// UncoveredDeploymentFlowEdges preserves (and duplicates) edges that were not
// covered by a deployment flow. As a special case, it preserves edges between
// Services and DeploymentConfigs.
func UncoveredDeploymentFlowEdges(covered osgraph.NodeSet) osgraph.EdgeFunc {
return func(g osgraph.Interface, head, tail graph.Node, edgeKind string) bool {
if edgeKind == kubeedges.ExposedThroughServiceEdgeKind {
return osgraph.AddReversedEdge(g, head, tail, osgraph.ReferencedByEdgeKind)
}
if covered.Has(head.ID()) && covered.Has(tail.ID()) {
return false
}
return osgraph.AddReversedEdge(g, head, tail, osgraph.ReferencedByEdgeKind)
}
}
// FindOverlappingHPAs scans the graph in search of HorizontalPodAutoscalers that are attempting to scale the same set of pods.
// This can occur in two ways:
// - 1. label selectors for two ReplicationControllers/DeploymentConfigs/etc overlap
// - 2. multiple HorizontalPodAutoscalers are attempting to scale the same ReplicationController/DeploymentConfig/etc
// Case 1 is handled by deconflicting the area of influence of ReplicationControllers/DeploymentConfigs/etc, and therefore we
// can assume that it will be handled before this step. Therefore, we are only concerned with finding HPAs that are trying to
// scale the same resources.
//
// The algorithm that is used to implement this check is described as follows:
// - create a sub-graph containing only HPA nodes and other nodes that can be scaled, as well as any scaling edges or other
// edges used to connect between objects that can be scaled
// - for every resulting edge in the new sub-graph, create an edge in the reverse direction
// - find the shortest paths between all HPA nodes in the graph
// - shortest paths connecting two horizontal pod autoscalers are used to create markers for the graph
func FindOverlappingHPAs(graph osgraph.Graph, namer osgraph.Namer) []osgraph.Marker {
markers := []osgraph.Marker{}
nodeFilter := osgraph.NodesOfKind(
kubenodes.HorizontalPodAutoscalerNodeKind,
kubenodes.ReplicationControllerNodeKind,
deploynodes.DeploymentConfigNodeKind,
)
edgeFilter := osgraph.EdgesOfKind(
kubegraph.ScalingEdgeKind,
deploygraph.DeploymentEdgeKind,
kubeedges.ManagedByControllerEdgeKind,
)
hpaSubGraph := graph.Subgraph(nodeFilter, edgeFilter)
for _, edge := range hpaSubGraph.Edges() {
osgraph.AddReversedEdge(hpaSubGraph, edge.From(), edge.To(), sets.NewString())
}
hpaNodes := hpaSubGraph.NodesByKind(kubenodes.HorizontalPodAutoscalerNodeKind)
for _, firstHPA := range hpaNodes {
// we can use Dijkstra's algorithm as we know we do not have any negative edge weights
shortestPaths := path.DijkstraFrom(firstHPA, hpaSubGraph)
for _, secondHPA := range hpaNodes {
if firstHPA == secondHPA {
continue
}
shortestPath, _ := shortestPaths.To(secondHPA)
if shortestPath == nil {
// if two HPAs have no path between them, no error exists
continue
}
markers = append(markers, osgraph.Marker{
Node: firstHPA,
Severity: osgraph.WarningSeverity,
RelatedNodes: shortestPath[1:],
Key: HPAOverlappingScaleRefWarning,
Message: fmt.Sprintf("%s and %s overlap because they both attempt to scale %s",
namer.ResourceName(firstHPA), namer.ResourceName(secondHPA), nameList(shortestPath[1:len(shortestPath)-1], namer)),
})
}
}
return markers
}
