learning/prompts/documentation-lookup/04_basic_pcollections.md - beam - Git at Google

 Prompt:
 What is a `PCollection` in Apache Beam?

 Response:
 A `PCollection` in Apache Beam is an unordered bag of elements. Each `PCollection` is a potentially distributed, homogeneous data set or data stream, and is owned by the specific `Pipeline` object for which it is created. It’s the primary data structure used in Apache Beam pipelines to handle large-scale data processing in batch and streaming modes.

 The following example shows how to create a `PCollection` using the `Create` transform:

 ```python
 import apache_beam as beam

 with beam.Pipeline() as pipeline:
   pcollection = pipeline | beam.Create([...])  # Create a PCollection
 ```

 A `PCollection` can either be bounded or unbounded, making it versatile for different [data source types](https://beam.apache.org/documentation/basics/#pcollection). Bounded `PCollection` objects represent a finite data set, such as files or databases, ideal for batch processing. Unbounded `PCollection` objects, on the other hand, represent data streams that continuously grow over time, such as real-time event logs, suitable for stream processing.

 Beam’s computational patterns and transforms are focused on situations where distributed data-parallel computation is required. Therefore, a `PCollection` has the following key characteristics:
    - All elements must be of the same type (with support of structured types).
    - Every `PCollection` has a coder, which is a specification of the binary format of the elements.
    - Elements are immutable. They can't be altered after creation.
    - Random access to individual elements of the collection isn't allowed.
    - Beam encodes each element for distributed processing.

 For more information about `PCollection` objects in Apache Beam and usage patterns, see [Beam Programming Guide: PCollections](https://beam.apache.org/documentation/programming-guide/#pcollections).
	Prompt:
	What is a `PCollection` in Apache Beam?

	Response:
	A `PCollection` in Apache Beam is an unordered bag of elements. Each `PCollection` is a potentially distributed, homogeneous data set or data stream, and is owned by the specific `Pipeline` object for which it is created. It’s the primary data structure used in Apache Beam pipelines to handle large-scale data processing in batch and streaming modes.

	The following example shows how to create a `PCollection` using the `Create` transform:

	```python
	import apache_beam as beam

	with beam.Pipeline() as pipeline:
	pcollection = pipeline \| beam.Create([...]) # Create a PCollection
	```

	A `PCollection` can either be bounded or unbounded, making it versatile for different [data source types](https://beam.apache.org/documentation/basics/#pcollection). Bounded `PCollection` objects represent a finite data set, such as files or databases, ideal for batch processing. Unbounded `PCollection` objects, on the other hand, represent data streams that continuously grow over time, such as real-time event logs, suitable for stream processing.

	Beam’s computational patterns and transforms are focused on situations where distributed data-parallel computation is required. Therefore, a `PCollection` has the following key characteristics:
	- All elements must be of the same type (with support of structured types).
	- Every `PCollection` has a coder, which is a specification of the binary format of the elements.
	- Elements are immutable. They can't be altered after creation.
	- Random access to individual elements of the collection isn't allowed.
	- Beam encodes each element for distributed processing.

	For more information about `PCollection` objects in Apache Beam and usage patterns, see [Beam Programming Guide: PCollections](https://beam.apache.org/documentation/programming-guide/#pcollections).