About the telemetry pipeline
Learn about the Gloo telemetry pipeline architecture, its components, and default pipelines that you can choose from.
You can gain insights into the health and performance of your cluster components by using the Gloo telemetry pipeline. Built on top of the OpenTelemetry open source project, the Gloo telemetry pipeline helps you to collect and export telemetry data, such as metrics, logs, traces, and Gloo insights, and to visualize this data by using Gloo observability tools.
Review the information on this page to learn more about the Gloo telemetry pipeline and how to use it in your cluster.
Setup
The Gloo telemetry pipeline is set up by default when you install Gloo Mesh.
To see the receivers, processors, and exporters that are set up by default for you, run the following commands:
kubectl get configmap gloo-telemetry-gateway-config -n gloo-mesh -o yaml
kubectl get configmap gloo-telemetry-collector-config -n gloo-mesh -o yaml
Disable the telemetry pipeline
If you want to disable the Gloo telemetry pipeline, follow the Upgrade guide and add the following configuration to your Helm values file:
telemetryCollector:
enabled: false
telemetryGateway:
enabled: false
Disabling the Gloo telemetry pipeline removes the Gloo telemetry gateway and collector agent pods from your cluster. If you previously collected telemetry data, and data was not exported to a different observability tool, all telemetry data is removed. To keep telemetry data, consider exporting data to other observability tools, such as Prometheus, Jaeger, or your own before disabling the telemetry pipeline.
Customize the pipeline
You can customize the Gloo telemetry pipeline and set up additional receivers, processors, and exporters in your pipeline. The Gloo telemetry pipeline is set up with pre-built pipelines that use a variety of receivers, processors, and exporters to collect and store telemetry data in your cluster. You can enable and disable these pipelines as part of your Helm installation.
Because the Gloo telemetry pipeline is built on top of the OpenTelemetry open source project, you also have the option to add your own custom receivers, processors, and exporters to the pipeline. For more information, see the pipeline architecture information in the OpenTelemetry documentation.
To see the receivers, processors, and exporters that are set up by default for you, run the following commands:
kubectl get configmap gloo-telemetry-gateway-config -n gloo-mesh -o yaml
kubectl get configmap gloo-telemetry-collector-config -n gloo-mesh -o yaml
To add more telemetry data to the Gloo telemetry pipeline, see Customize the pipeline.
Architecture
The Gloo telemetry pipeline is decoupled from the Gloo agents and management server core functionality, and consists of two main components: the Gloo telemetry collector agent and telemetry gateway.
Flip through the cards to see how these components are set up in a single and multicluster environment.
In single cluster setups, only a Gloo telemetry collector agent is deployed to the cluster. The agent is configured to scrape metrics from workloads in the cluster, and to enrich the data, such as by adding workload IDs that you can later use to filter metrics. In addition, it receives other telemetry data, such as traces. Depending on the type of telemetry data, the collector agent then forwards this data to other observability tools, such as Jaeger as shown in the following image.
You also have the option to set up your own exporter to forward telemetry data to an observability tool of your choice. To see an example for how to export data to Datadog, see Forward metrics to Datadog.
In multicluster setups, Gloo telemetry collector agents are deployed to the management cluster and each workload cluster. The agents are configured to scrape metrics from workloads in the cluster, and to enrich the data, such as by adding workload IDs that you can later use to filter metrics. In addition, they receive other telemetry data, such as traces and Gloo analyzer logs.
A Gloo telemetry gateway is also deployed to the management cluster and exposed with a Kubernetes load balancer service. The collector agents in each workload cluster send all their telemetry data to the telemetry gateway’s service endpoint. You can choose from a set of pre-built pipelines to configure how the telemetry gateway forwards telemetry data within the cluster.
You also have the option to forward telemetry data to an observability tool of your choice by adding custom exporters to either the telemetry gateway or to each collector agent. The option that is right for you depends on the size of your environment, the amount of telemetry data that you want to export, and the compute resources that are available to the Gloo telemetry pipeline components. To see an example for how to export data to Datadog, see Forward metrics to Datadog.
The diagram shows the default ports that are added as prometheus.io/port: "<port_number>" pod annotations to the workloads that expose metrics. This port is automatically used by the Gloo collector agent, Gloo telemetry gateway, and Prometheus to scrape the metrics from these workloads. You can change the port by changing the pod annotation. However, keep in mind that changing the default scraping ports might lead to unexpected results, because Gloo Mesh processes might depend on the default setting.
Learn more about the telemetry data that is collected in the Gloo telemetry pipeline.
Built-in telemetry pipelines
The Gloo telemetry pipeline is set up with default pipelines that you can enable to collect telemetry data in your cluster.
Default metrics in the pipeline
By default, the Gloo telemetry pipeline is configured to scrape the metrics that are required for the Gloo UI from various workloads in your cluster by using the metrics/ui and metrics/prometheus pipelines. The built-in Prometheus server is configured to scrape metrics from the Gloo collector agent (single cluster), or Gloo telemetry gateway and collector agent (multicluster). To reduce cardinality in the Gloo telemetry pipeline, only a few labels are collected for each metric. For more information, see Metric labels.
Review the metrics that are available in the Gloo telemetry pipeline. You can set up additional receivers to scrape other metrics, or forward the metrics to other observability tools, such as Datadog, by creating your own custom exporter for the Gloo telemetry gateway. To find an example setup, see Forward metrics to Datadog.
Istio proxy, ztunnel, and waypoint proxy metrics
| Metric | Description |
|---|---|
istio_requests_total | The number of requests that were processed for an Istio proxy. |
istio_request_duration_milliseconds | The time it takes for a request to reach its destination in milliseconds. |
istio_request_duration_milliseconds_bucket | The time it takes for a request to reach its destination in milliseconds. |
istio_request_duration_milliseconds_count | The total number of Istio requests since the Istio proxy was last started. |
istio_request_duration_milliseconds_sum | The sum of all request durations since the last start of the Istio proxy. |
istio_tcp_sent_bytes | The number of bytes that are sent in a response at a particular moment in time. |
istio_tcp_sent_bytes_total | The total number of bytes that are sent in a response. |
istio_tcp_received_bytes | The number of bytes that are received in a request at a particular moment in time. |
istio_tcp_received_bytes_total | The total number of bytes that are received in a request. |
istio_tcp_connections_opened | The number of open connections to an Istio proxy at a particular moment in time. |
istio_tcp_connections_opened_total | The total number of open connections to an Istio proxy. |
Istiod metrics
| Metric | Description |
|---|---|
pilot_proxy_convergence_time | The time it takes between applying a configuration change and the Istio proxy receiving the configuration change. |
Gloo management server metrics
| Metric | Description |
|---|---|
gloo_mesh_build_snapshot_metric_time_sec | The time in seconds for the Gloo management server to generate an output snapshot for connected Gloo agents. |
gloo_mesh_garbage_collection_time_sec | The time it takes for the garbage collector to clean up unused resources in seconds, such as after the custom resource translation. |
gloo_mesh_reconciler_time_sec_bucket | The time the Gloo management server needs to sync with the Gloo agents in the workload clusters to apply the translated resources. This metric is captured in seconds for the following intervals (buckets): 1, 2, 5, 10, 15, 30, 50, 80, 100, 200. |
gloo_mesh_redis_relation_err_total | The number of errors that occurred during a read or write operation of relationship data to Redis. |
gloo_mesh_redis_sync_err_total | The number of times the Gloo management server could not read from or write to the Gloo Redis instance. |
gloo_mesh_redis_write_time_sec | The time it takes in seconds for the Gloo management server to write to the Redis database. |
gloo_mesh_relay_client_delta_pull_time_sec | The time it takes for a Gloo agent to receive a delta output snapshot from the Gloo management server in seconds. |
gloo_mesh_relay_client_delta_pull_err | The number of errors that occurred while sending a delta output snapshot to a connected Gloo agent. |
gloo_mesh_relay_client_delta_push_last_loop_timestamp_seconds | The unix timestamp (in seconds) of the last time the relay agent created a delta snapshot. This metric is generated, even if the snapshot was empty and not sent to the management server. |
gloo_mesh_relay_client_delta_push_time_sec | The time it takes for a Gloo agent to send a delta input snapshot to the Gloo management server in seconds. |
gloo_mesh_relay_client_delta_push_err | The number of errors that occurred while sending a delta input snapshot from the Gloo agent to the Gloo management server. |
gloo_mesh_relay_client_last_delta_pull_received_timestamp_seconds | The unix timestamp (in seconds) of the last time the relay agent received a delta snapshot from the management server. |
gloo_mesh_relay_client_last_delta_push_timestamp_seconds | The unix timestamp (in seconds) of the last time the relay agent pushed a delta snapshot (either non-empty, or the initial snapshot). |
gloo_mesh_relay_client_last_server_communication_pull_stream_timestamp_seconds | The unix timestamp (in seconds) of the last time the relay agent received a response from the management server. |
gloo_mesh_snapshot_upserter_op_time_sec | The time it takes for a snapshot to be updated and/or inserted in the Gloo management server local memory in seconds. |
gloo_mesh_safe_mode_active | Indicates whether safe mode is enabled in the Gloo management server. For more information, see Redis safe mode options. |
gloo_mesh_translation_time_sec_bucket | The time the Gloo management server needs to translate Gloo resources into Istio or Envoy resources. This metric is captured in seconds for the following intervals (buckets): 1, 2, 5, 10, 15, 20, 25, 30, 45, 60, and 120. |
gloo_mesh_translator_concurrency | The number of translation operations that the Gloo management server can perform at the same time. |
object_write_fails_total | The number of times the Gloo agent tried to write invalid Istio configuration to the cluster that was rejected by the Istio control plane istiod. |
relay_pull_clients_connected | The number of Gloo agents that are connected to the Gloo management server. |
relay_push_clients_warmed | The number of Gloo agents that are ready to accept updates from the Gloo management server. |
solo_io_gloo_gateway_license | The number of minutes until the Gloo Mesh Gateway license expires. To prevent your management server from crashing when the license expires, make sure to upgrade the license before expiration. |
solo_io_gloo_mesh_license | The number of minutes until the Gloo Mesh Enterprise license expires. To prevent your management server from crashing when the license expires, make sure to upgrade the license before expiration. |
translation_error | The number of translation errors that were reported by the Gloo management server. |
translation_warning | The number of translation warnings that were reported by the Gloo management server. |
Gloo telemetry pipeline metrics
| Metric | Description |
|---|---|
otelcol_processor_refused_metric_points | The number of metrics that were refused by the Gloo telemetry pipeline processor. For example, metrics might be refused to prevent collector agents from being overloaded in the case of insufficient memory resources. |
otelcol_receiver_refused_metric_points | The number of metrics that were refused by the Gloo telemetry pipeline receiver. For example, metrics might be refused to prevent collector agents from being overloaded in the case of insufficient memory resources. |
otelcol_processor_refused_spans | The metric spans that were refused by the memory_limiter in the Gloo telemetry pipeline to prevent collector agents from being overloaded. |
otelcol_exporter_queue_capacity | The amount of telemetry data that can be stored in memory while waiting on a worker in the collector agent to become available to send the data. |
otelcol_exporter_queue_size | The amount of telemetry data that is currently stored in memory. If the size is equal or larger than otelcol_exporter_queue_capacity, new telemetry data is rejected. |
otelcol_loadbalancer_backend_latency | The time the collector agents need to export telemetry data. |
otelcol_exporter_send_failed_spans | The number of telemetry data spans that could not be sent to a backend. |
Metrics labels
To reduce cardinality in the Gloo telemetry pipeline, only the following labels are collected for each metric.
| Metric group | Labels |
|---|---|
| Istio | [“cluster”, “collector_pod” , “connection_security_policy”, “destination_cluster”, “destination_principal”, “destination_service”, “destination_workload”, “destination_workload_id”, “destination_workload_namespace”, “gloo_mesh”, “namespace”, “pod_name”, “reporter”, “response_code”, “source_cluster”, “source_principal”, “source_workload”, “source_workload_namespace”, “version”, “workload_id”] |
| Telemetry pipeline | [“app”, “cluster”, “collector_name”, “collector_pod”, “component”, “exporter”, “namespace”, “pod_template_generation”, “processor”, “service_version”] |
Observability tools
The Gloo observability pipeline comes with several observability tools that help you monitor the health of Gloo and Istio components and the workloads in your cluster.
Gloo Mesh health
View the configuration and status of Gloo custom resources. You can also view the health of the clusters that are registered with the Gloo management server. To monitor the health of your Gloo Mesh components, such as the Gloo management server or Gloo telemetry collector agent, use the Gloo UI log viewer to view, filter, search, or download logs for these components.
Use the Prometheus expression browser to run PromQL queries to analyze and aggregate Gloo Mesh metrics. To view metrics that are collected by default, see Gloo management server metrics. To view the alerts that are automatically set up for you, see Alerts.
Ingress gateway
View the components of your gateway setup. To monitor the traffic to your gateway, you can access the Gloo UI Graph.
Check the Istio insights that the Gloo analyzer collects for your gateways and reports in the Gloo UI. These insights can help determine the security posture of your setup, the gateway health, and production readiness. The insights give you a checklist to address issues that might otherwise be hard to detect across your environment.
The Gloo telemetry pipeline collects Istio metrics from the ingress gateway proxy and exposes those metrics so that the built-in Prometheus server can scrape them. To view the metrics that are collected by default, see Istio proxy metrics. You can access these metrics by running PromQL queries in the Prometheus expression browser. To find example queries that you can run, see Ingress gateway queries.
You can enable request tracing for the ingress gateway and add these traces to the Gloo telemetry pipeline so that they can be forwarded to the built-in or a custom Jaeger instance. For more information about how to set up tracing, and how to enable Jaeger, see Add Istio request traces.
Leverage the default Envoy access log collector to record logs for the apps that send requests to the Istio ingress gateway. You can review these logs to troubleshoot issues as-needed, or scrape these logs to view them in your larger platform logging system.
Service mesh
View your service mesh workloads. To monitor the traffic to your service mesh workloads, you can access the Gloo UI Graph.
Check the Istio insights that the Gloo analyzer collects for your service mesh workloads and reports in the Gloo UI. These insights can help determine the security posture of your workloads, their health, and production readiness. The insights give you a checklist to address issues that might otherwise be hard to detect across your environment.
The Gloo telemetry pipeline collects Istio metrics from the Istio-enabled workloads and exposes those metrics so that the built-in Prometheus server can scrape them. To view the metrics that are collected by default, see Istio proxy metrics. You can access these metrics by running PromQL queries in the Prometheus expression browser. To find example queries that you can run, see Service mesh workload queries.
You can enable request tracing for Istio-enabled workloads and add these traces to the Gloo telemetry pipeline so that they can be forwarded to the built-in or a custom Jaeger instance. For more information about how to set up tracing, and how to enable Jaeger, see Add Istio request traces.
Leverage the default Envoy access log collector to record logs for the apps that send requests to Istio-enabled workloads in your service mesh. You can review these logs to troubleshoot issues as-needed, or scrape these logs to view them in your larger platform logging system.