A custom destination for pino that takes advantage of the unique environment in AWS Lambda functions. ref

By default, this destination reformats the log output so it matches the existing Cloudwatch format. The default pino log format loses some of the built in support for request ID tracing that lambda has built into to support Cloudwatch insights and Xray tracing.

It also automatically tracks the request id, correlation ids, and xray tracing from upstream services.

• Capture and forward correlation IDs through different Lambda event sources
• Decorated Lambda Handlers

Basic usage for most applications

import pino from 'pino';
import { lambdaRequestTracker, pinoLambdaDestination } from 'pino-lambda';

// custom destination formatter
const destination = pinoLambdaDestination();
const logger = pino(
  {
    // typical pino options
  },
  destination,
);
const withRequest = lambdaRequestTracker();

async function handler(event, context) {
  // automatic request tracing across all instances of pino
  // called once at the beginning of your Lambda handler
  withRequest(event, context);

  // typical logging methods
  logger.info({ data: 'Some data' }, 'A log message');
}

Cloudwatch output will now match the native console.log output, correctly preserving @requestid, @timestamp, and @message properties for use in Cloudwatch Insights and other Cloudwatch aware tools such as Datadog and Splunk.

2018-12-20T17:05:25.330Z    6fccb00e-0479-11e9-af91-d7ab5c8fe19e    INFO  A log message
{
   "awsRequestId": "6fccb00e-0479-11e9-af91-d7ab5c8fe19e",
   "x-correlation-id": "238da608-0542-11e9-8eb2-f2801f1b9fd1",
   "x-correlation-trace-id": "Root=1-5c1bcbd2-9cce3b07143efd5bea1224f2;Parent=07adc05e4e92bf13;Sampled=1",
   "level": 30,
   "message": "A log message",
   "data": "Some data"
}

The request logging context can be updated downstream by calling the updateContext function. Any duplicate values will overwrite previous values.

Note: If you provide a custom storage context, you will need to update that storage context directly (this is not typical)

import { GlobalContextStorageProvider } from 'pino-lambda';
GlobalContextStorageProvider.updateContext({ userId: '12' });

With context tracing enabled, all instances of pino that use one of the built in formatters will automatically log the request id and other details so you don't need to pass an instance of a logger to all of your functions.

Every AWS Lambda request contains a unique request ID, context.awsRequestId. If the request originated outside of the AWS platform, the request ID will match the event.header.x-correlation-id value. However, if the request originated from within the AWS platform, the event.header.x-correlation-id will be set to the request ID of the calling service. This allows you to trace a request across the entire platform.

Amazon XRAY also has a unique tracing ID that is propagated across the requests and can be tracked as well.

You can customize the data that is tracked for each request by adding a per-request mixin. The request mixin takes the Lambda event and context and returns an object.

This differs from the built in pino mixin as it only executes once per request where the built in pino mixin runs once per log entry.

import pino from 'pino';
import { lambdaRequestTracker, pinoLambdaDestination } from 'pino-lambda';

const destination = pinoLambdaDestination();
const logger = pino(destination);
const withRequest = lambdaRequestTracker({
  requestMixin: (event, context) => {
    return {
      // add request header host name
      host: event.headers?.host,

      // you can also set any request property to undefined
      // which will remove it from the output
      'x-correlation-id': undefined,

      // add any type of static data
      brand: 'famicom',
    };
  },
});

async function handler(event, context) {
  withRequest(event, context);
  logger.info({ data: 'Some data' }, 'A log message');
}

Output

2018-12-20T17:05:25.330Z    6fccb00e-0479-11e9-af91-d7ab5c8fe19e    INFO  A log message
{
   "awsRequestId": "6fccb00e-0479-11e9-af91-d7ab5c8fe19e",
   "x-correlation-trace-id": "Root=1-5c1bcbd2-9cce3b07143efd5bea1224f2;Parent=07adc05e4e92bf13;Sampled=1",
   "level": 30,
   "host": "www.host.com",
   "brand": "famicom",
   "message": "A log message",
   "data": "Some data"
}

By default, the pinoLambdaDestination uses the CloudwatchLogFormatter.

If you would like to use the new AWS Lambda Advanced Logging Controls format for your logs, ensure your Lambda function is properly configured and enable StructuredLogFormatter in pino-lambda.

import pino from 'pino';
import { lambdaRequestTracker, pinoLambdaDestination, StructuredLogFormatter } from 'pino-lambda';

const destination = pinoLambdaDestination({
  formatter: new StructuredLogFormatter()
});
const logger = pino(destination);
const withRequest = lambdaRequestTracker();

async function handler(event, context) {
  withRequest(event, context);
  logger.info({ data: 'Some data' }, 'A log message');
}

Output

{
  "timestamp": "2016-12-01T06:00:00.000Z",
  "requestId": "6fccb00e-0479-11e9-af91-d7ab5c8fe19e",
  "level": "INFO",
  "message": {
    "msg": "A log message",
    "data": "Some data",
    "x-correlation-trace-id": "Root=1-5c1bcbd2-9cce3b07143efd5bea1224f2;Parent=07adc05e4e92bf13;Sampled=1"
  }
}

If you want the request tracing features of pino-lambda, but don't need the Cloudwatch format, you can use the PinoLogFormatter which matches the default object output format of pino.

import pino from 'pino';
import { lambdaRequestTracker, pinoLambdaDestination, PinoLogFormatter } from 'pino-lambda';

const destination = pinoLambdaDestination({
  formatter: new PinoLogFormatter(),
});
const logger = pino(destination);
const withRequest = lambdaRequestTracker();

async function handler(event, context) {
  withRequest(event, context);
  logger.info({ data: 'Some data' }, 'A log message');
}

Output

{
  "awsRequestId": "6fccb00e-0479-11e9-af91-d7ab5c8fe19e",
  "x-correlation-trace-id": "Root=1-5c1bcbd2-9cce3b07143efd5bea1224f2;Parent=07adc05e4e92bf13;Sampled=1",
  "level": 30,
  "time": 1480572000000,
  "msg": "A log message",
  "data": "Some data"
}

The formatter function can also be replaced with any custom implementation you need by using the supplied interface.

import { LogData, ILogFormatter } from 'pino-lambda';

class BananaLogFormatter implements ILogFormatter {
  format(data: LogData) {
    return `[BANANA] ${JSON.stringify(data)}`;
  }
}

const destination = pinoLambdaDestination({
  formatter: new BananaLogFormatter(),
});
const logger = pino(destination);

Output

[BANANA]
{
   "awsRequestId": "6fccb00e-0479-11e9-af91-d7ab5c8fe19e",
   "x-correlation-trace-id": "Root=1-5c1bcbd2-9cce3b07143efd5bea1224f2;Parent=07adc05e4e92bf13;Sampled=1",
   "level": 30,
   "msg": "A log message",
   "data": "Some data"
}

Unless your application is small, it can be useful to split the logger into its own module for easier reuse across your application code. This ensures that all your logging calls receive the correct formatting and context across the request.

// logger.ts
import pino from 'pino';
import { pinoLambdaDestination } from 'pino-lambda';

const destination = pinoLambdaDestination();
export const logger = pino(destination);

// handler.ts
import { lambdaRequestTracker } from 'pino-lambda';
import { logger } from './logger';

const withRequest = lambdaRequestTracker();

async function handler(event, context) {
  // automatic request tracing across all instances of pino
  // called once at the beginning of your Lambda handler
  withRequest(event, context);

  // typical logging methods
  logger.info({ data: 'Some data' }, 'A log message');
}

You can use the this wrapper outside of the AWS lambda function in any place you want. This is especially useful in private npm modules that will be used by your AWS Lambda function. The default logger context is a shared instance, so it inherits all properties the default is configured for, and will emit request information for all logs. This effectively allows you to track a request across its entire set of log entries.

Please see our contributing guide.

Active: Formidable is actively working on this project, and we expect to continue for work for the foreseeable future. Bug reports, feature requests and pull requests are welcome.