Prompt:
How to get, set, and create new coders for my Apache Beam pipeline?

Response:
Every `PCollection` in an Apache Beam pipeline requires a corresponding `Coder`. In most cases, the Apache Beam SDKs can automatically infer a `Coder` for a `PCollection` based on its element type or the producing transform. However, in some instances, you may need to explicitly set a `Coder` or create a custom `Coder`.

In the Apache Beam SDKs for Python and Java, the `Coder` type provides the necessary methods for encoding and decoding data. To get, set, or register a coder for a particular pipeline, you can access and modify the pipeline’s `CoderRegistry` object.

The following examples demonstrate how to get, set, and create a new `Coder` in an Apache Beam pipeline using the Python and Java SDKs.

**Python SDK:**

In the Python SDK, you can use the following methods:
* `coders.registry`: retrieves the pipeline’s `CoderRegistry` object.
* `CoderRegistry.get_coder`: retrieves the default `Coder` for a type.
* `CoderRegistry.register_coder`: sets a new `Coder` for the target type.

Here is an example illustrating how to set the default `Coder` in the Python SDK:

```python
apache_beam.coders.registry.register_coder(int, BigEndianIntegerCoder)
```

The provided example sets a default `Coder`, specifically `BigEndianIntegerCoder`, for `int` values in the pipeline.

For custom or complex nested data types, you can implement a custom coder for your pipeline. To create a new `Coder`, you need to define a class that inherits from `Coder` and implement the required methods:
* `encode`: takes input values and encodes them into byte strings.
* `decode`: decodes the encoded byte string into its corresponding object.
* `is_deterministic`: specifies whether this coder encodes values deterministically or not. A deterministic coder produces the same encoded representation of a given object every time, even if it is called on different workers at different moments. The method returns `True` or `False` based on your implementation.

Here is an example of a custom `Coder` implementation in the Python SDK:

```python
from apache_beam.coders import Coder

class CustomCoder(Coder):
    def encode(self, value):
        # Implementation for encoding 'value' into byte strings
        pass

    def decode(self, encoded):
        # Implementation for decoding byte strings into the original object
        pass

    def is_deterministic(self):
        # Specify whether this coder produces deterministic encodings
        return True  # or False based on your implementation
```

**Java SDK:**

In the Java SDK, you can use the following methods:
* `Pipeline.getCoderRegistry`: retrieves the pipeline’s `CoderRegistry` object.
* `getCoder`: retrieves the coder for an existing `PCollection`.
* `CoderRegistry.getCoder`: retrieves the default `Coder` for a type.
* `CoderRegistry.registerCoder`: sets a new default `Coder` for the target type.

Here is an example of how you can set the default ‘Coder’ in the Java SDK:

```java
PipelineOptions options = PipelineOptionsFactory.create();
Pipeline p = Pipeline.create(options);

CoderRegistry cr = p.getCoderRegistry();
cr.registerCoder(Integer.class, BigEndianIntegerCoder.class);
```

In this example, you use the method `CoderRegistry.registerCoder` to register `BigEndianIntegerCoder` for the target `integer` type.

For custom or complex nested data types, you can implement a custom coder for your pipeline. For this, the `Coder` class exposes the following key methods:
* `encode`: takes input values and encodes them into byte strings.
* `decode`: decodes the encoded byte string into its corresponding object.
* `verifyDeterministic`: specifies whether this coder produces deterministic encodings. A deterministic coder produces the same encoded representation of a given object every time, even if it is called on different workers at different moments. The method will return `NonDeterministicException` if a coder is not deterministic.

Here’s an example of a custom `Coder` implementation in the Java SDK:

```java
import org.apache.beam.sdk.coders.CoderException;
import org.apache.beam.sdk.coders.StructuredCoder;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;

public class CustomCoder extends StructuredCoder<YourType> {
    @Override
    public void encode(YourType value, OutputStream outStream) throws CoderException, IOException {
        // Implementation for encoding 'value' into byte strings
    }

    @Override
    public YourType decode(InputStream inStream) throws CoderException, IOException {
        // Implementation for decoding byte strings into the original object
    }

    @Override
    public void verifyDeterministic() throws NonDeterministicException {
        // Specify whether this coder produces deterministic encodings
        // Throw NonDeterministicException if not deterministic
    }
}
```

Replace `YourType` with the actual type for which you want to create a new `Coder`, and implement the necessary methods based on your encoding/decoding logic.
