### Describe Your Changes
Change response code to 502 to align it with behaviour of other existing
reverse proxies. Currently, the following reverse proxies will return
502 in case an upstream is not available: nginx, traefik, caddy, apache.
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
The %q formatter may result in incorrectly formatted JSON string if the original string
contains special chars such as \x1b . They must be encoded as \u001b , otherwise the resulting JSON string
cannot be parsed by JSON parsers.
This is a follow-up for c0caa69939
See https://github.com/VictoriaMetrics/victorialogs-datasource/issues/24
### Describe Your Changes
`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>
- Rename overrideHostHeader() function to hasEmptyHostHeader()
- Rename overrideHostHeader field at UserInfo to useBackendHostHeader
This should simplify the future maintenance of the code
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6525
This reverts commit 4d66e042e3.
Reasons for revert:
- The commit makes unrelated invalid changes to docs/CHANGELOG.md
- The changes at app/vmauth/main.go are too complex. It is better splitting them into two parts:
- pooling readTrackingBody struct for reducing pressure on GC
- avoiding to use readTrackingBody when -maxRequestBodySizeToRetry command-line flag is set to 0
Let's make this in the follow-up commits!
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6445
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6533
- Move the test for SRV discovery into a separate function. This allows verifying round-robin discovery across SRV records.
- Restore the original netutil.Resolver after the test finishes, so it doesn't interfere with other tests.
- Move the description of the bugfix into the correct place at docs/CHANGELOG.md - it should be placed under v1.102.0-rc2
instead of v1.102.0-rc1.
- Remove unneeded code in URLPrefix.sanitizeAndInitialize(), since it is expected this function is called only once
for finishing URLPrefix initializiation. In this case URLPrefix.nextDiscoveryDeadline and URLPrefix.n are equal to 0
according to https://pkg.go.dev/sync/atomic#Uint64
- Properly fix the bug at URLPrefix.discoverBackendAddrsIfNeeded() - it is expected that hostToAddrs map uses
the original hostname keys, including 'srv+' prefix, so it shouldn't be removed when looping over up.busOriginal.
Instead, the 'srv+' prefix must be removed from the hostname only locally before passing the hostname to netutil.Resolver.LookupSRV.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6401
- Rename GetStatDialFunc to NewStatDialFunc, since it returns new function with every call
- NewStatDialFunc isn't related to http in any way, so it must be moved from lib/httputils to lib/netutil
- Simplify the implementation of NewStatDialFunc by removing sync.Map from there.
- Use netutil.NewStatDialFunc at app/vmauth and lib/promscrape/discoveryutils
- Use gauge instead of counter type for *_conns metric
This is a follow-up for d7b5062917
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6299
- Move the remaining code responsible for stream aggregation initialization from remotewrite.go to streamaggr.go .
This improves code maintainability a bit.
- Properly shut down streamaggr.Aggregators initialized inside remotewrite.CheckStreamAggrConfigs().
This prevents from potential resource leaks.
- Use separate functions for initializing and reloading of global stream aggregation and per-remoteWrite.url stream aggregation.
This makes the code easier to read and maintain. This also fixes INFO and ERROR logs emitted by these functions.
- Add an ability to specify `name` option in every stream aggregation config. This option is used as `name` label
in metrics exposed by stream aggregation at /metrics page. This simplifies investigation of the exposed metrics.
- Add `path` label additionally to `name`, `url` and `position` labels at metrics exposed by streaming aggregation.
This label should simplify investigation of the exposed metrics.
- Remove `match` and `group` labels from metrics exposed by streaming aggregation, since they have little practical applicability:
it is hard to use these labels in query filters and aggregation functions.
- Rename the metric `vm_streamaggr_flushed_samples_total` to less misleading `vm_streamaggr_output_samples_total` .
This metric shows the number of samples generated by the corresponding streaming aggregation rule.
This metric has been added in the commit 861852f262 .
See https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6462
- Remove the metric `vm_streamaggr_stale_samples_total`, since it is unclear how it can be used in practice.
This metric has been added in the commit 861852f262 .
See https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6462
- Remove Alias and aggrID fields from streamaggr.Options struct, since these fields aren't related to optional params,
which could modify the behaviour of the constructed streaming aggregator.
Convert the Alias field to regular argument passed to LoadFromFile() function, since this argument is mandatory.
- Pass Options arg to LoadFromFile() function by reference, since this structure is quite big.
This also allows passing nil instead of Options when default options are enough.
- Add `name`, `path`, `url` and `position` labels to `vm_streamaggr_dedup_state_size_bytes` and `vm_streamaggr_dedup_state_items_count` metrics,
so they have consistent set of labels comparing to the rest of streaming aggregation metrics.
- Convert aggregator.aggrStates field type from `map[string]aggrState` to `[]aggrOutput`, where `aggrOutput` contains the corresponding
`aggrState` plus all the related metrics (currently only `vm_streamaggr_output_samples_total` metric is exposed with the corresponding
`output` label per each configured output function). This simplifies and speeds up the code responsible for updating per-output
metrics. This is a follow-up for the commit 2eb1bc4f81 .
See https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6604
- Added missing urls to docs ( https://docs.victoriametrics.com/stream-aggregation/ ) in error messages. These urls help users
figuring out why VictoriaMetrics or vmagent generates the corresponding error messages. The urls were removed for unknown reason
in the commit 2eb1bc4f81 .
- Fix incorrect update for `vm_streamaggr_output_samples_total` metric in flushCtx.appendSeriesWithExtraLabel() function.
While at it, reduce memory usage by limiting the maximum number of samples per flush to 10K.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5467
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6268
The old link was changed globally to the new link in the commit f4b1cbfef0 .
Unfortunately, old links are still posted in new commits :(
This is a follow-up for 680b8c25c8 .
While at it, remove duplicate 'len(*remoteWriteURLs) > 0' check in the remotewrite.Init() functions,
since this check is already made at the beginning of the function.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6253
- Drop samples and return true from remotewrite.TryPush() at fast path when all the remote storage
systems are configured with the disabled on-disk queue, every in-memory queue is full
and -remoteWrite.dropSamplesOnOverload is set to true. This case is quite common,
so it should be optimized. Previously additional CPU time was spent on per-remoteWriteCtx
relabeling and other processing in this case.
- Properly count the number of dropped samples inside remoteWriteCtx.pushInternalTrackDropped().
Previously dropped samples were counted only if -remoteWrite.dropSamplesOnOverload flag is set.
In reality, the samples are dropped when they couldn't be sent to the queue because in-memory queue is full
and on-disk queue is disabled.
The remoteWriteCtx.pushInternalTrackDropped() function is called by streaming aggregation for pushing
the aggregated data to the remote storage. Streaming aggregation cannot wait until the remote storage
processes pending data, so it drops aggregated samples in this case.
- Clarify the description for -remoteWrite.disableOnDiskQueue command-line flag at -help output,
so it is clear that this flag can be set individually per each -remoteWrite.url.
- Make the -remoteWrite.dropSamplesOnOverload flag global. If some of the remote storage systems
are configured with the disabled on-disk queue, then there is no sense in keeping samples
on some of these systems, while dropping samples on the remaining systems, since this
will result in global stall on the remote storage system with the disabled on-disk queue
and with the -remoteWrite.dropSamplesOnOverload=false flag. vmagent will always return false
from remotewrite.TryPush() in this case. This will result in infinite duplicate samples
written to the remaining remote storage systems. That's why the -remoteWrite.dropSamplesOnOverload
is forcibly set to true if more than one -remoteWrite.disableOnDiskQueue flag is set.
This allows proceeding with newly scraped / pushed samples by sending them to the remaining
remote storage systems, while dropping them on overloaded systems with the -remoteWrite.disableOnDiskQueue flag set.
- Verify that the remoteWriteCtx.TryPush() returns true in the TestRemoteWriteContext_TryPush_ImmutableTimeseries test.
- Mention in vmagent docs that the -remoteWrite.disableOnDiskQueue command-line flag can be set individually per each -remoteWrite.url.
See https://docs.victoriametrics.com/vmagent/#disabling-on-disk-persistence
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6248
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6065
This makes test code more clear and reduces the number of code lines by 500.
This also simplifies debugging tests. See https://itnext.io/f-tests-as-a-replacement-for-table-driven-tests-in-go-8814a8b19e9e
While at it, consistently use t.Fatal* instead of t.Error* across tests, since t.Error*
requires more boilerplate code, which can result in additional bugs inside tests.
While t.Error* allows writing logging errors for the same, this doesn't simplify fixing
broken tests most of the time.
This is a follow-up for a9525da8a4
This simplifies debugging tests and makes the test code more clear and concise.
See https://itnext.io/f-tests-as-a-replacement-for-table-driven-tests-in-go-8814a8b19e9e
While at is, consistently use t.Fatal* instead of t.Error* across tests, since t.Error*
requires more boilerplate code, which can result in additional bugs inside tests.
While t.Error* allows writing logging errors for the same, this doesn't simplify fixing
broken tests most of the time.
This is a follow-up for a9525da8a4
Consistently using t.Fatal* simplifies the test code and makes it less fragile, since it is common error
to forget to make proper cleanup after t.Error* call. Also t.Error* calls do not provide any practical
benefits when some tests fail. They just clutter test output with additional noise information,
which do not help in fixing failing tests most of the time.
While at it, improve errors generated at app/victoria-metrics tests, so they contain more useful information
when debugging failed tests.
This is a follow-up for a9525da8a4
### Describe Your Changes
In most cases histograms are exposed in sorted manner with lower buckets
being first. This means that during scraping buckets with lower bounds
have higher chance of being updated earlier than upper ones.
Previously, values were propagated from upper to lower bounds, which
means that in most cases that would produce results higher than expected
once all buckets will become updated.
Propagating from upper bound effectively limits highest value of
histogram to the value of previous scrape. Once the data will become
consistent in the subsequent evaluation this causes spikes in the
result.
Changing propagation to be from lower to higher buckets reduces value
spikes in most cases due to nature of the original inconsistency.
See: https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4580
An example histogram with previous(red) and updated(blue) versions:
This also makes logic of filling nan values with lower buckets values: [1 2 3 nan nan nan] => [1 2 3 3 3 3] obsolete.
Since buckets are now fixed from lower ones to upper this happens in the main loop, so there is no need in a second one.
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Andrii Chubatiuk <andrew.chubatiuk@gmail.com>
Co-authored-by: hagen1778 <roman@victoriametrics.com>
'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{}'.
The change adds a new docs section with examples on how source can be
overridden. It should address questions like
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6536
While there, fix the example in `external.alert.source` cmd-line flag
and docker-compose examples.
### Checklist
The following checks are **mandatory**:
- [x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Reason for revert:
There are many statsd servers exist:
- https://github.com/statsd/statsd - classical statsd server
- https://docs.datadoghq.com/developers/dogstatsd/ - statsd server from DataDog built into DatDog Agent ( https://docs.datadoghq.com/agent/ )
- https://github.com/avito-tech/bioyino - high-performance statsd server
- https://github.com/atlassian/gostatsd - statsd server in Go
- https://github.com/prometheus/statsd_exporter - statsd server, which exposes the aggregated data as Prometheus metrics
These servers can be used for efficient aggregating of statsd data and sending it to VictoriaMetrics
according to https://docs.victoriametrics.com/#how-to-send-data-from-graphite-compatible-agents-such-as-statsd (
the https://github.com/prometheus/statsd_exporter can be scraped as usual Prometheus target
according to https://docs.victoriametrics.com/#how-to-scrape-prometheus-exporters-such-as-node-exporter ).
Adding support for statsd data ingestion protocol into VictoriaMetrics makes sense only if it provides
significant advantages over the existing statsd servers, while has no significant drawbacks comparing
to existing statsd servers.
The main advantage of statsd server built into VictoriaMetrics and vmagent - getting rid of additional statsd server.
The main drawback is non-trivial and inconvenient streaming aggregation configs, which must be used for the ingested statsd metrics (
see https://docs.victoriametrics.com/stream-aggregation/ ). These configs are incompatible with the configs for standalone statsd servers.
So you need to manually translate configs of the used statsd server to stream aggregation configs when migrating
from standalone statsd server to statsd server built into VictoriaMetrics (or vmagent).
Another important drawback is that it is very easy to shoot yourself in the foot when using built-in statsd server
with the -statsd.disableAggregationEnforcement command-line flag or with improperly configured streaming aggregation.
In this case the ingested statsd metrics will be stored to VictoriaMetrics as is without any aggregation.
This may result in high CPU usage during data ingestion, high disk space usage for storing all the unaggregated
statsd metrics and high CPU usage during querying, since all the unaggregated metrics must be read, unpacked and processed
during querying.
P.S. Built-in statsd server can be added to VictoriaMetrics and vmagent after figuring out more ergonomic
specialized configuration for aggregating of statsd metrics. The main requirements for this configuration:
- easy to write, read and update (ideally it should work out of the box for most cases without additional configuration)
- hard to misconfigure (e.g. hard to shoot yourself in the foot)
It would be great if this configuration will be compatible with the configuration of the most widely used statsd server.
In the mean time it is recommended continue using external statsd server.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6265
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5053
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5052
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/206
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4600
This reverts commit 5a3abfa041.
Reason for revert: exemplars aren't in wide use because they have numerous issues which prevent their adoption (see below).
Adding support for examplars into VictoriaMetrics introduces non-trivial code changes. These code changes need to be supported forever
once the release of VictoriaMetrics with exemplar support is published. That's why I don't think this is a good feature despite
that the source code of the reverted commit has an excellent quality. See https://docs.victoriametrics.com/goals/ .
Issues with Prometheus exemplars:
- Prometheus still has only experimental support for exemplars after more than three years since they were introduced.
It stores exemplars in memory, so they are lost after Prometheus restart. This doesn't look like production-ready feature.
See 0a2f3b3794/content/docs/instrumenting/exposition_formats.md (L153-L159)
and https://prometheus.io/docs/prometheus/latest/feature_flags/#exemplars-storage
- It is very non-trivial to expose exemplars alongside metrics in your application, since the official Prometheus SDKs
for metrics' exposition ( https://prometheus.io/docs/instrumenting/clientlibs/ ) either have very hard-to-use API
for exposing histograms or do not have this API at all. For example, try figuring out how to expose exemplars
via https://pkg.go.dev/github.com/prometheus/client_golang@v1.19.1/prometheus .
- It looks like exemplars are supported for Histogram metric types only -
see https://pkg.go.dev/github.com/prometheus/client_golang@v1.19.1/prometheus#Timer.ObserveDurationWithExemplar .
Exemplars aren't supported for Counter, Gauge and Summary metric types.
- Grafana has very poor support for Prometheus exemplars. It looks like it supports exemplars only when the query
contains histogram_quantile() function. It queries exemplars via special Prometheus API -
https://prometheus.io/docs/prometheus/latest/querying/api/#querying-exemplars - (which is still marked as experimental, btw.)
and then displays all the returned exemplars on the graph as special dots. The issue is that this doesn't work
in production in most cases when the histogram_quantile() is calculated over thousands of histogram buckets
exposed by big number of application instances. Every histogram bucket may expose an exemplar on every timestamp shown on the graph.
This makes the graph unusable, since it is litterally filled with thousands of exemplar dots.
Neither Prometheus API nor Grafana doesn't provide the ability to filter out unneeded exemplars.
- Exemplars are usually connected to traces. While traces are good for some
I doubt exemplars will become production-ready in the near future because of the issues outlined above.
Alternative to exemplars:
Exemplars are marketed as a silver bullet for the correlation between metrics, traces and logs -
just click the exemplar dot on some graph in Grafana and instantly see the corresponding trace or log entry!
This doesn't work as expected in production as shown above. Are there better solutions, which work in production?
Yes - just use time-based and label-based correlation between metrics, traces and logs. Assign the same `job`
and `instance` labels to metrics, logs and traces, so you can quickly find the needed trace or log entry
by these labes on the time range with the anomaly on metrics' graph.
