You can instrument a Node.js JavaScript application to collect trace and metric data with the [OpenTelemetry](https://opentelemetry.io/docs/what-is-opentelemetry/) SDK and an OpenTelemetry collector and to write correlated, structured JSON logs to standard output. To experiment with this instrumentation, you can download and run the sample [Fastify](https://fastify.dev/) application, which generates log, metric, and trace data and exports it to your Google Cloud project.
## About the instrumentation approach
When you use an OpenTelemetry collector, you instrument your application with
the SDK and the SDK's OTLP in-process exporter. This instrumentation is
vendor neutral. You also deploy an OpenTelemetry collector that receives telemetry
from the in-process exporter and then exports that
telemetry to your Google Cloud project. To learn more about collectors, see
[Google-Built OpenTelemetry Collector](https://docs.cloud.google.com/stackdriver/docs/instrumentation/google-built-otel.md.txt).
We recommend that you use an OpenTelemetry collector to export your telemetry data
when your environment supports the use of a collector. For some environments,
you must use an in-process exporter that directly sends data to your
Google Cloud project. To learn about in-process instrumentation, see
[Migrate from the Trace exporter to the OTLP endpoint](https://docs.cloud.google.com/trace/docs/migrate-to-otlp-endpoints.md.txt).
To learn more about instrumentation, see the following documents:
- [Observability for application developers](https://docs.cloud.google.com/stackdriver/docs/opentelemetry/application-developers.md.txt)
- [Choose an instrumentation approach](https://docs.cloud.google.com/stackdriver/docs/instrumentation/choose-approach.md.txt)
## About manual and zero-code instrumentation
For this language, OpenTelemetry defines *zero-code instrumentation* as
the practice of collecting telemetry from
libraries and frameworks without making code changes. However, you must
install modules and set environment variables.
This document doesn't describe zero-code instrumentation. For information about
that topic, see
[JavaScript zero-code instrumentation](https://opentelemetry.io/docs/languages/js/automatic/).
For general information, see
[OpenTelemetry Instrumentation for Node](https://opentelemetry.io/docs/languages/js/instrumentation/).
## Before you begin
1.
To get the permissions that
you need to have the sample application write log, metric, and trace data,
ask your administrator to grant you the
following IAM roles:
- [Logs Writer](https://docs.cloud.google.com/iam/docs/roles-permissions/logging.md.txt#logging.logWriter) (`roles/logging.logWriter`) on your project
- [Monitoring Metric Writer](https://docs.cloud.google.com/iam/docs/roles-permissions/monitoring.md.txt#monitoring.metricWriter) (`roles/monitoring.metricWriter`) on your project
- [Cloud Telemetry Traces Writer](https://docs.cloud.google.com/iam/docs/roles-permissions/telemetry.md.txt#telemetry.tracesWriter) (`roles/telemetry.tracesWriter`) on your project
- [Service Usage Consumer](https://docs.cloud.google.com/iam/docs/roles-permissions/serviceusage.md.txt#serviceusage.serviceUsageConsumer) (`roles/serviceusage.serviceUsageConsumer`) on your quota project
These permissions are sufficient if you run the sample in the
Cloud Shell, on Google Cloud resources, or on a local
development environment. To learn how to configure a quota project, see
[Set the quota project](https://docs.cloud.google.com/docs/quotas/set-quota-project.md.txt).
> [!NOTE]
> **Important**: Production applications typically rely on a service account to provide the credentials required to write log, metric, and trace data. In those scenarios, grant the previous roles to the service account.
To get the permissions that
you need to view your log, metric, and trace data,
ask your administrator to grant you the
following IAM roles on your project:
- [Logs Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/logging.md.txt#logging.viewer) (`roles/logging.viewer`)
- [Monitoring Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/monitoring.md.txt#monitoring.viewer) (`roles/monitoring.viewer`)
- [Cloud Trace User](https://docs.cloud.google.com/iam/docs/roles-permissions/cloudtrace.md.txt#cloudtrace.user) (`roles/cloudtrace.user`)
For more information about granting roles, see [Manage access to projects, folders, and organizations](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access.md.txt).
You might also be able to get
the required permissions through [custom
roles](https://docs.cloud.google.com/iam/docs/creating-custom-roles) or other [predefined
roles](https://docs.cloud.google.com/iam/docs/roles-overview#predefined).
## Instrument your app to collect traces, metrics, and logs
> [!NOTE]
> **Note:** This document displays only selected portions of a working application. For example, the sample doesn't display the list of imported packages. However, the full application is available on GitHub. To view the full sample, in the sample's toolbar, select the GitHub logo.
To instrument your app to collect trace and metric data and to write
structured JSON logs to standard output, perform the following steps:
1. [Configure OpenTelemetry](https://docs.cloud.google.com/trace/docs/setup/nodejs-ot.md.txt#config-otel)
2. [Configure your app to preload the OpenTelemetry configuration](https://docs.cloud.google.com/trace/docs/setup/nodejs-ot.md.txt#config-preload)
3. [Configure structured logging](https://docs.cloud.google.com/trace/docs/setup/nodejs-ot.md.txt#config-structured-logging)
4. [Write structured logs](https://docs.cloud.google.com/trace/docs/setup/nodejs-ot.md.txt#write-structured-logging)
### Configure OpenTelemetry
The default configuration for the OpenTelemetry Node.js SDK exports traces by using
the [OTLP protocol](https://opentelemetry.io/docs/specs/otlp/). It also configures OpenTelemetry to use the [W3C
Trace Context](https://www.w3.org/TR/trace-context/) format for [propagating trace
context](https://opentelemetry.io/docs/concepts/context-propagation/#propagation). This configuration ensures that spans have the
correct parent-child relationship within a trace.
The following code sample illustrates a JavaScript module to set up OpenTelemetry.
To view the full sample, in the sample's toolbar,
select the GitHub logo.
```typescript
diag.setLogger(
new DiagConsoleLogger(),
opentelemetry.core.diagLogLevelFromString(
opentelemetry.core.getStringFromEnv('OTEL_LOG_LEVEL'),
),
);
const sdk = new opentelemetry.NodeSDK({
instrumentations: getNodeAutoInstrumentations({
// Disable noisy instrumentations
'@opentelemetry/instrumentation-fs': {enabled: false},
}),
resourceDetectors: getResourceDetectorsFromEnv(),
metricReader: getMetricReader(),
});
try {
sdk.start();
diag.info('OpenTelemetry automatic instrumentation started successfully');
} catch (error) {
diag.error(
'Error initializing OpenTelemetry SDK. Your application is not instrumented and will not produce telemetry',
error,
);
}
// Gracefully shut down the SDK to flush telemetry when the program exits
process.on('SIGTERM', () => {
sdk
.shutdown()
.then(() => diag.debug('OpenTelemetry SDK terminated'))
.catch(error => diag.error('Error terminating OpenTelemetry SDK', error));
});
```
The previous code sample configures OpenTelemetry to export metrics using the [OTLP
protocol](https://opentelemetry.io/docs/specs/otlp/), and it uses the `@opentelemetry/auto-instrumentations-node`
package to configure all available Node.js instrumentations.
To ensure that all pending telemetry is flushed and that connections are closed
gracefully before the application shuts down, the `SIGTERM` handler calls
`shutdown`.
For more information and configuration options, see
[Zero-Code Instrumentation Configuration](https://opentelemetry.io/docs/languages/js/automatic/configuration).
### Configure your app to preload the OpenTelemetry configuration
To configure the app to write structured logs and to collect metrics and trace
data by using OpenTelemetry, update the invocation of your app to preload the
instrumentation module with the Node.js [`--require` flag](https://nodejs.org/api/cli.html#-r---require-module). Using
the `--require` flag ensures that OpenTelemetry is initialized before your app
starts. For more information, see [OpenTelemetry Node.js Getting
Started](https://opentelemetry.io/docs/languages/js/getting-started/nodejs/#instrumentation).
The following code sample illustrates a Dockerfile passing the `--require` flag:
```docker
CMD node --require ./build/src/instrumentation.js build/src/index.js 2>&1 | tee /var/log/app.log
```
### Configure structured logging
To include the trace information as part of the JSON-formatted logs written to
standard output, configure your app to output structured logs in JSON format.
> [!NOTE]
> **Note:** The following code sample depends on Fastify, which uses the [Pino log framework](https://getpino.io/#/) and provides a logger in each request handler. The `timestamp` function defined in the sample returns a key-value pair, where the key is labeled `"timestamp"`. If your log data isn't appearing as expected, then try setting the key to `"time"` instead of `"timestamp"`.
The following code sample illustrates a Pino `LoggerOptions`
object that configures the app to output JSON structured logs:
```javascript
// Expected attributes that OpenTelemetry adds to correlate logs with spans
interface LogRecord {
trace_id?: string;
span_id?: string;
trace_flags?: string;
[key: string]: unknown;
}
// https://cloud.google.com/logging/docs/reference/v2/rest/v2/LogEntry#logseverity
const PinoLevelToSeverityLookup: Record = {
trace: 'DEBUG',
debug: 'DEBUG',
info: 'INFO',
warn: 'WARNING',
error: 'ERROR',
fatal: 'CRITICAL',
};
export const loggerConfig = {
messageKey: 'message',
// Same as pino.stdTimeFunctions.isoTime but uses "timestamp" key instead of "time"
timestamp(): string {
return `,"timestamp":"${new Date(Date.now()).toISOString()}"`;
},
formatters: {
log(object: LogRecord): Record {
// Add trace context attributes following Cloud Logging structured log format described
// in https://cloud.google.com/logging/docs/structured-logging#special-payload-fields
const {trace_id, span_id, trace_flags, ...rest} = object;
return {
'logging.googleapis.com/trace': trace_id,
'logging.googleapis.com/spanId': span_id,
'logging.googleapis.com/trace_sampled': trace_flags
? trace_flags === '01'
: undefined,
...rest,
};
},
// See
// https://getpino.io/#/docs/help?id=mapping-pino-log-levels-to-google-cloud-logging-stackdriver-severity-levels
level(label: string) {
return {
severity:
PinoLevelToSeverityLookup[label] ?? PinoLevelToSeverityLookup['info'],
};
},
},
} satisfies LoggerOptions;
```
The previous configuration extracts information about the active span from the
log message and then adds that information as attributes to the JSON structured
log. These attributes can then be used to correlate a log with a trace:
- `logging.googleapis.com/trace`: Resource name of the trace associated with the log entry.
- `logging.googleapis.com/spanId`: The span ID within the trace that is associated with the log entry.
- `logging.googleapis.com/trace_sampled`: The value of this field must be `true` or `false`.
For more information about these fields, see the [`LogEntry`](https://docs.cloud.google.com/logging/docs/reference/v2/rest/v2/LogEntry.md.txt)
structure.
To use the Pino configuration with Fastify, pass the logger config object when
creating the Fastify app:
```javascript
// Create the Fastify app providing the Pino logger config
const fastify = Fastify({
logger: loggerConfig,
});
```
### Write structured logs
To write structured logs that link to a trace, use the [Fastify-provided Pino
logger](https://fastify.dev/docs/v5.0.x/Reference/Logging/#usage). For example, the following statement shows how to call
the `Logger.info()` method:
```typescript
request.log.info({subRequests}, 'handle /multi request');
```
OpenTelemetry automatically populates the Pino log entries with the [span
context](https://opentelemetry.io/docs/concepts/signals/traces/#span-context) of the current active span in the [OpenTelemetry
Context](https://opentelemetry.io/docs/specs/otel/context/). This span context is then included in the JSON logs as
described in
[Configure structured logging](https://docs.cloud.google.com/trace/docs/setup/nodejs-ot.md.txt#config-structured-logging).
## Run a sample app configured to collect telemetry
The instrumentation in the sample app uses vendor-neutral formats, like JSON
for log data and OTLP for metric and trace data. The app also uses
the [Fastify framework](https://fastify.dev/). The OpenTelemetry `Collector` sends
log, metric, and trace data to your project by using the Telemetry API,
which uses OTLP.
The app has two endpoints:
- The `/multi` endpoint is handled by the `handleMulti` function. The load
generator in the app issues requests to the `/multi` endpoint. When this
endpoint receives a request, it sends between three and seven requests to
the `/single` endpoint on the local server.
```javascript
/**
* handleMulti handles an http request by making 3-7 http requests to the /single endpoint.
*
* OpenTelemetry instrumentation requires no changes here. It will automatically generate a
* span for the handler body.
*/
fastify.get('/multi', async request => {
const subRequests = randInt(3, 8);
request.log.info({subRequests}, 'handle /multi request');
for (let i = 0; i < subRequests; i++) {
await axios.get(`http://localhost:${port}/single`);
}
return 'ok';
});
```
- The `/single` endpoint is handled by the `handleSingle` function. When this
endpoint receives a request, it sleeps for a short delay and then responds
with a string.
```javascript
/**
* handleSingle handles an http request by sleeping for 100-200 ms. It writes the number of
* milliseconds slept as its response.
*/
fastify.get('/single', async request => {
// Sleep between 100-200 milliseconds
const sleepMillis = randInt(100, 200);
request.log.info({sleepMillis}, 'Going to sleep');
await sleep(sleepMillis);
return `slept ${sleepMillis}\n`;
});
```
### Download and deploy the app
> [!NOTE]
> **Note:** We recommend running the sample app by using Cloud Shell. However, if you want to run the sample app locally on Linux or Mac, then skip the first step in the following instructions.
To run the sample, do the following:
1. In the Google Cloud console, activate Cloud Shell.
[Activate Cloud Shell](https://console.cloud.google.com/?cloudshell=true)
At the bottom of the Google Cloud console, a
[Cloud Shell](https://docs.cloud.google.com/shell/docs/how-cloud-shell-works.md.txt)
session starts and displays a command-line prompt. Cloud Shell is a shell environment
with the Google Cloud CLI
already installed and with values already set for
your current project. It can take a few seconds for the session to initialize.
2. Clone the repository:
```bash
git clone https://github.com/GoogleCloudPlatform/opentelemetry-samples
```
3. Go to the sample directory:
```bash
cd opentelemetry-samples/javascript/instrumentation-quickstart
```
4. Build and run the sample:
```bash
docker compose up --abort-on-container-exit
```
If you aren't running on Cloud Shell, then run the application with the
`GOOGLE_APPLICATION_CREDENTIALS` environment variable pointing to a
credentials file. [Application Default
Credentials](https://docs.cloud.google.com/docs/authentication/application-default-credentials#personal)
provides a credentials file at
`$HOME/.config/gcloud/application_default_credentials.json`.
```bash
# Set environment variables
export GOOGLE_CLOUD_PROJECT="PROJECT_ID"
export GOOGLE_APPLICATION_CREDENTIALS="$HOME/.config/gcloud/application_default_credentials.json"
export USERID="$(id -u)"
# Run
docker compose -f docker-compose.yaml -f docker-compose.creds.yaml up --abort-on-container-exit
```
### View your metrics
The OpenTelemetry instrumentation in the sample app generates Prometheus
metrics that you can view by using the [Metrics Explorer](https://docs.cloud.google.com/monitoring/charts/metrics-explorer.md.txt):
- `Prometheus/http_server_duration_milliseconds/histogram`
records the duration of server requests and stores the results
in a histogram.
- `Prometheus/http_client_duration_milliseconds/histogram`
records the duration of client requests and stores the results
in a histogram.
To view the metrics generated by the sample app, do the following:
1. In the Google Cloud console, go to the
**Metrics explorer** page:
[Go to **Metrics explorer**](https://console.cloud.google.com/monitoring/metrics-explorer)
If you use the search bar to find this page, then select the result whose subheading is
**Monitoring**.
2. In the toolbar of the Google Cloud console, select your Google Cloud project. For [App Hub](https://docs.cloud.google.com/app-hub/docs/overview.md.txt) configurations, select the App Hub host project or the app-enabled folder's management project.
3. In the **Metric** element, expand the **Select a metric** menu, enter `http_server` in the filter bar, and then use the submenus to select a specific resource type and metric:
1. In the **Active resources** menu, select **Prometheus Target**.
2. In the **Active metric categories** menu, select **Http**.
3. In the **Active metrics** menu, select a metric.
4. Click **Apply**.
4. To add filters, which remove time series from the query results, use the
[**Filter** element](https://docs.cloud.google.com/monitoring/charts/metrics-selector.md.txt#filter-option).
5. Configure how the data is viewed.
When the measurements for a metric are
cumulative, Metrics Explorer automatically normalizes the measured data by
the alignment period, which results in the chart displaying a rate. For
more information, see
[Kinds, types, and conversions](https://docs.cloud.google.com/monitoring/api/v3/aggregation.md.txt#ts-conversion).
When integer or double values are measured, such as with the two
`counter` metrics, Metrics Explorer automatically sums all time series.
To view the data for the `/multi` and `/single` HTTP routes,
set the first menu of the **Aggregation** entry to **None**.
For more information about configuring a chart, see
[Select metrics when using Metrics Explorer.](https://docs.cloud.google.com/monitoring/charts/metrics-selector.md.txt)
### View your traces
It might take several minutes before your trace data is available. For example,
when trace data is received by your project, Google Cloud Observability might need to create
a database to store that data. The creation of the database can take a few
minutes, and during that period no trace data is available to view.
To view your trace data, do the following:
1. In the Google Cloud console, go to the
**Trace explorer** page:
[Go to **Trace explorer**](https://console.cloud.google.com/traces/explorer)
You can also find this page by using the search bar.
2. In the table section of the page, select a row with the span name `/multi`.
3. In the Gantt chart on the **Trace details** panel,
select the span labeled `/multi`.
A panel opens that displays information about the HTTP request. These
details include the method, status code, number of bytes, and the
user agent of the caller.
4. To view the logs associated with this trace,
select the **Logs \& Events** tab.
The tab shows individual logs. To view the details of the log entry,
expand the log entry. You can also click **View Logs** and view the log
by using the Logs Explorer.
For more information about using the **Trace Explorer** page, see
[Find and explore traces](https://docs.cloud.google.com/trace/docs/finding-traces.md.txt).
### View your logs
From the Logs Explorer, you can inspect your logs, and you can also
view associated traces, when they exist.
1. In the Google Cloud console, go to the
**Logs Explorer** page:
[Go to **Logs Explorer**](https://console.cloud.google.com/logs/query)
If you use the search bar to find this page, then select the result whose subheading is
**Logging**.
2. Locate a log with the description of `handle /multi request`.
To view the details of the log, expand the log entry.
3. Click  **Traces** on a log entry with the
"handle /multi request" message, and then select **View trace details**.
A **Trace details** panel opens and displays the selected trace.
Your log data might be available several minutes before your trace
data is available. If you encounter an error when viewing trace data
either by searching for a trace by ID or by following the steps in
this task, then wait a minute or two and retry the action.
For more information about using the Logs Explorer, see
[View logs by using the Logs Explorer](https://docs.cloud.google.com/logging/docs/view/logs-explorer-interface.md.txt).
## What's next
- [OpenTelemetry](https://opentelemetry.io/docs/what-is-opentelemetry/)
- [OTLP specification](https://opentelemetry.io/docs/specs/otlp/)
- [Structured logging](https://docs.cloud.google.com/logging/docs/structured-logging.md.txt)
- [Troubleshooting Managed Service for Prometheus](https://docs.cloud.google.com/stackdriver/docs/managed-prometheus/troubleshooting.md.txt)
- [Troubleshoot Cloud Trace](https://docs.cloud.google.com/trace/docs/troubleshooting.md.txt)