This codelab explains how to instrument your microservices. How to analyze the trace on Cloud Trace. How to propagate span contexts across the wire between app components.How to get started with the OpenTelemetry Trace libraries in Go project.After that, you will embed a profiler agent in the server and investigate the bottleneck. You will instrument the trace information across the request. Server accepts the query from the client, fetches all Shakespare works in text format from Google Cloud Storage, searches the lines that contain the query and return the number of the line that matched to the client.Clients pass through the query from the loadgen to the server in gRPC.Loadgen sends a query string to the client in HTTP.The architecture of Shakesapp is as described below: Shakesapp) that runs on a Google Kubernetes Engine cluster. In this codelab, you're going to instrument trace information in the services called "Shakespeare application" (a.k.a. Google Cloud Trace is one of the options for distributed trace backend and it is well integrated with other products in Google Cloud. Spans often have hierarchical relationships between each other - in the picture below all smaller spans are child spans of a large /messages span, and are assembled into one Trace that shows the path of work through a system. Span represents an individual unit of work done in a distributed system, recording start and stop times. In distributed trace, we handle a set of calls to process a single request to the system entrypoint in a form of Trace containing multiple Spans. When analyzing distributed traces, the trace data visualization is the key to grasp overall system latencies at a glance. Especially in the era of microservices, distributed trace is the strong driver to find out latency bottlenecks in the overall distributed system. Distributed TraceĪmong logs, metrics, and traces, trace is the telemetry that tells the latency of a specific part of the process in the system. Part 2 will cover continuous profiling with Cloud Profiler. This codelab is part 1 of the series and covers instrumenting distributed traces in microservices with OpenTelemetry and Cloud Trace. Cloud Profiler is one of the originators and provides an easy interface to drill down the performance metrics in the application call stacks. By utilizing libraries that the project and its ecosystem provide, developers are able to instrument their applications in the vendor neutral way and against multiple architectures.Īlso in addition to the three pillars of observability, continuous profiling is another key component for observability and it is expandings the user base in the industry. OpenTelemetry is an open standard and community driven project under CNCF. OpenTelemetry is a set of specifications, libraries and agents that accelerate the instrumentation and export of telemetry data (logs, metrics, and traces) that observability requires. In that context, three pillars of observability logs, metrics, and traces are the fundamental instrumentation for the system to acquire observability. A system with observability allows teams to actively debug their system. Observability is the term used to describe an attribute of a system. Last Updated: Observability of the application Observability and OpenTelemetry
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