# Targets

There are two fundamental building blocks to a Prism project: tasks and targets.

## What are targets?

The second fundamental building block to Prism projects are targets. 

Targets enable you to cache the results of your tasks. Put differently, targets are used to store the results of your task at an external location (e.g., a CSV on your local machine, a table in your data warehouse, a file in an S3 bucket, and so on). In doing so, they prevent repetitive and costly task re-runs.

For example, let's say you have a project with two tasks: `long`, and `short`, and that `short` depends on the output of `long`. Based on prior runs, we know that `long` takes 10 minutes to execute. If we don't want to re-run `long` every time we make updates to `short`, we can specify a target for `long` to save its output to an external location for easy access.

## How do you use targets?

To specify a target for a task, use the `prism.decorators.target` decorator on the `run` function as follows:

```python
# modules/hello_world.py

import prism.task
import prism.target
import prism.decorators

class HelloWorld(prism.task.PrismTask):
    
    @prism.decorators.target(
        type=prism.target.Txt, 
        loc="/Users/hello_world.txt", 
        **kwargs
    )
    def run(self, tasks, hooks):
        test_str = "Hello, world!"
        return test_str
```

The decorator takes two arguments: `type` and `loc`. The `type` parameter should be `PrismTarget` class, and it controls how the output should be saved (e.g., as a `.txt` file, as a parquet file, or so on). The `loc` parameter should be a `pathlib.Path` or a string, and it controls where the output should be saved.

In addition, you can add additional keyword arguments to customize the target's saving behavior (e.g., removing the index from CSVs when saving a Pandas DataFrame).

{% hint style="warning" %}
**Critical:** specifying a target changes the output of the task. Usually, the task output is some sort of object (e.g., a DataFrame). However, targets change it to instead be the location where the object is stored. Put differently, targets cause the output of the task to be the `loc` parameter of the `prism.decorators.target` decorator.
{% endhint %}

In the example above, the output of task `HelloWorld` is the path `"/Users/hello_world.txt"`. This will be reflected in downstream `tasks.ref(...)` calls:

```python
# modules/second_task.py

import prism.task
import prism.target
import prism.decorators

class SecondTask(prism.task.PrismTask):

    def run(self, tasks, hooks):
        tasks.ref("hello_world.py")  # returns "/Users/hello_world.txt"
```

## What kinds of targets are available?

There are several targets available out-of-the-box. These include `Txt`, `NumpyTxt`, `PandasCsv`, and `PysparkParquet`. We're always looking to add targets and improve the Prism functionality, so please let us know if there's a target you want us to include in the next update!

If the pre-defined targets are not sufficient for your use case, then you can define your own `PrismTarget` class. These classes are pretty simple. They have three attributes: `obj` (i.e., the output to save), `loc` (the location to save the output), and `hooks` (see [here](https://docs.runprism.com/v0.1.9rc1/fundamentals/tasks/hooks) for more information). And, they have one method called `save` that specifies how `obj` should be saved to `loc`. 

For reference, here is the full code for the `prism.target.Txt` class:

```python
class Txt(PrismTarget):

    def save(self, **kwargs):
        with open(self.loc, "w") as f:
            f.write(self.obj, **kwargs)
        f.close()
```