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fe0859aa5c
Notable next steps include: * Intelligently select the aggregation method (rate/avg_over_time/etc). * Intelligenty select the aggregation window. * Figure out how to handle "namespace", if at all. * Fix the crazy type conversion in `sample_converter.go`.
111 lines
3.7 KiB
Go
111 lines
3.7 KiB
Go
package provider
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import (
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"fmt"
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s "strings"
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provider "github.com/directxman12/k8s-prometheus-adapter/pkg/custom-provider/metric-converter"
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"k8s.io/apimachinery/pkg/labels"
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"k8s.io/apimachinery/pkg/selection"
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)
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type ExternalMetricQueryBuilder interface {
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BuildPrometheusQuery(namespace string, metricName string, metricSelector labels.Selector, queryMetadata provider.QueryMetadata) string
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}
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type externalMetricQueryBuilder struct {
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}
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func NewExternalMetricQueryBuilder() ExternalMetricQueryBuilder {
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return &externalMetricQueryBuilder{}
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}
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func (p *externalMetricQueryBuilder) BuildPrometheusQuery(namespace string, metricName string, metricSelector labels.Selector, queryMetadata provider.QueryMetadata) string {
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//TODO: At least for my Prometheus install, the "namespace" label doesn't seem to be
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//directly applied to the time series. I'm using prometheus-operator. The grafana dashboards
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//seem to query for the pods in a namespace from kube_pod_info and then apply pod-specific
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//label filters. This might need some more thought. Disabling for now.
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// namespaceSelector := p.makeLabelFilter("namespace", "=", namespace)
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labelSelectors := p.convertSelectors(metricSelector)
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joinedLabels := s.Join(labelSelectors, ", ")
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//TODO: Both the aggregation method and window should probably be configurable.
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//I don't think we can make assumptions about the nature of someone's metrics.
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//I'm guessing this might be covered by the recently added advanced configuration
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//code, but I haven't yet had an opportunity to dig into that and understand it.
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//We'll leave this here for testing purposes for now.
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//As reasonable defaults, maybe:
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//rate(...) for counters
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//avg_over_time(...) for gauges
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//I'm guessing that SeriesRegistry might store the metric type, but I haven't looked yet.
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aggregation := queryMetadata.Aggregation
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window := queryMetadata.WindowInSeconds
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return fmt.Sprintf("%s(%s{%s}[%ss])", aggregation, metricName, joinedLabels, window)
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}
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func (p *externalMetricQueryBuilder) makeLabelFilter(labelName string, operator string, targetValue string) string {
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return fmt.Sprintf("%s%s\"%s\"", labelName, operator, targetValue)
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}
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func (p *externalMetricQueryBuilder) convertSelectors(metricSelector labels.Selector) []string {
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requirements, _ := metricSelector.Requirements()
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selectors := []string{}
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for i := 0; i < len(requirements); i++ {
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selector := p.convertRequirement(requirements[i])
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selectors = append(selectors, selector)
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}
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return selectors
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}
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func (p *externalMetricQueryBuilder) convertRequirement(requirement labels.Requirement) string {
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labelName := requirement.Key()
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values := requirement.Values().List()
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stringValues := values[0]
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valueCount := len(values)
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if valueCount > 1 {
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stringValues = s.Join(values, "|")
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}
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operator := p.selectOperator(requirement.Operator(), valueCount)
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return p.makeLabelFilter(labelName, operator, stringValues)
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}
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func (p *externalMetricQueryBuilder) selectOperator(operator selection.Operator, valueCount int) string {
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if valueCount > 1 {
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return p.selectRegexOperator(operator)
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}
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return p.selectSingleValueOperator(operator)
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}
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func (p *externalMetricQueryBuilder) selectRegexOperator(operator selection.Operator) string {
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switch operator {
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case selection.Equals:
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case selection.In:
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return "=~"
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case selection.NotIn:
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case selection.NotEquals:
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return "!~"
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}
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//TODO: Cover more cases, supply appropriate errors for any unhandled cases.
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return "="
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}
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func (p *externalMetricQueryBuilder) selectSingleValueOperator(operator selection.Operator) string {
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switch operator {
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case selection.Equals:
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return "="
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case selection.NotEquals:
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return "!="
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}
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//TODO: Cover more cases, supply appropriate errors for any unhandled cases.
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return "="
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}
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