries](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986).
|
|
VictoriaMetrics also provides [query tracer](https://docs.victoriametrics.com/#query-tracing)
|
|
to help identify the source of heavy query.
|
|
|
|
3. Lack of free memory for processing workload spikes. If VictoriaMetrics components use almost all the available memory
|
|
under the current workload, then it is recommended migrating to a host with bigger amounts of memory.
|
|
This would protect from possible OOM crashes on workload spikes. It is recommended to have at least 50%
|
|
of free memory for graceful handling of possible workload spikes.
|
|
See [capacity planning for single-node VictoriaMetrics](https://docs.victoriametrics.com/#capacity-planning)
|
|
and [capacity planning for cluster version of VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#capacity-planning).
|
|
|
|
|
|
## Cluster instability
|
|
|
|
VictoriaMetrics cluster may become unstable if there is no enough free resources (CPU, RAM, disk IO, network bandwidth)
|
|
for processing the current workload.
|
|
|
|
The most common sources of cluster instability are:
|
|
|
|
- Workload spikes. For example, if the number of active time series increases by 2x while
|
|
the cluster has no enough free resources for processing the increased workload,
|
|
then it may become unstable.
|
|
VictoriaMetrics provides various configuration settings, which can be used for limiting unexpected workload spikes.
|
|
See [these docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#resource-usage-limits) for details.
|
|
|
|
- Various maintenance tasks such as rolling upgrades or rolling restarts during configuration changes.
|
|
For example, if a cluster contains `N=3` `vmstorage` nodes and they are restarted one-by-one (aka rolling restart),
|
|
then the cluster will have only `N-1=2` healthy `vmstorage` nodes during the rolling restart.
|
|
This means that the load on healthy `vmstorage` nodes increases by at least `100%/(N-1)=50%`
|
|
comparing to the load before rolling restart. E.g. they need to process 50% more incoming
|
|
data and to return 50% more data during queries. In reality, the load on the remaining `vmstorage`
|
|
nodes increases even more because they need to register new time series, which were re-routed
|
|
from temporarily unavailable `vmstorage` node. If `vmstorage` nodes had less than 50%
|
|
of free resources (CPU, RAM, disk IO) before the rolling restart, then it
|
|
can lead to cluster overload and instability for both data ingestion and querying.
|
|
|
|
The workload increase during rolling restart can be reduced by increasing
|
|
the number of `vmstorage` nodes in the cluster. For example, if VictoriaMetrics cluster contains
|
|
`N=11` `vmstorage` nodes, then the workload increase during rolling restart of `vmstorage` nodes
|
|
would be `100%/(N-1)=10%`. It is recommended to have at least 8 `vmstorage` nodes in the cluster.
|
|
The recommended number of `vmstorage` nodes should be multiplied by `-replicationFactor` if replication is enabled -
|
|
see [replication and data safety docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety)
|
|
for details.
|
|
|
|
The obvious solution against VictoriaMetrics cluster instability is to make sure cluster components
|
|
have enough free resources for graceful processing of the increased workload.
|
|
See [capacity planning docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#capacity-planning)
|
|
and [cluster resizing and scalability docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#cluster-resizing-and-scalability)
|
|
for details.
|
|
|
|
|
|
## Monitoring
|
|
|
|
Having proper [monitoring](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#monitoring)
|
|
would help identify and prevent most of the issues listed above.
|
|
|
|
[Grafana dasbhoards](https://grafana.com/orgs/victoriametrics/dashboards) contain panels reflecting the
|
|
health state, resource usage and other specific metrics for VictoriaMetrics components.
|
|
|
|
Alerting rules for [single-node](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/deployment/docker/alerts.yml)
|
|
and [cluster](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/cluster/deployment/docker/alerts.yml) versions
|
|
of VictoriaMetrics will notify about issues with Victoriametrics components and provide recommendations for how to solve them.
|
|
|
|
Internally, we heavily rely both on dashboards and alerts, and constantly improve them.
|
|
It is important to stay up to date with such changes.
|