# Thesis

Inspired by https://github.com/github/scientist

Thesis provides the `Experiment` struct, which represents an experiment to
run which compares the return values of multiple methods for accomplishing
the same task.

Let's imagine that we already have a function called `load_data_from_db`,
which loads some data from a database. We want to refactor this to instead
load the same data from redis. We write a new function called
`load_data_from_redis` to accomplish the same task, but with redis instead of
a DB. We want to use the redis version on only a very small percentage of
traffic, say 0.5% of incoming requests, and we want to log it out if the
redis data doesn't match the DB data, treating the DB data as accurate and
discarding the redis data if so. Here's how we can use an `Experiment` to do
this.

```rust
use thesis::{Experiment, rollout::Percent};

async fn load_data_from_db(id: i32) -> i32 { id }
async fn load_data_from_redis(id: i32) -> i32 { id }

let id = 4;
let result = Experiment::new("load_data_from_db => load_data_from_redis")
    .control(load_data_from_db(id))
    .experimental(load_data_from_redis(id))
    .rollout_strategy(Percent::new(0.5))
    .on_mismatch(|mismatch| {
        eprintln!(
            "DB & Redis data differ - db={}, redis={}",
            mismatch.control,
            mismatch.experimental,
        );

        // the `control` value here comes from the DB
        mismatch.control
    })
    .run()
    .await;

assert_eq!(result, 4);
```

# Monitoring

Because thesis is designed to be used for refactoring operations in
production systems, there are a few built-in features for monitoring and
observability. Some contextual information is provided via spans created with
the `tracing` crate, as well as some metrics via the `metrics` crate.

## Metrics provided (with tags)

- `thesis_experiment_run_total` - counter incremented each time the `run`
  function is called
    - `name` - name of the experiment provided to the constructor
- `thesis_experiment_run_variant` - counter incremented each time a
  variant (defined as control vs experimental) is run
    - `name` - name of the experiment
    - `kind` - one of `control`, `experimental`, `experimental_and_compare`
- `thesis_experiment_outcome` - counter incremented each time an experiment
  has an observable outcome
    - `name` - name of the experiment
    - `kind` - one of `control`, `experimental`, `experimental_and_compare`
    - `outcome` - one of `ok`, `error`, `mismatch` (ok/error only produced
    via `Experiment::run_result`)

# Result handling

If your experimental (or control) methods may return an error, you should use
the `run_result` method on the `Experiment` builder. This method has special
handling and metrics reporting for `Result` types. When
`RolloutDecision::UseControl` is used and the experimental method is not
called, `run_result` works the same as `run`. Nothing special happens, even
if the control method returns an error. Here's what happens when
`RolloutDecision::UseExperimentalAndCompare` is used.

| Control  | Experimental | Return Value             | Metrics (label values of `thesis_experiment_outcome`)                                                                   | Logs                                                                                                      |
|----------|--------------|--------------------------|-------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|
| `Ok(X)`  | `Ok(X)`      | `Ok(X)`                  | `{kind=control, outcome=ok}`, `{kind=experimental, outcome=ok}`                                                         |                                                                                                           |
| `Ok(X)`  | `Ok(Y)`      | Result of `on_mismatch`  | `{kind=control, outcome=ok}`, `{kind=experimental, outcome=ok}`, `{kind=experimental_and_compare, outcome=mismatch}`    |                                                                                                           |
| `Ok(X)`  | `Err(e)`     | `Ok(X)`                  | `{kind=control, outcome=ok}`, `{kind=experimental, outcome=error}`, `{kind=experimental_and_compare, outcome=mismatch}` | `"thesis experiment error" kind=experimental, error=e`                                                    |
| `Err(e)` | `Ok(x)`      | Result of  `on_mismatch` | `{kind=control, outcome=error}`, `{kind=experimental, outcome=ok}`, `{kind=experimental_and_compare, outcome=mismatch}` | `"thesis experiment error" kind=control, error=e`                                                         |
| `Err(e)` | `Err(f)`     | `Err(e)`                 | `{kind=control, outcome=error}`, `{kind=experimental, outcome=error}`                                                   | `"thesis experiment error" kind=control, error=e`, `"thesis experiment error" kind=experimental, error=f` |

# Limitations

- The `control` and `experimental` futures must both have the same `Output`
  types
- There are no defaults provided for `control`, `experimental`, or
  `rollout_strategy`, all of these methods must be called or the experiment
  will not compile.
- `control` and `experimental` must both be futures. A non-async version of
  `Experiment` could be written, but this library does not currently provide
  one.
- The `name` provided to the experiment must be a `&'static str`. We use the
  `metrics` library for reporting metric information, which requires us to
  either to use an owned `String` each time an `Experiment` is created, or to
  require a static string. Allocating a `String` seems more wasteful than
  limiting dynamicly created experiment names.
- When using `run_result`, both `Result` types must have the same `Err` type.