Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/defra/defra-logging-metrics
Provides a library to measure the duration of functions execution and log them into Azure Application Insights as custom metrics with optional custom dimensions
https://github.com/defra/defra-logging-metrics
Last synced: 6 days ago
JSON representation
Provides a library to measure the duration of functions execution and log them into Azure Application Insights as custom metrics with optional custom dimensions
- Host: GitHub
- URL: https://github.com/defra/defra-logging-metrics
- Owner: DEFRA
- License: other
- Created: 2023-12-15T14:59:31.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-01-23T16:58:06.000Z (10 months ago)
- Last Synced: 2024-11-07T12:04:04.970Z (12 days ago)
- Language: JavaScript
- Size: 132 KB
- Stars: 2
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Defra Logging Metrics
This Defra Common Platform npm package provides a library to measure the duration of functions execution and log them into Azure Application Insights as custom metrics with optional custom dimensions.
## Run tests
```shell
npm install
```
```shell
npm test
```
## Use the package from npm
1. Install the package
```shell
npm install @defra/logging-metrics --save
```
1. Import the module in your script
```js
import MetricsService from '@defra/logging-metrics'
```
1. Initialize the metrics service
```js
let metricsService = new MetricsService()
```
1. If you are already running application insights and already have a `TelemetryClient` configured in your application, you can create the metrics service and pass the telemetry client to the constructor:
```js
let metricsService = new MetricsService(telemetryClient)
```
Alternatively you can create a new telemetry client and provide the configuration with a connection string and any other [configuration options](https://learn.microsoft.com/en-us/azure/azure-monitor/app/nodejs#advanced-configuration-options) you wish e.g.:
```js
let telemetryClientConfig = {
connectionString: "InstrumentationKey=myKey;IngestionEndpoint=https://applicationinsights.azure.com/;LiveEndpoint=https://livediagnostics.monitor.azure.com/",
samplingPercentage: 100
}
[...]
let { duration, result } = metricsService.execute(myFunction, metric, telemetryClientConfig)
```
If you don't provide a telemetry client in the constructor and you don't provide a telemetry client configuration, a new telemetry client will be created by reading the connection string from the `APPLICATIONINSIGHTS_CONNECTION_STRING` environment variable.
1. Name the metric you want to log with along with any other custom properties to be recorded e.g.:
```js
let metric = {
name: "timeToCallDatabase",
properties: { source: "CosmosDB" }
}
```
1. Execute your function via one of the following methods, providing the `config` and `metric` from the previous steps along with the function you want to call and measure:
- If your function does not return any kind of `Promise`:
```js
let { duration, result } = metricsService.execute(myFunction, metric, config)
console.log(`Took ${duration} seconds for ${result}`)
```
- If your function returns a `Promise`:
```js
metricsService.executeAsync(asyncFunction, metric, config)
.then(({ result, duration }) => {
console.log(`Took ${duration} seconds for ${result}`)
})
```
- If you need to use a `Promise.all()`-like behavior and record each function call:
```js
metricsService.executeAllFailFastAsync(() => [asyncFunction1, asyncFunction2], metric, config)
.then(({ results, durations }) => {
results.forEach((result, i) => {
console.log(`Took ${durations[i]} seconds for ${result}`)
})
})
```
- If you need to use a `Promise.allSettled()`-like behavior and record each function call:
```js
metricsService.executeAllAsync(() => [asyncFunction1, asyncFunction2], metric, config)
.then(({ results, durations }) => {
results.forEach((result, i) => {
console.log(`Took ${durations[i]} seconds for ${result}`)
})
})
```
1. As advised in Application Insights documentation:
> If your application has a short lifespan, such as a CLI tool, it might be necessary to manually flush your buffered telemetry [...]
Flush is supported by calling `metricsService.flushClient()`
1. If you wish to further use, configure or access the Microsoft Azure Application Insights `TelemetryClient` within the metrics service, it can be accessed through the `metricsService.telemetryClient` variable
## Full example
A full example can be found [here](sample/README.md).