- Documentation
- Red Hat OpenShift Serverless
- Observability
- Tracing
- Tracing requests history bug_report picture_as_pdf
- About OpenShift Serverless
- Installing OpenShift Serverless
- Preparing to install OpenShift Serverless
- Installing the OpenShift Serverless Operator
- Installing the Knative CLI
- Installing Knative Serving
- Installing Knative Eventing
- Configuring Knative for Apache Kafka
- Configuring kube-rbac-proxy resources for Knative for Apache Kafka
- Configuring OpenShift Serverless Functions
- Installing the OpenShift Serverless Logic Operator
- Installing the OpenShift Serverless Logic Knative Workflow plugin
- OpenShift Serverless upgrades
- Functions
- Getting started with functions
- Creating functions
- Running functions locally
- Deploying functions
- Building functions
- Listing existing functions
- Invoking functions
- Deleting functions
- Building and deploying functions on the cluster
- Connecting an event source to a function
- Subscribing functions to CloudEvents
- Functions development reference guide
- Configuring functions
- Serving
- Getting started with Knative Serving
- Scalability and Performance
- Autoscaling
- Configuring OpenShift Serverless applications
- Debugging OpenShift Serverless applications
- Kourier and Istio ingresses
- Serving transport encryption
- Traffic splitting
- External and Ingress routing
- HTTP configuration
- Configuring access to Knative services
- Configuring kube-rbac-proxy resources for Serving
- Configuring burst and QPS for net-kourier
- Configuring custom domains for Knative services
- High availability configuration for Knative Serving
- Tuning serving configuration
- Configuring Queue Proxy resources
- Eventing
- Knative Eventing
- Event sources
- Event sinks
- Brokers
- Triggers
- Channels
- Subscriptions
- Event delivery
- Event discovery
- Tuning eventing configuration
- Configuring TLS encryption in Eventing
- Configuring kube-rbac-proxy resources for Eventing
- Using ContainerSource with OpenShift Service Mesh
- Using a sink binding with OpenShift Service Mesh
- Serverless Logic
- Knative CLI
- Observability
- Integrations
- Integrating Service Mesh with OpenShift Serverless
- Using Service Mesh to isolate network traffic with OpenShift Serverless
- Integrating OpenShift Serverless with the cost management service
- Integrating OpenShift Serverless with OpenShift Pipelines
- Using NVIDIA GPU resources with serverless applications
- Removing OpenShift Serverless
Tracing requests
Distributed tracing records the path of a request through the various services that make up an application. It is used to tie information about different units of work together, to understand a whole chain of events in a distributed transaction. The units of work might be executed in different processes or hosts.
Distributed tracing overview
As a service owner, you can use distributed tracing to instrument your services to gather insights into your service architecture. You can use distributed tracing for monitoring, network profiling, and troubleshooting the interaction between components in modern, cloud-native, microservices-based applications.
With distributed tracing you can perform the following functions:
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Monitor distributed transactions
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Optimize performance and latency
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Perform root cause analysis
Red Hat OpenShift distributed tracing consists of two main components:
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Red Hat OpenShift distributed tracing platform - This component is based on the open source Jaeger project.
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Red Hat OpenShift distributed tracing data collection - This component is based on the open source OpenTelemetry project.
Both of these components are based on the vendor-neutral OpenTracing APIs and instrumentation.