Key Metrics

Tetrate Service Bridge collects a large number of metrics. This page is generated from dashboards ran internally at Tetrate and will be updated periodically based on best practices learned from operational experiences in Tetrate and from user deployments. Each heading represents a different dashboard, and each sub-heading is a panel on this dashboard. For this reason, you may see metrics appear multiple times.

The metrics in this document refer to TSB components, so be sure to check the TSB architecture to get a good understanding of each component and its function.

The list of available dashboards can be obtained with the tctl experimental grafana dashboard command. To download a dashboard in JSON format to upload it to Grafana, you can run the command as follows:

tctl experimental grafana dashboard <dashboard file name> -o json

You can also upload de dashboards directly to your Grafana instance using the tctl experimental grafana upload command.

Control Plane Operator metrics

Dashboard to show the status of control plane operator metrics.

Control Plane Mode

This panel represents the control plane mode configured in the control plane cluster.

Possible values are:

• CONTROL(Default)
• OBSERVE

Metric Name	Labels	PromQL Expression
control_plane_mode	N/A	control_plane_mode == 1

Closest Token To Expire

The 10 clusters that have a token that will expire the soonest.
Tokens are rotated halfway through their validity period. By default, tokens are valid for one hour, so they are rotated 30 minutes after creation. If a cluster's time is displayed in red or with a negative value, it indicates that at least one token has expired without being rotated. In such cases, please review the "Token Rotations Execution Failed" chart to check for any failed token rotation attempts.

Metric Name	Labels	PromQL Expression
token_expiration_timestamp	cluster_name	max(max_over_time(token_expiration_timestamp{cluster_name=~"${cluster}"}[1m])) by (cluster_name) - time()

Clusters Not Validating Tokens

Each cluster should validate all its token every 60 seconds. If a cluster shows up it means that the last time it validated the tokens and reported the result is over 120 seconds ago.

This doesn't necessarily mean that any token is invalid or is not being rotated properly. For example, the metrics may not have been reported correctly or in a timely manner, or that there is a problem with the TSB Control Plane operator.

Metric Name	Labels	PromQL Expression
last_tokens_validation_timestamp	cluster_name	max(time() - max_over_time(last_tokens_validation_timestamp{cluster_name=~"${cluster}"}[1m])) by (cluster_name)

Valid Tokens

Number of valid control plane tokens.

Metric Name	Labels	PromQL Expression
valid_tokens	cluster_name	max(max_over_time(valid_tokens{cluster_name=~"${cluster}"}[1m])) by (cluster_name)

Token Rotation Executions

Number of token rotation executions. An execution doesn't necessarily mean than a token has been rotated as they might still be valid.

Metric Name	Labels	PromQL Expression
token_rotation_executions_count_total	cluster_name	sum(increase(token_rotation_executions_count_total{cluster_name=~"${cluster}"}[1m])) by(cluster_name)

Token Rotation Execution Failed

Number of failed token rotation executions. If there are errors in this chart it means that the token could not be rotated. Please check the TSB Control Plane operator logs to find the cause. Use the following command: kubectl logs -n istio-system -l name=tsb-operator --tail=-1 | grep "token rotation failed, retrying in"

Metric Name	Labels	PromQL Expression
token_rotation_executions_count_total	cluster_name status	sum(increase(token_rotation_executions_count_total{cluster_name=~"${cluster}", status="failed"}[5m])) by(cluster_name)

Tokens Exceeded Rotation Time

Number of control plane tokens that exceeded their rotation time.

Metric Name	Labels	PromQL Expression
token_exceeded_rotation_time	cluster_name	max(max_over_time(token_exceeded_rotation_time{cluster_name=~"${cluster}"}[1m])) by (cluster_name)

Tokens Rotated Successfully.

Number of tokens that have been rotated successfully. If the values are 0 it might mean that the token rotation execution is failing or that TSB Control Plane operator is not even running.

Metric Name	Labels	PromQL Expression
token_rotations_count_total	cluster_name status	sum(increase(token_rotations_count_total{cluster_name=~"${cluster}", status="success"}[1m])) by(cluster_name)

Failed Tokens to Rotate.

Number of tokens that have been could not be rotated.

Metric Name	Labels	PromQL Expression
token_rotations_count_total	cluster_name status	sum(increase(token_rotations_count_total{cluster_name=~"${cluster}", status="failed"}[1m])) by(cluster_name)

Tokens Exceeded Rotation Timeline

Number of control plane tokens that exceeded their rotation time.

Metric Name	Labels	PromQL Expression
token_exceeded_rotation_time	cluster_name	max(max_over_time(token_exceeded_rotation_time{cluster_name=~"${cluster}"}[1m])) by (cluster_name)

Valid Tokens Timeline

Number of valid control plane tokens.

Metric Name	Labels	PromQL Expression
valid_tokens	cluster_name	sum(valid_tokens{cluster_name=~"${cluster}"}) by (cluster_name)

Invalid Tokens

Number of invalid control plane tokens grouped by the reason.

Metric Name	Labels	PromQL Expression
invalid_tokens	cluster_name	sum by (reason) (invalid_tokens{cluster_name=~"$cluster"})
token_missing_rotate_at_annotation	cluster_name	sum by (name) (token_missing_rotate_at_annotation{cluster_name=~"$cluster"})

GitOps Operational Status

Operational metrics to indicate Cluster GitOps health

GitOps Status

Shows the status of the GitOps component for each cluster.

Metric Name	Labels	PromQL Expression
gitops_enabled	N/A	gitops_enabled

Accepted Admission Requests

Accepted admission requests for each cluster. This is the rate at which operations are processed by the GitOps relay and sent to TSB.

Metric Name	Labels	PromQL Expression
gitops_admission_count_total	allowed	sum(rate(gitops_admission_count_total{allowed="true"}[1h])) by (cluster_name, component)

Rejected Admission Requests

Rejected admission requests for each cluster. This is the rate at which operations are processed by the GitOps relay and sent to TSB.

A spike in these metrics may indicate an increase in invalid TSB resources being applied to the Kubernetes clusters, or error in the admission webhook processing.

Metric Name	Labels	PromQL Expression
gitops_admission_count_total	allowed	sum(rate(gitops_admission_count_total{allowed="false"}[1h])) by (cluster_name, component)

Admission Review Latency

Admission review latency percentiles grouped by cluster.

The GitOps admission reviews make decisions by forwarding the objects to the Management Plane. This metric helps understand the time it takes to make such decisions.

A spike here may indicate network issues or connectivity issues between the Control Plane and the Management Plane.

Metric Name	Labels	PromQL Expression
gitops_admission_duration_bucket	N/A	histogram_quantile(0.99, sum(rate(gitops_admission_duration_bucket[1h])) by (cluster_name, component, le))
gitops_admission_duration_bucket	N/A	histogram_quantile(0.95, sum(rate(gitops_admission_duration_bucket[1h])) by (cluster_name, component, le))

Resources Pushed to TSB

Number of resources pushed to the Management Plane.

This should be equivalent to the admission requests in most cases, but this will also account for object pushes that are done by the background reconcile processes.

Metric Name	Labels	PromQL Expression
gitops_push_count_total	success	sum(rate(gitops_push_count_total{success="true"}[1h])) by (cluster_name, component)

Failed pushes to TSB

Number of resource pushes to the Management Plane that failed.

This should be equivalent to the failed admission requests in most cases, but this will also account for object pushes that are done by the background reconciliation processes.

Metric Name	Labels	PromQL Expression
gitops_push_count_total	code	sum(rate(gitops_push_count_total{code!="OK"}[1h])) by (cluster_name, component, code)

Resources Conversions

Number of Kubernetes resources that have been read from the cluster and successfully converted into TSB objects to be pushed to the Management plane.

The values for this metric should be the same as the Pushed Objects. If there is a difference between them, it probably means some issue when converting the Kubernetes objects to TSB objects.

Metric Name	Labels	PromQL Expression
gitops_convert_count_total	success	sum(rate(gitops_convert_count_total{success="true"}[1h])) by (cluster_name, component)

Resources conversions errors

Number of Kubernetes resources that have been read from the cluster and failed to be converted into TSB objects.

A spike on this metric indicates that the Kubernetes objects could not be converted to TSB objects and that those resources were not sent to the Management Plane.

Metric Name	Labels	PromQL Expression
gitops_convert_count_total	success	sum(rate(gitops_convert_count_total{success="false"}[1h])) by (cluster_name, component)

Global Configuration Distribution

These metrics indicate the overall health of Tetrate Service Bridge and should be considered the starting point for any investigation into issues with Tetrate Service Bridge.

Connected Clusters

This details all clusters connected to and receiving configuration from the management plane.

If this number drops below 1 or a given cluster does not appear in this table it means that the cluster is disconnected. This may happen for a brief period of time during upgrades/re-deploys.

Metric Name	Labels	PromQL Expression
xcp_central_current_edge_connections	N/A	xcp_central_current_edge_connections

TSB Error Rate (Humans)

Rate of failed requests to the TSB apiserver from the UI and CLI.

Metric Name	Labels	PromQL Expression
grpc_server_handled_total	component grpc_code grpc_method grpc_type	sum(rate(grpc_server_handled_total{component="tsb", grpc_code!="OK", grpc_type="unary", grpc_method!="SendAuditLog"}[1m])) by (grpc_code) OR on() vector(0)

Istio-Envoy Sync Time (99th Percentile)

Once XCP has synced with the management plane it creates resources for Istio to configure Envoy. Istio usually distributes these within a second.

If this number starts to exceed 10 seconds then you may need to scale out istiod. In small clusters, it is possible this number is too small to be handled by the histogram buckets so may be nil.

Metric Name	Labels	PromQL Expression
pilot_proxy_convergence_time_bucket	N/A	histogram_quantile(0.99, sum(rate(pilot_proxy_convergence_time_bucket[1m])) by (le, cluster_name))

XCP central -> edge Sync Time (99th Percentile)

MPC component translates TSB configuration into XCP objects. XCP central then sends these objects to every Edge connected to it.

This is the time taken for XCP central to send the configs to edges in ms.

Metric Name	Labels	PromQL Expression
xcp_central_config_propagation_time_ms_bucket	N/A	histogram_quantile(0.99, sum(rate(xcp_central_config_propagation_time_ms_bucket[1m])) by (le, edge))

Istiod Errors

Rate of istiod errors broken down by cluster. This graph helps identify clusters that may be experiencing problems. Typically, there should be no errors. Any non-transient errors should be investigated.

Sometimes this graph will show "No data" or these metrics won't exist. This is because istiod only emits these metrics if the errors occur.

Metric Name	Labels	PromQL Expression
pilot_total_xds_internal_errors	N/A	sum(rate(pilot_xds_write_timeout[1m])) by (cluster_name) + sum(rate(pilot_total_xds_internal_errors[1m])) by (cluster_name) + sum(rate(pilot_total_xds_rejects[1m])) by (cluster_name) + sum(rate(pilot_xds_expired_nonce[1m])) by (cluster_name) + sum(rate(pilot_xds_push_context_errors[1m])) by (cluster_name) + sum(rate(pilot_xds_pushes{type=~".*_senderr"}[1m])) by (cluster_name) OR on() vector(0)
pilot_total_xds_rejects	N/A	sum(rate(pilot_xds_write_timeout[1m])) by (cluster_name) + sum(rate(pilot_total_xds_internal_errors[1m])) by (cluster_name) + sum(rate(pilot_total_xds_rejects[1m])) by (cluster_name) + sum(rate(pilot_xds_expired_nonce[1m])) by (cluster_name) + sum(rate(pilot_xds_push_context_errors[1m])) by (cluster_name) + sum(rate(pilot_xds_pushes{type=~".*_senderr"}[1m])) by (cluster_name) OR on() vector(0)
pilot_xds_expired_nonce	N/A	sum(rate(pilot_xds_write_timeout[1m])) by (cluster_name) + sum(rate(pilot_total_xds_internal_errors[1m])) by (cluster_name) + sum(rate(pilot_total_xds_rejects[1m])) by (cluster_name) + sum(rate(pilot_xds_expired_nonce[1m])) by (cluster_name) + sum(rate(pilot_xds_push_context_errors[1m])) by (cluster_name) + sum(rate(pilot_xds_pushes{type=~".*_senderr"}[1m])) by (cluster_name) OR on() vector(0)
pilot_xds_push_context_errors	N/A	sum(rate(pilot_xds_write_timeout[1m])) by (cluster_name) + sum(rate(pilot_total_xds_internal_errors[1m])) by (cluster_name) + sum(rate(pilot_total_xds_rejects[1m])) by (cluster_name) + sum(rate(pilot_xds_expired_nonce[1m])) by (cluster_name) + sum(rate(pilot_xds_push_context_errors[1m])) by (cluster_name) + sum(rate(pilot_xds_pushes{type=~".*_senderr"}[1m])) by (cluster_name) OR on() vector(0)
pilot_xds_pushes	type	sum(rate(pilot_xds_write_timeout[1m])) by (cluster_name) + sum(rate(pilot_total_xds_internal_errors[1m])) by (cluster_name) + sum(rate(pilot_total_xds_rejects[1m])) by (cluster_name) + sum(rate(pilot_xds_expired_nonce[1m])) by (cluster_name) + sum(rate(pilot_xds_push_context_errors[1m])) by (cluster_name) + sum(rate(pilot_xds_pushes{type=~".*_senderr"}[1m])) by (cluster_name) OR on() vector(0)
pilot_xds_write_timeout	N/A	sum(rate(pilot_xds_write_timeout[1m])) by (cluster_name) + sum(rate(pilot_total_xds_internal_errors[1m])) by (cluster_name) + sum(rate(pilot_total_xds_rejects[1m])) by (cluster_name) + sum(rate(pilot_xds_expired_nonce[1m])) by (cluster_name) + sum(rate(pilot_xds_push_context_errors[1m])) by (cluster_name) + sum(rate(pilot_xds_pushes{type=~".*_senderr"}[1m])) by (cluster_name) OR on() vector(0)

Istio Operational Status

Operational metrics for istiod health.

Connected Envoys

Count of Envoys connected to istiod. This should represent the total number of endpoints in the selected cluster.

If this number significantly decreases for longer than 5 minutes without an obvious reason (e.g. a scale-down event) then you should investigate. This may indicate that Envoys have been disconnected from istiod and are unable to reconnect.

Metric Name	Labels	PromQL Expression
pilot_xds	cluster_name istio_revision	sum(pilot_xds{cluster_name="$cluster",istio_revision="$istio_revision"})

Total Error Rate

The total error rate for Istio when configuring Envoy, including generation and transport errors.

Any errors (current and historic) should be investigated using the more detailed split below.

Metric Name	Labels	PromQL Expression
pilot_total_xds_internal_errors	cluster_name istio_revision	sum(rate(pilot_xds_write_timeout{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_pushes{cluster_name="$cluster",istio_revision="$istio_revision",type=~".*_senderr"}[1m])) OR on() vector(0)
pilot_total_xds_rejects	cluster_name istio_revision	sum(rate(pilot_xds_write_timeout{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_pushes{cluster_name="$cluster",istio_revision="$istio_revision",type=~".*_senderr"}[1m])) OR on() vector(0)
pilot_xds_expired_nonce	cluster_name istio_revision	sum(rate(pilot_xds_write_timeout{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_pushes{cluster_name="$cluster",istio_revision="$istio_revision",type=~".*_senderr"}[1m])) OR on() vector(0)
pilot_xds_push_context_errors	cluster_name istio_revision	sum(rate(pilot_xds_write_timeout{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_pushes{cluster_name="$cluster",istio_revision="$istio_revision",type=~".*_senderr"}[1m])) OR on() vector(0)
pilot_xds_pushes	cluster_name istio_revision type	sum(rate(pilot_xds_write_timeout{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_pushes{cluster_name="$cluster",istio_revision="$istio_revision",type=~".*_senderr"}[1m])) OR on() vector(0)
pilot_xds_write_timeout	cluster_name istio_revision	sum(rate(pilot_xds_write_timeout{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) + sum(rate(pilot_xds_pushes{cluster_name="$cluster",istio_revision="$istio_revision",type=~".*_senderr"}[1m])) OR on() vector(0)

Median Proxy Convergence Time

The median (50th percentile) delay between istiod receiving configuration changes and the proxy receiving all required configuration in the selected cluster. This number indicates how stale the proxy configuration is. As this number increases, it may start to impact application traffic.

This number is typically in the hundreds of milliseconds. In small clusters, this number may be zero.

If this number creeps up to 30s for an extended period, istiod likely needs to be scaled out (or up).

Metric Name	Labels	PromQL Expression
pilot_proxy_convergence_time_bucket	cluster_name istio_revision	histogram_quantile(0.5, sum(rate(pilot_proxy_convergence_time_bucket{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) by (le))

Time to Root CA expiration

Time remaining until expiration of the Istiod root CA (displayed as 'days hours:minutes:seconds'). The value will turn yellow (warning) when the time to expiration is less than 6 month, and will turn red (critical) when it expires in less than a month.

Metric Name	Labels	PromQL Expression
citadel_server_root_cert_expiry_timestamp	cluster_name istio_revision	citadel_server_root_cert_expiry_timestamp{cluster_name="$cluster",istio_revision="$istio_revision"} - time()

Time to cert chain expiration

Time remaining until expiration of the Istiod certificate chain (the certificates istiod uses to sign workload certificates). The value will turn yellow (warning) when the time to expiration is less than 6 month, and will turn red (critical) when it expires in less than a month.

If this shows no value, it means your istiod instances are using a self-signed certificate or a root CA certificate to issue workload certificates, instead of using an intermediate CA. If that is the case, this panel is meaningless to you, but you should consider using an intermediate CA for your istio control planes.

Metric Name	Labels	PromQL Expression
citadel_server_cert_chain_expiry_timestamp	cluster_name istio_revision	citadel_server_cert_chain_expiry_timestamp{cluster_name="$cluster",istio_revision="$istio_revision"} - time()

Applied Envoy Filters

Number of successfully applied Envoy Filters in the cluster.

Metric Name	Labels	PromQL Expression
pilot_envoy_filter_status	cluster_name istio_revision result	sum(pilot_envoy_filter_status{cluster_name="$cluster",istio_revision="$istio_revision",result="applied"})

Errored Envoy Filters

Number of Envoy Filters in the cluster that failed to apply.

Metric Name	Labels	PromQL Expression
pilot_envoy_filter_status	cluster_name istio_revision result	sum(pilot_envoy_filter_status{cluster_name="$cluster",istio_revision="$istio_revision",result="error"})

Envoy Filter status

Status of each Envoy Filter in the cluster (applied/error).

Metric Name	Labels	PromQL Expression
pilot_envoy_filter_status	cluster_name istio_revision	pilot_envoy_filter_status{cluster_name="$cluster",istio_revision="$istio_revision"}

Istiod Push Rate

The rate of istiod pushes to Envoy grouped by discovery service. Istiod pushes clusters (CDS), endpoints (EDS), listeners (LDS) or routes (RDS) any time it receives a configuration change.

Changes are triggered by a user interacting with TSB or a change in infrastructure such as a new endpoint (service instance/pod) creation.

In small relatively static clusters these values can be zero most of the time.

Metric Name	Labels	PromQL Expression
pilot_xds_pushes	cluster_name istio_revision type	sum(irate(pilot_xds_pushes{cluster_name="$cluster",istio_revision="$istio_revision",type=~"cds|eds|rds|lds"}[1m])) by (type)

Istiod Error Rate

The different error rates for Istio during general operations. Including the generation and distribution of Envoy configuration.

pilot_xds_write_timeout Rate of connection timeouts between Envoy and istiod. This number indicates that an Envoy has taken too long to acknowledge a configuration change from Istio. An increase in these errors typically indicates network issues, envoy resource limits or istiod resource limits (usually cpu)

pilot_total_xds_internal_errors Rate of errors thrown inside istiod whilst generating Envoy configuration. Check the istiod logs for more details if you see internal errors.

pilot_total_xds_rejects Rate of rejected configuration from Envoy. Istio should never produce any invalid Envoy configuration so any errors here warrants investigation, starting with the istiod logs.

pilot_xds_expired_nonce Rate of expired nonces from Envoys. This number indicates that an Envoy has responded to the wrong request sent from Istio. An increase in these errors typically indicates network issues (saturation or partition), Envoy resource limits or istiod resource limits (usually cpu).

pilot_xds_push_context_errors Rate of errors setting a connection with an Envoy instance. An increase in these errors typically indicates network issues (saturation or partition), Envoy resource limits or istiod resource limits (usually cpu). Check istiod logs for further details.

pilot_xds_pushes Rate of transport errors sending configuration to Envoy. An increase in these errors typically indicates network issues (saturation or partition), Envoy resource limits or istiod resource limits (usually cpu).

Metric Name	Labels	PromQL Expression
pilot_total_xds_internal_errors	cluster_name istio_revision	sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m]))
pilot_total_xds_rejects	cluster_name istio_revision	sum(rate(pilot_total_xds_rejects{cluster_name="$cluster",istio_revision="$istio_revision"}[1m]))
pilot_xds_expired_nonce	cluster_name istio_revision	sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster",istio_revision="$istio_revision"}[1m]))
pilot_xds_push_context_errors	cluster_name istio_revision	sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster",istio_revision="$istio_revision"}[1m]))
pilot_xds_pushes	cluster_name istio_revision type	sum(rate(pilot_xds_pushes{cluster_name="$cluster",istio_revision="$istio_revision",type=~".*_senderr"}[1m])) by (type)
pilot_xds_write_timeout	cluster_name istio_revision	sum(rate(pilot_xds_write_timeout{cluster_name="$cluster",istio_revision="$istio_revision"}[1m]))

Proxy Convergence Time

The delay between an istiod receiving configuration changes and a proxy receiving all required configuration in the cluster. Broken down by percentiles.

This number indicates how stale the proxy configuration is. As this number increases it may start to affect application traffic.

This number is typically in the hundreds of milliseconds. If this number creeps up to 30s for an extended period of time, it is likely that istiod needs to be scaled out (or up) as it is likely pinned up against its CPU limits.

Metric Name	Labels	PromQL Expression
pilot_proxy_convergence_time_bucket	cluster_name istio_revision	histogram_quantile(0.5, sum(rate(pilot_proxy_convergence_time_bucket{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) by (le))
pilot_proxy_convergence_time_bucket	cluster_name istio_revision	histogram_quantile(0.90, sum(rate(pilot_proxy_convergence_time_bucket{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) by (le))
pilot_proxy_convergence_time_bucket	cluster_name istio_revision	histogram_quantile(0.99, sum(rate(pilot_proxy_convergence_time_bucket{cluster_name="$cluster",istio_revision="$istio_revision"}[1m])) by (le))
pilot_proxy_convergence_time_bucket	cluster_name	histogram_quantile(0.999, sum(rate(pilot_proxy_convergence_time_bucket{cluster_name="$cluster"}[1m])) by (le))

Configuration Validation

Success and failure rate of istio configuration validation requests. This is triggered when TSB configuration is created or updated.

Any failures here should be investigated in the istiod and edge logs.

If there are TSB configuration changes being made that affect the selected cluster and the success number is zero then there is an issue with configuration propagation. Check the XCP edge logs to debug further.

Metric Name	Labels	PromQL Expression
galley_validation_failed	cluster_name	sum(rate(galley_validation_failed{cluster_name="$cluster"}[1m]))
galley_validation_passed	cluster_name istio_revision	sum(rate(galley_validation_passed{cluster_name="$cluster",istio_revision="$istio_revision"}[1m]))

Sidecar Injection

Rate of sidecar injection requests. Sidecar injection is triggered whenever a new instance/pod is created.

Any errors displayed here should be investigated further by checking the istiod logs.

Metric Name	Labels	PromQL Expression
sidecar_injection_failure_total	cluster_name	sum(rate(sidecar_injection_failure_total{cluster_name="$cluster"}[1m]))
sidecar_injection_success_total	cluster_name istio_revision	sum(rate(sidecar_injection_success_total{cluster_name="$cluster",istio_revision="$istio_revision"}[1m]))

MPC Operational Status

Operational metrics to indicate Management Plane Controller (MPC) health.

Get All Config Objects duration

How long does it take to execute the GetAllConfigObjects grpc method to retrieve all the configuration objects. If you want to know the error cause take a look at the grpc charts that contain the status response codes.

Metric Name	Labels	PromQL Expression
mpc_get_all_config_objects_time	component error	sum(max_over_time(mpc_get_all_config_objects_time{error="", component="mpc"}[5m])) by (component)
mpc_get_all_config_objects_time	component error	sum(max_over_time(mpc_get_all_config_objects_time{error="true", component="mpc"}[5m])) by (component)

Received configs

The number of resources that sent from TSB to MPC.

This metric shows the number of objects that are created, updated, and deleted as part of a configuration push from MPC to XCP.

This metric can be used together with the XCP push operations and push duration to get an understanding of how the amount of resources being pushed to XCP affects the time it takes for the entire configuration push operation to complete.

Metric Name	Labels	PromQL Expression
mpc_tsb_config_received_count	component resource	sum(max_over_time(mpc_tsb_config_received_count{resource="", component="mpc"}[5m])) by(component)

Config Processing duration

Time it takes to process an entire config set. It shows the details about the amount of time spent pre-processing the configurations, converting them to XCP, and pushing them to the k8s cluster

Metric Name	Labels	PromQL Expression
mpc_config_conversion_time	component error	sum(max_over_time(mpc_config_conversion_time{error="", component="mpc"}[5m])) by (component)
mpc_config_pre_process_time	component error	sum(max_over_time(mpc_config_pre_process_time{error="", component="mpc"}[5m])) by (component)
mpc_config_total_process_time	component error	sum(max_over_time(mpc_config_total_process_time{error="", component="mpc"}[5m])) by (component)
mpc_xcp_config_push_time	component error	sum(max_over_time(mpc_xcp_config_push_time{error="", component="mpc"}[5m])) by (component)

Received configs by type

Configuration updates received from TSB are processed by MPC and translated into XCP resources. This metric shows the number of objects of each type MPC will convert.

Metric Name	Labels	PromQL Expression
mpc_tsb_config_received_count	component resource	sum(max_over_time(mpc_tsb_config_received_count{resource!="", component="mpc"}[5m])) by(component, resource)

Total Conversion Time by Type every 5m

Time it takes to convert TSB resources to the XCP APIs.

Metric Name	Labels	PromQL Expression
mpc_xcp_conversion_duration_sum	component	sum(rate(mpc_xcp_conversion_duration_sum{component="mpc"}[5m])) by (resource)

Conversion Time by Type every 5m

Time it takes to convert TSB resources to the XCP APIs.

Metric Name	Labels	PromQL Expression
mpc_xcp_conversion_duration_bucket	N/A	histogram_quantile(0.99, sum(rate(mpc_xcp_conversion_duration_bucket[5m])) by (le, resource))

Conversions by Resource every 5m

Conversions by resource executed in a time period. This can be used to understand the throughput of the MPC conversions.

Metric Name	Labels	PromQL Expression
mpc_xcp_conversion_duration_count	component resource	sum(increase(mpc_xcp_conversion_duration_count{resource!="", component="mpc"}[5m])) by (resource)

Conversions Invalidations by Resource every 5m

Conversions Invalidations by resource executed in a time period. This can be used to understand the throughput of the MPC conversions.

Metric Name	Labels	PromQL Expression
mpc_xcp_conversion_invalidation_duration_count	component resource	sum(increase(mpc_xcp_conversion_invalidation_duration_count{component="mpc", resource!=""}[5m])) by (resource)

Conversion Invalidation Time every 5m

Time it takes to invalidate TSB resources for a given conversion to the XCP APIs.

Metric Name	Labels	PromQL Expression
mpc_xcp_conversion_invalidation_duration_bucket	component resource	histogram_quantile(0.99, sum(rate(mpc_xcp_conversion_invalidation_duration_bucket{component="mpc", resource!=""}[5m])) by (le, resource))

Updates from TSB every 5m

Configuration and onboarded cluster messages received from TSB.

The number of update messages may increase or decrease based on the time it takes for MPC to fully process the messages. The more time it takes to process, the less frequent config updates will be retrieved.

Metric Name	Labels	PromQL Expression
grpc_client_handled_total	component grpc_code grpc_method	sum(increase(grpc_client_handled_total{component="mpc", grpc_method="GetAllConfigObjects", grpc_code="OK"}[5m])) or on() vector(0)
grpc_client_handled_total	component grpc_code grpc_method	sum(increase(grpc_client_handled_total{component="mpc", grpc_method="GetAllClusters", grpc_code="OK"}[5m])) or on() vector(0)
grpc_client_handled_total	component grpc_code grpc_method	sum(increase(grpc_client_handled_total{component="mpc", grpc_method="GetAllConfigObjects", grpc_code!="OK"}[5m])) or on() vector(0)
grpc_client_handled_total	component grpc_code grpc_method	sum(increase(grpc_client_handled_total{component="mpc", grpc_method="GetAllClusters", grpc_code!="OK"}[5m])) or on() vector(0)

MPC to XCP pushed configs

The number of resources that are pushed to XCP.

This metric shows the number of objects that are created, updated, and deleted as part of a configuration push from MPC to XCP. It also shows how many fetch calls to the k8s api server are done.

This metric can be used together with the TSB tp MPC sent configs and XCP push operations and push duration to get an understanding of how the amount of resources being pushed to XCP affects the time it takes for the entire configuration push operation to complete.

Metric Name	Labels	PromQL Expression
mpc_xcp_config_create_ops	component	sum(mpc_xcp_config_create_ops{component="mpc"})
mpc_xcp_config_delete_ops	component	sum(mpc_xcp_config_delete_ops{component="mpc"})
mpc_xcp_config_fetch_ops	component	sum(mpc_xcp_config_fetch_ops{component="mpc"})
mpc_xcp_config_update_ops	component	sum(mpc_xcp_config_update_ops{component="mpc"})

MCP to XCP pushed configs error

The number of resources that failed while pushing to XCP.

This metric shows the number of objects that fail when they are tried to be created, updated, and deleted as part of a configuration push from MPC to XCP. It also shows the number of failed fetch calls to the k8s api server.

This metric can be used together with the MPC to TSB push configs and the XCP push operations and push duration to get an understanding of how the amount of resources being pushed to XCP affects the time it takes for the entire configuration push operation to complete.

Metric Name	Labels	PromQL Expression
mpc_xcp_config_create_ops_err	component	sum(mpc_xcp_config_create_ops_err{component="mpc"})
mpc_xcp_config_delete_ops_err	component	sum(mpc_xcp_config_delete_ops_err{component="mpc"})
mpc_xcp_config_fetch_ops_err	component	sum(mpc_xcp_config_fetch_ops_err{component="mpc"})
mpc_xcp_config_update_ops_err	component	sum(mpc_xcp_config_update_ops_err{component="mpc"})

Config Status updates every 5m

Config Status update messages sent over the gRPC streams, from XCP to MPC to XCP.

This metric can help understand how messages are queued in TSB when it is under load. The value for both metrics should always be the same. If the Received by TSB metric has a value lower than the MPC one, it means TSB is under load and cannot process all messages sent by MPC as fast as MPC is sending them.

Metric Name	Labels	PromQL Expression
grpc_client_msg_received_total	component grpc_method	sum(increase(grpc_client_msg_received_total{grpc_method="Report",component="mpc"}[5m])) or on() vector(0)
grpc_client_msg_sent_total	component grpc_method	sum(increase(grpc_client_msg_sent_total{grpc_method="PushStatus",component="mpc"}[5m])) or on() vector(0)
grpc_server_msg_received_total	component grpc_method	sum(increase(grpc_server_msg_received_total{grpc_method="PushStatus", component="tsb"}[5m])) or on() vector(0)

Config Status updates processed every 5m

This is the number of config status updates that are processed by the Management Plane Controller (MPC), that are received from XCP and to be sent to TSB.

There are two gRPC streams, one that connects XCP to MPC and another one that connects MPC to TSB.

Metric Name	Labels	PromQL Expression
permanent_stream_operation_total	component error name	sum(increase(permanent_stream_operation_total{name="StatusPush", error="", component="mpc"}[5m])) or on() vector(0)
permanent_stream_operation_total	component error name	sum(increase(permanent_stream_operation_total{name="StatusPull", error="", component="mpc"}[5m])) or on() vector(0)
permanent_stream_operation_total	component error name	sum(increase(permanent_stream_operation_total{name="StatusPush", error!="", component="mpc"}[5m])) or on() vector(0)
permanent_stream_operation_total	component error name	sum(increase(permanent_stream_operation_total{name="StatusPull", error!="", component="mpc"}[5m])) or on() vector(0)

Config Status stream connection attempts every 5m

The number of connection (and reconnection) attempts on the config status updates streams. MPC sends the config status updates over a permanently connected gRPC stream to TSB. At the same time, XCP sends them to MPC. This metric shows the number of connections and reconnections that happened on each stream.

Metric Name	Labels	PromQL Expression
permanent_stream_connection_attempts_total	error name	sum(increase(permanent_stream_connection_attempts_total{name="StatusPull", error="" }[5m])) or on() vector(0)
permanent_stream_connection_attempts_total	error name	sum(increase(permanent_stream_connection_attempts_total{name="StatusPull", error!="" }[5m])) or on() vector(0)
permanent_stream_connection_attempts_total	error name	sum(increase(permanent_stream_connection_attempts_total{name="StatusPush", error="" }[5m])) or on() vector(0)
permanent_stream_connection_attempts_total	error name	sum(increase(permanent_stream_connection_attempts_total{name="StatusPush", error!="" }[5m])) or on() vector(0)

TSB Handled Status Reports 5m

Number of config status reports handled by TSB. Each received config status report is either handled or skipped.

• Handled: Process and store the status report directly, or if --max-status-report-workers is > 1, enqueued for async processing.
• Skipped: For duplicated status reports.

Metric Name	Labels	PromQL Expression
config_handling_duration_count	component	sum(increase(config_handling_duration_count{component="tsb"}[5m])) by(skip)

Config status cache operations every 5m

Number of operations done in the config status cache when receiving new config statuses.

Metric Name	Labels	PromQL Expression
config_status_cache_add_total	error	sum(increase(config_status_cache_add_total{error="false"}[5m]))
config_status_cache_add_total	error	sum(increase(config_status_cache_add_total{error="true"}[5m])) or on() vector(0)
config_status_cache_check_total	N/A	sum(increase(config_status_cache_check_total[5m]))
config_status_cache_check_total	error	sum(increase(config_status_cache_check_total{error="true"}[5m])) or on() vector(0)
config_status_cache_invalidate_total	N/A	sum(increase(config_status_cache_invalidate_total[5m]))
grpc_server_msg_received_total	component grpc_method	sum(increase(grpc_server_msg_received_total{grpc_method="PushStatus", component="tsb"}[5m]))

TSB Processed Status Reports in 5m

The number of config status reports processed by TSB. This number must be the same as the number of handled ones without the skipped ones.

Metric Name	Labels	PromQL Expression
config_status_report_work_duration_bucket	component	histogram_quantile(0.99, sum(rate(config_status_report_work_duration_bucket{component="tsb"}[5m])) by (le, skip))

TSB Handling Status Reports Duration 5m

The P99 duration in milliseconds of handling a received status reports handled by TSB. Each received config status report is either handled or skipped.

• Handled: Process and store the status report directly, or if --max-status-report-workers is > 1, enqueued for async processing.
• Skipped: For duplicated status reports.

Metric Name	Labels	PromQL Expression
config_handling_duration_bucket	component	histogram_quantile(0.99, sum(rate(config_handling_duration_bucket{component="tsb"}[5m])) by (le, skip))

Config status cache operations by event type every 5m

Number of operations done in the cache by event type.

This metric helps understand the amount of event processing that can be skipped on the TSB side when receiving events because TSB already knows about them, and help understand how status event reporting relates to load on the TSB side.

Metric Name	Labels	PromQL Expression
config_status_cache_add_total	component error	sum(increase(config_status_cache_add_total{error="false", component="tsb"}[5m])) by (type)
config_status_cache_check_total	component error	sum(increase(config_status_cache_check_total{error="false", component="tsb"}[5m])) by (type)

TSB Processing Status Reports Duration 5m

The P99 duration in milliseconds of processing config status reports handled by TSB. Processing of a config status report involves analyse, apply/storing the result, propagate to parents and dependants.

Metric Name	Labels	PromQL Expression
config_status_report_work_duration_bucket	component	histogram_quantile(0.99, sum(rate(config_status_report_work_duration_bucket{component="tsb"}[5m])) by (le, skip))

Status Reports Work per Shard Distribution

Only applies when tsb --max-status-report-workers is > 1. Distribution of the status report work across the different shards.

Metric Name	Labels	PromQL Expression
sharded_queue_work_duration_count	component name	sum(increase(sharded_queue_work_duration_count{name="status-reports", component="tsb"}[5m])) by (component, shard)

Work executions every 5m

Only applies when tsb --max-status-report-workers is > 1. Amount of status processing jobs processed

Metric Name	Labels	PromQL Expression
sharded_queue_work_duration_count	component	sum(increase(sharded_queue_work_duration_count{component="tsb"}[5m])) by (name)

Status updates worker time every 5m

Only applies when tsb --max-status-report-workers is > 1. Time it takes for workers to process a single status update event.

Metric Name	Labels	PromQL Expression
sharded_queue_work_duration_bucket	N/A	histogram_quantile(0.99, sum(rate(sharded_queue_work_duration_bucket[5m])) by (le, name))

TSB Status Updates Enqueue Delay 5m

Only applies when tsb --max-status-report-workers is > 1. The P99 delay is milliseconds since a config status report started the enqueuing until the queue accepted(enqueued) the config status report.

If the P99 delay goes high it means that that a shard of the queue is filled up reaching its max capacity. If it goes over minutes, it means that there is deadlock probably.

Metric Name	Labels	PromQL Expression
sharded_queue_enqueue_delay_bucket	name	histogram_quantile(0.99, sum(rate(sharded_queue_enqueue_delay_bucket{name="status-reports"}[5m])) by (le, name))

TSB Status Updates Enqueue Delay 5m

Only applies when tsb --max-status-report-workers is > 1. The delay is milliseconds since a config status report started the enqueuing until the queue accepted(enqueued) the config status report.

The config status report queue has a fixed size of element in can't hold per bucket. If the enqueuing latency goes up, it means that there is contention in the queue and more elements cannot be placed until the queue releases spaces by consuming its enqueued elements.

Metric Name	Labels	PromQL Expression
sharded_queue_enqueue_delay_bucket	name	sum(rate(sharded_queue_enqueue_delay_bucket{name="status-reports"}[5m])) by (le)

TSB Status Updates Worker Delay 5m

Only applies when tsb --max-status-report-workers is > 1. The delay is milliseconds since a config status report is received and processed by the work queue.

Metric Name	Labels	PromQL Expression
sharded_queue_work_delay_bucket	name	histogram_quantile(0.99, sum(rate(sharded_queue_work_delay_bucket{name="status-reports"}[5m])) by (le, name))

TSB Status Updates Worker Delay 5m

Only applies when tsb --max-status-report-workers is > 1. The delay is milliseconds since a config status report is received and processed by the work queue.

Metric Name	Labels	PromQL Expression
sharded_queue_work_delay_bucket	name	sum(rate(sharded_queue_work_delay_bucket{name="status-reports"}[5m])) by (le)

Cluster Status Update from XCP every 5m

Cluster status update messages received from XCP over a gRPC stream.

Metric Name	Labels	PromQL Expression
grpc_client_msg_received_total	component grpc_method	sum(increase(grpc_client_msg_received_total{component="mpc", grpc_method="GetClusterState" }[5m])) or on() vector(0)

Cluster updates from XCP processed every 5m

The number of cluster status updates received by the Management Plane Controller (MPC) from XCP that must be processed and sent to TSB.

XCP sends the cluster status updates (e.g. services deployed in the cluster) over a permanently connected gRPC stream to MPC. This metric shows the number of messages received and processed by MPC on that stream.

Metric Name	Labels	PromQL Expression
permanent_stream_operation_total	error name	sum(increase(permanent_stream_operation_total{name="ClusterStateFromXCP", error="" }[5m])) or on() vector(0)
permanent_stream_operation_total	error name	sum(increase(permanent_stream_operation_total{name="ClusterStateFromXCP", error!="" }[5m])) or on() vector(0)

XCP cluster status updates Sent to TSB every 5m

This is the number of cluster status updates that are processed by the Management Plane Controller (MPC) to be sent to TSB.

MPC sends the cluster status updates over a gRPC stream that is permanently connected to TSB, and this metric shows the number of cluster updates that are processed by MPC and sent to TSB on that stream.

Metric Name	Labels	PromQL Expression
permanent_stream_operation_total	error name	sum(increase(permanent_stream_operation_total{name="ClusterUpdates", error=""}[5m])) or on() vector(0)
permanent_stream_operation_total	error name	sum(increase(permanent_stream_operation_total{name="ClusterUpdates", error!=""}[5m])) or on() vector(0)

Cluster status updates to TSB stream connection attempts every 5m

The number of connection (and reconnection) attempts on the cluster status updates stream. MPC sends the cluster status updates over a permanently connected gRPC stream to TSB. This metric shows the number of connections and reconnections that happened on that stream.

Metric Name	Labels	PromQL Expression
permanent_stream_connection_attempts_total	error name	sum(increase(permanent_stream_connection_attempts_total{name="ClusterUpdates", error=""}[5m])) or on() vector(0)
permanent_stream_connection_attempts_total	error name	sum(increase(permanent_stream_connection_attempts_total{name="ClusterUpdates", error!=""}[5m])) or on() vector(0)

Cluster updates from XCP stream connection attempts every 5m

The number of connection (and reconnection) attempts on the cluster status updates from XCP stream. XCP sends the cluster status updates over a permanently connected gRPC stream to MPC. This metric shows the number of connections and reconnections that happened on that stream.

Metric Name	Labels	PromQL Expression
permanent_stream_connection_attempts_total	error name	sum(increase(permanent_stream_connection_attempts_total{name="ClusterStateFromXCP", error="" }[5m])) or on() vector(0)
permanent_stream_connection_attempts_total	error name	sum(increase(permanent_stream_connection_attempts_total{name="ClusterStateFromXCP", error!="" }[5m])) or on() vector(0)

GC Count by Component in Management Plane

Metric Name	Labels	PromQL Expression
go_gc_duration_seconds_count	component plane	sum(rate(go_gc_duration_seconds_count{component=~"tsb|mpc|xcp", plane="management"}[5m])) by (component)

GC Duration by Component in Management Plane

Metric Name	Labels	PromQL Expression
go_gc_duration_seconds_count	component plane	sum(rate(go_gc_duration_seconds_sum{component="tsb|mpc|xcp", plane="management"}[5m]) / rate(go_gc_duration_seconds_count{component="tsb|mpc|xcp", plane="management"}[5m])) by (component)
go_gc_duration_seconds_sum	component plane	sum(rate(go_gc_duration_seconds_sum{component="tsb|mpc|xcp", plane="management"}[5m]) / rate(go_gc_duration_seconds_count{component="tsb|mpc|xcp", plane="management"}[5m])) by (component)

Heap Allocations by Component in Management Plane

Metric Name	Labels	PromQL Expression
go_memstats_heap_alloc_bytes	component plane	sum(max_over_time(go_memstats_heap_alloc_bytes{component=~"tsb|mpc|xcp", plane="management"}[5m])) by (component)

Heap Objects by Component in Management Plane

Metric Name	Labels	PromQL Expression
go_memstats_heap_objects	component plane	sum(max_over_time(go_memstats_heap_objects{component=~"tsb|mpc|xcp", plane="management"}[5m])) by (component)

Next GC Target by Component in Management Plane

The heap memory size during the next GC cycle. GC is used to guarantee that the value is no less than the value of

Metric Name	Labels	PromQL Expression
go_memstats_next_gc_bytes	component plane	sum(max_over_time(go_memstats_next_gc_bytes{component=~"tsb|mpc|xcp", plane="management"}[5m])) by (component)

Heap Utilization Percentage by Component in Management Plane

Metric Name	Labels	PromQL Expression
go_memstats_heap_idle_bytes	component	( sum(go_memstats_heap_inuse_bytes{component="xcp"}) by (component) / sum(go_memstats_heap_idle_bytes{component="xcp"} + go_memstats_heap_inuse_bytes{component="xcp"}) by (component) ) * 100
go_memstats_heap_idle_bytes	component	( sum(go_memstats_heap_inuse_bytes{component="tsb"}) by (component) / sum(go_memstats_heap_idle_bytes{component="tsb"} + go_memstats_heap_inuse_bytes{component="tsb"}) by (component) ) * 100
go_memstats_heap_idle_bytes	component	( sum(go_memstats_heap_inuse_bytes{component="mpc"}) by (component) / sum(go_memstats_heap_idle_bytes{component="mpc"} + go_memstats_heap_inuse_bytes{component="mpc"}) by (component) ) * 100
go_memstats_heap_inuse_bytes	component	( sum(go_memstats_heap_inuse_bytes{component="xcp"}) by (component) / sum(go_memstats_heap_idle_bytes{component="xcp"} + go_memstats_heap_inuse_bytes{component="xcp"}) by (component) ) * 100
go_memstats_heap_inuse_bytes	component	( sum(go_memstats_heap_inuse_bytes{component="tsb"}) by (component) / sum(go_memstats_heap_idle_bytes{component="tsb"} + go_memstats_heap_inuse_bytes{component="tsb"}) by (component) ) * 100
go_memstats_heap_inuse_bytes	component	( sum(go_memstats_heap_inuse_bytes{component="mpc"}) by (component) / sum(go_memstats_heap_idle_bytes{component="mpc"} + go_memstats_heap_inuse_bytes{component="mpc"}) by (component) ) * 100

GC CPU Fraction by Component in Management Plane

Metric Name	Labels	PromQL Expression
go_gc_duration_seconds_sum	component plane	sum( rate(go_gc_duration_seconds_sum{component="tsb|mpc|xcp", plane="management"}[5m]) / rate(process_cpu_seconds_total{component="tsb|mpc|xcp", plane="management"}[5m]) ) by (component)* 100
process_cpu_seconds_total	component plane	sum( rate(go_gc_duration_seconds_sum{component="tsb|mpc|xcp", plane="management"}[5m]) / rate(process_cpu_seconds_total{component="tsb|mpc|xcp", plane="management"}[5m]) ) by (component)* 100

Goroutines by Component in Management Plane

Metric Name	Labels	PromQL Expression
go_goroutines	component plane	sum(max_over_time(go_goroutines{component=~"tsb|mpc|xcp", plane="management"}[5m])) by (component)

Heap Sys by Component in Management Plane

Metric Name	Labels	PromQL Expression
go_memstats_heap_sys_bytes	component plane	sum(max_over_time(go_memstats_heap_sys_bytes{component=~"tsb|mpc|xcp", plane="management"}[5m])) by (component)

gRPC Server Calls Started Rate

The rate of RPCs started on the server.

Metric Name	Labels	PromQL Expression
grpc_server_started_total	component grpc_method	sum(rate(grpc_server_started_total{component="tsb", grpc_method=~"GetAllClusters|UpdateClusterState|GetAllConfigObjects"}[5m])) by (grpc_method, component)

gRPC Server Handled Rate

The rate of RPCs completed on the server.

Metric Name	Labels	PromQL Expression
grpc_server_handled_total	component grpc_method	sum(rate(grpc_server_handled_total{component="tsb", grpc_method=~"GetAllClusters|UpdateClusterState|GetAllConfigObjects"}[5m])) by (grpc_method, component)

gRPC Client Calls Started Rate

The rate of the RPCs started on the client.

Metric Name	Labels	PromQL Expression
grpc_client_started_total	component	sum(rate(grpc_client_started_total{component="mpc"}[5m])) by (grpc_method, component)

gRPC Client Handled Rate

The rate of RPCs completed on the client.

Metric Name	Labels	PromQL Expression
grpc_client_handled_total	component	sum(rate(grpc_client_handled_total{component="mpc"}[5m])) by (grpc_method, component)

gRPC Server Handled Status Rate

Metric Name	Labels	PromQL Expression
grpc_server_handled_total	component grpc_method	sum(rate(grpc_server_handled_total{component="tsb", grpc_method=~"GetAllClusters|UpdateClusterState|GetAllConfigObjects"}[5m])) by (component, grpc_code)

gRPC Client Handled Status Rate

Metric Name	Labels	PromQL Expression
grpc_client_handled_total	component	max(rate(grpc_client_handled_total{component="mpc"}[5m])) by (grpc_code, component)

gRPC Server Msg Sent Rate

Metric Name	Labels	PromQL Expression
grpc_server_msg_sent_total	component grpc_method	sum(rate(grpc_server_msg_sent_total{component="tsb", grpc_method=~"GetAllClusters|UpdateClusterState|GetAllConfigObjects"}[5m])) by (grpc_method, component)

gRPC Client Msg Received Rate

Metric Name	Labels	PromQL Expression
grpc_client_msg_received_total	component	sum(rate(grpc_client_msg_received_total{component="mpc"}[5m])) by (grpc_method, component)

gRPC Client Msg Sent Rate

Metric Name	Labels	PromQL Expression
grpc_server_msg_sent_total	component grpc_method	sum(rate(grpc_server_msg_sent_total{component="tsb", grpc_method=~"GetAllClusters|UpdateClusterState|GetAllConfigObjects"}[5m])) by (grpc_method, component)

gRPC Server Msg Received Rate

Metric Name	Labels	PromQL Expression
grpc_server_msg_received_total	component grpc_method	max(rate(grpc_server_msg_received_total{component="tsb", grpc_method=~"GetAllClusters|UpdateClusterState|GetAllConfigObjects|PushStatus"}[5m])) by (grpc_method, component)

OAP Operational Status

Operational metrics to indicate Tetrate Service Bridge OAP stack health.

OAP Request Rate

The request rate to OAP, by status.

Metric Name	Labels	PromQL Expression
envoy_cluster_upstream_rq_xx_total	envoy_cluster_name plane	sum by (envoy_response_code_class) (rate(envoy_cluster_upstream_rq_xx_total{envoy_cluster_name="oap-grpc", plane="management"}[1m]))

OAP Request Latency

The OAP, request latency.

Metric Name	Labels	PromQL Expression
envoy_cluster_upstream_rq_time_bucket	envoy_cluster_name plane	histogram_quantile(0.99, sum(rate(envoy_cluster_upstream_rq_time_bucket{envoy_cluster_name="oap-grpc", plane="management"}[1m])) by (le))
envoy_cluster_upstream_rq_time_bucket	envoy_cluster_name plane	histogram_quantile(0.95, sum(rate(envoy_cluster_upstream_rq_time_bucket{envoy_cluster_name="oap-grpc", plane="management"}[1m])) by (le))
envoy_cluster_upstream_rq_time_bucket	envoy_cluster_name plane	histogram_quantile(0.90, sum(rate(envoy_cluster_upstream_rq_time_bucket{envoy_cluster_name="oap-grpc", plane="management"}[1m])) by (le))
envoy_cluster_upstream_rq_time_bucket	envoy_cluster_name plane	histogram_quantile(0.75, sum(rate(envoy_cluster_upstream_rq_time_bucket{envoy_cluster_name="oap-grpc", plane="management"}[1m])) by (le))
envoy_cluster_upstream_rq_time_bucket	envoy_cluster_name plane	histogram_quantile(0.50, sum(rate(envoy_cluster_upstream_rq_time_bucket{envoy_cluster_name="oap-grpc", plane="management"}[1m])) by (le))

OAP Aggregation Request Rate

OAP Aggregation Request Rate, by type:

• central aggregation service handler received
• central application aggregation received
• central service aggregation received

Metric Name	Labels	PromQL Expression
central_aggregation_handler_total	N/A	sum(rate(central_aggregation_handler_total[1m]))
central_app_aggregation_total	N/A	sum(rate(central_app_aggregation_total[1m]))
central_service_aggregation_total	N/A	sum(rate(central_service_aggregation_total[1m]))

OAP Aggregation Rows

Cumulative rate of rows in OAP aggreagation.

Metric Name	Labels	PromQL Expression
metrics_aggregation_total	plane	sum(rate(metrics_aggregation_total{plane="management"}[1m]))

OAP Mesh Analysis Latency

The process latency of OAP service mesh telemetry streaming process.

Metric Name	Labels	PromQL Expression
mesh_analysis_latency_bucket	component plane	histogram_quantile(0.99, sum(rate(mesh_analysis_latency_bucket{plane="control", component="oap"}[1m])) by (le))
mesh_analysis_latency_bucket	component plane	histogram_quantile(0.95, sum(rate(mesh_analysis_latency_bucket{plane="control", component="oap"}[1m])) by (le))
mesh_analysis_latency_bucket	component plane	histogram_quantile(0.90, sum(rate(mesh_analysis_latency_bucket{plane="control", component="oap"}[1m])) by (le))
mesh_analysis_latency_bucket	component plane	histogram_quantile(0.75, sum(rate(mesh_analysis_latency_bucket{plane="control", component="oap"}[1m])) by (le))

OAP Zipkin Trace Rate

The OAP Zipkin processing trace rate

Metric Name	Labels	PromQL Expression
trace_in_latency_count	plane protocol	sum(rate(trace_in_latency_count{protocol='zipkin-http',plane='control'}[1m]))

OAP Zipkin Trace Latency

The OAP trace processing latency

Metric Name	Labels	PromQL Expression
trace_in_latency_bucket	N/A	histogram_quantile(0.99, sum(rate(trace_in_latency_bucket[5m])) by (le))
trace_in_latency_bucket	N/A	histogram_quantile(0.95, sum(rate(trace_in_latency_bucket[5m])) by (le))
trace_in_latency_bucket	N/A	histogram_quantile(0.90, sum(rate(trace_in_latency_bucket[5m])) by (le))
trace_in_latency_bucket	N/A	histogram_quantile(0.75, sum(rate(trace_in_latency_bucket[5m])) by (le))
trace_in_latency_bucket	N/A	histogram_quantile(0.50, sum(rate(trace_in_latency_bucket[5m])) by (le))

OAP Zipkin Trace Error Rate

The OAP Zipkin processing trace error rate

Metric Name	Labels	PromQL Expression
trace_analysys_error_count	plane protocol	sum(rate(trace_analysys_error_count{protocol='zipkin-http',plane='control'}[1m]))

JVM Threads

Numbed of threads in OAP JVM

Metric Name	Labels	PromQL Expression
jvm_threads_current	component plane	sum(jvm_threads_current{component="oap", plane="management"})
jvm_threads_daemon	component plane	sum(jvm_threads_daemon{component="oap", plane="management"})
jvm_threads_deadlocked	component plane	sum(jvm_threads_deadlocked{component="oap", plane="management"})
jvm_threads_peak	component plane	sum(jvm_threads_peak{component="oap", plane="management"})

JVM Memory

JVM Memory stats of OAP JVM instances.

Metric Name	Labels	PromQL Expression
jvm_memory_bytes_max	component plane	sum by (area, instance) (jvm_memory_bytes_max{component="oap", plane="management"})
jvm_memory_bytes_used	component plane	sum by (area, instance) (jvm_memory_bytes_used{component="oap", plane="management"})

TSB Health

TSB Health fast diagnosis

MPC Health

MPC Health Status. Three metrics define the health of this component:

1. If mpc_info stops reporting, then it is KO.
2. If mpc has create operations errors, then it is degraded (!!!).
3. If mpc has fetch operations errors, then it is degraded (!!!).
4. If mpc gRPC streams had more than X errors, then it is degraded (!!!)

If 2 and 3, then it is KO

Metric Name	Labels	PromQL Expression
mpc_info	N/A	absent(mpc_info) OR on() vector(0)
mpc_xcp_config_create_ops_err	N/A	rate(mpc_xcp_config_create_ops_err[20m]) OR on() vector(1) > 0
mpc_xcp_config_fetch_ops_err	N/A	rate(mpc_xcp_config_fetch_ops_err[20m]) OR on() vector(1) > 0
permanent_stream_operation_total	component error	(sum(increase(permanent_stream_operation_total{error!="", component="mpc"}[5m])) or on() vector(0)) > bool 5

XCP Central Health

CentralXCP Health Status.

1. If the number of grpc connections from Central to edges and MPC is less than 1, then KO. Edge connections of type cluster_state should be 1 for tsb api and 1 for each cluster. Because in previous versions we had some scenarios with negative values, we account for it with the less tha 1.
2. If the propagation time is greater than 10 seconds, it is degraded (!!!). If it is equal or greater than 20 seconds, then it is KO.

Metric Name	Labels	PromQL Expression
xcp_central_config_propagation_time_ms_bucket	N/A	max(histogram_quantile(0.95, sum(rate(xcp_central_config_propagation_time_ms_bucket[60m])) by (le, edge))) OR on() vector(0)
xcp_central_current_edge_connections	component connection_type	(sum(xcp_central_current_edge_connections{connection_type="cluster_state", component="xcp"}) OR on() vector(0)) < bool 1

XCP Edge Health

XCP Edge Health

1. This is a key metric about messages received by central from edges. If some of the edges stop reporting, there's a problem with the edges.
2. This is a key metric about time passed since edges synced with central. If it is more than 10 minutes, there's a problem with one of the edges.

Metric Name	Labels	PromQL Expression
xcp_central_config_propagation_event_count_total	status type	(min(increase(xcp_central_config_propagation_event_count_total{status="received",type="config_resync_request"}[8m])) OR on() vector(0)) == bool 0
xcp_central_current_onboarded_edge	N/A	max (time() - max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge[2m]) == 0) + on(edge,instance) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", type="cluster_state"} /1000) by (edge,type,instance) > bool 700)
xcp_central_current_onboarded_edge_total	N/A	max (time() - max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge[2m]) == 0) + on(edge,instance) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", type="cluster_state"} /1000) by (edge,type,instance) > bool 700)
xcp_central_last_config_propagation_event_timestamp_ms	edge type	max (time() - max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge[2m]) == 0) + on(edge,instance) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", type="cluster_state"} /1000) by (edge,type,instance) > bool 700)

TSB API Health

TSB API Health Status.

1. If OK grpc codes reported by TSB API are 0 or not being reported, this silence indicates an error. IF KO, use tctl or UI to check if tsb api is returning. If everything's alright, any call should set this metric to OK.

Metric Name	Labels	PromQL Expression
grpc_server_handled_total	component grpc_code grpc_type	(sum(rate(grpc_server_handled_total{component="tsb", grpc_code="OK", grpc_type="unary"}[5m])) by (grpc_code) OR on() vector(0)) == bool 0

IAM Health

IAM Health Status.

1. If no authentication operations are reported, IAM is having an issue.
2. If the difference between the short and middle term latencies for JWT is more than 1 second, then IAM is degraded.
3. If the difference between the short and middle term latencies for JWT is more than 5 seconds, then IAM is having an issue.
4. If the difference between the short and middle term latencies for non-JWT is more than 5 seconds, then IAM is degraded.
5. If the difference between the short and middle term latencies for non-JWT is more than 30 seconds, then IAM is having an issue.

Metric Name	Labels	PromQL Expression
iam_auth_time_bucket	error provider	(abs((histogram_quantile(0.95, sum(rate(iam_auth_time_bucket{error="", provider="jwt.Provider"}[5m])) by (le)) OR on() vector(1)) - (histogram_quantile(0.95, sum(rate(iam_auth_time_bucket{error="", provider="jwt.Provider"}[30m])) by (le)) OR on() vector(1))) / 1000) > bool 1
iam_auth_time_bucket	error provider	(abs((histogram_quantile(0.95, sum(rate(iam_auth_time_bucket{error="", provider="jwt.Provider"}[5m])) by (le)) OR on() vector(1)) - (histogram_quantile(0.95, sum(rate(iam_auth_time_bucket{error="", provider="jwt.Provider"}[30m])) by (le)) OR on() vector(1))) / 1000) > bool 5
iam_auth_time_bucket	error provider	(abs((histogram_quantile(0.95, sum(rate(iam_auth_time_bucket{error="", provider!="jwt.Provider"}[5m])) by (le)) OR on() vector(1)) - (histogram_quantile(0.95, sum(rate(iam_auth_time_bucket{error="", provider!="jwt.Provider"}[30m])) by (le)) OR on() vector(1))) / 1000) > bool 5
iam_auth_time_bucket	error provider	(abs((histogram_quantile(0.95, sum(rate(iam_auth_time_bucket{error="", provider!="jwt.Provider"}[5m])) by (le)) OR on() vector(1)) - (histogram_quantile(0.95, sum(rate(iam_auth_time_bucket{error="", provider!="jwt.Provider"}[30m])) by (le)) OR on() vector(1))) / 1000) > bool 30
iam_auth_time_count	error	(max(sum(rate(iam_auth_time_count{error=""}[1m])) by (provider)) OR on() vector(0)) == bool 0

OAP Health

OAP Health Status.

1. If OAP's JVM are not reported, then OAP in the management plane has an issue.
2. If the number of reporting clusters to xcp central is less than the number of control planes OAPs, theres an issue with one or more OAPs in the CPs. The dependency on xcp central health is controlled by only accounting for positive differences.

Metric Name	Labels	PromQL Expression
jvm_threads_current	component plane	(sum(jvm_threads_current{component="oap", plane="management"}) OR on() vector(0)) == bool 0
jvm_threads_current	component plane	count(rate(xcp_central_config_propagation_event_count_total{status="received",type="config_resync_request"}[5m]) OR on() vector(0)) - count(jvm_threads_current{component="oap", plane="control"} OR on() vector(0)) > bool 0
xcp_central_config_propagation_event_count_total	status type	count(rate(xcp_central_config_propagation_event_count_total{status="received",type="config_resync_request"}[5m]) OR on() vector(0)) - count(jvm_threads_current{component="oap", plane="control"} OR on() vector(0)) > bool 0

Front Envoy Health

Front Envoy Health Status. 1.If the difference between the short and the long average response time from its upstream transactions exceeds a given threshold in ms (defined by the divisor).

Metric Name	Labels	PromQL Expression
envoy_cluster_internal_upstream_rq_time_bucket	component	histogram_quantile(0.95, sum(rate(envoy_cluster_internal_upstream_rq_time_bucket{component="front-envoy"}[5m])) by (le)) OR on() vector(2000)
envoy_cluster_internal_upstream_rq_time_bucket	component	histogram_quantile(0.95, sum(rate(envoy_cluster_internal_upstream_rq_time_bucket{component="front-envoy"}[60m])) by (le)) OR on() vector(1000)

TSB Operational Status

Operational metrics to indicate Tetrate Service Bridge API server health.

Front Envoy Success Rate

Rate of successful requests to Front Envoy. This includes all user and cluster requests into the management plane.

Note: This indicates the health of the AuthZ server not whether the user or cluster making the request has the correct permissions.

Metric Name	Labels	PromQL Expression
envoy_cluster_internal_upstream_rq_total	component envoy_response_code	sum(rate(envoy_cluster_internal_upstream_rq_total{envoy_response_code=~"2.|3.|401", component="front-envoy"}[1m])) by (envoy_cluster_name)

Front Envoy Error Rate

The error rate of requests to the Front Envoy server. This includes all user and cluster requests into the management plane. Note: This indicates the health of the AuthZ server not whether the user or cluster making the request has the correct permissions.

Metric Name	Labels	PromQL Expression
envoy_cluster_internal_upstream_rq_total	component envoy_response_code	sum(rate(envoy_cluster_internal_upstream_rq_total{envoy_response_code!~"2.|3.|401", component="front-envoy"}[1m])) by (envoy_cluster_name, envoy_response_code)

Front Envoy Latency

Front Envoy request latency percentiles.

Metric Name	Labels	PromQL Expression
envoy_cluster_internal_upstream_rq_time_bucket	component	histogram_quantile(0.99, sum(rate(envoy_cluster_internal_upstream_rq_time_bucket{component="front-envoy"}[1m])) by (le, envoy_cluster_name))
envoy_cluster_internal_upstream_rq_time_bucket	component	histogram_quantile(0.95, sum(rate(envoy_cluster_internal_upstream_rq_time_bucket{component="front-envoy"}[1m])) by (le, envoy_cluster_name))

TSB Success Rate

Rate of successful requests to the TSB apiserver from the UI and CLI.

Metric Name	Labels	PromQL Expression
grpc_server_handled_total	component grpc_code grpc_method grpc_type	sum(rate(grpc_server_handled_total{component="tsb", grpc_code="OK", grpc_type="unary", grpc_method!="SendAuditLog"}[1m])) by (grpc_method)

TSB Error Rate

Rate of failed requests to the TSB apiserver from the UI and CLI.

Metric Name	Labels	PromQL Expression
grpc_server_handled_total	component grpc_code grpc_method grpc_type	sum(rate(grpc_server_handled_total{component="tsb", grpc_code!="OK", grpc_type="unary", grpc_method!="SendAuditLog"}[1m])) by (grpc_code, grpc_method)

Authentication Success Rate

The success rate for authentication operations for each type of authentication provider.

Metric Name	Labels	PromQL Expression
iam_auth_time_count	error	sum(rate(iam_auth_time_count{error=""}[1m])) by (provider)

Authentication Error Rate

The error rate for authentication operations for each type of authentication provider.

Spikes may indicate problems with the provider or the given credentials, such as expired JWT tokens.

Metric Name	Labels	PromQL Expression
iam_auth_time_count	error	sum(rate(iam_auth_time_count{error!=""}[1m])) by (provider)

Authentication Latency

The latency for authentication operations for each type of authentication provider.

Spikes in the latency may indicate that the authentication provider has a sub-optimal configuration (such as too wide LDAP queries).

Metric Name	Labels	PromQL Expression
iam_auth_time_bucket	error	histogram_quantile(0.99, sum(rate(iam_auth_time_bucket{error=""}[1m])) by (le, provider))
iam_auth_time_bucket	error	histogram_quantile(0.95, sum(rate(iam_auth_time_bucket{error=""}[1m])) by (le, provider))

Data Store Operations Rate

Request rate for operations persisting data to the datastore grouped by method and kind.

Metric Name	Labels	PromQL Expression
persistence_operation_total	N/A	sum(rate(persistence_operation_total[1m])) by (kind, method)

Data Store Operations Error Rate

The request error rate for operations persisting data to the datastore grouped by method and kind. This graph also includes transactions. These are standard SQL transactions and consists of multiple operations. Note: The graph explicitly excludes "resource not found" errors. A small number of "not found" responses are normal as TSB for optimization often uses Get queries instead of Exists to determine the resource existence.

Metric Name	Labels	PromQL Expression
persistence_operation_total	error kind	sum(rate(persistence_operation_total{error="true", kind!="iam_revoked_token"}[1m])) by (kind, method, error)

Data Store Operations Latency

The request latency for operations persisting data to the datastore grouped by method.

Metric Name	Labels	PromQL Expression
persistence_operation_duration_bucket	N/A	histogram_quantile(0.99, sum(rate(persistence_operation_duration_bucket[1m])) by (le, method))

Data Store Transaction Rate

The rate of newly creates transactions dry run mode. These are standard SQL transactions and consist of multiple operations.

Metric Name	Labels	PromQL Expression
persistence_transaction_total	N/A	sum(rate(persistence_transaction_total[1m])) by (dry_run_mode)

Data Store Transaction Error Rate

The rate of transactions that failed at execution time by dry run mode. These are standard SQL transactions and consist of multiple operations.

Metric Name	Labels	PromQL Expression
persistence_transaction_total	error	sum(rate(persistence_transaction_total{error="true"}[1m])) by (dry_run_mode)

Data Store Transactions Latency

TheP99 latency of transactions execution grouped by dry run mode.

Metric Name	Labels	PromQL Expression
persistence_transaction_duration_bucket	N/A	histogram_quantile(0.99, sum(rate(persistence_transaction_duration_bucket[1m])) by (le, dry_run_mode))

Active Transactions

The number of running transactions on the datastore.

This graph shows how many active transactions are running at a given point in time. It helps you understand the load of the system generated by concurrent access to the platform.

Metric Name	Labels	PromQL Expression
persistence_concurrent_transaction	N/A	sum(persistence_concurrent_transaction) by (dry_run_mode)

Mismatching Transactions dry run mode

This is the number of child transactions that do not match parent's transaction dry run mode.

This means that the dry run mode has not properly been propagated to child transactions.

Metric Name	Labels	PromQL Expression
persistence_dry_run_transaction_mismatch_total	N/A	sum(persistence_dry_run_transaction_mismatch_total)

In Use Connections

The number of connections currently in use.

Metric Name	Labels	PromQL Expression
go_sql_in_use_connections	db_name	sum(max_over_time(go_sql_in_use_connections{db_name="dbpool"}[1m])) by(component)

Open Connections/Max Connections

The number of established connections both in use and idle. Also the maximum allowed number of connections is displayed.

Metric Name	Labels	PromQL Expression
go_sql_max_open_connections	db_name	sum(max_over_time(go_sql_max_open_connections{db_name="dbpool"}[1m])) by(component)
go_sql_open_connections	db_name	sum(max_over_time(go_sql_open_connections{db_name="dbpool"}[1m])) by(component)

Connections Waited

The total number of connections waited for.

Metric Name	Labels	PromQL Expression
go_sql_wait_count_total	db_name	sum(increase(go_sql_wait_count_total{db_name="dbpool"}[1m])) by(component)

Time Waiting for Connections

The total time blocked waiting for a new connection.

Metric Name	Labels	PromQL Expression
go_sql_wait_duration_seconds_total	component db_name	sum(increase(go_sql_wait_duration_seconds_total{component="tsb", db_name="dbpool"}[1m])) by(component)

Created Connections

The number of created connections

Metric Name	Labels	PromQL Expression
db_sql_created_connections_duration_count	N/A	sum(increase(db_sql_created_connections_duration_count[1m])) by (component)

Create Connections Latency

The p99 duration in milliseconds for creating new connection.

Metric Name	Labels	PromQL Expression
db_sql_created_connections_duration_bucket	N/A	histogram_quantile(0.99, sum(rate(db_sql_created_connections_duration_bucket[1m])) by (le, component))

Closed Connections

The number of closed connections by reasons.

• Max Idle: The total number of connections closed due to SetMaxIdleConns.
• Idle Time: The total number of connections closed due to SetConnMaxIdleTime.
• Max Lifetime: The total number of connections closed due to SetConnMaxLifetime.

Metric Name	Labels	PromQL Expression
go_sql_max_idle_closed_total	db_name	sum(increase(go_sql_max_idle_closed_total{db_name="dbpool"}[1m])) by(component)
go_sql_max_idle_time_closed_total	db_name	sum(increase(go_sql_max_idle_time_closed_total{db_name="dbpool"}[1m])) by(component)
go_sql_max_lifetime_closed_total	db_name	sum(increase(go_sql_max_lifetime_closed_total{db_name="dbpool"}[1m])) by(component)

Idle Connections

The number of idle connections.

Metric Name	Labels	PromQL Expression
go_sql_idle_connections	db_name	sum(max_over_time(go_sql_idle_connections{db_name="dbpool"}[1m])) by(component)

Service Registry Operations

This metric shows the amount of operations done by the service registry. The service registry will handle all service changes across the clusters, detecting and persisting them in the database.

Metric Name	Labels	PromQL Expression
service_registry_operation_duration_count	error	sum(increase(service_registry_operation_duration_count{error=""}[1m])) by (operation)
service_registry_operation_duration_count	error	sum(increase(service_registry_operation_duration_count{error!=""}[1m])) by (operation)

Service Registry Operations Duration

Duration of operations performed by the service registry.

This graph also includes the total duration of the reconciliation process during which the service registry iterates through all clusters to identify changes that need to be persisted in the database.

Metric Name	Labels	PromQL Expression
service_registry_operation_duration_bucket	N/A	histogram_quantile(0.99, sum(rate(service_registry_operation_duration_bucket[1m])) by (le, operation))
service_registry_total_duration_bucket	N/A	histogram_quantile(0.99, sum(rate(service_registry_total_duration_bucket[1m])) by (le))

PDP Success Rate

Successful request rate of PDP grouped by method. NGAC is a graph-based authorization framework that consists of three main components: Policy Information Point (PIP): Maintains the NGAC graph. It creates the nodes and edges in the graph that represents the state of the system. The other components of NGAC use this graph to perform access decisions. Policy Decision Point (PDP): Performs access decisions based on the NGAC graph's policies. The PDP is used to perform binary Allow/Deny access decisions (Check) and determine the objects a user has access to (List). These access decisions are enforced at the Policy Enforcement Point (PEP). Policy Enforcement Point (PEP): Enforces access control by calling the PDP to get an access decision. Successful requests to the PDP show the number of requests that the PEP has successfully made to the PDP. A successful request does not represent "access granted" decisions; they represent the access decision requests for which a verdict was obtained. A drop in this metric may show that operations against the graph are failing. This may mean that the graph is unavailable for reads and this is usually a consequence of failures in the PIP. Failures in the PIP for write operations will result in the graph not being properly updated to the latest status, resulting in access decisions based on stale models.

Metric Name	Labels	PromQL Expression
ngac_pdp_operation_total	error	sum(rate(ngac_pdp_operation_total{error=""}[1m])) by (method)

PDP Error Rate

Rate of errors for PDP requests grouped by method. NGAC is a graph-based authorization framework that consists of three main components: Policy Information Point (PIP): Maintains the NGAC graph. It creates the nodes and edges in the graph that represents the state of the system. The other components of NGAC use this graph to perform access decisions. Policy Decision Point (PDP): Performs access decisions based on the NGAC graph's policies. The PDP is used to perform binary Allow/Deny access decisions (Check) and determine the objects a user has access to (List). These access decisions are enforced at the Policy Enforcement Point (PEP). Policy Enforcement Point (PEP): Enforces access control by calling the PDP to get an access decision. Successful requests to the PDP show the number of requests that the PEP has successfully made to the PDP. A successful request does not represent "access granted" decisions; they represent the access decision requests where a verdict was obtained. Failed requests to the PDP show the number of requests from the PEP to the PDP that have failed. They do not represent "access denied" decisions; they represent the access decision requests where a verdict could not be obtained. A rise in this metric may show that operations against the graph are failing. This may mean that the graph is unavailable for reads and this is usually a consequence of failures in the PIP. Failures in the PIP for write operations will result in the graph not being correctly updated to the latest status, resulting in access decisions based on stale models.

Metric Name	Labels	PromQL Expression
ngac_pdp_operation_total	error	sum(rate(ngac_pdp_operation_total{error!=""}[1m])) by (method)

PDP Latency

PDP latency percentiles grouped by method. NGAC is a graph-based authorization framework that consists of three main components: Policy Information Point (PIP): Maintains the NGAC graph. It creates the nodes and edges in the graph that represents the state of the system. The other components of NGAC use this graph to perform access decisions. Policy Decision Point (PDP): Performs access decisions based on the NGAC graph's policies. The PDP is used to perform binary Allow/Deny access decisions (Check) and determine the objects a user has access to (List). These access decisions are enforced at the Policy Enforcement Point (PEP). Policy Enforcement Point (PEP): Enforces access control by calling the PDP to get an access decision. Successful requests to the PDP show the number of requests that the PEP has successfully made to the PDP. A successful request does not represent "access granted" decisions; they represent the access decision requests for which a verdict was obtained. This metric shows the time it takes to get an access decision for authorization requests. Degradation in PDP operations may result in general degradation of the system. PDP latency represents the time it takes to make access decisions, and that will impact user experience since access decisions are made and enforced for every operation.

Metric Name	Labels	PromQL Expression
ngac_pdp_operation_duration_bucket	N/A	histogram_quantile(0.99, sum(rate(ngac_pdp_operation_duration_bucket[1m])) by (method, le))
ngac_pdp_operation_duration_bucket	N/A	histogram_quantile(0.95, sum(rate(ngac_pdp_operation_duration_bucket[1m])) by (method, le))

PIP Success Rate

Successful request rate of PIP grouped by method.

NGAC is a graph based authorization framework that consists on three main components:

• Policy Information Point (PIP): Maintains the NGAC graph. It creates the nodes and edges in the graph that represents the state of the system. This graph is used by the other components of NGAC to perform access decisions.
• Policy Decision Point (PDP): Performs access decisions based on the policies configured in the NGAC graph. The PDP is used to perform binary Allow/Deny access decisions (Check), and to determine the objects a user has access to (List). These access decisions are enforced at the Policy Enforcement Point (PEP).
• Policy Enforcement Point (PEP): Enforces access control by calling the PDP to get an access decision. Successful requests to the PDP show the number of requests that the PEP has successfully made to the PDP. Successful request do not represent "access granted" decisions; they just represent the access decision requests for which a verdict was obtained.

PIP operations are executed against the NGAC graph to represent and maintain the objects in the system and the relationships between them.

A drop in this metric may show that operations against the graph are failing. This may mean that the graph is unavailable for reads, or that it is failing to persist data. Read failures may result in failed access decisions (in the PDP) and user interaction with the system may be rejected as well. Failures in write operations will result in the graph not being properly updated to the latest status and that could result in access decisions based on stale models.

Metric Name	Labels	PromQL Expression
ngac_pip_operation_total	error	sum(rate(ngac_pip_operation_total{error=""}[1m])) by (method)

PIP Latency

PiP latency percentiles grouped by method.

NGAC is a graph based authorization framework that consists on three main components:

• Policy Information Point (PIP): Maintains the NGAC graph. It creates the nodes and edges in the graph that represents the state of the system. This graph is used by the other components of NGAC to perform access decisions.
• Policy Decision Point (PDP): Performs access decisions based on the policies configured in the NGAC graph. The PDP is used to perform binary Allow/Deny access decisions (Check), and to determine the objects a user has access to (List). These access decisions are enforced at the Policy Enforcement Point (PEP).
• Policy Enforcement Point (PEP): Enforces access control by calling the PDP to get an access decision. Successful requests to the PDP show the number of requests that the PEP has successfully made to the PDP. Successful request do not represent "access granted" decisions; they just represent the access decision requests for which a verdict was obtained.

This metric shows the time it takes for a PIP operation to complete and, in the case of write operations, to have data persisted in the NGAC graph.

Degradation in PIP operations may result in general degradation of the system. PIP latency represents the time it takes to access the NGAC graph, and this directly affects the PDP when running access decisions. A degraded PIP may result in a degraded PDP, and that will impact user experience, as access decisions are made and enforced for every operation.

Metric Name	Labels	PromQL Expression
ngac_pip_operation_duration_bucket	N/A	histogram_quantile(0.99, sum(rate(ngac_pip_operation_duration_bucket[1m])) by (method, le))
ngac_pip_operation_duration_bucket	N/A	histogram_quantile(0.95, sum(rate(ngac_pip_operation_duration_bucket[1m])) by (method, le))

PIP Error Rate

Rate of errors for PIP requests grouped by method.

NGAC is a graph based authorization framework that consists on three main components:

• Policy Information Point (PIP): Maintains the NGAC graph. It creates the nodes and edges in the graph that represents the state of the system. This graph is used by the other components of NGAC to perform access decisions.
• Policy Decision Point (PDP): Performs access decisions based on the policies configured in the NGAC graph. The PDP is used to perform binary Allow/Deny access decisions (Check), and to determine the objects a user has access to (List). These access decisions are enforced at the Policy Enforcement Point (PEP).
• Policy Enforcement Point (PEP): Enforces access control by calling the PDP to get an access decision. Successful requests to the PDP show the number of requests that the PEP has successfully made to the PDP. Successful request do not represent "access granted" decisions; they just represent the access decision requests for which a verdict was obtained.

PIP operations are executed against the NGAC graph to represent and maintain the objects in the system and the relationships between them.

Note: the "Node not found" errors are explicitly excluded as TSB often uses GetNode method instead of Exists to determine the node existence, for the purposes of optimisation.

A general raise in this metric may show that operations against the graph are failing. This may mean that the graph is unavailable for reads, or that it is failing to persist data. Read failures may result in failed access decisions (in the PDP) and user interaction with the system may be rejected as well. Failures in write operations will result in the graph not being properly updated to the latest status and that could result in access decisions based on stale models.

Metric Name	Labels	PromQL Expression
ngac_pip_operation_total	error	sum(rate(ngac_pip_operation_total{error!="", error!="Node not found"}[1m])) by (method)

Active PIP Transactions

The number of running transactions on the NGAC PIP. NGAC is a graph-based authorization framework that consists on three main components: Policy Information Point (PIP): Maintains the NGAC graph. It creates the nodes and edges in the graph that represents the state of the system. The other components of NGAC use this graph to perform access decisions. Policy Decision Point (PDP): Performs access decisions based on the NGAC graph's policies. The PDP is used to perform binary Allow/Deny access decisions (Check) and determine the objects a user has access to (List). These access decisions are enforced at the Policy Enforcement Point (PEP). Policy Enforcement Point (PEP): Enforces access control by calling the PDP to get an access decision. Successful requests to the PDP show the number of requests that the PEP has successfully made to the PDP. A successful request does not represent "access granted" decisions; they represent the access decision requests for which a verdict was obtained. This metric shows the number of active write operations against the NGAC graph. It can be useful to understand the load of the system generated by concurrent access to the platform.

Metric Name	Labels	PromQL Expression
ngac_pip_concurrent_transaction	N/A	sum(ngac_pip_concurrent_transaction)

TSB webhooks

Operational status of the Tetrate Service Bridge webhooks

Management Plane webhook requests

Shows the rate of requests for the webhooks in each management plane component

Metric Name	Labels	PromQL Expression
controller_runtime_webhook_requests_total	plane	sum by(component) (rate(controller_runtime_webhook_requests_total{plane="management"}[1m]))

Control Plane webhooks requests

Shows the rate of requests for the webhooks in each control plane component for the selected cluster

Metric Name	Labels	PromQL Expression
controller_runtime_webhook_requests_total	cluster_name plane	sum by(component) (rate(controller_runtime_webhook_requests_total{plane="control", cluster_name="$cluster"}[1m]))

Management Plane webhook latency

Shows the latency percentiles across all management plane webhooks

Metric Name	Labels	PromQL Expression
controller_runtime_webhook_latency_seconds_bucket	plane	histogram_quantile(0.5, sum by(le) (rate(controller_runtime_webhook_latency_seconds_bucket{plane="management"}[1m])))
controller_runtime_webhook_latency_seconds_bucket	plane	histogram_quantile(0.95, sum by(le) (rate(controller_runtime_webhook_latency_seconds_bucket{plane="management"}[1m])))
controller_runtime_webhook_latency_seconds_bucket	plane	histogram_quantile(0.99, sum by(le) (rate(controller_runtime_webhook_latency_seconds_bucket{plane="management"}[1m])))

Control Plane webhook latency

Shows the latency percentiles across all control plane webhooks for the selected cluster

Metric Name	Labels	PromQL Expression
controller_runtime_webhook_latency_seconds_bucket	cluster_name plane	histogram_quantile(0.5, sum by(le) (rate(controller_runtime_webhook_latency_seconds_bucket{plane="control", cluster_name="$cluster"}[1m])))
controller_runtime_webhook_latency_seconds_bucket	cluster_name plane	histogram_quantile(0.95, sum by(le) (rate(controller_runtime_webhook_latency_seconds_bucket{plane="control", cluster_name="$cluster"}[1m])))
controller_runtime_webhook_latency_seconds_bucket	cluster_name plane	histogram_quantile(0.99, sum by(le) (rate(controller_runtime_webhook_latency_seconds_bucket{plane="control", cluster_name="$cluster"}[1m])))

Management Plane deletion protection webhook

Shows the number of deletion requests denied and invalid requests received by the deletion protection webhook in the management plane

Metric Name	Labels	PromQL Expression
deletion_protection_webhook_denied_total	plane	sum by(component) (rate(deletion_protection_webhook_denied_total{plane="management"}[1m]))
deletion_protection_webhook_invalid_total	plane	sum by(component) (rate(deletion_protection_webhook_invalid_total{plane="management"}[1m]))

Control Plane deletion protection webhooks

Shows the number of deletion requests denied and invalid requests received by the deletion protection webhook in the control plane for the selected cluster

Metric Name	Labels	PromQL Expression
deletion_protection_webhook_denied_total	cluster_name plane	sum by(component) (rate(deletion_protection_webhook_denied_total{plane="control", cluster_name="$cluster"}[1m]))
deletion_protection_webhook_invalid_total	cluster_name plane	sum by(component) (rate(deletion_protection_webhook_invalid_total{plane="control", cluster_name="$cluster"}[1m]))

XCP Central Operational Status

Operational metrics to indicate XCP Central health.

Metric Name	Labels	PromQL Expression
process_start_time_seconds	component plane	time() - process_start_time_seconds{component="xcp",plane="management"}

XCP Central Version

Metric Name	Labels	PromQL Expression
xcp_central_version	N/A	label_replace(xcp_central_version, "xcp_version", "$1", "version", "(.*)")

Time since last cluster state received from the edge (seconds)

Since the default cluster state resync time is 10 minutes, any value higher than 600-700 seconds is considered abnormal.

Metric Name	Labels	PromQL Expression
xcp_central_current_onboarded_edge_total	N/A	time() - max(max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge_total[2m]) == 0)) by (edge) + on(edge) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", status="received" , type="cluster_state"} /1000) by (edge,type)
xcp_central_last_config_propagation_event_timestamp_ms	edge status type	time() - max(max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge_total[2m]) == 0)) by (edge) + on(edge) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", status="received" , type="cluster_state"} /1000) by (edge,type)

Time since cluster states were sent to the MPC and Edges clients (seconds)

Metric Name	Labels	PromQL Expression
xcp_central_current_onboarded_edge	N/A	time() - max((xcp_central_last_cluster_state_event_timestamp_ms / 1000 unless on(peer_cluster_name) label_replace(increase(xcp_central_current_onboarded_edge[5m]),"peer_cluster_name", "$1", "edge", "(.)") == 0) unless on(cluster_state_event_cluster_name) label_replace(increase(xcp_central_current_onboarded_edge[5m]),"cluster_state_event_cluster_name", "$1", "edge", "(.)") == 0) by (peer_cluster_name, cluster_state_event_cluster_name)
xcp_central_last_cluster_state_event_timestamp_ms	N/A	time() - max((xcp_central_last_cluster_state_event_timestamp_ms / 1000 unless on(peer_cluster_name) label_replace(increase(xcp_central_current_onboarded_edge[5m]),"peer_cluster_name", "$1", "edge", "(.)") == 0) unless on(cluster_state_event_cluster_name) label_replace(increase(xcp_central_current_onboarded_edge[5m]),"cluster_state_event_cluster_name", "$1", "edge", "(.)") == 0) by (peer_cluster_name, cluster_state_event_cluster_name)

Time since config resync request is received from the edge (seconds)

Because regular periodic resync requests would be coming, a high value than the resync period, 60 sec default, is not normal.

Metric Name	Labels	PromQL Expression
xcp_central_current_onboarded_edge_total	N/A	time() - max(max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge_total[2m]) == 0)) by (edge) + on(edge) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", status="received" , type="config_resync_request"} /1000) by (edge,type)
xcp_central_last_config_propagation_event_timestamp_ms	edge status type	time() - max(max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge_total[2m]) == 0)) by (edge) + on(edge) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", status="received" , type="config_resync_request"} /1000) by (edge,type)

Time since config CRs sent to the edge (seconds)

Sent: Time since configs like workspaces, traffic groups etc were sent to the edge. In steady state, a very high value is fine

Metric Name	Labels	PromQL Expression
xcp_central_current_onboarded_edge_total	N/A	time() - max( max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge_total[2m]) == 0)) by (edge) + on(edge) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", status="sent"} /1000) by (edge,type)
xcp_central_last_config_propagation_event_timestamp_ms	edge status	time() - max( max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge_total[2m]) == 0)) by (edge) + on(edge) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", status="sent"} /1000) by (edge,type)

messages received by central from edges in last 5 min

Number of times any message is received by central from edges

Messages received by central from any edge are of three types:

1. Periodic(per minute by default) config resync request
2. cluster state
3. Header message to ack the config received

This number is combined count of all three in the last 5 min.

Metric Name	Labels	PromQL Expression
xcp_central_config_propagation_event_count_total	status type	increase(xcp_central_config_propagation_event_count_total{status="received",type="config_resync_request"}[5m]) unless on(edge) increase(xcp_central_current_onboarded_edge_total[5m]) == 0
xcp_central_config_propagation_event_count_total	status type	increase(xcp_central_config_propagation_event_count_total{status="received",type="cluster_state"}[5m]) unless on(edge) increase(xcp_central_current_onboarded_edge_total[5m]) == 0
xcp_central_current_onboarded_edge_total	N/A	increase(xcp_central_config_propagation_event_count_total{status="received",type="config_resync_request"}[5m]) unless on(edge) increase(xcp_central_current_onboarded_edge_total[5m]) == 0
xcp_central_current_onboarded_edge_total	N/A	increase(xcp_central_config_propagation_event_count_total{status="received",type="cluster_state"}[5m]) unless on(edge) increase(xcp_central_current_onboarded_edge_total[5m]) == 0

Number of times config CRs sent by central to the edges in last 5m

Number of times config CRs like workspaces. traffic groups etc sent by central in last 5m

Metric Name	Labels	PromQL Expression
xcp_central_config_propagation_event_count_total	status	increase(xcp_central_config_propagation_event_count_total{status="sent"}[5m]) unless on(edge) increase(xcp_central_current_onboarded_edge_total[5m]) == 0
xcp_central_current_onboarded_edge_total	N/A	increase(xcp_central_config_propagation_event_count_total{status="sent"}[5m]) unless on(edge) increase(xcp_central_current_onboarded_edge_total[5m]) == 0

Config Propagation Latency by Edge

Distribution of time to propagate updates from Central (Management plane) to Edges. If there is no config push in last one minute, you will see all 0s, which is expected.

Metric Name	Labels	PromQL Expression
xcp_central_config_propagation_time_ms_bucket	N/A	histogram_quantile(0.99, sum(rate(xcp_central_config_propagation_time_ms_bucket[1m])) by (le, edge)) unless on(edge) increase(xcp_central_current_onboarded_edge[5m]) == 0
xcp_central_config_propagation_time_ms_bucket	N/A	histogram_quantile(0.95, sum(rate(xcp_central_config_propagation_time_ms_bucket[1m])) by (le, edge)) unless on(edge) increase(xcp_central_current_onboarded_edge[5m]) == 0
xcp_central_config_propagation_time_ms_bucket	N/A	histogram_quantile(0.90, sum(rate(xcp_central_config_propagation_time_ms_bucket[1m])) by (le, edge)) unless on(edge) increase(xcp_central_current_onboarded_edge[5m]) == 0
xcp_central_config_propagation_time_ms_bucket	N/A	histogram_quantile(0.75, sum(rate(xcp_central_config_propagation_time_ms_bucket[1m])) by (le, edge)) unless on(edge) increase(xcp_central_current_onboarded_edge[5m]) == 0
xcp_central_config_propagation_time_ms_bucket	N/A	histogram_quantile(0.50, sum(rate(xcp_central_config_propagation_time_ms_bucket[1m])) by (le, edge)) unless on(edge) increase(xcp_central_current_onboarded_edge[5m]) == 0
xcp_central_current_onboarded_edge	N/A	histogram_quantile(0.99, sum(rate(xcp_central_config_propagation_time_ms_bucket[1m])) by (le, edge)) unless on(edge) increase(xcp_central_current_onboarded_edge[5m]) == 0
xcp_central_current_onboarded_edge	N/A	histogram_quantile(0.95, sum(rate(xcp_central_config_propagation_time_ms_bucket[1m])) by (le, edge)) unless on(edge) increase(xcp_central_current_onboarded_edge[5m]) == 0
xcp_central_current_onboarded_edge	N/A	histogram_quantile(0.90, sum(rate(xcp_central_config_propagation_time_ms_bucket[1m])) by (le, edge)) unless on(edge) increase(xcp_central_current_onboarded_edge[5m]) == 0
xcp_central_current_onboarded_edge	N/A	histogram_quantile(0.75, sum(rate(xcp_central_config_propagation_time_ms_bucket[1m])) by (le, edge)) unless on(edge) increase(xcp_central_current_onboarded_edge[5m]) == 0
xcp_central_current_onboarded_edge	N/A	histogram_quantile(0.50, sum(rate(xcp_central_config_propagation_time_ms_bucket[1m])) by (le, edge)) unless on(edge) increase(xcp_central_current_onboarded_edge[5m]) == 0

Errors in config push REQUESTS to the edges in last 5 minutes

Central enqueues the config push request to the debouncer(an internal component of central) when:

• It receives event about config resources from k8s apiserver , or
• Any edge connects first time, or
• It is handling a periodic resync request from any of the edges.

In either case, if central meets an error in the event handling before en-queuing the config push request to the debouncer, this metric gets incremented. So this panel is inversely related to "config push(to the edges) requests enqueued to debouncer in last 5 min".

Metric Name	Labels	PromQL Expression
xcp_central_config_update_error_count	N/A	increase(xcp_central_config_update_error_count[5m]) OR on() vector(0)

config push(to the edges) requests enqueued to debouncer in last 5 min

Number of times central enqueued config push(to the connected edges) request to the debouncer in last 5 min. Along with count of the request, reason for the config push request are also shown.

Note: This metric does not indicate the count of actual config push by central. Because of debouncing, actual config push will generally be lesser than this metric. In other words, this metric shows input events for config push. Output (config push on grpc channels to edges) will be lesser because of debouncer

Reasons could be:

1. ADD/DELETE/UPDATE : These are the events received by the central from the k8s apiserver. Example: ADD/IngressGateway means count of config push requests enqueued because of new IngressGateway CRs creation at k8s apiserver.
2. EDGE_RESYNC: This shows the count of config push requests when periodic config resync request from edge triggers config push. This will be non-zero only in rare cases when, for whatever reason, edge reported a stale set of configs and central triggers config push to refresh the configs
3. EDGE_FIRST_CONNECTION: When any edge connects to central, central syncs config to the edge. In steady state, its count must be 0. If its count is non-zero, that indicates grpc stream between central and edge is in error and getting reconnected.
4. CENTRAL_RESYNC: central enqueues a config push request every 5 minute to reconcile configs at edges. Note that this will result into actual config push only to those edges which are not actively sending their config version periodically. Since 1.4, edges request config resync and therefore central will actually push configs over grpc as a result of these request only if edge is < 1.4.

Metric Name	Labels	PromQL Expression
xcp_central_config_update_push_count_total	N/A	increase(xcp_central_config_update_push_count_total[5m])

Pending configurations (orphan configs)

Pending configurations are configs for which cluster could not be determined yet because the parent resource is missing. These metrics show which configurations are currently in Pending state, and the missing Parent group configuration due to which this is in Pending state.

For more information on the Pending configurations can be found by using the XCP central debug endpoint - /debug/cluster_scoped_configs/?pending=true

Metric Name	Labels	PromQL Expression
xcp_central_pending_configs_total	N/A	xcp_central_pending_configs_total

Number of connections(cluster state pushing and config pushing)

Central has two type of grpc connections:

1. edge_config_distribution: One grpc connection with each edge for pushing user configs like workspace, trafficgroup etc
2. cluster_state: One grpc connection with each edge for pushing learned cluster state(service discovery) from all other peer edges. In addition, one more grpc connection with the mpc for pushing all the learned cluster states to the tsb server.

count of edge_config_distribution will be equal to the number of edges connected to the central count of cluster_state connections will be one more that count of edge_config_distribution connections because of additional mpc connection.

IMPORTANT NOTE: If the cluster is not onboarded(TSB cluster object missing), but the edge is up and connected to central, in that scenario connection counts will include such edges

Metric Name	Labels	PromQL Expression
xcp_central_current_edge_connections	connection_type	xcp_central_current_edge_connections{connection_type="edge_config_distribution"} OR on() vector(0)
xcp_central_current_edge_connections	connection_type	xcp_central_current_edge_connections{connection_type="cluster_state"} OR on() vector(0)

Pending on reference configurations

Pending on reference configurations are configs referring to a missing configuration in the TSB hierarchy. The configs are propagated to edges with missing reference resolution metadata. Currently, only Security Settings refer other configurations. These metrics show which configurations are currently in PendingOnRef state, and the missing Parent group configuration due to which this is in PendingOnRef state.

Metric Name	Labels	PromQL Expression
xcp_central_pending_on_ref_configs_total	N/A	xcp_central_pending_on_ref_configs_total

validation webhook passed count in last 5 min

count of requests that validation webhook passed in last 5 minutes by GVK

Metric Name	Labels	PromQL Expression
xcp_central_validation_webhook_passed_count	N/A	increase(xcp_central_validation_webhook_passed_count[5m]) OR on() vector(0)

New connections per min(cluster state pushing and config pushing)

In steady state, edges should be reconnecting continuously to central for cluster state and config streams. Therefore, rate must be 0.

Metric Name	Labels	PromQL Expression
xcp_central_connection_register_count_total	connection_type	rate(xcp_central_connection_register_count_total{connection_type="cluster_state"}[1m]) * 60
xcp_central_connection_register_count_total	connection_type	rate(xcp_central_connection_register_count_total{connection_type="edge_config_distribution"}[1m]) * 60

Connection Push timeouts[5m]

This Panel represents the connection push timeouts in the last 5 minutes in Central grouped by connection types and peer cluster name.

There are two types of connections: Config Propagation and Cluster State Propagation. If the propagation of data(cluster_state/config_propagation) takes more than the configured timeout duration(2 mins by default), central drops the push. This metric can highlight if pushes of certain connection types are failing in certain edges, helping narrow down the problematic component. The push can get a timeout if the previous send to the edge is taking too long.

Metric Name	Labels	PromQL Expression
xcp_central_connection_push_timeout_count	N/A	sum by(connection_type, peer_cluster_name) (increase(xcp_central_connection_push_timeout_count[5m]))

All goroutines

Metric Name	Labels	PromQL Expression
go_goroutines	component plane	go_goroutines{component="xcp",plane="management"}

Rate of webhook validation errors

Rate of webhook validation errors by GVK

Metric Name	Labels	PromQL Expression
xcp_central_validation_webhook_failed_count	N/A	increase(xcp_central_validation_webhook_failed_count[5m]) OR on() vector(0)
xcp_central_validation_webhook_http_error_count	N/A	increase(xcp_central_validation_webhook_http_error_count[5m]) OR on() vector(0)

Central memory consumption

Metric Name	Labels	PromQL Expression
go_memstats_heap_inuse_bytes	component plane	go_memstats_heap_inuse_bytes{component="xcp",plane="management"}
go_memstats_stack_inuse_bytes	component plane	go_memstats_stack_inuse_bytes{component="xcp",plane="management"}

Central specific goroutines

This shows the number of active goroutines in XCP Central that are responsible for config pushes to edges.

Metric Name	Labels	PromQL Expression
go_goroutines	component plane	increase(go_goroutines{component="xcp",plane="management"}[1m])
xcp_central_go_routine_count_total	N/A	increase(xcp_central_go_routine_count_total[1m])

Edges' memory consumption

This shows the current memory usage for all Edges

Metric Name	Labels	PromQL Expression
go_memstats_heap_inuse_bytes	component plane	go_memstats_heap_inuse_bytes{component="xcp",plane="control"}

Central CPU consumption

Metric Name	Labels	PromQL Expression
process_cpu_seconds_total	job	rate(process_cpu_seconds_total{job="central-xcp"}[1m])

All edges' CPU consumption

Metric Name	Labels	PromQL Expression
process_cpu_seconds_total	job	rate(process_cpu_seconds_total{job="edge-xcp"}[1m])

XCP Central Coordinator running

This panel represents if the XCP Central Coordinator is running across the Central instances.

Metric Name	Labels	PromQL Expression
xcp_central_ha_coordinator_up	N/A	avg by(instance) (xcp_central_ha_coordinator_up)

XCP Central Leader

Metric Name	Labels	PromQL Expression
xcp_central_ha_coordinator_acting_as_leader	N/A	avg by(instance) (xcp_central_ha_coordinator_acting_as_leader)

XCP Central Followers

Metric Name	Labels	PromQL Expression
xcp_central_ha_coordinator_acting_as_follower	N/A	avg by(instance) (xcp_central_ha_coordinator_acting_as_follower)

XCP Central Coordinator Leader election loop[5m]

This panel represents how many times the XCP Central Coordinator started the leader election loop in the last 5 minutes.

Metric Name	Labels	PromQL Expression
xcp_central_ha_coordinator_leader_election_loops_total	N/A	sum by(instance) (increase(xcp_central_ha_coordinator_leader_election_loops_total[5m]))

XCP Central Primary Relay Streams Total

This panel represents how many streams are open in the XCP Central Primary relay server.

As the leader's switch, the instance name will change.

Metric Name	Labels	PromQL Expression
xcp_central_ha_primary_relay_server_streams_total	N/A	sum by(instance) (xcp_central_ha_primary_relay_server_streams_total)

Number of currently open relay streams at the H/A Primary (Relay Server)

This panel represents the currently open relay stream at the Primary relay server in the XCP Central instances.

Metric Name	Labels	PromQL Expression
xcp_central_ha_primary_relay_server_streams_open_count_total	N/A	sum by(instance) (xcp_central_ha_primary_relay_server_streams_open_count_total)

Relay streams rejected by primary relay server

Total number of relay streams rejected by the H/A Primary (Relay Server) because the current XCP Central instance is not the H/A Leader

Metric Name	Labels	PromQL Expression
xcp_central_ha_primary_relay_server_streams_rejected_total	N/A	sum by(instance) (increase(xcp_central_ha_primary_relay_server_streams_rejected_total[5m]))

Number of relay streams discontinued by primary relay server

Total number of relay streams closed forcibly by the H/A Primary (Relay Server) because current XCP Central instance stopped being the H/A Leader

Metric Name	Labels	PromQL Expression
xcp_central_ha_primary_relay_server_streams_discontinued_total	N/A	xcp_central_ha_primary_relay_server_streams_discontinued_total

Total cluster states sent by primary relay server(Leader->Follower)[5m]

Total number of Cluster state updates pushed by the H/A Primary (Relay Server) to H/A Secondary(s) (Relay Client(s))

Metric Name	Labels	PromQL Expression
xcp_central_ha_primary_relay_server_cluster_states_sent_total	N/A	sum by(instance) (increase(xcp_central_ha_primary_relay_server_cluster_states_sent_total[5m]))

Cluster States received by primary relay server(Follower->Leader)[5m]

Total number of Cluster state updates received by the H/A Primary (Relay Server) from H/A Secondary(s) (Relay Client(s))

Metric Name	Labels	PromQL Expression
xcp_central_ha_primary_relay_server_cluster_states_received_total	N/A	sum by(instance) (increase(xcp_central_ha_primary_relay_server_cluster_states_received_total[5m]))

Number of cluster state is sent by primary to Secondary for Different CPs[5m]

Total number of times a Cluster state has been pushed by the H/A Primary (Relay Server) to H/A Secondary(s) (Relay Client(s))

Metric Name	Labels	PromQL Expression
xcp_central_ha_primary_relay_server_cluster_state_sent_total	N/A	sum by(cluster) (increase(xcp_central_ha_primary_relay_server_cluster_state_sent_total[5m]))

Number of cluster states received by primary from secondaries for different CP[5m]

Total number of times a Cluster state has been received by the H/A Primary (Relay Server) from H/A Secondary(s) (Relay Client(s))

Metric Name	Labels	PromQL Expression
xcp_central_ha_primary_relay_server_cluster_state_received_total	N/A	sum by(cluster) (increase(xcp_central_ha_primary_relay_server_cluster_state_received_total[5m]))

Secondary relay client running

Flag indicating whether H/A Secondary (Relay Client) is running

Metric Name	Labels	PromQL Expression
xcp_central_ha_secondary_relay_client_up	N/A	avg by(instance) (xcp_central_ha_secondary_relay_client_up)

Total number of Second relay client stream

Total number of relay streams opened by the H/A Secondary (Relay Client)

Metric Name	Labels	PromQL Expression
xcp_central_ha_secondary_relay_client_streams_total	N/A	avg by(instance) (xcp_central_ha_secondary_relay_client_streams_total)

Number of open relay stream by secondary relay client

Number of currently open relay streams by the H/A Secondary (Relay Client)

Metric Name	Labels	PromQL Expression
xcp_central_ha_secondary_relay_client_streams_open_count_total	N/A	sum by(instance) (xcp_central_ha_secondary_relay_client_streams_open_count_total)

Number of cluster state updates recvd by secondary relay client from Primary[5m]

Total number of Cluster state updates received by the H/A Secondary (Relay Client) from the H/A Primary (Relay Server)

Metric Name	Labels	PromQL Expression
xcp_central_ha_secondary_relay_client_cluster_states_received_total	N/A	sum by(instance) (increase(xcp_central_ha_secondary_relay_client_cluster_states_received_total[5m]))

Number of cluster states sent by secondary relay client to primary[5m]

Total number of Cluster state updates pushed by the H/A Secondary (Relay Client) to the H/A Primary (Relay Server)

Metric Name	Labels	PromQL Expression
xcp_central_ha_secondary_relay_client_cluster_states_sent_total	N/A	sum by(instance) (increase(xcp_central_ha_secondary_relay_client_cluster_states_sent_total[5m]))

Number of cluster state updates recvd by secondary client from primary for different CPs[5m]

Total number of times a Cluster state has been received by the H/A Secondary (Relay Client) from the H/A Primary (Relay Server)

Metric Name	Labels	PromQL Expression
xcp_central_ha_secondary_relay_client_cluster_state_received_total	N/A	sum by(cluster) (increase(xcp_central_ha_secondary_relay_client_cluster_state_received_total[5m]))

Number of cluster state updates sent by secondary relay to primary relay for different CPs[5m]

Total number of times a Cluster state has been pushed by the H/A Secondary (Relay Client) to the H/A Primary (Relay Server)

Metric Name	Labels	PromQL Expression
xcp_central_ha_secondary_relay_client_cluster_state_sent_total	N/A	sum by(cluster) (increase(xcp_central_ha_secondary_relay_client_cluster_state_sent_total[5m]))

XCP Central Cross Partition enabled

Flag indicating whether support for Cross-Partition H/A is enabled

Metric Name	Labels	PromQL Expression
xcp_central_ha_cross_partition_enabled	N/A	avg(xcp_central_ha_cross_partition_enabled)

Total number of relay streams by H/A Cross-Paritition Requestor

Total number of relay streams opened by the H/A Cross-Partition Requestor (Relay Client)

Metric Name	Labels	PromQL Expression
xcp_central_ha_cross_partition_requestor_relay_client_streams_total	N/A	avg by(instance) (xcp_central_ha_cross_partition_requestor_relay_client_streams_total)

Number of open streams by Cross-partition requestor

Number of currently open relay streams by the H/A Cross-Partition Requestor (Relay Client)

Metric Name	Labels	PromQL Expression
xcp_central_ha_cross_partition_requestor_relay_client_streams_open_count	N/A	sum by(instance) (xcp_central_ha_cross_partition_requestor_relay_client_streams_open_count)

Number of cluster state updates recvd by H/A Cross-partition requestor[5m]

Total number of Cluster state updates received by the H/A Cross-Partition Requestor (Relay Client) from the H/A Cross-Partition Responder (Relay Server)

Metric Name	Labels	PromQL Expression
xcp_central_ha_cross_partition_requestor_relay_client_cluster_states_received_total	N/A	sum by(instance) (increase(xcp_central_ha_cross_partition_requestor_relay_client_cluster_states_received_total[5m]))

Number of cluster state updates recvd by H/A Cross partition requestor for different CPs[5m]

Total number of times a Cluster state has been received by the H/A Cross-Partition Requestor (Relay Client) from the H/A Cross-Partition Responder (Relay Server)

Metric Name	Labels	PromQL Expression
xcp_central_ha_cross_partition_requestor_relay_client_cluster_state_received_total	N/A	sum by(cluster) (increase(xcp_central_ha_cross_partition_requestor_relay_client_cluster_state_received_total[5m]))

Total number of relay streams by H/A Cross-Paritition Responder

Total number of relay streams opened by the H/A Cross-Partition Responder (Relay Client)

Metric Name	Labels	PromQL Expression
xcp_central_ha_cross_partition_responder_relay_server_streams_total	N/A	avg by(instance) (xcp_central_ha_cross_partition_responder_relay_server_streams_total)

Number of open streams by Cross-partition responder

Number of currently open relay streams at the H/A Cross-Partition Responder (Relay Server)

Metric Name	Labels	PromQL Expression
xcp_central_ha_cross_partition_requestor_relay_client_streams_open_count	N/A	sum by(instance) (xcp_central_ha_cross_partition_requestor_relay_client_streams_open_count)

Number of cluster state updates sent by H/A Cross-partition responder[5m]

Total number of Cluster state updates sent by the H/A Cross-Partition Responder (Relay Client) to the H/A Cross-Partition Requestor (Relay Server)

Metric Name	Labels	PromQL Expression
xcp_central_ha_cross_partition_responder_relay_server_cluster_states_sent_total	N/A	sum by(instance) (increase(xcp_central_ha_cross_partition_responder_relay_server_cluster_states_sent_total[5m]))

Number of cluster state updates sent by H/A Cross partition responder for different CPs[5m]

Total number of times a Cluster state has been sent by the H/A Cross-Partition Responder (Relay Client) to the H/A Cross-Partition Requestor (Relay Server)

Metric Name	Labels	PromQL Expression
xcp_central_ha_cross_partition_responder_relay_server_cluster_state_sent_total	N/A	sum by(cluster) (increase(xcp_central_ha_cross_partition_responder_relay_server_cluster_state_sent_total[5m]))

XCP Edge status

Metric Name	Labels	PromQL Expression
process_start_time_seconds	cluster_name component	time() - process_start_time_seconds{cluster_name="$cluster",component="xcp"}

XCP Edge Version

Metric Name	Labels	PromQL Expression
xcp_edge_istio_versions	cluster_name	label_replace(xcp_edge_istio_versions{cluster_name="$cluster"}, "istio_versions", "$1", "version", "(.*)")
xcp_edge_version	cluster_name	label_replace(xcp_edge_version{cluster_name="$cluster"}, "xcp_version", "$1", "version", "(.*)")

Number of gatewayHost exposed

Metric Name	Labels	PromQL Expression
xcp_edge_gateway_hosts_count	cluster_name	xcp_edge_gateway_hosts_count{cluster_name="$cluster"}

Active connections to central

Current peer connections this edge holds against remote edges.

Metric Name	Labels	PromQL Expression
xcp_edge_stream_connect_count_total	cluster_name statusLabel	xcp_edge_stream_connect_count_total{statusLabel="ok", cluster_name="$cluster"} - ignoring(statusLabel) xcp_edge_stream_connect_count_total{statusLabel="close", cluster_name="$cluster"} OR on() xcp_edge_stream_connect_count_total{statusLabel="ok", cluster_name="$cluster"}

Time since any message sent to central on config stream (seconds)

Time since any of the following messages is sent by edge to central:

1. Periodic(per minute) config resync request
2. Ack of last config received
3. Cluster state Because regular periodic resync requests would be going out periodically, a high value than the resync period, 60 sec default, is not normal.

Metric Name	Labels	PromQL Expression
xcp_edge_last_config_resync_to_central_timestamp_ms	cluster_name	time() - xcp_edge_last_config_resync_to_central_timestamp_ms{cluster_name="$cluster"} / 1000
xcp_edge_last_push_to_central_timestamp_ms	cluster_name	time() - xcp_edge_last_push_to_central_timestamp_ms{cluster_name="$cluster"} / 1000

cluster-state build time percentiles(in secs)

Time (in ms) taken to build the local cluster state. This build time is subset of cluster-update-propagation time

Metric Name	Labels	PromQL Expression
xcp_edge_cluster_state_build_time_secs_bucket	cluster_name	histogram_quantile(0.5,sum(xcp_edge_cluster_state_build_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_cluster_state_build_time_secs_bucket	cluster_name	histogram_quantile(0.9,sum(xcp_edge_cluster_state_build_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_cluster_state_build_time_secs_bucket	cluster_name	histogram_quantile(0.95,sum(xcp_edge_cluster_state_build_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_cluster_state_build_time_secs_bucket	cluster_name	histogram_quantile(0.99,sum(xcp_edge_cluster_state_build_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_cluster_state_build_time_secs_bucket	cluster_name	histogram_quantile(1,sum(xcp_edge_cluster_state_build_time_secs_bucket{cluster_name="$cluster"}) by (le))

Number of times cluster states sent to central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_count	cluster_name	sum(xcp_edge_local_cluster_update_propagation_time_secs_count{cluster_name="$cluster"}) by (trigger_reason)

cluster-state propagation delay percentiles(in secs)

Time (in ms) taken to propagate a change in the local cluster state to remote central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name	histogram_quantile(0.5,sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name	histogram_quantile(0.9,sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name	histogram_quantile(0.95,sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name	histogram_quantile(0.99,sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name	histogram_quantile(1,sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster"}) by (le))

propagated to central in 0-1.5 secs

Time (in ms) taken to propagate a change in the local cluster state to remote central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name le	sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="1.5"}) by (trigger_reason)
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name le	xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="4"} - ignoring(le) xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="2"}

Number of times config status sent by edge to central in last 5 min

Number of times config statuses are sent by edge to central, with respective objects' Kind.

Messages received by central from any edge are of three types:

1. Periodic(per minute by default) config resync request
2. cluster state
3. Header message to ack the config received

This number is combined count of all three in the last 5 min.

Metric Name	Labels	PromQL Expression
xcp_edge_config_status_updates_sent_gvk_total	cluster_name	increase(xcp_edge_config_status_updates_sent_gvk_total{cluster_name="$cluster"}[5m])

propagated to central in 1.5-2.5 secs

Time (in ms) taken to propagate a change in the local cluster state to remote central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name le	sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="2.5"} - ignoring(le,cluster_name) xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="1.5"}) by (trigger_reason)

propagated to central in 2.5-4 secs

Time (in ms) taken to propagate a change in the local cluster state to remote central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name le	sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="4"} - ignoring(le,cluster_name) xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="2.5"}) by (trigger_reason)

Length of cluster state event queue

Length of the cluster state events queue at the current moment. This metric is useful to track the cluster state events currently in the queue and ready to be dequeued and sent to central. A high value of this metric means that events are getting enqueued but dequeuing is blocked because of some bottleneck at sending to the central part.

Metric Name	Labels	PromQL Expression
xcp_edge_current_state_state_events_queue_len	cluster_name	xcp_edge_current_state_state_events_queue_len{cluster_name="$cluster"}

propagated to central in 4-7 secs

Time (in ms) taken to propagate a change in the local cluster state to remote central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name le	sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="7"} - ignoring(le) xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="4"}) by (trigger_reason)

propagated to central in 11-15 secs

Time (in ms) taken to propagate a change in the local cluster state to remote central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name le	sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="15"} - ignoring(le) xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="11"}) by (trigger_reason)

propagated to central in 7-11 secs

Time (in ms) taken to propagate a change in the local cluster state to remote central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name le	sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="11"} - ignoring(le) xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="7"}) by (trigger_reason)

propagated to central in 15-20 secs

Time (in ms) taken to propagate a change in the local cluster state to remote central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name le	sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="20"} - ignoring(le) xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="15"}) by (trigger_reason)

propagated to central in 20-30 secs

Time (in ms) taken to propagate a change in the local cluster state to remote central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name le	sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="30"} - ignoring(le) xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="20"}) by (trigger_reason)

propagated to central in 30-40 secs

Time (in secs) taken to propagate a change in the local cluster state to remote central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name le	sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="40"} - ignoring(le) xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="30"}) by (trigger_reason)

propagated to central in more than 40 secs

Time (in ms) taken to propagate a change in the local cluster state to remote central

Metric Name	Labels	PromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucket	cluster_name le	sum(xcp_edge_local_cluster_update_propagation_time_secs_count{cluster_name="$cluster"} - ignoring(le) xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="40"}) by (trigger_reason)
xcp_edge_local_cluster_update_propagation_time_secs_count	cluster_name	sum(xcp_edge_local_cluster_update_propagation_time_secs_count{cluster_name="$cluster"} - ignoring(le) xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="40"}) by (trigger_reason)

Number of times cluster states received by edge from central in last 1 min

Number of times cluster states are received by edge from central in the last 1 min.

Metric Name	Labels	PromQL Expression
xcp_edge_cluster_state_received_from_central_count_total	cluster_name	increase(xcp_edge_cluster_state_received_from_central_count_total{cluster_name="$cluster"}[1m])

config translation duration percentiles(in ms)

Total time taken in completing Istio translation for all the app namespaces

Metric Name	Labels	PromQL Expression
xcp_edge_total_translation_time_in_ms_bucket	cluster_name	histogram_quantile(0.5,sum(xcp_edge_total_translation_time_in_ms_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_total_translation_time_in_ms_bucket	cluster_name	histogram_quantile(0.9,sum(xcp_edge_total_translation_time_in_ms_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_total_translation_time_in_ms_bucket	cluster_name	histogram_quantile(0.95,sum(xcp_edge_total_translation_time_in_ms_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_total_translation_time_in_ms_bucket	cluster_name	histogram_quantile(0.99,sum(xcp_edge_total_translation_time_in_ms_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_total_translation_time_in_ms_bucket	cluster_name	histogram_quantile(1,sum(xcp_edge_total_translation_time_in_ms_bucket{cluster_name="$cluster"}) by (le))

Number of times config CRs received by edge from central in last 5 min

Number of times cluster states are received by edge from central

Metric Name	Labels	PromQL Expression
xcp_edge_config_updates_received_count_total	cluster_name	increase(xcp_edge_config_updates_received_count_total{cluster_name="$cluster"}[5m])

Translation count per min

Number of Istio config translations in Edge per namespace per min

Metric Name	Labels	PromQL Expression
xcp_edge_istio_translations_count_total	cluster_name	increase(xcp_edge_istio_translations_count_total{cluster_name="$cluster"}[1m])

Number of configs created/updated by edge at k8s apiserver every 5 minutes

Shows the activity of Edge creating objects in K8s API, grouped by object kind.

Metric Name	Labels	PromQL Expression
xcp_edge_cr_added_total	cluster_name	increase(xcp_edge_cr_added_total{cluster_name="$cluster"}[5m]) OR increase(xcp_edge_cr_updated_total{cluster_name="$cluster"}[5m])
xcp_edge_cr_updated_total	cluster_name	increase(xcp_edge_cr_added_total{cluster_name="$cluster"}[5m]) OR increase(xcp_edge_cr_updated_total{cluster_name="$cluster"}[5m])

Number of configs deleted by edge from k8s apiserver every 5 minutes

Shows the activity of Edge deleting objects in K8s API, grouped by object kind.

Metric Name	Labels	PromQL Expression
xcp_edge_cr_deleted_total	cluster_name	increase(xcp_edge_cr_deleted_total{cluster_name="$cluster"}[5m])

k8s config apply duration P50 percentiles for each namespace (in ms)

Metric Name	Labels	PromQL Expression
xcp_edge_k8s_configs_apply_time_secs_bucket	cluster_name	histogram_quantile(0.5,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le,namespace))
xcp_edge_k8s_configs_apply_time_secs_bucket	cluster_name	histogram_quantile(0.9,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucket	cluster_name	histogram_quantile(0.95,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucket	cluster_name	histogram_quantile(0.99,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucket	cluster_name	histogram_quantile(1,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))

k8s config apply duration P90 percentiles for each namespace (in ms)

Metric Name	Labels	PromQL Expression
xcp_edge_k8s_configs_apply_time_secs_bucket	cluster_name	histogram_quantile(0.9,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le,namespace))

k8s config apply duration P99 percentiles for each namespace (in ms)

Metric Name	Labels	PromQL Expression
xcp_edge_k8s_configs_apply_time_secs_bucket	cluster_name	histogram_quantile(0.99,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le,namespace))
xcp_edge_k8s_configs_apply_time_secs_bucket	cluster_name	histogram_quantile(0.9,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucket	cluster_name	histogram_quantile(0.95,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucket	cluster_name	histogram_quantile(0.99,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucket	cluster_name	histogram_quantile(1,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))

All goroutines

Metric Name	Labels	PromQL Expression
go_goroutines	cluster_name component	go_goroutines{cluster_name="$cluster", component="xcp"}

Edge specific gorountines

This shows the number of active goroutines in XCP Edge that are responsible for config translation.

Metric Name	Labels	PromQL Expression
xcp_edge_go_routine_count_total	cluster_name	increase(xcp_edge_go_routine_count_total{cluster_name="$cluster"}[1m])

Edge CPU consumption

Metric Name	Labels	PromQL Expression
process_cpu_seconds_total	cluster_name job	rate(process_cpu_seconds_total{job="edge-xcp",cluster_name="$cluster"}[1m])

Edge memory consumption

Metric Name	Labels	PromQL Expression
go_memstats_heap_inuse_bytes	cluster_name component	go_memstats_heap_inuse_bytes{component="xcp",cluster_name="$cluster"}
go_memstats_stack_inuse_bytes	cluster_name component	go_memstats_stack_inuse_bytes{component="xcp",cluster_name="$cluster"}

Custom Resource events[5m]

This panel represents the increase in custom resource events received by the edge registry controller in the last 5 minutes.

Metric Name	Labels	PromQL Expression
xcp_edge_registry_kubernetes_custom_resource_events_total	cluster_name	sum by(kind) (increase(xcp_edge_registry_kubernetes_custom_resource_events_total{cluster_name="$cluster"}[5m]))

EDS Update events[5m]

This Panel represents the increase in EDS update events received by the Edge registry controller in the last 5 minutes.

Metric Name	Labels	PromQL Expression
xcp_edge_registry_kubernetes_eds_update_events_received_total	cluster_name	sum(rate(xcp_edge_registry_kubernetes_eds_update_events_received_total{cluster_name="$cluster"}[5m]))

Namespace Events[5m]

This panel represents the increase in namespace events received by the Edge in the last 5-minute interval. Edge responds to the namespace events through its namespace controller.

Metric Name	Labels	PromQL Expression
xcp_edge_registry_kubernetes_namespace_events_received_total	cluster_name	sum by(event_type) (increase(xcp_edge_registry_kubernetes_namespace_events_received_total{cluster_name="$cluster"}[5m]))

Node Events received[5m]

This panel represents the increase in node events received by the Edge Kubernetes registry controller in the last 5 minutes.

There are two different sources for node events. Edge responds differently for different node event sources:

• Node Controller: Used for Gateway Hold webhook
• XDS Updater Config Update: Used to update node port service addresses

Metric Name	Labels	PromQL Expression
xcp_edge_registry_kubernetes_node_events_received_total	cluster_name	sum by(node_event_source) (increase(xcp_edge_registry_kubernetes_node_events_received_total{cluster_name="$cluster"}[5m]))

SvcUpdate events[5m]

This panel represents the Svc Update events received by the edge Kubernetes registry controller in 5-minute intervals.

Metric Name	Labels	PromQL Expression
xcp_edge_registry_kubernetes_svc_update_events_received_total	cluster_name	sum(increase(xcp_edge_registry_kubernetes_svc_update_events_received_total{cluster_name="$cluster"}[5m]))

Service Entry events received[5m]

This Panel represents the increase in Service entry events received by the Edge Kubernetes registry controller in the last 5 minutes.

Metric Name	Labels	PromQL Expression
xcp_edge_registry_kubernetes_service_entry_events_received_total	cluster_name	sum by(event_type) (increase(xcp_edge_registry_kubernetes_service_entry_events_received_total{cluster_name="$cluster"}[5m]))