Fixes #RHIROS-1401 - Dropping csv records with missing resource usage…

internal/logging/logging.go

@@ -62,12 +62,13 @@ func GetLogger() *logrus.Entry {
 }
 
 func Set_request_details(data types.KafkaMsg) *logrus.Entry {
-	return log.WithFields(logrus.Fields{
+	log = log.WithFields(logrus.Fields{
 		"request_id":    data.Request_id,
 		"account":       data.Metadata.Account,
 		"org_id":        data.Metadata.Org_id,
 		"source_id":     data.Metadata.Source_id,
 		"cluster_uuid":  data.Metadata.Cluster_uuid,
 		"cluster_alias": data.Metadata.Cluster_alias,
 	})
+	return log
 }

internal/types/workload/workload.go

@@ -33,7 +33,7 @@ func (p WorkloadType) String() string {
 	case Replicationcontroller:
 		return "replicationcontroller"
 	case Statefulset:
-		return "statefulsets"
+		return "statefulset"
 	case Daemonset:
 		return "daemonset"
 	}

internal/utils/aggregator.go

@@ -6,40 +6,28 @@ import (
 	"github.com/go-gota/gota/dataframe"
 	"github.com/go-gota/gota/series"
 
+	"github.com/redhatinsights/ros-ocp-backend/internal/logging"
 	w "github.com/redhatinsights/ros-ocp-backend/internal/types/workload"
 )
 
 func Aggregate_data(df dataframe.DataFrame) dataframe.DataFrame {
-	df = df.FilterAggregation(
-		dataframe.And,
-		dataframe.F{Colname: "owner_kind", Comparator: series.Neq, Comparando: ""},
-		dataframe.F{Colname: "owner_name", Comparator: series.Neq, Comparando: ""},
-		dataframe.F{Colname: "workload", Comparator: series.Neq, Comparando: ""},
-		dataframe.F{Colname: "workload_type", Comparator: series.Neq, Comparando: ""},
-	)
+	log = logging.GetLogger()
+	df = determine_k8s_object_type(df)
 
-	columns := df.Names()
-	index_of_owner_name := findInStringSlice("owner_name", columns)
-	index_of_owner_kind := findInStringSlice("owner_kind", columns)
-	index_of_workload := findInStringSlice("workload", columns)
-	index_of_workload_type := findInStringSlice("workload_type", columns)
+	// filter out only valid workload type
+	df = filter_valid_k8s_object_types(df)
 
-	s := df.Rapply(func(s series.Series) series.Series {
-		owner_name := s.Elem(index_of_owner_name).String()
-		owner_kind := s.Elem(index_of_owner_kind).String()
-		workload := s.Elem(index_of_workload).String()
-		workload_type := s.Elem(index_of_workload_type).String()
-		if strings.ToLower(owner_kind) == string(w.Replicaset) && workload == "<none>" {
-			return series.Strings([]string{string(w.Replicaset), owner_name})
-		} else if strings.ToLower(owner_kind) == string(w.Replicationcontroller) && workload == "<none>" {
-			return series.Strings([]string{string(w.Replicationcontroller), owner_name})
-		} else {
-			return series.Strings([]string{workload_type, workload})
-		}
-	})
+	// Validation to check if metrics for cpuUsage, memoryUsage and memoryRSS are missing
+	df, no_of_dropped_records := filter_valid_csv_records(df)
+	if no_of_dropped_records != 0 {
+		invalidDataPoints.Add(float64(no_of_dropped_records))
+		log.Infof("Invalid records in CSV - %v", no_of_dropped_records)
+	}
+
+	if df.Nrow() == 0 {
+		return df
+	}
 
-	df = df.Mutate(s.Col("X0")).Rename("k8s_object_type", "X0")
-	df = df.Mutate(s.Col("X1")).Rename("k8s_object_name", "X1")
 	dfGroups := df.GroupBy(
 		"namespace",
 		"k8s_object_type",
@@ -87,3 +75,75 @@ func Aggregate_data(df dataframe.DataFrame) dataframe.DataFrame {
 	df = dfGroups.Aggregation(columnsAggregationType, columnsToAggregate)
 	return df
 }
+
+func filter_valid_csv_records(main_df dataframe.DataFrame) (dataframe.DataFrame, int) {
+	df := main_df.FilterAggregation(
+		dataframe.And,
+		dataframe.F{Colname: "memory_rss_usage_container_sum", Comparator: series.GreaterEq, Comparando: 0},
+		dataframe.F{Colname: "memory_rss_usage_container_max", Comparator: series.GreaterEq, Comparando: 0},
+		dataframe.F{Colname: "memory_rss_usage_container_min", Comparator: series.GreaterEq, Comparando: 0},
+		dataframe.F{Colname: "memory_rss_usage_container_avg", Comparator: series.GreaterEq, Comparando: 0},
+		dataframe.F{Colname: "memory_usage_container_sum", Comparator: series.GreaterEq, Comparando: 0},
+		dataframe.F{Colname: "memory_usage_container_max", Comparator: series.GreaterEq, Comparando: 0},
+		dataframe.F{Colname: "memory_usage_container_min", Comparator: series.GreaterEq, Comparando: 0},
+		dataframe.F{Colname: "memory_usage_container_avg", Comparator: series.GreaterEq, Comparando: 0},
+		dataframe.F{Colname: "cpu_usage_container_sum", Comparator: series.GreaterEq, Comparando: 0},
+		dataframe.F{Colname: "cpu_usage_container_max", Comparator: series.GreaterEq, Comparando: 0},
+		dataframe.F{Colname: "cpu_usage_container_min", Comparator: series.GreaterEq, Comparando: 0},
+		dataframe.F{Colname: "cpu_usage_container_avg", Comparator: series.GreaterEq, Comparando: 0},
+	)
+
+	no_of_dropped_records := main_df.Nrow() - df.Nrow()
+
+	return df, no_of_dropped_records
+}
+
+func filter_valid_k8s_object_types(df dataframe.DataFrame) dataframe.DataFrame {
+	return df.Filter(
+		dataframe.F{
+			Colname:    "k8s_object_type",
+			Comparator: series.In,
+			Comparando: []string{
+				w.Daemonset.String(),
+				w.Deployment.String(),
+				w.Deploymentconfig.String(),
+				w.Replicaset.String(),
+				w.Replicationcontroller.String(),
+				w.Statefulset.String(),
+			}},
+	)
+}
+
+func determine_k8s_object_type(df dataframe.DataFrame) dataframe.DataFrame {
+	df = df.FilterAggregation(
+		dataframe.And,
+		dataframe.F{Colname: "owner_kind", Comparator: series.Neq, Comparando: ""},
+		dataframe.F{Colname: "owner_name", Comparator: series.Neq, Comparando: ""},
+		dataframe.F{Colname: "workload", Comparator: series.Neq, Comparando: ""},
+		dataframe.F{Colname: "workload_type", Comparator: series.Neq, Comparando: ""},
+	)
+
+	columns := df.Names()
+	index_of_owner_name := findInStringSlice("owner_name", columns)
+	index_of_owner_kind := findInStringSlice("owner_kind", columns)
+	index_of_workload := findInStringSlice("workload", columns)
+	index_of_workload_type := findInStringSlice("workload_type", columns)
+
+	s := df.Rapply(func(s series.Series) series.Series {
+		owner_name := s.Elem(index_of_owner_name).String()
+		owner_kind := s.Elem(index_of_owner_kind).String()
+		workload := s.Elem(index_of_workload).String()
+		workload_type := s.Elem(index_of_workload_type).String()
+		if strings.ToLower(owner_kind) == string(w.Replicaset) && workload == "<none>" {
+			return series.Strings([]string{string(w.Replicaset), owner_name})
+		} else if strings.ToLower(owner_kind) == string(w.Replicationcontroller) && workload == "<none>" {
+			return series.Strings([]string{string(w.Replicationcontroller), owner_name})
+		} else {
+			return series.Strings([]string{workload_type, workload})
+		}
+	})
+
+	df = df.Mutate(s.Col("X0")).Rename("k8s_object_type", "X0")
+	df = df.Mutate(s.Col("X1")).Rename("k8s_object_name", "X1")
+	return df
+}

internal/utils/aggregator_test.go

@@ -0,0 +1,152 @@
+package utils
+
+import (
+	"fmt"
+	"testing"
+
+	"github.com/go-gota/gota/dataframe"
+)
+
+type UsageData struct {
+	Report_period_start            string `dataframe:"report_period_start,string"`
+	Report_period_end              string `dataframe:"report_period_end,string"`
+	Interval_start                 string `dataframe:"interval_start,string"`
+	Interval_end                   string `dataframe:"interval_end,string"`
+	Container_name                 string `dataframe:"container_name,string"`
+	Pod                            string `dataframe:"pod,string"`
+	Owner_name                     string `dataframe:"owner_name,string"`
+	Owner_kind                     string `dataframe:"owner_kind,string"`
+	Workload                       string `dataframe:"workload,string"`
+	Workload_type                  string `dataframe:"workload_type,string"`
+	Namespace                      string `dataframe:"namespace,string"`
+	Image_name                     string `dataframe:"image_name,string"`
+	Node                           string `dataframe:"node,string"`
+	Resource_id                    string `dataframe:"resource_id,string"`
+	Cpu_request_container_avg      string `dataframe:"cpu_request_container_avg,float"`
+	Cpu_request_container_sum      string `dataframe:"cpu_request_container_sum,float"`
+	Cpu_limit_container_avg        string `dataframe:"cpu_limit_container_avg,float"`
+	Cpu_limit_container_sum        string `dataframe:"cpu_limit_container_sum,float"`
+	Cpu_usage_container_avg        string `dataframe:"cpu_usage_container_avg,float"`
+	Cpu_usage_container_min        string `dataframe:"cpu_usage_container_min,float"`
+	Cpu_usage_container_max        string `dataframe:"cpu_usage_container_max,float"`
+	Cpu_usage_container_sum        string `dataframe:"cpu_usage_container_sum,float"`
+	Cpu_throttle_container_avg     string `dataframe:"cpu_throttle_container_avg,float"`
+	Cpu_throttle_container_max     string `dataframe:"cpu_throttle_container_max,float"`
+	Cpu_throttle_container_sum     string `dataframe:"cpu_throttle_container_sum,float"`
+	Memory_request_container_avg   string `dataframe:"memory_request_container_avg,float"`
+	Memory_request_container_sum   string `dataframe:"memory_request_container_sum,float"`
+	Memory_limit_container_avg     string `dataframe:"memory_limit_container_avg,float"`
+	Memory_limit_container_sum     string `dataframe:"memory_limit_container_sum,float"`
+	Memory_usage_container_avg     string `dataframe:"memory_usage_container_avg,float"`
+	Memory_usage_container_min     string `dataframe:"memory_usage_container_min,float"`
+	Memory_usage_container_max     string `dataframe:"memory_usage_container_max,float"`
+	Memory_usage_container_sum     string `dataframe:"memory_usage_container_sum,float"`
+	Memory_rss_usage_container_avg string `dataframe:"memory_rss_usage_container_avg,float"`
+	Memory_rss_usage_container_min string `dataframe:"memory_rss_usage_container_min,float"`
+	Memory_rss_usage_container_max string `dataframe:"memory_rss_usage_container_max,float"`
+	Memory_rss_usage_container_sum string `dataframe:"memory_rss_usage_container_sum,float"`
+}
+
+func Test_filter_valid_k8s_object_types(t *testing.T) {
+	// Check valid k8s object type
+	usage_data := []UsageData{
+		// k8s object type DaemonSet
+		{
+			"2023-02-01 00:00:00 +0000 UTC", "2023-03-01 00:00:00 +0000 UTC", "2023-06-02 00:00:01 +0000 UTC", "2023-06-02 00:15:00 +0000 UTC",
+			"Yuptoo-service", "Yuptoo-app-standalone-1", "Yuptoo-app", "DaemonSet", "testdeploymentconfig", "daemonset", "Yuptoo-prod",
+			"quay.io/cloudservices/yuptoo", "ip-10-0-176-227.us-east-2.compute.internal", "i-0dfbb3fa4d0e8fc94",
+			"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
+		},
+		// k8s object type Replicaset
+		{
+			"2023-02-01 00:00:00 +0000 UTC", "2023-03-01 00:00:00 +0000 UTC", "2023-06-02 00:00:01 +0000 UTC", "2023-06-02 00:15:00 +0000 UTC",
+			"Yuptoo-service", "Yuptoo-app-standalone-1", "Yuptoo-app", "ReplicaSet", "<none>", "deployment", "Yuptoo-prod",
+			"quay.io/cloudservices/yuptoo", "ip-10-0-176-227.us-east-2.compute.internal", "i-0dfbb3fa4d0e8fc94",
+			"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
+		},
+		// k8s object type Deployment
+		{
+			"2023-02-01 00:00:00 +0000 UTC", "2023-03-01 00:00:00 +0000 UTC", "2023-06-02 00:00:01 +0000 UTC", "2023-06-02 00:15:00 +0000 UTC",
+			"Yuptoo-service", "Yuptoo-app-standalone-1", "Yuptoo-app", "ReplicaSet", "testdeployment", "deployment", "Yuptoo-prod",
+			"quay.io/cloudservices/yuptoo", "ip-10-0-176-227.us-east-2.compute.internal", "i-0dfbb3fa4d0e8fc94",
+			"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
+		},
+		// k8s object type ReplicationController
+		{
+			"2023-02-01 00:00:00 +0000 UTC", "2023-03-01 00:00:00 +0000 UTC", "2023-06-02 00:00:01 +0000 UTC", "2023-06-02 00:15:00 +0000 UTC",
+			"Yuptoo-service", "Yuptoo-app-standalone-1", "Yuptoo-app", "ReplicationController", "<none>", "deploymentconfig", "Yuptoo-prod",
+			"quay.io/cloudservices/yuptoo", "ip-10-0-176-227.us-east-2.compute.internal", "i-0dfbb3fa4d0e8fc94",
+			"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
+		},
+		// k8s object type Deploymentconfig
+		{
+			"2023-02-01 00:00:00 +0000 UTC", "2023-03-01 00:00:00 +0000 UTC", "2023-06-02 00:00:01 +0000 UTC", "2023-06-02 00:15:00 +0000 UTC",
+			"Yuptoo-service", "Yuptoo-app-standalone-1", "Yuptoo-app", "ReplicationController", "testdeploymentconfig", "deploymentconfig", "Yuptoo-prod",
+			"quay.io/cloudservices/yuptoo", "ip-10-0-176-227.us-east-2.compute.internal", "i-0dfbb3fa4d0e8fc94",
+			"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
+		},
+		// k8s object type StatefulSet
+		{
+			"2023-02-01 00:00:00 +0000 UTC", "2023-03-01 00:00:00 +0000 UTC", "2023-06-02 00:00:01 +0000 UTC", "2023-06-02 00:15:00 +0000 UTC",
+			"Yuptoo-service", "Yuptoo-app-standalone-1", "Yuptoo-app", "StatefulSet", "testdeploymentconfig", "statefulset", "Yuptoo-prod",
+			"quay.io/cloudservices/yuptoo", "ip-10-0-176-227.us-east-2.compute.internal", "i-0dfbb3fa4d0e8fc94",
+			"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
+		},
+	}
+	df := dataframe.LoadStructs(usage_data)
+	df = determine_k8s_object_type(df)
+	result := filter_valid_k8s_object_types(df)
+	fmt.Println(result.Nrow())
+	if result.Nrow() != 6 {
+		t.Error("Data not filtered properly. Some of the valid k8s object type got dropped")
+	}
+
+	// check if Invalid k8s object type is dropped
+	usage_data = []UsageData{
+		// k8s object type Job
+		{
+			"2023-02-01 00:00:00 +0000 UTC", "2023-03-01 00:00:00 +0000 UTC", "2023-06-02 00:00:01 +0000 UTC", "2023-06-02 00:15:00 +0000 UTC",
+			"Yuptoo-service", "Yuptoo-app-standalone-1", "Yuptoo-app", "Job", "testdeploymentconfig", "job", "Yuptoo-prod",
+			"quay.io/cloudservices/yuptoo", "ip-10-0-176-227.us-east-2.compute.internal", "i-0dfbb3fa4d0e8fc94",
+			"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
+		},
+	}
+	df = dataframe.LoadStructs(usage_data)
+	df = determine_k8s_object_type(df)
+	result = filter_valid_k8s_object_types(df)
+	if result.Nrow() != 0 {
+		t.Error("Invalid k8s object type did not get dropped")
+	}
+}
+
+func Test_filter_valid_csv_records(t *testing.T) {
+	usage_data := []UsageData{
+		// k8s object with missing data
+		{
+			"2023-02-01 00:00:00 +0000 UTC", "2023-03-01 00:00:00 +0000 UTC", "2023-06-02 00:00:01 +0000 UTC", "2023-06-02 00:15:00 +0000 UTC",
+			"Yuptoo-service", "Yuptoo-app-standalone-1", "Yuptoo-app", "ReplicaSet", "testdeployment", "deployment", "Yuptoo-prod",
+			"quay.io/cloudservices/yuptoo", "ip-10-0-176-227.us-east-2.compute.internal", "i-0dfbb3fa4d0e8fc94",
+			"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "",
+		},
+		{
+			"2023-02-01 00:00:00 +0000 UTC", "2023-03-01 00:00:00 +0000 UTC", "2023-06-02 00:00:01 +0000 UTC", "2023-06-02 00:15:00 +0000 UTC",
+			"Yuptoo-service", "Yuptoo-app-standalone-1", "Yuptoo-app", "ReplicaSet", "testdeployment", "deployment", "Yuptoo-prod",
+			"quay.io/cloudservices/yuptoo", "ip-10-0-176-227.us-east-2.compute.internal", "i-0dfbb3fa4d0e8fc94",
+			"1", "1", "1", "1", "", "", "", "", "1", "1", "1", "1", "1", "1", "1", "", "", "", "", "", "", "", "",
+		},
+		// k8s object with 0 CPU, Memory and RSS usage
+		{
+			"2023-02-01 00:00:00 +0000 UTC", "2023-03-01 00:00:00 +0000 UTC", "2023-06-02 00:00:01 +0000 UTC", "2023-06-02 00:15:00 +0000 UTC",
+			"Yuptoo-service", "Yuptoo-app-standalone-1", "Yuptoo-app", "ReplicaSet", "testdeployment", "deployment", "Yuptoo-prod",
+			"quay.io/cloudservices/yuptoo", "ip-10-0-176-227.us-east-2.compute.internal", "i-0dfbb3fa4d0e8fc94",
+			"1", "1", "1", "1", "0", "0", "0", "0", "1", "1", "1", "1", "1", "1", "1", "0", "0", "0", "0", "0", "0", "0", "0",
+		},
+	}
+	df := dataframe.LoadStructs(usage_data)
+	df = determine_k8s_object_type(df)
+	result, no_of_dropped_records := filter_valid_csv_records(df)
+	if result.Nrow() != 1 || no_of_dropped_records != 2 {
+		t.Error("Invalid k8s object type did not get dropped")
+	}
+
+}

internal/utils/metrics.go

@@ -0,0 +1,13 @@
+package utils
+
+import (
+	"github.com/prometheus/client_golang/prometheus"
+	"github.com/prometheus/client_golang/prometheus/promauto"
+)
+
+var (
+	invalidDataPoints = promauto.NewCounter(prometheus.CounterOpts{
+		Name: "rosocp_invalid_datapoints_total",
+		Help: "The total number of invalid datapoints(rows) found in received CSVs",
+	})
+)