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[Cloud Run] Split up In-Process docs (#30742)
* update containers overview page with tabs * create empty pages for each in-process language * fill out nodejs in-process * fix layout * fix layout * cleanup nodejs page; fix links * reorganize files * add configuration section * update tab structure * add python instructions * remove empty page * make explanation and alternative configuration collapsible * fix `gcloud run deploy` shell * fix expandable content * fix languages tabs at top * update collapsible * fix links; update collapsible * nits * add more information to "Alternative configuration" * Update description for `DD_SOURCE` and `DD_TAGS`; make `COPY` explanation more clear; add more logs information; clarify that `winston` and `structlog` are external packages * Move `NODE_OPTIONS` to tracer installation instructions * clarify DD_SITE * fill in `go.md` * nits go.md * try using hugo shortcodes to simplify step 2 * try to fix shortcode * Reorder languages * Fix nested shortcode * fix escaped quotes * update go and nodejs to use shortcode * implement java.md * nits * implement dotnet.md * nits * implement ruby.md * implement php.md * add troubleshooting instructions * add google cloud integration setup * add more info about serverless-init image and tags * fix markdown link * add aliases for old paths * add `DD_SOURCE=nodejs` recommendation * update titles * Add software catalog troubleshooting and mark required env vars as recommended * fix link * optimize PHP dockerfile by combining RUN instructions * nits * Only show relevant language docs in configure env vars table * fix table * formatting * install the tracer * small edit --------- Co-authored-by: cecilia saixue watt <[email protected]>
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‎content/en/serverless/google_cloud_run/containers.md renamed to ‎content/en/serverless/google_cloud_run/containers/_index.md

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---
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title: Choosing an Instrumentation Method for Google Cloud Run Containers
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title: Choosing an Instrumentation Method for Containers
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further_reading:
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- link: "/integrations/google-cloud-run/"
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tag: "Documentation"
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text: "Google Cloud Run Integration"
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- link: 'https://www.datadoghq.com/blog/collect-traces-logs-from-cloud-run-with-datadog/'
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tag: 'Blog'
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text: 'Collect traces, logs, and custom metrics from Cloud Run services'
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- link: "/serverless/google_cloud_run/containers_in_process/"
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- link: "/serverless/google_cloud_run/containers/in_process/"
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tag: 'Documentation'
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text: 'Instrument your container with the in-process approach'
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- link: "/serverless/google_cloud_run/containers_sidecar/"
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- link: "/serverless/google_cloud_run/containers/sidecar/"
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tag: 'Documentation'
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text: 'Instrument your container with the sidecar approach'
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---
@@ -19,7 +19,7 @@ To instrument your Google Cloud Run containers with Datadog, choose one of two o
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{{% google-cloud-run-container-options %}}
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- [**In-process**][1]: Wraps your application container with the Datadog Agent. Choose this option for a simpler setup, lower cost overhead, and direct log piping.
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- [**In-process**][1]: Wraps your application container with the Datadog Agent. Choose this option for a simpler setup, lower cost overhead, and direct log piping.
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- [**Sidecar**][2]: Deploys the Datadog Agent in a separate container alongside your app container. Choose this option if you have multiple containers in a single service, if you prefer strict isolation of the Datadog Agent, or if you have performance-sensitive workloads.
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## Comparison: in-process versus sidecar instrumentation
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{{< partial name="whats-next/whats-next.html" >}}
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[1]: /serverless/google_cloud_run/containers_in_process
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[2]: /serverless/google_cloud_run/containers_sidecar
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[1]: /serverless/google_cloud_run/containers/in_process
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[2]: /serverless/google_cloud_run/containers/sidecar

‎content/en/serverless/google_cloud_run/containers/in_process/_index.md

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---
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title: In-Process Instrumentation
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type: multi-code-lang
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aliases:
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- /serverless/google_cloud_run/containers_in_process/
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---
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First, set up the **[Google Cloud Integration][1]** to collect metrics and logs from Google Cloud services. Remember to add the `cloud asset viewer` role to your service account and enable the Cloud Asset Inventory API in Google Cloud.
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Then, instrument your application using one of the following guides:
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{{< partial name="serverless/in-process-languages.html" >}}
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[1]: /integrations/google-cloud-platform/

‎content/en/serverless/google_cloud_run/containers/in_process/dotnet.md

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---
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title: Instrumenting a .NET Cloud Run Container In-Process
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code_lang: dotnet
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type: multi-code-lang
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code_lang_weight: 50
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further_reading:
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- link: '/tracing/trace_collection/automatic_instrumentation/dd_libraries/dotnet-core/?tab=linux'
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tag: 'Documentation'
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text: 'Tracing .NET Core Applications'
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- link: '/tracing/other_telemetry/connect_logs_and_traces/dotnet/'
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tag: 'Documentation'
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text: 'Correlating .NET Logs and Traces'
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---
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## Setup
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1. **Install the Datadog .NET tracer** in your Dockerfile.
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Because GitHub requests are rate limited, you must pass a GitHub token saved in the environment variable `GITHUB_TOKEN` as a [Docker build secret][1] `--secret id=github-token,env=GITHUB_TOKEN`.
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{{< tabs >}}
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{{% tab "Linux/AMD64" %}}
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{{< code-block lang="dockerfile" filename="Dockerfile" disable_copy="false" collapsible="true" >}}
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RUN --mount=type=secret,id=github-token,env=GITHUB_TOKEN \
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chmod +x /app/dotnet.sh && /app/dotnet.sh
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{{< /code-block >}}
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{{% /tab %}}
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{{% tab "Alpine" %}}
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{{< code-block lang="dockerfile" filename="Dockerfile" disable_copy="false" collapsible="true" >}}
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# For alpine use datadog-dotnet-apm-2.57.0-musl.tar.gz
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ARG TRACER_VERSION
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ADD https://github.com/DataDog/dd-trace-dotnet/releases/download/v${TRACER_VERSION}/datadog-dotnet-apm-${TRACER_VERSION}.tar.gz /tmp/datadog-dotnet-apm.tar.gz
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RUN mkdir -p /dd_tracer/dotnet/ && tar -xzvf /tmp/datadog-dotnet-apm.tar.gz -C /dd_tracer/dotnet/ && rm /tmp/datadog-dotnet-apm.tar.gz
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{{< /code-block >}}
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{{% /tab %}}
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{{< /tabs >}}
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For more information, see [Tracing .NET applications][2].
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2. **Install serverless-init**.
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{{% gcr-install-serverless-init cmd="\"dotnet\", \"dotnet.dll\"" %}}
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3. **Set up logs**.
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To enable logging, set the environment variable `DD_LOGS_ENABLED=true`. This allows `serverless-init` to read logs from stdout and stderr.
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Datadog also recommends setting the environment variable `DD_SOURCE=csharp` to enable advanced Datadog log parsing.
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If you want multiline logs to be preserved in a single log message, Datadog recommends writing your logs in JSON format. For example, you can use a third-party logging library such as `Serilog`:
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{{< code-block lang="csharp" disable_copy="false" >}}
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using Serilog;
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builder.Host.UseSerilog((context, config) =>
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{
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config.WriteTo.Console(new Serilog.Formatting.Json.JsonFormatter(renderMessage: true));
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});
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logger.LogInformation("Hello World!");
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{{< /code-block >}}
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For more information, see [Correlating .NET Logs and Traces][3].
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4. **Configure your application**.
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{{% gcr-configure-env-vars language="csharp" %}}
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## Troubleshooting
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{{% gcr-troubleshooting %}}
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## Further reading
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{{< partial name="whats-next/whats-next.html" >}}
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[1]: https://docs.docker.com/build/building/secrets/
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[2]: /tracing/trace_collection/automatic_instrumentation/dd_libraries/dotnet-core/?tab=linux
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[3]: /tracing/other_telemetry/connect_logs_and_traces/dotnet/

‎content/en/serverless/google_cloud_run/containers/in_process/go.md

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---
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title: Instrumenting a Go Cloud Run Container In-Process
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code_lang: go
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type: multi-code-lang
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code_lang_weight: 30
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further_reading:
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- link: '/tracing/trace_collection/automatic_instrumentation/dd_libraries/go/'
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tag: 'Documentation'
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text: 'Tracing Go Applications'
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- link: '/tracing/other_telemetry/connect_logs_and_traces/go/'
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tag: 'Documentation'
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text: 'Correlating Go Logs and Traces'
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---
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## Setup
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1. **Install the Datadog Go tracer**.
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1. In your main application, add the tracing library from `dd-trace-go`.
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{{< code-block lang="shell" disable_copy="false" >}}
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go get github.com/DataDog/dd-trace-go/v2/ddtrace/tracer
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{{< /code-block >}}
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2. Add the following to your application code to initialize the tracer:
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{{< code-block lang="go" disable_copy="false" >}}
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tracer.Start()
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defer tracer.Stop()
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{{< /code-block >}}
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You can also add additional packages:
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{{< code-block lang="shell" disable_copy="false" >}}
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# Enable Profiling
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go get github.com/DataDog/dd-trace-go/v2/profiler
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# Patch /net/http
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go get github.com/DataDog/dd-trace-go/contrib/net/http/v2
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{{< /code-block >}}
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For more information, see [Tracing Go Applications][1] and the [Tracer README][2].
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2. **Install serverless-init**.
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{{% gcr-install-serverless-init cmd="./your-binary" %}}
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3. **Set up logs**.
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To enable logging, set the environment variable `DD_LOGS_ENABLED=true`. This allows `serverless-init` to read logs from stdout and stderr.
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Datadog also recommends setting the environment variable `DD_SOURCE=go` to enable advanced Datadog log parsing.
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If you want multiline logs to be preserved in a single log message, Datadog recommends writing your logs in JSON format. For example, you can use a third-party logging library such as `logrus`:
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```go
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logrus.SetFormatter(&logrus.JSONFormatter{})
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logrus.AddHook(&dd_logrus.DDContextLogHook{})
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logrus.WithContext(ctx).Info("Hello World!")
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```
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For more information, see [Correlating Go Logs and Traces][3].
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4. **Configure your application**.
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{{% gcr-configure-env-vars language="go" %}}
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## Troubleshooting
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{{% gcr-troubleshooting %}}
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## Further reading
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{{< partial name="whats-next/whats-next.html" >}}
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[1]: /tracing/trace_collection/automatic_instrumentation/dd_libraries/go/
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[2]: https://github.com/DataDog/dd-trace-go?tab=readme-ov-file#installing
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[3]: /tracing/other_telemetry/connect_logs_and_traces/go/

‎content/en/serverless/google_cloud_run/containers/in_process/java.md

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---
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title: Instrumenting a Java Cloud Run Container In-Process
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code_lang: java
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type: multi-code-lang
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code_lang_weight: 40
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further_reading:
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- link: '/tracing/trace_collection/automatic_instrumentation/dd_libraries/java/'
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tag: 'Documentation'
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text: 'Tracing Java Applications'
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- link: '/tracing/other_telemetry/connect_logs_and_traces/java/'
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tag: 'Documentation'
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text: 'Correlating Java Logs and Traces'
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---
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## Setup
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1. **Install the Datadog Java tracer**.
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1. Add the Datadog Java tracer to your Dockerfile:
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{{< code-block lang="dockerfile" filename="Dockerfile" disable_copy="false" collapsible="true" >}}
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ADD 'https://dtdg.co/latest-java-tracer' agent.jar
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ENV JAVA_TOOL_OPTIONS="-javaagent:agent.jar"
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{{< /code-block >}}
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2. Add the tracer artifacts.
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{{< tabs >}}
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{{% tab "Maven" %}}
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{{< code-block lang="xml" disable_copy="false" >}}
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<dependency>
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<groupId>com.datadoghq</groupId>
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<artifactId>dd-trace-api</artifactId>
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<version>DD_TRACE_JAVA_VERSION_HERE</version>
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</dependency>
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{{< /code-block >}}
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{{% /tab %}}
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{{% tab "Gradle" %}}
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{{< code-block lang="groovy" disable_copy="false" >}}
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implementation 'com.datadoghq:dd-trace-api:DD_TRACE_JAVA_VERSION_HERE'
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{{< /code-block >}}
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{{% /tab %}}
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{{< /tabs >}}
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3. Add the `@Trace` annotation to any method you want to trace.
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For more information, see [Tracing Java Applications][1].
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2. **Install serverless-init**.
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{{% gcr-install-serverless-init cmd="\"./mvnw\", \"spring-boot:run\"" %}}
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3. **Set up logs**.
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To enable logging, set the environment variable `DD_LOGS_ENABLED=true`. This allows `serverless-init` to read logs from stdout and stderr.
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Datadog also recommends setting the environment variable `DD_SOURCE=java` to enable advanced Datadog log parsing.
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If you want multiline logs to be preserved in a single log message, Datadog recommends writing your logs in *compact* JSON format. For example, you can use a third-party logging library such as `Log4j 2`:
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{{< code-block lang="java" disable_copy="false" >}}
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private static final Logger logger = LogManager.getLogger(App.class);
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logger.info("Hello World!");
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{{< /code-block >}}
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{{< code-block lang="xml" filename="resources/log4j2.xml" disable_copy="false" >}}
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<Configuration>
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<Appenders>
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<Console name="Console"><JsonLayout compact="true" eventEol="true" properties="true"/></Console>
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</Appenders>
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<Loggers><Root level="info"><AppenderRef ref="Console"/></Root></Loggers>
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</Configuration>
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{{< /code-block >}}
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For more information, see [Correlating Java Logs and Traces][2].
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4. **Configure your application**.
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{{% gcr-configure-env-vars language="java" %}}
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## Troubleshooting
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{{% gcr-troubleshooting %}}
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## Further reading
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{{< partial name="whats-next/whats-next.html" >}}
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[1]: /tracing/trace_collection/automatic_instrumentation/dd_libraries/java/
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[2]: /tracing/other_telemetry/connect_logs_and_traces/java/

‎content/en/serverless/google_cloud_run/containers/in_process/nodejs.md

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---
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title: Instrumenting a Node.js Cloud Run Container In-Process
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code_lang: nodejs
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type: multi-code-lang
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code_lang_weight: 20
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further_reading:
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- link: '/tracing/trace_collection/automatic_instrumentation/dd_libraries/nodejs/'
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tag: 'Documentation'
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text: 'Tracing Node.js Applications'
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- link: '/tracing/other_telemetry/connect_logs_and_traces/nodejs/'
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tag: 'Documentation'
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text: 'Correlating Node.js Logs and Traces'
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---
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## Setup
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1. **Install the Datadog Node.js tracer**.
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1. In your main application, add `dd-trace-js`.
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{{< code-block lang="shell" disable_copy="false" >}}
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npm install dd-trace --save
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{{< /code-block >}}
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2. Add the following to your application code to initialize the tracer:
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{{< code-block lang="javascript" disable_copy="false" >}}
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const tracer = require('dd-trace').init({
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logInjection: true,
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});
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{{< /code-block >}}
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3. Set the following environment variable to specify that the `dd-trace/init` module is required when the Node.js process starts:
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{{< code-block lang="dockerfile" disable_copy="false" >}}
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ENV NODE_OPTIONS="--require dd-trace/init"
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{{< /code-block >}}
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For more information, see [Tracing Node.js applications][1].
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2. **Install serverless-init**.
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{{% gcr-install-serverless-init cmd="\"/nodejs/bin/node\", \"/path/to/your/app.js\"" %}}
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3. **Set up logs**.
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To enable logging, set the environment variable `DD_LOGS_ENABLED=true`. This allows `serverless-init` to read logs from stdout and stderr.
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Datadog also recommends setting the environment variable `DD_SOURCE=nodejs` to enable advanced Datadog log parsing.
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If you want multiline logs to be preserved in a single log message, Datadog recommends writing your logs in JSON format. For example, you can use a third-party logging library such as `winston`:
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{{< code-block lang="javascript" disable_copy="false" >}}
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const tracer = require('dd-trace').init({
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logInjection: true,
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});
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const { createLogger, format, transports } = require('winston');
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const logger = createLogger({
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level: 'info',
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exitOnError: false,
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format: format.json(),
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transports: [
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new transports.Console()
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],
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});
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logger.info(`Hello world!`);
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{{< /code-block >}}
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For more information, see [Correlating Node.js Logs and Traces][2].
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4. **Configure your application**.
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{{% gcr-configure-env-vars language="nodejs" %}}
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## Troubleshooting
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{{% gcr-troubleshooting %}}
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## Further reading
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{{< partial name="whats-next/whats-next.html" >}}
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[1]: /tracing/trace_collection/automatic_instrumentation/dd_libraries/nodejs/
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[2]: /tracing/other_telemetry/connect_logs_and_traces/nodejs/

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