Skip to main content
logoTetrate Service BridgeVersion: next

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 described in this document build up a series of Grafana dashboards that can be downloaded from here, so you can import them into your Grafana setup. For more information on how to import the dashboard into Grafana, please refer to the Grafana documentation.

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

Download

Dashboard 1 Dashboard 1

GitOps Operational Status

Operational metrics to indicate Cluster GitOps health

GitOps Status

Shows the status of the GitOps component for each cluster.

Metric NameLabelsPromQL Expression
gitops_enabledN/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 NameLabelsPromQL Expression
gitops_admission_count_totalallowed
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 NameLabelsPromQL Expression
gitops_admission_count_totalallowed
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 NameLabelsPromQL Expression
gitops_admission_duration_bucketN/A
histogram_quantile(0.99, sum(rate(gitops_admission_duration_bucket[1h])) by (cluster_name, component, le))
gitops_admission_duration_bucketN/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 NameLabelsPromQL Expression
gitops_push_count_totalsuccess
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 reconcile processes.

Metric NameLabelsPromQL Expression
gitops_push_count_totalsuccess
sum(rate(gitops_push_count_total{success="false"}[1h])) by (cluster_name, component)

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 NameLabelsPromQL Expression
gitops_convert_count_totalsuccess
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 NameLabelsPromQL Expression
gitops_convert_count_totalsuccess
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 NameLabelsPromQL Expression
xcp_central_current_edge_connectionsN/A
xcp_central_current_edge_connections

TSB Error Rate (Humans)

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

Metric NameLabelsPromQL Expression
grpc_server_handled_totalcomponent 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 NameLabelsPromQL Expression
pilot_proxy_convergence_time_bucketN/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 NameLabelsPromQL Expression
xcp_central_config_propagation_time_ms_bucketN/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 NameLabelsPromQL Expression
pilot_total_xds_internal_errorsN/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_rejectsN/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_nonceN/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_errorsN/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_pushestype
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_timeoutN/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 NameLabelsPromQL Expression
pilot_xdscluster_name
sum(pilot_xds{cluster_name="$cluster"})

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 NameLabelsPromQL Expression
pilot_total_xds_internal_errorscluster_name
sum(rate(pilot_xds_write_timeout{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_pushes{cluster_name="$cluster", type=~".*_senderr"}[1m])) OR on() vector(0)
pilot_total_xds_rejectscluster_name
sum(rate(pilot_xds_write_timeout{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_pushes{cluster_name="$cluster", type=~".*_senderr"}[1m])) OR on() vector(0)
pilot_xds_expired_noncecluster_name
sum(rate(pilot_xds_write_timeout{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_pushes{cluster_name="$cluster", type=~".*_senderr"}[1m])) OR on() vector(0)
pilot_xds_push_context_errorscluster_name
sum(rate(pilot_xds_write_timeout{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_pushes{cluster_name="$cluster", type=~".*_senderr"}[1m])) OR on() vector(0)
pilot_xds_pushescluster_name type
sum(rate(pilot_xds_write_timeout{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_pushes{cluster_name="$cluster", type=~".*_senderr"}[1m])) OR on() vector(0)
pilot_xds_write_timeoutcluster_name
sum(rate(pilot_xds_write_timeout{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster"}[1m])) + sum(rate(pilot_total_xds_rejects{cluster_name="$cluster"}[1m])) + sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster"}[1m])) +   sum(rate(pilot_xds_pushes{cluster_name="$cluster", 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 NameLabelsPromQL Expression
pilot_proxy_convergence_time_bucketcluster_name
histogram_quantile(0.5, sum(rate(pilot_proxy_convergence_time_bucket{cluster_name="$cluster"}[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 NameLabelsPromQL Expression
citadel_server_root_cert_expiry_timestampN/A
citadel_server_root_cert_expiry_timestamp - 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 NameLabelsPromQL Expression
citadel_server_cert_chain_expiry_timestampN/A
citadel_server_cert_chain_expiry_timestamp - time()

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 NameLabelsPromQL Expression
pilot_xds_pushescluster_name type
sum(irate(pilot_xds_pushes{cluster_name="$cluster", 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 NameLabelsPromQL Expression
pilot_total_xds_internal_errorscluster_name
sum(rate(pilot_total_xds_internal_errors{cluster_name="$cluster"}[1m]))
pilot_total_xds_rejectscluster_name
sum(rate(pilot_total_xds_rejects{cluster_name="$cluster"}[1m]))
pilot_xds_expired_noncecluster_name
sum(rate(pilot_xds_expired_nonce{cluster_name="$cluster"}[1m]))
pilot_xds_push_context_errorscluster_name
sum(rate(pilot_xds_push_context_errors{cluster_name="$cluster"}[1m]))
pilot_xds_pushescluster_name type
sum(rate(pilot_xds_pushes{cluster_name="$cluster", type=~".*_senderr"}[1m])) by (type)
pilot_xds_write_timeoutcluster_name
sum(rate(pilot_xds_write_timeout{cluster_name="$cluster"}[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 NameLabelsPromQL Expression
pilot_proxy_convergence_time_bucketcluster_name
histogram_quantile(0.5, sum(rate(pilot_proxy_convergence_time_bucket{cluster_name="$cluster"}[1m])) by (le))
pilot_proxy_convergence_time_bucketcluster_name
histogram_quantile(0.90, sum(rate(pilot_proxy_convergence_time_bucket{cluster_name="$cluster"}[1m])) by (le))
pilot_proxy_convergence_time_bucketcluster_name
histogram_quantile(0.99, sum(rate(pilot_proxy_convergence_time_bucket{cluster_name="$cluster"}[1m])) by (le))
pilot_proxy_convergence_time_bucketcluster_name
histogram_quantile(0.999, sum(rate(pilot_proxy_convergence_time_bucket{cluster_name="$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 NameLabelsPromQL Expression
galley_validation_failedcluster_name
sum(rate(galley_validation_failed{cluster_name="$cluster"}[1m]))
galley_validation_passedcluster_name
sum(rate(galley_validation_passed{cluster_name="$cluster"}[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 NameLabelsPromQL Expression
sidecar_injection_failure_totalcluster_name
sum(rate(sidecar_injection_failure_total{cluster_name="$cluster"}[1m]))
sidecar_injection_success_totalcluster_name
sum(rate(sidecar_injection_success_total{cluster_name="$cluster"}[1m]))

MPC Operational Status

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

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 NameLabelsPromQL Expression
mpc_tsb_config_received_countresource
mpc_tsb_config_received_count{resource=""}

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 NameLabelsPromQL Expression
mpc_config_conversion_timeN/A
mpc_config_conversion_time or on() vector(0)
mpc_config_pre_process_timeN/A
mpc_config_pre_process_time or on() vector(0)
mpc_config_total_process_timeerror
mpc_config_total_process_time{error=""} or on() vector(0)
mpc_xcp_config_push_timeerror
mpc_xcp_config_push_time{error=""} or on() vector(0)

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 NameLabelsPromQL Expression
mpc_tsb_config_received_countresource
mpc_tsb_config_received_count{resource!=""}

Conversion Time every 5m

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

Metric NameLabelsPromQL Expression
mpc_xcp_conversion_duration_bucketN/A
histogram_quantile(0.99, sum(rate(mpc_xcp_conversion_duration_bucket[5m])) by (le, resource))

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 NameLabelsPromQL Expression
mpc_xcp_config_create_opsN/A
sum(mpc_xcp_config_create_ops)
mpc_xcp_config_delete_opsN/A
sum(mpc_xcp_config_delete_ops)
mpc_xcp_config_fetch_opsN/A
sum(mpc_xcp_config_fetch_ops)
mpc_xcp_config_update_opsN/A
sum(mpc_xcp_config_update_ops)

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 NameLabelsPromQL Expression
grpc_client_handled_totalcomponent 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_totalcomponent 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_totalcomponent 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_totalcomponent grpc_code grpc_method
sum(increase(grpc_client_handled_total{component="mpc", grpc_method="GetAllClusters", grpc_code!="OK"}[5m])) or on() vector(0)

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 NameLabelsPromQL Expression
mpc_xcp_conversion_duration_sumN/A
sum(rate(mpc_xcp_conversion_duration_sum[5m])) by (resource)

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 NameLabelsPromQL Expression
mpc_xcp_config_create_ops_errN/A
sum(mpc_xcp_config_create_ops_err)
mpc_xcp_config_delete_ops_errN/A
sum(mpc_xcp_config_delete_ops_err)
mpc_xcp_config_fetch_ops_errN/A
sum(mpc_xcp_config_fetch_ops_err)
mpc_xcp_config_update_ops_errN/A
sum(mpc_xcp_config_update_ops_err)

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 NameLabelsPromQL Expression
grpc_client_msg_received_totalcomponent grpc_method
sum(increase(grpc_client_msg_received_total{grpc_method="Report",component="mpc"}[5m])) or on() vector(0)
grpc_client_msg_sent_totalcomponent grpc_method
sum(increase(grpc_client_msg_sent_total{grpc_method="PushStatus",component="mpc"}[5m])) or on() vector(0)
grpc_server_msg_received_totalcomponent 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 NameLabelsPromQL Expression
permanent_stream_operation_totalerror name
sum(increase(permanent_stream_operation_total{name="StatusPush", error=""}[5m])) or on() vector(0)
permanent_stream_operation_totalerror name
sum(increase(permanent_stream_operation_total{name="StatusPush", error!=""}[5m])) or on() vector(0)
permanent_stream_operation_totalerror name
sum(increase(permanent_stream_operation_total{name="StatusPull", error=""}[5m])) or on() vector(0)
permanent_stream_operation_totalerror name
sum(increase(permanent_stream_operation_total{name="StatusPull", error!=""}[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 NameLabelsPromQL Expression
permanent_stream_connection_attempts_totalerror name
sum(increase(permanent_stream_connection_attempts_total{name="StatusPull", error="" }[5m])) or on() vector(0)
permanent_stream_connection_attempts_totalerror name
sum(increase(permanent_stream_connection_attempts_total{name="StatusPull", error!="" }[5m])) or on() vector(0)
permanent_stream_connection_attempts_totalerror name
sum(increase(permanent_stream_connection_attempts_total{name="StatusPush", error="" }[5m])) or on() vector(0)
permanent_stream_connection_attempts_totalerror name
sum(increase(permanent_stream_connection_attempts_total{name="StatusPush", error!="" }[5m])) or on() vector(0)

Config status cache operations every 5m

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

Metric NameLabelsPromQL Expression
config_status_cache_adderror
sum(increase(config_status_cache_add{error="false"}[5m]))
config_status_cache_adderror
sum(increase(config_status_cache_add{error="true"}[5m]))
config_status_cache_checkN/A
sum(increase(config_status_cache_check[5m]))
config_status_cache_checkerror
sum(increase(config_status_cache_check{error="true"}[5m]))
config_status_cache_invalidateN/A
sum(increase(config_status_cache_invalidate[5m]))
grpc_server_msg_received_totalcomponent grpc_method
sum(increase(grpc_server_msg_received_total{grpc_method="PushStatus", component="tsb"}[5m])) or on() vector(0)

Work executions every 5m

Amount of status processing jobs processed

Metric NameLabelsPromQL Expression
config_status_report_work_duration_countN/A
sum(increase(config_status_report_work_duration_count[5m])) by (skip)
sharded_queue_work_duration_countN/A
sum(increase(sharded_queue_work_duration_count[5m])) by (name)

Status reports shard distribution

Distribution of the status report work across the different shards

Metric NameLabelsPromQL Expression
sharded_queue_work_duration_countname
sharded_queue_work_duration_count{name="status-reports"}

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 vent 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 NameLabelsPromQL Expression
config_status_cache_add_totalerror
sum(increase(config_status_cache_add_total{error="false"}[5m])) by (type)
config_status_cache_check_totalerror
sum(increase(config_status_cache_check_total{error="false"}[5m])) by (type)

Status updates worker time every 5m

Time it takes for workers to process a single status update event.

Metric NameLabelsPromQL Expression
config_status_report_work_duration_bucketN/A
histogram_quantile(0.99, sum(rate(config_status_report_work_duration_bucket[5m])) by (le, skip))
sharded_queue_work_duration_bucketN/A
histogram_quantile(0.99, sum(rate(sharded_queue_work_duration_bucket[5m])) by (le, name))

Cluster Status Update from XCP every 5m

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

Metric NameLabelsPromQL Expression
grpc_client_msg_received_totalcomponent 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 NameLabelsPromQL Expression
permanent_stream_operation_totalerror name
sum(increase(permanent_stream_operation_total{name="ClusterStateFromXCP", error="" }[5m])) or on() vector(0)
permanent_stream_operation_totalerror name
sum(increase(permanent_stream_operation_total{name="ClusterStateFromXCP", 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 NameLabelsPromQL Expression
permanent_stream_connection_attempts_totalerror name
sum(increase(permanent_stream_connection_attempts_total{name="ClusterStateFromXCP", error="" }[5m])) or on() vector(0)
permanent_stream_connection_attempts_totalerror name
sum(increase(permanent_stream_connection_attempts_total{name="ClusterStateFromXCP", error!="" }[5m])) or on() vector(0)

XCP cluster status updates processed 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 NameLabelsPromQL Expression
permanent_stream_operation_totalerror name
sum(increase(permanent_stream_operation_total{name="ClusterUpdates", error=""}[5m])) or on() vector(0)
permanent_stream_operation_totalerror 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 NameLabelsPromQL Expression
permanent_stream_connection_attempts_totalerror name
sum(increase(permanent_stream_connection_attempts_total{name="ClusterUpdates", error=""}[5m])) or on() vector(0)
permanent_stream_connection_attempts_totalerror name
sum(increase(permanent_stream_connection_attempts_total{name="ClusterUpdates", error!=""}[5m])) or on() vector(0)

OAP Operational Status

Operational metrics to indicate Tetrate Service Bridge OAP stack health.

OAP Request Rate

The request rate to OAP, by status.

Metric NameLabelsPromQL Expression
envoy_cluster_upstream_rq_xx_totalenvoy_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 NameLabelsPromQL Expression
envoy_cluster_upstream_rq_time_bucketenvoy_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_bucketenvoy_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_bucketenvoy_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_bucketenvoy_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_bucketenvoy_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 NameLabelsPromQL Expression
central_aggregation_handler_totalN/A
sum(rate(central_aggregation_handler_total[1m]))
central_app_aggregation_totalN/A
sum(rate(central_app_aggregation_total[1m]))
central_service_aggregation_totalN/A
sum(rate(central_service_aggregation_total[1m]))

OAP Aggregation Rows

Cumulative rate of rows in OAP aggreagation.

Metric NameLabelsPromQL Expression
metrics_aggregation_totalplane
sum(rate(metrics_aggregation_total{plane="management"}[1m]))

OAP Mesh Analysis Latency

The process latency of OAP service mesh telemetry streaming process.

Metric NameLabelsPromQL Expression
mesh_analysis_latency_bucketcomponent plane
histogram_quantile(0.99, sum(rate(mesh_analysis_latency_bucket{plane="control", component="oap"}[1m])) by (le))
mesh_analysis_latency_bucketcomponent plane
histogram_quantile(0.95, sum(rate(mesh_analysis_latency_bucket{plane="control", component="oap"}[1m])) by (le))
mesh_analysis_latency_bucketcomponent plane
histogram_quantile(0.90, sum(rate(mesh_analysis_latency_bucket{plane="control", component="oap"}[1m])) by (le))
mesh_analysis_latency_bucketcomponent 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 NameLabelsPromQL Expression
trace_in_latency_countplane protocol
sum(rate(trace_in_latency_count{protocol='zipkin-http',plane='control'}[1m]))

OAP Zipkin Trace Latency

The OAP trace processing latency

Metric NameLabelsPromQL Expression
trace_in_latency_bucketN/A
histogram_quantile(0.99, sum(rate(trace_in_latency_bucket[5m])) by (le))
trace_in_latency_bucketN/A
histogram_quantile(0.95, sum(rate(trace_in_latency_bucket[5m])) by (le))
trace_in_latency_bucketN/A
histogram_quantile(0.90, sum(rate(trace_in_latency_bucket[5m])) by (le))
trace_in_latency_bucketN/A
histogram_quantile(0.75, sum(rate(trace_in_latency_bucket[5m])) by (le))
trace_in_latency_bucketN/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 NameLabelsPromQL Expression
trace_analysys_error_countplane protocol
sum(rate(trace_analysys_error_count{protocol='zipkin-http',plane='control'}[1m]))

JVM Threads

Numbed of threads in OAP JVM

Metric NameLabelsPromQL Expression
jvm_threads_currentcomponent plane
sum(jvm_threads_current{component="oap", plane="management"})
jvm_threads_daemoncomponent plane
sum(jvm_threads_daemon{component="oap", plane="management"})
jvm_threads_deadlockedcomponent plane
sum(jvm_threads_deadlocked{component="oap", plane="management"})
jvm_threads_peakcomponent plane
sum(jvm_threads_peak{component="oap", plane="management"})

JVM Memory

JVM Memory stats of OAP JVM instances.

Metric NameLabelsPromQL Expression
jvm_memory_bytes_maxcomponent plane
sum by (area, instance) (jvm_memory_bytes_max{component="oap", plane="management"})
jvm_memory_bytes_usedcomponent 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 NameLabelsPromQL Expression
mpc_infoN/A
absent(mpc_info) OR on() vector(0)
mpc_xcp_config_create_ops_errN/A
rate(mpc_xcp_config_create_ops_err[20m]) OR on() vector(1) > 0
mpc_xcp_config_fetch_ops_errN/A
rate(mpc_xcp_config_fetch_ops_err[20m]) OR on() vector(1) > 0
permanent_stream_operation_totalcomponent 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 NameLabelsPromQL Expression
xcp_central_config_propagation_time_ms_bucketN/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_connectionscomponent 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 NameLabelsPromQL Expression
xcp_central_config_propagation_event_count_totalstatus 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_edgeN/A
max (time() - max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge[2m]) == 0) + on(edge) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", type="cluster_state"} /1000) by (edge,type) > bool 700)
xcp_central_current_onboarded_edge_totalN/A
max (time() - max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge[2m]) == 0) + on(edge) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", type="cluster_state"} /1000) by (edge,type) > bool 700)
xcp_central_last_config_propagation_event_timestamp_msedge type
max (time() - max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge[2m]) == 0) + on(edge) group_right xcp_central_last_config_propagation_event_timestamp_ms{edge!="", type="cluster_state"} /1000) by (edge,type) > 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 NameLabelsPromQL Expression
grpc_server_handled_totalcomponent 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 NameLabelsPromQL Expression
iam_auth_time_bucketerror 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_bucketerror 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_bucketerror 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_bucketerror 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_counterror
(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 NameLabelsPromQL Expression
jvm_threads_currentcomponent plane
(sum(jvm_threads_current{component="oap", plane="management"}) OR on() vector(0)) == bool 0
jvm_threads_currentcomponent 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_totalstatus 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 NameLabelsPromQL Expression
envoy_cluster_internal_upstream_rq_time_bucketcomponent
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_bucketcomponent
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 NameLabelsPromQL Expression
envoy_cluster_internal_upstream_rq_totalcomponent 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 NameLabelsPromQL Expression
envoy_cluster_internal_upstream_rq_totalcomponent 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 NameLabelsPromQL Expression
envoy_cluster_internal_upstream_rq_time_bucketcomponent
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_bucketcomponent
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 NameLabelsPromQL Expression
grpc_server_handled_totalcomponent 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 NameLabelsPromQL Expression
grpc_server_handled_totalcomponent 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 NameLabelsPromQL Expression
iam_auth_time_counterror
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 NameLabelsPromQL Expression
iam_auth_time_counterror
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 NameLabelsPromQL Expression
iam_auth_time_bucketerror
histogram_quantile(0.99, sum(rate(iam_auth_time_bucket{error=""}[1m])) by (le, provider))
iam_auth_time_bucketerror
histogram_quantile(0.95, sum(rate(iam_auth_time_bucket{error=""}[1m])) by (le, provider))

Data Store Success Rate

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

This graph also includes transactions. These are standard SQL transactions and consist of multiple operations.

Metric NameLabelsPromQL Expression
persistence_operation_totalerror
sum(rate(persistence_operation_total{error=""}[1m])) by (kind, method)
persistence_transaction_totalerror
sum(rate(persistence_transaction_total{error=""}[1m]))

Data Store Latency

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

This graph also includes transactions. These are standard SQL transactions and consist of multiple operations.

Metric NameLabelsPromQL Expression
persistence_operation_duration_bucketN/A
histogram_quantile(0.99, sum(rate(persistence_operation_duration_bucket[1m])) by (le, method))
persistence_transaction_duration_bucketN/A
histogram_quantile(0.99, sum(rate(persistence_transaction_duration_bucket[1m])) by (le))

Data Store 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 NameLabelsPromQL Expression
persistence_operation_totalerror kind
sum(rate(persistence_operation_total{error!="", kind!="iam_revoked_token"}[1m])) by (kind, method, error)
persistence_transaction_totalerror
sum(rate(persistence_transaction_total{error!=""}[1m])) by (error)

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 NameLabelsPromQL Expression
persistence_concurrent_transactionN/A
sum(persistence_concurrent_transaction)

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 NameLabelsPromQL Expression
service_registry_operation_duration_counterror
sum(increase(service_registry_operation_duration_count{error=""}[1m])) by (operation)
service_registry_operation_duration_counterror
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 NameLabelsPromQL Expression
service_registry_operation_duration_bucketN/A
histogram_quantile(0.99, sum(rate(service_registry_operation_duration_bucket[1m])) by (le, operation))
service_registry_total_duration_bucketN/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 NameLabelsPromQL Expression
ngac_pdp_operation_totalerror
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 NameLabelsPromQL Expression
ngac_pdp_operation_totalerror
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 NameLabelsPromQL Expression
ngac_pdp_operation_duration_bucketN/A
histogram_quantile(0.99, sum(rate(ngac_pdp_operation_duration_bucket[1m])) by (method, le))
ngac_pdp_operation_duration_bucketN/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 NameLabelsPromQL Expression
ngac_pip_operation_totalerror
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 NameLabelsPromQL Expression
ngac_pip_operation_duration_bucketN/A
histogram_quantile(0.99, sum(rate(ngac_pip_operation_duration_bucket[1m])) by (method, le))
ngac_pip_operation_duration_bucketN/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 NameLabelsPromQL Expression
ngac_pip_operation_totalerror
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 NameLabelsPromQL Expression
ngac_pip_concurrent_transactionN/A
sum(ngac_pip_concurrent_transaction)

XCP Central Operational Status

Operational metrics to indicate XCP Central health.

Metric NameLabelsPromQL Expression
process_start_time_secondscomponent plane
time() - process_start_time_seconds{component="xcp",plane="management"}

XCP Central Version

Metric NameLabelsPromQL Expression
xcp_central_versionN/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 NameLabelsPromQL Expression
xcp_central_current_onboarded_edge_totalN/A
time() - max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge_total[2m]) == 0) + 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_msedge status type
time() - max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge_total[2m]) == 0) + 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 NameLabelsPromQL Expression
xcp_central_current_onboarded_edgeN/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_msN/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 NameLabelsPromQL Expression
xcp_central_current_onboarded_edge_totalN/A
time() - max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge_total[2m]) == 0) + 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_msedge status type
time() - max((increase(xcp_central_current_onboarded_edge_total[2m]) unless increase(xcp_central_current_onboarded_edge_total[2m]) == 0) + 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 NameLabelsPromQL Expression
xcp_central_current_onboarded_edge_totalN/A
time() - max((increase(xcp_central_current_onboarded_edge_total[1m]) unless increase(xcp_central_current_onboarded_edge_total[1m]) == 0) + 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_msedge status
time() - max((increase(xcp_central_current_onboarded_edge_total[1m]) unless increase(xcp_central_current_onboarded_edge_total[1m]) == 0) + 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 NameLabelsPromQL Expression
xcp_central_config_propagation_event_count_totalstatus 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_totalstatus 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_totalN/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_totalN/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 NameLabelsPromQL Expression
xcp_central_config_propagation_event_count_totalstatus
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_totalN/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 NameLabelsPromQL Expression
xcp_central_config_propagation_time_ms_bucketN/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_bucketN/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_bucketN/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_bucketN/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_bucketN/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_edgeN/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_edgeN/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_edgeN/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_edgeN/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_edgeN/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 NameLabelsPromQL Expression
xcp_central_config_update_error_countN/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 NameLabelsPromQL Expression
xcp_central_config_update_push_count_totalN/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 NameLabelsPromQL Expression
xcp_central_pending_configs_totalN/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 NameLabelsPromQL Expression
xcp_central_current_edge_connectionsconnection_type
xcp_central_current_edge_connections{connection_type="edge_config_distribution"} OR on() vector(0)
xcp_central_current_edge_connectionsconnection_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 NameLabelsPromQL Expression
xcp_central_pending_on_ref_configs_totalN/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 NameLabelsPromQL Expression
xcp_central_validation_webhook_passed_countN/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 NameLabelsPromQL Expression
xcp_central_connection_register_count_totalconnection_type
rate(xcp_central_connection_register_count_total{connection_type="cluster_state"}[1m]) * 60
xcp_central_connection_register_count_totalconnection_type
rate(xcp_central_connection_register_count_total{connection_type="edge_config_distribution"}[1m]) * 60

Rate of webhook validation errors

Rate of webhook validation errors by GVK

Metric NameLabelsPromQL Expression
xcp_central_validation_webhook_failed_countN/A
increase(xcp_central_validation_webhook_failed_count[5m]) OR on() vector(0)
xcp_central_validation_webhook_http_error_countN/A
increase(xcp_central_validation_webhook_http_error_count[5m]) OR on() vector(0)

All goroutines

Metric NameLabelsPromQL Expression
go_goroutinescomponent plane
go_goroutines{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 NameLabelsPromQL Expression
go_goroutinescomponent plane
increase(go_goroutines{component="xcp",plane="management"}[1m])
xcp_central_go_routine_count_totalN/A
increase(xcp_central_go_routine_count_total[1m])

Central memory consumption

Metric NameLabelsPromQL Expression
go_memstats_heap_inuse_bytescomponent plane
go_memstats_heap_inuse_bytes{component="xcp",plane="management"}
go_memstats_stack_inuse_bytescomponent plane
go_memstats_stack_inuse_bytes{component="xcp",plane="management"}

Edges' memory consumption

This shows the current memory usage for all Edges

Metric NameLabelsPromQL Expression
go_memstats_heap_inuse_bytescomponent plane
go_memstats_heap_inuse_bytes{component="xcp",plane="control"}

Central CPU consumption

Metric NameLabelsPromQL Expression
process_cpu_seconds_totaljob
rate(process_cpu_seconds_total{job="central-xcp"}[1m])

All edges' CPU consumption

Metric NameLabelsPromQL Expression
process_cpu_seconds_totaljob
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 NameLabelsPromQL Expression
xcp_central_ha_coordinator_upN/A
avg by(instance) (xcp_central_ha_coordinator_up)

XCP Central Leader

Metric NameLabelsPromQL Expression
xcp_central_ha_coordinator_acting_as_leaderN/A
avg by(instance) (xcp_central_ha_coordinator_acting_as_leader)

XCP Central Followers

Metric NameLabelsPromQL Expression
xcp_central_ha_coordinator_acting_as_followerN/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 NameLabelsPromQL Expression
xcp_central_ha_coordinator_leader_election_loops_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_primary_relay_server_streams_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_primary_relay_server_streams_open_count_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_primary_relay_server_streams_rejected_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_primary_relay_server_streams_discontinued_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_primary_relay_server_cluster_states_sent_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_primary_relay_server_cluster_states_received_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_primary_relay_server_cluster_state_sent_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_primary_relay_server_cluster_state_received_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_secondary_relay_client_upN/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 NameLabelsPromQL Expression
xcp_central_ha_secondary_relay_client_streams_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_secondary_relay_client_streams_open_count_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_secondary_relay_client_cluster_states_received_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_secondary_relay_client_cluster_states_sent_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_secondary_relay_client_cluster_state_received_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_secondary_relay_client_cluster_state_sent_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_cross_partition_enabledN/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 NameLabelsPromQL Expression
xcp_central_ha_cross_partition_requestor_relay_client_streams_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_cross_partition_requestor_relay_client_streams_open_countN/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 NameLabelsPromQL Expression
xcp_central_ha_cross_partition_requestor_relay_client_cluster_states_received_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_cross_partition_requestor_relay_client_cluster_state_received_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_cross_partition_responder_relay_server_streams_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_cross_partition_requestor_relay_client_streams_open_countN/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 NameLabelsPromQL Expression
xcp_central_ha_cross_partition_responder_relay_server_cluster_states_sent_totalN/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 NameLabelsPromQL Expression
xcp_central_ha_cross_partition_responder_relay_server_cluster_state_sent_totalN/A
sum by(cluster) (increase(xcp_central_ha_cross_partition_responder_relay_server_cluster_state_sent_total[5m]))

XCP Edge status

Metric NameLabelsPromQL Expression
process_start_time_secondscluster_name component
time() - process_start_time_seconds{cluster_name="$cluster",component="xcp"}

XCP Edge Version

Metric NameLabelsPromQL Expression
xcp_edge_istio_versionscluster_name
label_replace(xcp_edge_istio_versions{cluster_name="$cluster"}, "istio_versions", "$1", "version", "(.*)")
xcp_edge_versioncluster_name
label_replace(xcp_edge_version{cluster_name="$cluster"}, "xcp_version", "$1", "version", "(.*)")

Number of gatewayHost exposed

Metric NameLabelsPromQL Expression
xcp_edge_gateway_hosts_countcluster_name
xcp_edge_gateway_hosts_count{cluster_name="$cluster"}

Active connections to central

Current peer connections this edge holds against remote edges.

Metric NameLabelsPromQL Expression
xcp_edge_stream_connect_count_totalcluster_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 NameLabelsPromQL Expression
xcp_edge_last_config_resync_to_central_timestamp_mscluster_name
time() - xcp_edge_last_config_resync_to_central_timestamp_ms{cluster_name="$cluster"} / 1000
xcp_edge_last_push_to_central_timestamp_mscluster_name
time() - xcp_edge_last_push_to_central_timestamp_ms{cluster_name="$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 NameLabelsPromQL Expression
xcp_edge_cluster_state_build_time_secs_bucketcluster_name
histogram_quantile(0.5,sum(xcp_edge_cluster_state_build_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_cluster_state_build_time_secs_bucketcluster_name
histogram_quantile(0.9,sum(xcp_edge_cluster_state_build_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_cluster_state_build_time_secs_bucketcluster_name
histogram_quantile(0.95,sum(xcp_edge_cluster_state_build_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_cluster_state_build_time_secs_bucketcluster_name
histogram_quantile(0.99,sum(xcp_edge_cluster_state_build_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_cluster_state_build_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_countcluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_name
histogram_quantile(0.5,sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_name
histogram_quantile(0.9,sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_name
histogram_quantile(0.95,sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_name
histogram_quantile(0.99,sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_name le
sum(xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$cluster", le="1.5"}) by (trigger_reason)
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_name le
xcp_edge_local_cluster_update_propagation_time_secs_bucket{cluster_name="$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 NameLabelsPromQL Expression
xcp_edge_config_status_updates_sent_gvk_totalcluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_current_state_state_events_queue_lencluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_local_cluster_update_propagation_time_secs_bucketcluster_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_countcluster_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 NameLabelsPromQL Expression
xcp_edge_cluster_state_received_from_central_count_totalcluster_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 NameLabelsPromQL Expression
xcp_edge_total_translation_time_in_ms_bucketcluster_name
histogram_quantile(0.5,sum(xcp_edge_total_translation_time_in_ms_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_total_translation_time_in_ms_bucketcluster_name
histogram_quantile(0.9,sum(xcp_edge_total_translation_time_in_ms_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_total_translation_time_in_ms_bucketcluster_name
histogram_quantile(0.95,sum(xcp_edge_total_translation_time_in_ms_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_total_translation_time_in_ms_bucketcluster_name
histogram_quantile(0.99,sum(xcp_edge_total_translation_time_in_ms_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_total_translation_time_in_ms_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_config_updates_received_count_totalcluster_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 NameLabelsPromQL Expression
xcp_edge_istio_translations_count_totalcluster_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 NameLabelsPromQL Expression
xcp_edge_cr_added_totalcluster_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_totalcluster_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 NameLabelsPromQL Expression
xcp_edge_cr_deleted_totalcluster_name
increase(xcp_edge_cr_deleted_total{cluster_name="$cluster"}[5m])

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

Metric NameLabelsPromQL Expression
xcp_edge_k8s_configs_apply_time_secs_bucketcluster_name
histogram_quantile(0.5,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le,namespace))
xcp_edge_k8s_configs_apply_time_secs_bucketcluster_name
histogram_quantile(0.9,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucketcluster_name
histogram_quantile(0.95,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucketcluster_name
histogram_quantile(0.99,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_k8s_configs_apply_time_secs_bucketcluster_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 NameLabelsPromQL Expression
xcp_edge_k8s_configs_apply_time_secs_bucketcluster_name
histogram_quantile(0.99,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le,namespace))
xcp_edge_k8s_configs_apply_time_secs_bucketcluster_name
histogram_quantile(0.9,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucketcluster_name
histogram_quantile(0.95,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucketcluster_name
histogram_quantile(0.99,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))
xcp_edge_k8s_configs_apply_time_secs_bucketcluster_name
histogram_quantile(1,sum(xcp_edge_k8s_configs_apply_time_secs_bucket{cluster_name="$cluster"}) by (le))

All goroutines

Metric NameLabelsPromQL Expression
go_goroutinescluster_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 NameLabelsPromQL Expression
xcp_edge_go_routine_count_totalcluster_name
increase(xcp_edge_go_routine_count_total{cluster_name="$cluster"}[1m])

Edge CPU consumption

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

Edge memory consumption

Metric NameLabelsPromQL Expression
go_memstats_heap_inuse_bytescluster_name component
go_memstats_heap_inuse_bytes{component="xcp",cluster_name="$cluster"}
go_memstats_stack_inuse_bytescluster_name component
go_memstats_stack_inuse_bytes{component="xcp",cluster_name="$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 NameLabelsPromQL Expression
xcp_edge_registry_kubernetes_custom_resource_events_totalcluster_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 NameLabelsPromQL Expression
xcp_edge_registry_kubernetes_eds_update_events_received_totalcluster_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 NameLabelsPromQL Expression
xcp_edge_registry_kubernetes_namespace_events_received_totalcluster_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 NameLabelsPromQL Expression
xcp_edge_registry_kubernetes_node_events_received_totalcluster_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 NameLabelsPromQL Expression
xcp_edge_registry_kubernetes_svc_update_events_received_totalcluster_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 NameLabelsPromQL Expression
xcp_edge_registry_kubernetes_service_entry_events_received_totalcluster_name
sum by(event_type) (increase(xcp_edge_registry_kubernetes_service_entry_events_received_total{cluster_name="$cluster"}[5m]))