Advanced Guide
Publishing Custom Metrics
GoFr publishes some default metrics.
GoFr can handle multiple different metrics concurrently, each uniquely identified by its name during initialization. It supports the following metrics types in Prometheus format:
CounterUpDownCounterHistogramGauge
If any custom metric is required, it can be created by using custom metrics as shown below:
Usage
1. Counter Metrics
Counter is a synchronous Instrument which supports non-negative increments.
Usage
package main
import (
"gofr.dev/pkg/gofr"
)
func main() {
// initialise gofr object
app := gofr.New()
app.Metrics().NewCounter("transaction_success", "used to track the count of successful transactions")
app.POST("/transaction", func(ctx *gofr.Context) (interface{}, error) {
ctx.Metrics().IncrementCounter(ctx, "transaction_success")
return "Transaction Successful", nil
})
app.Run()
}
2. UpDown Counter Metrics
UpDownCounter is a synchronous Instrument which supports increments and decrements. Note: If the value is monotonically increasing, use Counter instead.
Usage
package main
import (
"gofr.dev/pkg/gofr"
)
func main() {
// initialise gofr object
app := gofr.New()
app.Metrics().NewUpDownCounter("total_credit_day_sale", "used to track the total credit sales in a day")
app.POST("/sale", func(ctx *gofr.Context) (interface{}, error) {
ctx.Metrics().DeltaUpDownCounter(ctx, "total_credit_day_sale", 1000)
return "Sale Completed", nil
})
app.Run()
}
3. Histogram Metrics
Histogram is a synchronous Instrument which can be used to report arbitrary values that are likely to be statistically meaningful. It is intended for statistics such as histograms, summaries, and percentile.
Usage
package main
import (
"gofr.dev/pkg/gofr"
)
func main() {
// initialise gofr object
app := gofr.New()
app.Metrics().NewHistogram("transaction_time", "used to track the time taken by a transaction",
5, 10, 15, 20, 25, 35)
app.POST("/transaction", func(ctx *gofr.Context) (interface{}, error) {
transactionStartTime := time.Now()
// transaction logic
tranTime := time.Now().Sub(transactionStartTime).Milliseconds()
ctx.Metrics().RecordHistogram(ctx, "transaction_time", float64(tranTime))
return "Transaction Completed", nil
})
app.Run()
}
4. Gauge Metrics
Gauge is a synchronous Instrument which can be used to record non-additive value(s) when changes occur.
Usage
package main
import (
"gofr.dev/pkg/gofr"
)
func main() {
// initialise gofr object
app := gofr.New()
app.Metrics().NewGauge("product_stock", "used to track the number of products in stock")
app.POST("/sale", func(ctx *gofr.Context) (interface{}, error) {
ctx.Metrics().SetGauge("product_stock", 10)
return "Sale Completed", nil
})
app.Run()
}
Adding Labels to Custom Metrics
GoFr leverages metrics support by enabling labels. Labels are a key feature in metrics that allow you to categorize and filter metrics based on relevant information.
Understanding Labels
Labels are key-value pairs attached to metrics. They provide additional context about the metric data.
Common examples of labels include:
- environment: (e.g., "production", "staging")
- service: (e.g., "api-gateway", "database")
- status: (e.g., "success", "failure")
By adding labels, you can create different time series for the same metric based on the label values. This allows for more granular analysis and visualization in Grafana (or any other) dashboards.
Additional Considerations
- Prefer to keep the number of labels manageable to avoid overwhelming complexity.
- Choose meaningful label names that clearly describe the data point.
- Ensure consistency in label naming conventions across your application.
By effectively using labels in GoFr, you can enrich your custom metrics and gain deeper insights into your application's performance and behavior.
Usage:
Labels are added while populating the data for metrics, by passing them as arguments (comma separated key-value pairs) in the GoFr's methods (namely: IncreamentCounter, DeltaUpDownCounter, RecordHistogram, SetGauge).
Example: c.Metrics().IncrementCounter(c, "metric-name", "metric-value", "label-1", "value-1", "label-2", "value-2")
package main
import (
"gofr.dev/pkg/gofr"
)
func main() {
// Initialise gofr object
a := gofr.New()
// Add custom metrics
a.Metrics().NewUpDownCounter("total_credit_day_sale", "used to track the total credit sales in a day")
// Add all the routes
a.POST("/sale", SaleHandler)
a.POST("/return", ReturnHandler)
// Run the application
a.Run()
}
func SaleHandler(c *gofr.Context) (interface{}, error) {
// logic to create sales
c.Metrics().DeltaUpDownCounter(c, "total_credit_day_sale", 10, "sale_type", "credit", "product_type", "beverage") // Here "sale_type" & "product_type" are the labels and "credit" & "beverage" are the values
return "Sale Successful", nil
}
func ReturnHandler(c *gofr.Context) (interface{}, error) {
// logic to create a sales return
c.Metrics().DeltaUpDownCounter(c, "total_credit_day_sale", -5, "sale_type", "credit_return", "product_type", "dairy")
return "Return Successful", nil
}
Good To Know
While registering a metrics 2 key pieces of information of required:
- Name
- Description
When a registered metrics has to be used 3 key pieces of information are required:
- Name
- Value
- A set of key-value pairs called tags or labels.
A permutation of these key-value values provides the metric cardinality.
Lower the cardinality, faster the query performance and lower the monitoring resource utilisation.