VictoriaMetrics/app/vmagent/remotewrite/pendingseries_test.go

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package remotewrite
import (
"fmt"
"math"
"testing"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/prompbmarshal"
)
func TestPushWriteRequest(t *testing.T) {
rowsCounts := []int{1, 10, 100, 1e3, 1e4}
Revert "Exemplar support (#5982)" This reverts commit 5a3abfa0414ab495cbc34a58146b540aa8289636. 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 https://github.com/prometheus/docs/blob/0a2f3b37940e2949eefe752ed7b6c768e0b00128/content/docs/instrumenting/exposition_formats.md?plain=1#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. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5982
2024-07-03 15:30:11 +02:00
expectedBlockLensProm := []int{216, 1848, 16424, 169882, 1757876}
expectedBlockLensVM := []int{138, 492, 3927, 34995, 288476}
for i, rowsCount := range rowsCounts {
expectedBlockLenProm := expectedBlockLensProm[i]
expectedBlockLenVM := expectedBlockLensVM[i]
t.Run(fmt.Sprintf("%d", rowsCount), func(t *testing.T) {
testPushWriteRequest(t, rowsCount, expectedBlockLenProm, expectedBlockLenVM)
})
}
}
func testPushWriteRequest(t *testing.T, rowsCount, expectedBlockLenProm, expectedBlockLenVM int) {
f := func(isVMRemoteWrite bool, expectedBlockLen int, tolerancePrc float64) {
t.Helper()
wr := newTestWriteRequest(rowsCount, 20)
pushBlockLen := 0
pushBlock := func(block []byte) bool {
if pushBlockLen > 0 {
panic(fmt.Errorf("BUG: pushBlock called multiple times; pushBlockLen=%d at first call, len(block)=%d at second call", pushBlockLen, len(block)))
}
pushBlockLen = len(block)
return true
}
app/vmagent: follow-up for 090cb2c9de8d533eaba45a3ebbdb0d2503e97e00 - Add Try* prefix to functions, which return bool result in order to improve readability and reduce the probability of missing check for the result returned from these functions. - Call the adjustSampleValues() only once on input samples. Previously it was called on every attempt to flush data to peristent queue. - Properly restore the initial state of WriteRequest passed to tryPushWriteRequest() before returning from this function after unsuccessful push to persistent queue. Previously a part of WriteRequest samples may be lost in such case. - Add -remoteWrite.dropSamplesOnOverload command-line flag, which can be used for dropping incoming samples instead of returning 429 Too Many Requests error to the client when -remoteWrite.disableOnDiskQueue is set and the remote storage cannot keep up with the data ingestion rate. - Add vmagent_remotewrite_samples_dropped_total metric, which counts the number of dropped samples. - Add vmagent_remotewrite_push_failures_total metric, which counts the number of unsuccessful attempts to push data to persistent queue when -remoteWrite.disableOnDiskQueue is set. - Remove vmagent_remotewrite_aggregation_metrics_dropped_total and vm_promscrape_push_samples_dropped_total metrics, because they are replaced with vmagent_remotewrite_samples_dropped_total metric. - Update 'Disabling on-disk persistence' docs at docs/vmagent.md - Update stale comments in the code Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5088 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2110
2023-11-25 10:31:30 +01:00
if !tryPushWriteRequest(wr, pushBlock, isVMRemoteWrite) {
t.Fatalf("cannot push data to remote storage")
app/vmagent: follow-up for 090cb2c9de8d533eaba45a3ebbdb0d2503e97e00 - Add Try* prefix to functions, which return bool result in order to improve readability and reduce the probability of missing check for the result returned from these functions. - Call the adjustSampleValues() only once on input samples. Previously it was called on every attempt to flush data to peristent queue. - Properly restore the initial state of WriteRequest passed to tryPushWriteRequest() before returning from this function after unsuccessful push to persistent queue. Previously a part of WriteRequest samples may be lost in such case. - Add -remoteWrite.dropSamplesOnOverload command-line flag, which can be used for dropping incoming samples instead of returning 429 Too Many Requests error to the client when -remoteWrite.disableOnDiskQueue is set and the remote storage cannot keep up with the data ingestion rate. - Add vmagent_remotewrite_samples_dropped_total metric, which counts the number of dropped samples. - Add vmagent_remotewrite_push_failures_total metric, which counts the number of unsuccessful attempts to push data to persistent queue when -remoteWrite.disableOnDiskQueue is set. - Remove vmagent_remotewrite_aggregation_metrics_dropped_total and vm_promscrape_push_samples_dropped_total metrics, because they are replaced with vmagent_remotewrite_samples_dropped_total metric. - Update 'Disabling on-disk persistence' docs at docs/vmagent.md - Update stale comments in the code Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5088 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2110
2023-11-25 10:31:30 +01:00
}
if math.Abs(float64(pushBlockLen-expectedBlockLen)/float64(expectedBlockLen)*100) > tolerancePrc {
t.Fatalf("unexpected block len for rowsCount=%d, isVMRemoteWrite=%v; got %d bytes; expecting %d bytes +- %.0f%%",
rowsCount, isVMRemoteWrite, pushBlockLen, expectedBlockLen, tolerancePrc)
}
}
// Check Prometheus remote write
f(false, expectedBlockLenProm, 3)
// Check VictoriaMetrics remote write
f(true, expectedBlockLenVM, 15)
}
func newTestWriteRequest(seriesCount, labelsCount int) *prompbmarshal.WriteRequest {
var wr prompbmarshal.WriteRequest
for i := 0; i < seriesCount; i++ {
var labels []prompbmarshal.Label
for j := 0; j < labelsCount; j++ {
labels = append(labels, prompbmarshal.Label{
Name: fmt.Sprintf("label_%d_%d", i, j),
Value: fmt.Sprintf("value_%d_%d", i, j),
})
}
wr.Timeseries = append(wr.Timeseries, prompbmarshal.TimeSeries{
Labels: labels,
Samples: []prompbmarshal.Sample{
{
Value: float64(i),
Timestamp: 1000 * int64(i),
},
},
})
}
return &wr
}