> ## Documentation Index
> Fetch the complete documentation index at: https://docs.beam.cloud/llms.txt
> Use this file to discover all available pages before exploring further.

# Distributed Maps

> Using Beam's distributed Map

Beam includes a concurrency-safe distributed map, accessible both locally and within remote containers. Serialization is done using cloudpickle, so any pickleable object will work. The interface is that of a standard python dictionary, but unlike a typical dicitonary it will persist between runs.

## Example: Accessing a map locally and remotely

In the following example, we create a distributed map. Our first function is invoked remotely using `.remote()`, and it sets the value a key in our map. The second function is invoked locally using `.local()`, and it sets another value. Finally, we print the result of our third, remotely invoked function, which retrieves the values we just set.

```python theme={null}
from beam import Map, function


@function()
def first():
    m = Map(name="m")
    m["beam"] = "me up"
    return


@function()
def second():
    m = Map(name="m")
    m["speed"] = "of light"
    return


@function()
def third():
    m = Map(name="m")
    return [m["beam"], m["speed"]]


if __name__ == '__main__':
    first.remote()
    second.local()
    print(third.remote())
```

You can run the example above with `python app.py`. The output will be:

```bash theme={null}
['me up', 'of light']
```