[COST-6491] Add ROS namespace queries

[djnakabaale]

Jira Ticket

COST-6491

Description

This change will enable gathering and reporting namespace metrics for resource optimization.

Testing instructions

Prerequisites:

• Access to an OpenShift cluster.
• A public Quay.io repository (or other container registry) to push the operator image.

1. Prepare the operator image

1. Checkout feature branch

2. Build and push the operator to your quay repo.Replace <USERNAME> with your Quay.io username and <TAG> with your desired image tag

   make docker-buildx IMG=quay.io/<USERNAME>/koku-metrics-operator:<TAG>

2. Deploy the operator to your OpenShift cluster

1. login into your cluster

2. create a koku-metrics-operator namespace

   oc new-project koku-metrics-operator

3. enable ROS for the namespace

   oc label namespace koku-metrics-operator cost_management_optimizations="true"

4. Pull the image to make sure its available on your machine

   docker pull quay.io/<USERNAME>/koku-metrics-operator:<TAG>

5. Deploy the operator

   make deploy IMG=quay.io/<USERNAME>/koku-metrics-operator:<TAG>

3. Configure metrics collection

1. Copy the yaml below to create a costmanagementmetricsconfig.yaml file

   apiVersion: costmanagement-metrics-cfg.openshift.io/v1beta1
   kind: CostManagementMetricsConfig
   metadata:
       name: costmanagementmetricscfg-sample-v1beta1
       namespace: koku-metrics-operator
   spec:
       upload:
           ingress_path: /api/ingress/v1/upload
           upload_toggle: false
           validate_cert: true
       packaging:
           max_reports_to_store: 30
           max_size_MB: 100
       api_url: 'https://console.redhat.com'
       prometheus_config:
           collect_previous_data: true
           disable_metrics_collection_cost_management: true
           disable_metrics_collection_resource_optimization: false
           service_address: 'https://thanos-querier.openshift-monitoring.svc:9091'
       authentication:
           type: token
       source:
           create_source: false
           name: ''

2. deploy the configuration

   oc apply -f costmanagementmetricsconfig.yaml

4. Verify setrics collection

1. You should see logs on querying ros metrics and writing results to files similar to below:

   2025-07-15T16:03:06.070744221Z INFO collector.GenerateReports querying for resource-optimization container metrics for ts: 2025-07-15 15:15:00 +0000 UTC
   2025-07-15T16:03:06.186342346Z INFO collector.GenerateReports.writeResults writing resource-optimization container results to file {"filename": "ros-openshift-container-202507.csv"}
   2025-07-15T16:03:06.188814287Z INFO collector.GenerateReports querying for resource-optimization namespace metrics for ts: 2025-07-15 15:15:00 +0000 UTC
   2025-07-15T16:03:06.262305141Z INFO collector.GenerateReports.writeResults writing resource-optimization namespace results to file {"filename": "ros-openshift-namespace-202507.csv"}
   

2. Additionally retrieve the reports and inspect the reports gathered by following the instructions on downloading reports from the operator.

Summary by Sourcery

Add namespace-level resource optimization (ROS) queries and reporting

New Features:

• Introduce new ROS namespace Prometheus queries for CPU, memory, pod, and throttle metrics
• Implement rosNamespaceQueries and corresponding rosNamespaceRow type to capture namespace-level metrics
• Extend the collector to query, process, and write namespace-level ROS CSV reports with a dedicated file prefix

Build:

• Update Makefile get-token-and-cert target to retrieve the CA certificate from a configmap instead of a secret

Tests:

• Add test fixtures and mock results for new ROS namespace queries
• Update report count assertions to account for the additional namespace-level ROS report

Summary by Sourcery

Add namespace-level resource optimization queries and reporting in the metrics collector and update CI build script.

New Features:

• Introduce namespace-level ROS Prometheus queries for CPU, memory, pod counts, and throttle metrics
• Implement rosNamespaceQueries and rosNamespaceRow to capture and format namespace-level ROS metrics into CSV

Enhancements:

• Extend resource optimization report generation to produce a separate namespace-level CSV report alongside container-level metrics

Build:

• Update Makefile get-token-and-cert target to retrieve the CA certificate from a configmap

Tests:

• Add test fixtures and mock data for the new namespace-level ROS queries and adjust expected report counts

[sourcery-ai]

Reviewer's Guide

Adds namespace-level resource optimization (ROS) metric support by defining new Prometheus queries, data structures, and report generation, along with corresponding test updates and Makefile adjustments.

Sequence diagram for generating ROS namespace reports

sequenceDiagram
    participant Operator
    participant PrometheusCollector
    participant Prometheus
    participant FileSystem

    Operator->>PrometheusCollector: generateResourceOpimizationReports()
    PrometheusCollector->>Prometheus: getQueryResults(rosNamespaceQueries)
    Prometheus-->>PrometheusCollector: namespace metrics data
    PrometheusCollector->>FileSystem: writeReport(ros-openshift-namespace-<month>.csv)
    FileSystem-->>PrometheusCollector: report written
    PrometheusCollector-->>Operator: return

Loading

Class diagram for new and updated ROS namespace metric types

classDiagram
    class rosNamespaceRow {
        +dateTimes
        +string Namespace
        +string CPURequestSum
        +string CPULimitSum
        +string CPUUsageAvg
        +string CPUUsageMax
        +string CPUUsageMin
        +string CPUThrottleAvg
        +string CPUThrottleMax
        +string CPUThrottleMin
        +string MemoryRequestSum
        +string MemoryLimitSum
        +string MemoryUsageAvg
        +string MemoryUsageMax
        +string MemoryUsageMin
        +string MemoryRSSUsageAvg
        +string MemoryRSSUsageMax
        +string MemoryRSSUsageMin
        +string PodsRunningMax
        +string PodsRunningAvg
        +string PodsTotalMax
        +string PodsTotalAvg
        +csvHeader() []string
        +csvRow() []string
        +string() string
    }
    class resourceOptimizationRow {
        +dateTimes
        ...
    }
    rosNamespaceRow --|> dateTimes
    resourceOptimizationRow --|> dateTimes

Loading

Class diagram for new ROS namespace queries variable

classDiagram
    class query {
        +string Name
        +string QueryString
        +staticFields MetricKey
        +saveQueryValue QueryValue
        +[]model.LabelName RowKey
    }
    class querys {
        +[]query
    }
    querys "1" -- "*" query : contains

Loading

File-Level Changes

Change	Details	Files
Introduce namespace-level ROS Prometheus queries and test fixtures	Add multiple ros:... namespace queries to QueryMap Define rosNamespaceQueries slice for namespace metrics Include rosNamespaceQueries loop in collector tests and adjust expected outcomes	internal/collector/queries.go internal/collector/collector_test.go
Define rosNamespaceRow struct and CSV formatting	Add new rosNamespaceRow type with mapstructure tags Implement csvHeader and csvRow methods for namespace rows	internal/collector/types.go
Extend report generator to handle namespace-level ROS reports	Add new file prefix rosNamespaceFilePrefix Update generateResourceOpimizationReports to query and write namespace reports	internal/collector/collector.go
Adjust Makefile token and certificate retrieval	Remove secret-based CA certificate extraction Use kube-root-ca.crt ConfigMap for service-ca.crt in get-token-and-cert target	Makefile

---

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[codecov]

Codecov Report

Attention: Patch coverage is 88.42105% with 11 lines in your changes missing coverage. Please review.

Project coverage is 83.34%. Comparing base (bc32688) to head (223eb84).
Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
internal/collector/collector.go	69.44%	8 Missing and 3 partials ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #635      +/-   ##
==========================================
+ Coverage   83.16%   83.34%   +0.17%     
==========================================
  Files          16       16              
  Lines        3065     3152      +87     
==========================================
+ Hits         2549     2627      +78     
- Misses        431      437       +6     
- Partials       85       88       +3     

Flag	Coverage Δ
unittests	83.34% <88.42%> (+0.17%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files with missing lines	Coverage Δ
internal/collector/types.go	100.00% <100.00%> (ø)
internal/collector/collector.go	83.57% <69.44%> (-1.16%)	⬇️

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[sourcery-ai]

Hey @djnakabaale - I've reviewed your changes - here's some feedback:

• The rosNamespaceQueries block is extremely repetitive—consider generating the namespace query definitions programmatically or extracting the common logic into a helper to reduce boilerplate.
• generateResourceOpimizationReports now handles both container and namespace reports in one function—splitting it into two dedicated functions would improve readability and maintainability.
• Your tests duplicate the logic for loading namespace query fixtures in multiple places—extract that into a shared helper to keep the test setup DRY.

Prompt for AI Agents

Please address the comments from this code review:
## Overall Comments
- The rosNamespaceQueries block is extremely repetitive—consider generating the namespace query definitions programmatically or extracting the common logic into a helper to reduce boilerplate.
- generateResourceOpimizationReports now handles both container and namespace reports in one function—splitting it into two dedicated functions would improve readability and maintainability.
- Your tests duplicate the logic for loading namespace query fixtures in multiple places—extract that into a shared helper to keep the test setup DRY.

## Individual Comments

### Comment 1
<location> `internal/collector/collector.go:431` </location>
<code_context>

 func generateResourceOpimizationReports(log gologr.Logger, c *PrometheusCollector, dirCfg *dirconfig.DirectoryConfig, nodeRows mappedCSVStruct, yearMonth string) error {
 	ts := c.TimeSeries.End
-	log.Info(fmt.Sprintf("querying for resource-optimization for ts: %+v", ts))
</code_context>

<issue_to_address>
Typo in function name: 'generateResourceOpimizationReports' should be 'generateResourceOptimizationReports'.

Please correct the spelling in the function name to maintain consistency and avoid confusion.

Suggested implementation:

```golang
func generateResourceOptimizationReports(log gologr.Logger, c *PrometheusCollector, dirCfg *dirconfig.DirectoryConfig, nodeRows mappedCSVStruct, yearMonth string) error {

```

You must also update all call sites of `generateResourceOpimizationReports` to use the new name `generateResourceOptimizationReports` throughout the codebase.
</issue_to_address>

### Comment 2
<location> `internal/collector/types.go:525` </location>
<code_context>
+	PodsTotalAvg      string `mapstructure:"pods-total-namespace-avg"`
+}
+
+func (rosNamespaceRow) csvHeader() []string {
+	return []string{
+		"report_period_start",
</code_context>

<issue_to_address>
Consider replacing the manual csvHeader and csvRow methods with a generic reflection-based approach using struct tags.

You can completely eliminate the hand-rolled `csvHeader`/`csvRow` methods by driving both from your existing `mapstructure` tags + a tiny bit of reflection.  This keeps the field order (including your embedded `dateTimes`) and means you only ever list each column once:

```go
// csv.go (new!)

import (
  "reflect"
  "strings"
)

// buildCSVHeaders returns all non-empty “mapstructure” tags (flattening embedded structs)
func buildCSVHeaders(v interface{}) []string {
  t := reflect.TypeOf(v)
  if t.Kind() == reflect.Ptr { t = t.Elem() }
  var hdr []string
  for i := 0; i < t.NumField(); i++ {
    f := t.Field(i)
    if f.Anonymous && f.Type.Kind() == reflect.Struct {
      // recurse into embedded structs
      embedded := reflect.New(f.Type).Interface()
      hdr = append(hdr, buildCSVHeaders(embedded)...)
      continue
    }
    if tag := f.Tag.Get("mapstructure"); tag != "" {
      hdr = append(hdr, tag)
    }
  }
  return hdr
}

// buildCSVRow returns the string values of all fields tagged “mapstructure”
func buildCSVRow(v interface{}) []string {
  val := reflect.ValueOf(v)
  if val.Kind() == reflect.Ptr { val = val.Elem() }
  t := val.Type()
  var row []string
  for i := 0; i < t.NumField(); i++ {
    f := t.Field(i)
    fv := val.Field(i)
    if f.Anonymous && f.Type.Kind() == reflect.Struct {
      row = append(row, buildCSVRow(fv.Interface())...)
      continue
    }
    if f.Tag.Get("mapstructure") != "" {
      row = append(row, fv.String())
    }
  }
  return row
}

// stringifyCSV just joins with commas
func stringifyCSV(fields []string) string {
  return strings.Join(fields, ",")
}
```

Then your `rosNamespaceRow` becomes just:

```go
type rosNamespaceRow struct {
  *dateTimes
  Namespace         string `mapstructure:"namespace"`
  CPURequestSum     string `mapstructure:"cpu-request-namespace-sum"`
  // … all your other fields with the same tags …
}

func (r rosNamespaceRow) csvHeader() []string { return buildCSVHeaders(r) }
func (r rosNamespaceRow) csvRow() []string    { return buildCSVRow(r) }
func (r rosNamespaceRow) string() string      { return stringifyCSV(r.csvRow()) }
```

You can apply the same two‐liner to every other `*Row` type and immediately delete 90+ lines of almost-identical slice literals.
</issue_to_address>

---

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[sourcery-ai]

issue (complexity): Consider replacing the manual csvHeader and csvRow methods with a generic reflection-based approach using struct tags.

You can completely eliminate the hand-rolled csvHeader/csvRow methods by driving both from your existing mapstructure tags + a tiny bit of reflection. This keeps the field order (including your embedded dateTimes) and means you only ever list each column once:

// csv.go (new!)

import (
  "reflect"
  "strings"
)

// buildCSVHeaders returns all non-empty “mapstructure” tags (flattening embedded structs)
func buildCSVHeaders(v interface{}) []string {
  t := reflect.TypeOf(v)
  if t.Kind() == reflect.Ptr { t = t.Elem() }
  var hdr []string
  for i := 0; i < t.NumField(); i++ {
    f := t.Field(i)
    if f.Anonymous && f.Type.Kind() == reflect.Struct {
      // recurse into embedded structs
      embedded := reflect.New(f.Type).Interface()
      hdr = append(hdr, buildCSVHeaders(embedded)...)
      continue
    }
    if tag := f.Tag.Get("mapstructure"); tag != "" {
      hdr = append(hdr, tag)
    }
  }
  return hdr
}

// buildCSVRow returns the string values of all fields tagged “mapstructure”
func buildCSVRow(v interface{}) []string {
  val := reflect.ValueOf(v)
  if val.Kind() == reflect.Ptr { val = val.Elem() }
  t := val.Type()
  var row []string
  for i := 0; i < t.NumField(); i++ {
    f := t.Field(i)
    fv := val.Field(i)
    if f.Anonymous && f.Type.Kind() == reflect.Struct {
      row = append(row, buildCSVRow(fv.Interface())...)
      continue
    }
    if f.Tag.Get("mapstructure") != "" {
      row = append(row, fv.String())
    }
  }
  return row
}

// stringifyCSV just joins with commas
func stringifyCSV(fields []string) string {
  return strings.Join(fields, ",")
}

Then your rosNamespaceRow becomes just:

type rosNamespaceRow struct {
  *dateTimes
  Namespace         string `mapstructure:"namespace"`
  CPURequestSum     string `mapstructure:"cpu-request-namespace-sum"`
  // … all your other fields with the same tags …
}

func (r rosNamespaceRow) csvHeader() []string { return buildCSVHeaders(r) }
func (r rosNamespaceRow) csvRow() []string    { return buildCSVRow(r) }
func (r rosNamespaceRow) string() string      { return stringifyCSV(r.csvRow()) }

You can apply the same two‐liner to every other *Row type and immediately delete 90+ lines of almost-identical slice literals.