@connectedcars/logutil
v6.1.2
Published
Simple log formatting for Node
Downloads
1,830
Keywords
Readme
node-logutil
Installation
npm install https://github.com/connectedcars/node-logutil#v1.1.3
API
log.debug(message[, context])
Logs the provided message
with the optional context
as a JSON string, if the log level is set to output debug.
The method takes most input, but the combination described above is the recommended one.
message
Type: string
Descriptive log message
context
Type: object
Object hash to provide log context.
log.statistic(message[, context])
Logs the provided message
with the optional context
as a JSON string, if the log level is set to output statistic.
The method takes most input, but the combination described above is the recommended one.
message
Type: string
Descriptive log message
context
Type: object
Object hash to provide log context.
log.info(message[, context])
Logs the provided message
with the optional context
as a JSON string, if the log level is set to output info.
The method takes most input, but the combination described above is the recommended one.
message
Type: string
Descriptive log message
context
Type: object
Object hash to provide log context.
log.warn(message[, context])
Logs the provided message
with the optional context
as a JSON string, if the log level is set to output warnings.
The method takes most input, but the combination described above is the recommended one.
message
Type: string
Descriptive log message
context
Type: object
Object hash to provide log context.
log.error(message[, context])
Logs the provided message
with the optional context
as a JSON string, if the log level is set to output errors.
The method takes most input, but the combination described above is the recommended one.
message
Type: string
Descriptive log message
context
Type: object
Object hash to provide log context.
log.critical(message[, context])
Logs the provided message
with the optional context
as a JSON string, if the log level is set to output critical.
The method takes most input, but the combination described above is the recommended one.
message
Type: string
Descriptive log message
context
Type: object
Object hash to provide log context.
log.MetricRegistry()
A singleton instance that holds the state of all metrics in the application at a given point in time. The class should be instantiated globally and kept through the process lifetime.
log.MetricsRegistry.prototype.cumulative(name, value[, labels])
A monotically increasing number that. E.g. the number of requests since the process started.
name
Type: string
Name of the metric to write. The recommended format is [namespace]/[metric-name]
. E.g. ingester/queue-size
value
Type: number
A value to write. It's worth noting that we're always using floating-point precision for these values.
labels
Type: object
An optional label map that denotes some specific properties you want to group by / filter on later. Please note that this map must have hashable values. Thus, you're not allowed to nest objects.
It's also worth noting that the MetricRegistry
will log all permutations of the label, so please only use it to store categorical types and not, say carId
s.
Example: {tableName: 'LatestCarPositions'}
Scenarios where you'd want to use this metric type
Generally every time you're interested in monitoring:
- the relative change in a value (marginal difference)
- or the value relative to the time period
E.g.
- Inserted rows since last time
- Incoming HTTP requests
log.MetricsRegistry.prototype.gauge(name, value[, labels, reducerFn])
Please refer to log.MetricsRegistry.prototype.cumulative()
for a definition of the arguments name, value and labels
An instantaneous measurement of a varying number. Per default, we will only store the value and timestamp of the latest inserted value. So if you insert two values, e.g. gauge('foo', 2)
at time 0 and gauge('foo', 4)
at time 2, and the metrics are scraped at time 3, we will report value=4,time=2
to the log. If you for some reason don't want this, you can specify an optional reducerFn
argument:
// Function that takes the mean over all values in that timespan
const fn = (arr) => arr.reduce((a, b) => a+b)) / arr.length
// Sample some data
registry.gauge('foo', 10, null, fn)
registry.gauge('foo', 20, null, fn)
// logMetric reports value=30 and generates a timestamp
registry.logMetrics()
Scenarios where you'd want to use this metric type
Every time you want to capture a state at a particular time. E.g.:
- Size of a queue at a particular time
- CPU utilization
A rule of thumb: If a value can go vary (i.e. go up and down), and it should be captured at a specific time, then it's a good candidate for a gauge.
log.MetricRegistry.prototype.logMetrics()
Captures a snapshot of the image state and adds endTime=Date.now()
to all objects. Afterwards all metrics are dumped to stdout using node-logutil
's log.statistic
function. All metrics are dumped using LOG_LEVEL=INFO
, so make sure your application uses at least that log level.
This function is intended to be used together with metrics-subscriber-api
log.MetricRegistry.prototype.getMetrics()
Returns all metrics as an array of objects. Example:
{
name: 'gauge-metric',
type: 'GAUGE',
value: 50,
labels: { brand: 'vw' }
}
log.MetricRegistry.prototype.getPrometheusMetrics()
Returns an array of strings formatted using the Prometheus exposition format The format should follow their EBNF definition, but is not tested against a parser as of this moment.
This function is intended to be exposed through a small HTTP server and used in conjunction with Prometheus in the future.
Usage
const log = require('logutil')
log.debug('This is a debug message')
// Outputs to stdout: {"message":"This is a debug message","severity":"DEBUG","timestamp":"2017-09-01T13:37:42Z"}
log.debug('This is a statistic value', { foo: 42, bar: 1337 })
// Outputs to stdout: {"message":"Statistics","severity":"STATISTIC","timestamp":"2017-09-01T13:37:42Z", "content":{"foo":42,"bar":1337}}
log.info('This is an info message')
// Outputs to stdout: {"message":"This is an info message","severity":"INFO","timestamp":"2017-09-01T13:37:42Z"}
log.warn('This is a warning message', { type: 'missing-item' })
// Outputs to stderr: {"message":"This is a warning message","severity":"WARNING","timestamp":"2017-09-01T13:37:42Z","context":{"type":"missing-item"}}
log.error('This is an error message', { context: { items: ['foo', 'bar'] } })
// Outputs to stderr: {"message":"This is an error message","severity":"ERROR","timestamp":"2017-09-01T13:37:42Z","context":{"items":["foo","bar"]}}
log.critical('This is a critical message', { context: { items: ['foo', 'bar'] } })
// Outputs to stderr: {"message":"This is a critical message","severity":"CRITICAL","timestamp":"2017-09-01T13:37:42Z","context":{"items":["foo","bar"]}}
log.debug(() => {
// Only runs if log level is debug
return Promise.resolve('This is a debug message')
})
// Outputs to stdout: {"message":"This is a debug message","severity":"DEBUBG","timestamp":"2017-09-01T13:37:42Z"}
log.info(() => {
// Only runs if log level is info
return new Promise(resolve => {
setTimeout(() => {
resolve('This is an info message')
}, 500)
})
})
// Outputs to stdout (after 500 ms): {"message":"This is an info message","severity":"INFO","timestamp":"2017-09-01T13:37:42Z"}
// Instantiate as a singleton
const registry = log.MetricRegistry()
// Write some data
await registry.gauge('namespace/metric-name', 20, {brand: 'vw'})
await registry.cumulative('namespace/cumulative-metric-name', 40, {brand: 'seat'})
// In case you want to do aggregations more granular than the regular dump, you can pass in a reducer function taking an array of numbers.
await registry.gauge('namespace/metric-name', 20, {brand: 'vw'}, (arr) => arr.reduce((a, b) => a+b)) / arr.length
// Dump metrics to stdout regularly
setInterval(registry.logMetrics, 30000)
Configuration
Determining what to output depends on the environment variable LOG_LEVEL
. This variable can be DEBUG
, STATISTIC
, INFO
, WARN
, ERROR
, or CRITICAL
but defaults to WARN
.