# okit

Short for "observability kit", `okit` aims to provide an all-in-one solution to application observability.

## Why

Traditional approaches to observability treat logging, application metrics, and tracing as independent operations, with
independent data streams. In practice, these elements are more or less all the same, with some minor differences between
them. Developers often have to choose between logging a message, emitting a metric, or expanding a trace.

**Logging**

- Used by developers and operators to determine what issues an application may be facing.
- High cardinality data (errors, stack traces, user id / signature).
- Often sent to stdout/stderr and can optionally be captured by traditional logging solutions.

**Tracing**

- Used by developers and operators to troubleshoot performance issues across a set of distributed systems.
- Medium cardinality data (consistent structure, high variability in tag values)
- Typically available in real-time to assess product performance.

**Metrics**

- Used by developers and product managers to determine details about how their product is doing.
- Medium/High cardinality data (user id / signature, other metadata fields).
- Typically available in real-time to assess user experience / feature performance.

In addition to the complexity that each of these solutions bring with them, you often need to import a custom library
for each, and thus increase your dependency footprint.

## Usage

### Basic

Basic usage for `okit` is fairly straight forward. You can create dedicated client, use the default, or even replace the
default. The example below demonstrates how to use the default client.

```go
package main

import (
	"context"

	"go.pitz.tech/okit"
)

func main() {
	var tags []okit.Tag

	// Metric emission
	okit.Observe("temperature_c", 20.9, tags...)

	// Multi-dimensional events
	okit.Emit("user_signup", tags...)

	// Tracing
	ctx := context.Background()
	defer okit.Trace(&ctx, tags...).Done()

	// Logging
	okit.Debug("a message used for debugging", tags...)
	okit.Info("an informational message for the user", tags...)
	okit.Warn("a warning indicating an issue with the system", tags...)
	okit.Error("the system encountered an error", tags...)
}
```

### HTTP

`okit` comes with built-in functionality to make it easy to trace HTTP operations.

```go
package main

import (
	"net/http"

	okithttp "go.pitz.tech/okit/http"
)

func main() {
	// Instrument HTTP Clients
	okithttp.InstrumentClient(http.DefaultClient)

	mux := http.NewServeMux()

	{ // Add reporting endpoints
		endpoint := okithttp.NewEndpoint()
		mux.HandleFunc("/health", endpoint.Health)
		mux.HandleFunc("/metrics", endpoint.Metrics)
		mux.HandleFunc("/trace", endpoint.Trace)
	}

	// Instrument HTTP Handlers
	handler := okithttp.InstrumentHandler(mux)
	err := http.ListenAndServe("0.0.0.0:8080", handler)
	if err != nil {
		panic(err)
	}
}
```

## Implementation

### Wire Protocol

TBD

### Tracing

Because `okit` aims at providing an all-in-one solution, tracing not only produces spec compliant traces for ingestion
into remote systems but also produces bookend log events for developers and operators.

### Logging

Logging follows a fairly standard implementation. It allows messages to be logged at different levels including `debug`,
`info`, `warn`, and `error`. A fifth, `trace` level is also available that allows tracing bookend events to be enabled
disabled. Logging output can be written as text or as JSON.

### Metrics

Metrics are implemented using an observation based approach. Results are recorded and stored locally for administrators
to be able to query.

### Events

Multi-dimensional events are a lot like metrics. The most common use case is when metrics have a statically coded value
of 1. For example, page views, checkout, and many other user-driven actions.