alxp1982 commented on code in PR #29507: URL: https://github.com/apache/beam/pull/29507#discussion_r1408012743
########## prompts/documentation-lookup/01_basic_learning_apache_beam.md: ########## @@ -0,0 +1,11 @@ +Prompt: +How can I learn Apache Beam? +Response: +Learning Apache Beam is a valuable skill for data processing and analysis. Here are some resources to get you started: +For a hands-on introduction, you can start by exploring the [WordCount](https://beam.apache.org/get-started/wordcount-example/) - a simple example that demonstrates the basic concepts of Apache Beam. Review Comment: Mention 'Get Started' on Beam website here? ########## prompts/documentation-lookup/01_basic_learning_apache_beam.md: ########## @@ -0,0 +1,11 @@ +Prompt: +How can I learn Apache Beam? +Response: +Learning Apache Beam is a valuable skill for data processing and analysis. Here are some resources to get you started: +For a hands-on introduction, you can start by exploring the [WordCount](https://beam.apache.org/get-started/wordcount-example/) - a simple example that demonstrates the basic concepts of Apache Beam. +The [Tour of Beam](https://tour.beam.apache.org/) is an interactive tour that allows you to learn how to write Beam code. It provides a sandbox environment to write and run pipelines while exploring different concepts. +The [Beam Playground](https://play.beam.apache.org/) offers an interactive space to experiment with Beam's transforms and examples without installing anything on your system. Once you have a basic understanding of Apache Beam, you can start to build your own pipelines. Review Comment: The [Beam Playground](https://play.beam.apache.org/) offers an interactive space to experiment with Beam's transforms and examples without installing anything on your system. Once you have a basic understanding of Apache Beam, you can start building your own pipelines. ########## prompts/documentation-lookup/01_basic_learning_apache_beam.md: ########## @@ -0,0 +1,11 @@ +Prompt: +How can I learn Apache Beam? +Response: +Learning Apache Beam is a valuable skill for data processing and analysis. Here are some resources to get you started: +For a hands-on introduction, you can start by exploring the [WordCount](https://beam.apache.org/get-started/wordcount-example/) - a simple example that demonstrates the basic concepts of Apache Beam. +The [Tour of Beam](https://tour.beam.apache.org/) is an interactive tour that allows you to learn how to write Beam code. It provides a sandbox environment to write and run pipelines while exploring different concepts. Review Comment: The [Tour of Beam](https://tour.beam.apache.org/) is an interactive tour that teaches you core Beam concepts. It provides a sandbox environment to write and run pipelines while exploring different topics. ########## learning/prompts/documentation-lookup/03_basic_configuring_pipelines.md: ########## @@ -0,0 +1,29 @@ +Prompt: +How do you configure a pipeline in Apache Beam? +Response: +Configuring pipeline options in Apache Beam is crucial for specifying the execution environment, managing resources, and tailoring the pipeline's behavior to meet specific data processing needs and performance requirements. + +Pipeline options can be set programmatically or passed through the command line. These options include runner-specific settings, job name, project ID (for cloud runners), machine types, number of workers, and more. + +Apache Beam offers a variety of [standard pipeline options](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/options/pipeline_options.py) that allow users to customize and optimize their data processing pipelines. + +Beam SDKs includes a command-line parser that you can use to set fields in PipelineOptions using command-line arguments in the `--<option>=<value>` format. For example, the following command sets the `--runner` option to `DirectRunner` and the `--project` option to `my-project-id`: + +```bash +python my-pipeline.py --runner=DirectRunner --project=my-project-id +``` + +To set the pipeline options programmatically, you can use the `PipelineOptions` class. For example, the following code sets the `--runner` option to `DirectRunner` and the `--project` option to `my-project-id`: + +```python +from apache_beam import Pipeline +from apache_beam.options.pipeline_options import PipelineOptions + +options = PipelineOptions( + project='my-project-id', + runner='DirectRunner' +) +`````` +You can also add your own custom options in addition to the standard PipelineOptions. For a common pattern for configuring pipeline options see here [Pipeline option pattern](https://beam.apache.org/documentation/patterns/pipeline-options/). Review Comment: You can also add your own custom options in addition to the standard PipelineOptions. For a common pattern for configuring pipeline options, see here [Pipeline option pattern](https://beam.apache.org/documentation/patterns/pipeline-options/). ########## learning/prompts/documentation-lookup/04_basic_pcollections.md: ########## @@ -0,0 +1,22 @@ +Prompt: +Wahat is a PCollection in Apache Beam? +Response: +A `PCollection` in Apache Beam is a core abstractions representing a distributed, multi-element data set or data stream. It's the primary data structure used in Apache Beam pipelines to handle large-scale data processing, both in batch and streaming modes. Review Comment: A PCollection in Apache Beam is a core abstraction representing a distributed, multi-element data set or data stream. It’s the primary data structure used in Apache Beam pipelines to handle large-scale data processing in batch and streaming modes. ########## learning/prompts/documentation-lookup/06_basic_schema.md: ########## @@ -0,0 +1,26 @@ +Prompt: +What are schemas in Apache Beam +Response: + + A [Schema in Apache Beam](https://beam.apache.org/documentation/programming-guide/#schemas) is a language-independent type definition for a PCollection. Schema defines elements of that PCollection as an ordered list of named fields. + +In many cases, the element type in a PCollection has a structure that can be introspected. Some examples are JSON, Protocol Buffer, Avro, and database row objects. All of these formats can be converted to Beam Schemas Review Comment: In many cases, the element type in a PCollection has a structure that can be introspected. Some examples are JSON, Protocol Buffer, Avro, and database row objects. All of these formats can be converted to Beam Schemas. ########## learning/prompts/documentation-lookup/07_basic_runners.md: ########## @@ -0,0 +1,22 @@ +Prompt: +What is a Runner in Apache Beam? +Response: +Apache Beam Runners are the execution engines that execute the pipelines. They are responsible for translating or adapting the pipeline into a form that can be executed on a massively parallel big data processing system, such as Apache Flink, Apache Spark, Google Cloud Dataflow, and more. + +Choosing a runner is an important step in the pipeline development process. The runner you choose determines where and how your pipeline runs. See the [capabilities matrix](https://beam.apache.org/documentation/runners/capability-matrix/) for more information on awailable runners and their capabilities. Review Comment: Choosing a runner is an important step in the pipeline development process. The runner you choose determines where and how your pipeline runs. See the [capabilities matrix](https://beam.apache.org/documentation/runners/capability-matrix/) for more information on available runners and their capabilities. ########## learning/prompts/documentation-lookup/06_basic_schema.md: ########## @@ -0,0 +1,26 @@ +Prompt: +What are schemas in Apache Beam +Response: + + A [Schema in Apache Beam](https://beam.apache.org/documentation/programming-guide/#schemas) is a language-independent type definition for a PCollection. Schema defines elements of that PCollection as an ordered list of named fields. + +In many cases, the element type in a PCollection has a structure that can be introspected. Some examples are JSON, Protocol Buffer, Avro, and database row objects. All of these formats can be converted to Beam Schemas + +In order to take advantage of schemas, your PCollections must have a schema attached to it. Often, the source itself will attach a schema to the PCollection. Review Comment: To take advantage of schemas, your PCollections must have a schema attached. Often, the source itself will attach a schema to the PCollection. ########## learning/prompts/documentation-lookup/07_basic_runners.md: ########## @@ -0,0 +1,22 @@ +Prompt: +What is a Runner in Apache Beam? +Response: +Apache Beam Runners are the execution engines that execute the pipelines. They are responsible for translating or adapting the pipeline into a form that can be executed on a massively parallel big data processing system, such as Apache Flink, Apache Spark, Google Cloud Dataflow, and more. + +Choosing a runner is an important step in the pipeline development process. The runner you choose determines where and how your pipeline runs. See the [capabilities matrix](https://beam.apache.org/documentation/runners/capability-matrix/) for more information on awailable runners and their capabilities. + +Runner is specified using the `--runner` flag when executing the pipeline. For example, to run the WordCount pipeline on Google Cloud Dataflow, you would run the following command: Review Comment: let's put a link to WordCount example ########## learning/prompts/documentation-lookup/09_basic_triggers.md: ########## @@ -0,0 +1,30 @@ +Prompt: +What are Triggers in Apache Beam? +Response: + +Beam uses triggers to determine when to emit the aggregated results of each [window](https://beam.apache.org/documentation/programming-guide/#windowing) (referred to as a pane) + +Triggers provide two additional capabilities compared to [outputting at the end of a window](https://beam.apache.org/documentation/programming-guide/#default-trigger): allow early results to be output before the end of the window or allow late data to be handled after the end of the window Review Comment: Triggers provide two additional capabilities compared to [outputting at the end of a window](https://beam.apache.org/documentation/programming-guide/#default-trigger, allowing early results to be output before the end of the window or allowing late data to be handled after the end of the window. ########## learning/prompts/documentation-lookup/08_basic_windowing.md: ########## @@ -0,0 +1,28 @@ +Prompt: +What is Windowing in Apache Beam? +Response: +Windowing is a key concept in stream processing, as it allows you to divide streams of data into logical units for efficient and correct parallel processing. +With an unbounded data set, it is impossible to collect all of the elements, since new elements are constantly being added. In the Beam model, any PCollection (including unbounded PCollections) can be subdivided into [logical windows](https://beam.apache.org/documentation/programming-guide/#windowing-basics). Grouping transforms then consider each PCollection’s elements on a per-window basis. + +Since Beam's default windowing strategy is to assign each element to a single, global window, you must explicitly specify a [windowing function](https://beam.apache.org/documentation/programming-guide/#setting-your-pcollections-windowing-function) for your pipeline. + +The following code snippet shows how to divide a PCollection into 60-second windows: +```python +from apache_beam import beam +from apache_beam import window +fixed_windowed_items = ( + items | 'window' >> beam.WindowInto(window.FixedWindows(60))) +``` + +Beam provides a number of [built-in windowing functions](https://beam.apache.org/documentation/programming-guide/#provided-windowing-functions) that you can use to subdivide your data into windows: +- Fixed Time Windows +- Sliding Time Windows +- Per-Session Windows +- Single Global Window +- Calendar-based Windows (not supported by the Beam SDK for Python or Go) + +You can also create your own custom windowing function [WindowFn](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/transforms/window.py). + +You also need to specify a [triggering strategy](https://beam.apache.org/documentation/programming-guide/#triggers) to determine when to emit the results of your pipeline’s windowed computations. + +You can adjust the windowing strategy to allow for [late data](https://beam.apache.org/documentation/programming-guide/#watermarks-and-late-data), or data that arrives after the watermark has passed the end of the window. You can also specify how to handle late data, such as discarding it or adding it to the next window. Review Comment: You can adjust the windowing strategy to allow for [late data](https://beam.apache.org/documentation/programming-guide/#watermarks-and-late-data) or data that arrives after the watermark has passed the end of the window. You can also specify how to handle late data, such as discarding or adding it to the next window. ########## learning/prompts/documentation-lookup/12_basic_timers.md: ########## @@ -0,0 +1,12 @@ +Prompt: +What is a Timer in Apache Beam? +Response: +In Apache Beam a [Timer](https://beam.apache.org/documentation/basics/#state-and-timers) is a per-key timer callback API enabling delayed processing of data stored using the [State API](https://beam.apache.org/documentation/programming-guide/#state-and-timers) Review Comment: In Apache Beam, a [Timer](https://beam.apache.org/documentation/basics/#state-and-timers) is a per-key timer callback API enabling delayed processing of data stored using the [State API](https://beam.apache.org/documentation/programming-guide/#state-and-timers) ########## learning/prompts/documentation-lookup/12_basic_timers.md: ########## @@ -0,0 +1,12 @@ +Prompt: +What is a Timer in Apache Beam? +Response: +In Apache Beam a [Timer](https://beam.apache.org/documentation/basics/#state-and-timers) is a per-key timer callback API enabling delayed processing of data stored using the [State API](https://beam.apache.org/documentation/programming-guide/#state-and-timers) + +Apache Beam provides two [types of timers](https://beam.apache.org/documentation/programming-guide/#timers) - processing time timers and event time timers. Processing time timers are based on the system clock and event time timers are based on the timestamps of the data elements. + +Beam also supports dynamically setting a timer tag using TimerMap in the Java SDK. This allows for setting multiple different timers in a DoFn and allowing for the timer tags to be dynamically chosen - e.g. based on data in the input elements Review Comment: Beam also supports dynamically setting a timer tag using TimerMap in the Java SDK. This allows for setting multiple different timers in a DoFn and allowing for the timer tags to be dynamically chosen - e.g. based on data in the input elements. ########## learning/prompts/documentation-lookup/13_advanced_splittable_dofn.md: ########## @@ -0,0 +1,18 @@ +Prompt: +What is Splittable DoFn in Apache Beam? +Response: +Splittable DoFn (SDF) is a generalization of [DoFn](https://beam.apache.org/documentation/programming-guide/#pardo) that lets you process elements in a non-monolithic way. Splittable DoFn makes it easier to create complex, modular I/O connectors in Beam. +When you apply a splittable DoFn to an element, the runner has the option of splitting the element’s processing into smaller tasks. You can checkpoint the processing of an element, and you can split the remaining work to yield additional parallelism. Review Comment: When you apply a splittable DoFn to an element, the runner can split the element’s processing into smaller tasks. You can checkpoint the processing of an element, and you can split the remaining work to yield additional parallelism. ########## learning/prompts/documentation-lookup/13_advanced_splittable_dofn.md: ########## @@ -0,0 +1,18 @@ +Prompt: +What is Splittable DoFn in Apache Beam? +Response: +Splittable DoFn (SDF) is a generalization of [DoFn](https://beam.apache.org/documentation/programming-guide/#pardo) that lets you process elements in a non-monolithic way. Splittable DoFn makes it easier to create complex, modular I/O connectors in Beam. Review Comment: Splittable DoFn (SDF) is a generalization of [DoFn](https://beam.apache.org/documentation/programming-guide/#pardo) that lets you process elements in a non-monolithic way. Splittable DoFn makes creating complex, modular I/O connectors in Beam easier. ########## learning/prompts/documentation-lookup/03_basic_configuring_pipelines.md: ########## @@ -0,0 +1,29 @@ +Prompt: +How do you configure a pipeline in Apache Beam? +Response: +Configuring pipeline options in Apache Beam is crucial for specifying the execution environment, managing resources, and tailoring the pipeline's behavior to meet specific data processing needs and performance requirements. + +Pipeline options can be set programmatically or passed through the command line. These options include runner-specific settings, job name, project ID (for cloud runners), machine types, number of workers, and more. Review Comment: You can set pipeline options programmatically or pass through the command line. These options include runner-specific settings, job name, project ID (for cloud runners), machine types, number of workers, and more. ########## learning/prompts/documentation-lookup/05_basic_ptransforms.md: ########## @@ -0,0 +1,28 @@ +Prompt: +What is a PTransform in Apache Beam? +Response: + +A `PTransform` (or [Transform](https://beam.apache.org/documentation/programming-guide/#transforms)) represents a data processing operation, or a step, in a Beam pipeline. A transform is applied to zero or more PCollection objects, and produces zero or more PCollection objects. Review Comment: A P[Transform](https://beam.apache.org/documentation/programming-guide/#transforms) (or Transform) represents a data processing operation, or a step, in a Beam pipeline. A transform is applied to zero or more PCollection objects and produces zero or more PCollection objects. ########## prompts/documentation-lookup/01_basic_learning_apache_beam.md: ########## @@ -0,0 +1,11 @@ +Prompt: +How can I learn Apache Beam? Review Comment: I think it useful to start with 'what is Apache Beam'? ########## learning/prompts/documentation-lookup/03_basic_configuring_pipelines.md: ########## @@ -0,0 +1,29 @@ +Prompt: +How do you configure a pipeline in Apache Beam? +Response: +Configuring pipeline options in Apache Beam is crucial for specifying the execution environment, managing resources, and tailoring the pipeline's behavior to meet specific data processing needs and performance requirements. + +Pipeline options can be set programmatically or passed through the command line. These options include runner-specific settings, job name, project ID (for cloud runners), machine types, number of workers, and more. + +Apache Beam offers a variety of [standard pipeline options](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/options/pipeline_options.py) that allow users to customize and optimize their data processing pipelines. + +Beam SDKs includes a command-line parser that you can use to set fields in PipelineOptions using command-line arguments in the `--<option>=<value>` format. For example, the following command sets the `--runner` option to `DirectRunner` and the `--project` option to `my-project-id`: Review Comment: Beam SDKs include a command-line parser that you can use to set fields in PipelineOptions using command-line arguments in the `--<option>=<value>` format. For example, the following command sets the `--runner` option to `DirectRunner` and the `--project` option to `my-project-id`: ########## learning/prompts/documentation-lookup/17_advanced_ai_ml.md: ########## @@ -0,0 +1,18 @@ +Prompt: +What are AI and ML capabilities in Apache Beam? +Response: +Apache Beam has a number of built-in [AI and ML capabilities](https://beam.apache.org/documentation/ml/overview/) that enable you to: Review Comment: Apache Beam has several built-in [AI and ML capabilities](https://beam.apache.org/documentation/ml/overview/) that enable you to: ########## learning/prompts/documentation-lookup/04_basic_pcollections.md: ########## @@ -0,0 +1,22 @@ +Prompt: +Wahat is a PCollection in Apache Beam? Review Comment: What is a PCollection in Apache Beam? ########## learning/prompts/documentation-lookup/03_basic_configuring_pipelines.md: ########## @@ -0,0 +1,29 @@ +Prompt: +How do you configure a pipeline in Apache Beam? Review Comment: should it be 'How do I configure pipeline in Apache Beam'? ########## learning/prompts/documentation-lookup/15_advanced_xlang.md: ########## @@ -0,0 +1,15 @@ +Prompt: +What is a multi-language pipeline in Apache Beam? +Response: +Beam lets you combine transforms written in any supported SDK language (currently, [Java](https://beam.apache.org/documentation/programming-guide/#1311-creating-cross-language-java-transforms) and [Python](https://beam.apache.org/documentation/programming-guide/#1312-creating-cross-language-python-transforms)) and use them in one multi-language pipeline. For example, a pipeline that reads from a Python source, processes the data using a Java transform, and writes the data to a Python sink is a multi-language pipeline. Review Comment: Beam lets you combine transforms written in any supported SDK language (currently, [Java](https://beam.apache.org/documentation/programming-guide/#1311-creating-cross-language-java-transforms) and [Python](https://beam.apache.org/documentation/programming-guide/#1312-creating-cross-language-python-transforms)) and use them in one multi-language pipeline. For example, a pipeline that reads from a Python source processes the data using a Java transform, and writes the data to a Python sink is a multi-language pipeline. ########## learning/prompts/documentation-lookup/04_basic_pcollections.md: ########## @@ -0,0 +1,22 @@ +Prompt: +Wahat is a PCollection in Apache Beam? +Response: +A `PCollection` in Apache Beam is a core abstractions representing a distributed, multi-element data set or data stream. It's the primary data structure used in Apache Beam pipelines to handle large-scale data processing, both in batch and streaming modes. + +```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 types of [data source](https://beam.apache.org/documentation/basics/#pcollection). Bounded `PCollection`s represent a finite data set, such as files or databases, ideal for batch processing. Unbounded `PCollection`s, on the other hand, represent data streams that continuously grow over time, such as real-time event logs, suitable for stream processing. Review Comment: A PCollection can either be bounded or unbounded, making it versatile for different [data source](https://beam.apache.org/documentation/basics/#pcollection) types. Bounded PCollections represent a finite data set, such as files or databases, ideal for batch processing. Unbounded PCollections, on the other hand, represent data streams that continuously grow over time, such as real-time event logs, suitable for stream processing. ########## learning/prompts/documentation-lookup/02_basic_pipelines.md: ########## @@ -0,0 +1,20 @@ +Prompt: +What is a Pipeline in Apache Beam? +Response: +A [Pipeline](https://beam.apache.org/documentation/pipelines/design-your-pipeline/) in Apache Beam serves as an abstraction that encapsulates the entirety of a data processing task, including all the data and each step of the process. Essentially, it's a [Directed Acyclic Graph](https://en.wikipedia.org/wiki/Directed_acyclic_graph) of transformations (known as `PTransform`s) applied to collections of data (`PCollection`s). + +The simplest pipelines in Apache Beam follow a linear flow of operations, typically adhering to a read-process-write pattern. However, pipelines can also be significantly more complex, featuring multiple input sources, multiple output sinks, and operations (`PTransform`s) that can both read from and output to multiple `PCollection`s. + +For more information on pipeline design and best practices, see the [Common Pipeline Patterns](https://beam.apache.org/documentation/patterns/overview/) + +To use Beam, your driver program must first create an instance of the Beam SDK class `Pipeline` (typically in the `main()` function). + +```python +import apache_beam as beam + +with beam.Pipeline() as pipeline: + pass # build your pipeline here +``` + +When you create your `Pipeline`, you’ll also need to set some [configuration options](https://beam.apache.org/documentation/programming-guide/#configuring-pipeline-options). You can set your pipeline’s configuration options programmatically, but it’s often easier to set the options ahead of time (or read them from the command line) and pass them to the `Pipeline` object when you create the object. Review Comment: When you create your `Pipeline`, you'll also need to set [configuration options](https://beam.apache.org/documentation/programming-guide/#configuring-pipeline-options). You can set your pipeline's configuration options programmatically. Still, it's often easier to set the options ahead of time (or read them from the command line) and pass them to the `Pipeline` object when you create the object. ########## learning/prompts/documentation-lookup/02_basic_pipelines.md: ########## @@ -0,0 +1,20 @@ +Prompt: +What is a Pipeline in Apache Beam? +Response: +A [Pipeline](https://beam.apache.org/documentation/pipelines/design-your-pipeline/) in Apache Beam serves as an abstraction that encapsulates the entirety of a data processing task, including all the data and each step of the process. Essentially, it's a [Directed Acyclic Graph](https://en.wikipedia.org/wiki/Directed_acyclic_graph) of transformations (known as `PTransform`s) applied to collections of data (`PCollection`s). + +The simplest pipelines in Apache Beam follow a linear flow of operations, typically adhering to a read-process-write pattern. However, pipelines can also be significantly more complex, featuring multiple input sources, multiple output sinks, and operations (`PTransform`s) that can both read from and output to multiple `PCollection`s. + +For more information on pipeline design and best practices, see the [Common Pipeline Patterns](https://beam.apache.org/documentation/patterns/overview/) + +To use Beam, your driver program must first create an instance of the Beam SDK class `Pipeline` (typically in the `main()` function). Review Comment: Prompt: What is a [Pipeline](https://beam.apache.org/documentation/pipelines/design-your-pipeline/) in Apache Beam? Response: A Pipeline in Apache Beam serves as an abstraction that encapsulates the entirety of a data processing task, including all the data and each step of the process. Essentially, it's a [Directed Acyclic Graph](https://en.wikipedia.org/wiki/Directed_acyclic_graph) of transformations (known as PTransforms) applied to data collections (PCollections). The simplest pipelines in Apache Beam follow a linear flow of operations, typically adhering to a read-process-write pattern. However, pipelines can also be significantly more complex, featuring multiple input sources, output sinks, and operations (PTransforms) that can both read from and output to multiple PCollections. For more information on pipeline design and best practices, see the [Common Pipeline Patterns](https://beam.apache.org/documentation/patterns/overview/) To use Beam, your driver program must first create an instance of the Beam SDK class Pipeline (typically in the main() function). ########## learning/prompts/documentation-lookup/16_advanced_pipeline_lifecycle.md: ########## @@ -0,0 +1,36 @@ +Prompt: +What is a pipeline development lifecycle in Apache Beam? +Response: + +The Apache Beam pipeline development lifecycle is an iterative process that usually involves the following steps: + +- Design your pipeline. +- Develop your pipeline code. +- Test your pipeline. +- Deploy your pipeline. + +On each iteration, you may need to go back and forth between the different steps to refine your pipeline code and to fix any bugs that you find. + +Desiging a pipeline addresses the following questions: Review Comment: Designing a pipeline addresses the following questions: ########## learning/prompts/documentation-lookup/04_basic_pcollections.md: ########## @@ -0,0 +1,22 @@ +Prompt: +Wahat is a PCollection in Apache Beam? +Response: +A `PCollection` in Apache Beam is a core abstractions representing a distributed, multi-element data set or data stream. It's the primary data structure used in Apache Beam pipelines to handle large-scale data processing, both in batch and streaming modes. + +```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 types of [data source](https://beam.apache.org/documentation/basics/#pcollection). Bounded `PCollection`s represent a finite data set, such as files or databases, ideal for batch processing. Unbounded `PCollection`s, 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, PCollections 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 cannot be altered after creation (immutability) + - No random access to individual elements of collection Review Comment: No random access to individual elements of the collection ########## learning/prompts/documentation-lookup/05_basic_ptransforms.md: ########## @@ -0,0 +1,28 @@ +Prompt: +What is a PTransform in Apache Beam? +Response: + +A `PTransform` (or [Transform](https://beam.apache.org/documentation/programming-guide/#transforms)) represents a data processing operation, or a step, in a Beam pipeline. A transform is applied to zero or more PCollection objects, and produces zero or more PCollection objects. + +Key Transforms Characteristics +1. Versatility: Able to execute a diverse range of operations on PCollections. +2. Composability: Can be combined to form elaborate data processing pipelines. +3. Parallel Execution: Designed for distributed processing, allowing simultaneous execution across multiple workers. +4. Scalability: Apt for handling extensive data, suitable for both batch and streaming data. + +The Beam SDKs contain a number of different transforms that you can apply to your pipeline’s PCollections. Common transform types include: + - [Source transforms](https://beam.apache.org/documentation/programming-guide/#pipeline-io) such as TextIO.Read and Create. A source transform conceptually has no input. + - [Processing and conversion operations](https://beam.apache.org/documentation/programming-guide/#core-beam-transforms) such as ParDo, GroupByKey, CoGroupByKey, Combine, and Count. + - [Outputting transforms](https://beam.apache.org/documentation/programming-guide/#pipeline-io) such as TextIO.Write. + - User-defined, application-specific [composite transforms](https://beam.apache.org/documentation/programming-guide/#composite-transforms). + +Transform processing logic is provided in the form of a function object (colloquially referred to as “user code”), and this code is applied to each element of the input PCollection (or more than one PCollection). They can be linked together to create complex data processing sequences. +User code for transforms should satisfy the [requrements of the Beam model](https://beam.apache.org/documentation/programming-guide/#requirements-for-writing-user-code-for-beam-transforms). Review Comment: User code for transforms should satisfy the [requirements of the Beam model](https://beam.apache.org/documentation/programming-guide/#requirements-for-writing-user-code-for-beam-transforms). ########## learning/prompts/documentation-lookup/06_basic_schema.md: ########## @@ -0,0 +1,26 @@ +Prompt: +What are schemas in Apache Beam +Response: + + A [Schema in Apache Beam](https://beam.apache.org/documentation/programming-guide/#schemas) is a language-independent type definition for a PCollection. Schema defines elements of that PCollection as an ordered list of named fields. + +In many cases, the element type in a PCollection has a structure that can be introspected. Some examples are JSON, Protocol Buffer, Avro, and database row objects. All of these formats can be converted to Beam Schemas + +In order to take advantage of schemas, your PCollections must have a schema attached to it. Often, the source itself will attach a schema to the PCollection. + +While schemas themselves are language independent, they are designed to embed naturally into the programming languages of the Beam SDK being used. This allows Beam users to continue [using native types](https://beam.apache.org/documentation/programming-guide/#schemas-for-pl-types) while leveraging the advantage of having Beam understand their element schemas. Review Comment: While schemas themselves are language-independent, they are designed to embed naturally into the Beam SDK programming languages. This allows Beam users to continue [using native types](https://beam.apache.org/documentation/programming-guide/#schemas-for-pl-types) while leveraging the advantage of having Beam understand their element schemas. ########## learning/prompts/documentation-lookup/07_basic_runners.md: ########## @@ -0,0 +1,22 @@ +Prompt: +What is a Runner in Apache Beam? +Response: +Apache Beam Runners are the execution engines that execute the pipelines. They are responsible for translating or adapting the pipeline into a form that can be executed on a massively parallel big data processing system, such as Apache Flink, Apache Spark, Google Cloud Dataflow, and more. Review Comment: Apache Beam Runners are the execution engines that execute the pipelines. They translate or adapt the pipeline into a form that can be executed on a massively parallel big data processing system, such as Apache Flink, Apache Spark, Google Cloud Dataflow, and more. ########## learning/prompts/code-explaination/02_io_pubsub.md: ########## @@ -0,0 +1,34 @@ +Prompt: +What does this code do? +```python +pipeline_options = PipelineOptions(pipeline_args) + +with beam.Pipeline(options=options) as p: + (p | "Read" >> ReadFromPubSub(subscription=input_subscription) + | "Process" >> ParDo(ProcessMessage()) + | "Write" >> WriteToPubSub(topic=output_topic)) +``` + +Response: +This code reads messages from a [PubSub](https://cloud.google.com/pubsub) subscription `input_subscription` using the `ReadFromPubSub`, processes them and writes the resulting collection to a PubSub topic `output_topic` using the `WriteToPubSub` transform. Review Comment: This code reads messages from a [PubSub](https://cloud.google.com/pubsub) subscription `input_subscription` using the `ReadFromPubSub,` processes them and writes the resulting collection to a PubSub topic `output_topic` using the `WriteToPubSub` transform. ########## learning/prompts/documentation-lookup/17_advanced_ai_ml.md: ########## @@ -0,0 +1,18 @@ +Prompt: +What are AI and ML capabilities in Apache Beam? +Response: +Apache Beam has a number of built-in [AI and ML capabilities](https://beam.apache.org/documentation/ml/overview/) that enable you to: +- Process large datasets for both preprocessing and model inference. +- Conduct exploratory data analysis and smoothly scale up data pipelines in production as part of your MLOps ecosystem. +- Run your models in production with varying data loads, both in batch and streaming + +See [here](https://beam.apache.org/documentation/patterns/ai-platform/) for common AI Platform integration patterns in Apache Baam. + +The recommended way to implement inference in Apache Beam is by using the [RunInference API](https://beam.apache.org/documentation/sdks/python-machine-learning/). See [here](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_pytorch_tensorflow_sklearn.ipynb) for more details of how to use RunInference for PyTorch, scikit-learn, and TensorFlow. + +Using pre-trained models in Apache Beam is also supported with [PyTorch](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_pytorch.ipynb), [Scikit-learn](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_sklearn.ipynb), and [Tensorflow](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb). Running inference on [custom models](https://beam.apache.org/documentation/ml/about-ml/#use-custom-models) is also supported. + +Apache Beam also supports automatic model refresh, which allows you to update models, hot swapping them in a running streaming pipeline with no pause in processing the stream of data, avoiding downtime. See [here](https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh) for more details. +More on Apache Beam ML innovations for production can be found [here](https://cloud.google.com/blog/products/ai-machine-learning/dataflow-ml-innovations-on-apache-beam/). + +For more hands-on examples of using Apache Beam ML integration see [here](https://beam.apache.org/documentation/patterns/bqml/) Review Comment: For more hands-on examples of using Apache Beam ML integration, see [here](https://beam.apache.org/documentation/patterns/bqml/) ########## learning/prompts/documentation-lookup/08_basic_windowing.md: ########## @@ -0,0 +1,28 @@ +Prompt: +What is Windowing in Apache Beam? +Response: +Windowing is a key concept in stream processing, as it allows you to divide streams of data into logical units for efficient and correct parallel processing. Review Comment: Windowing is a key concept in stream processing, as it allows you to divide data streams into logical units for efficient and correct parallel processing. ########## learning/prompts/code-generation/02_io_pubsub.md: ########## @@ -0,0 +1,51 @@ +Prompt: +Write the python code to read messages from a PubSub subscription. +Response: +You can read messages from a PubSub subsription or topic using the `ReadFromPubSub` transform. PubSub is currently supported only in streaming pipelines. + +The following python code reads messages from a PubSub subscription. The subscription is provided as a command line argument. The messages are logged to the console: + +```python +import logging + +import apache_beam as beam +from apache_beam import Map +from apache_beam.io import ReadFromPubSub +from apache_beam.options.pipeline_options import PipelineOptions + +class PubSubReadOptions(PipelineOptions): +""" +Configure pipeline options for PubSub read transform. +""" + @classmethod + def _add_argparse_args(cls, parser): + parser.add_argument( + "--subscription", + required=True, + help="PubSub subscription to read from.") + +def read_subscription(): + """Read from PubSub subscription function.""" + + #parse pipeline options + #streaming=True is required for a streaming pipeline + options = PubSubReadOptions(streaming=True) + + with beam.Pipeline(options=options) as p: + #this pipeline reads from a PubSub subscription and logs the messages to the console + (p | "Read PubSub subscription" >> ReadFromPubSub(subscription=options.subscription) + | "Format message" >> Map(lambda message: f"Received message:\n{message}\n") + | Map(logging.info)) + +if __name__ == '__main__': + logging.getLogger().setLevel(logging.INFO) + read_subscription() + +``` +Reading messages directly from a topic is also supported. A temporary subscription will be created automatically. + +The messages could be returned as a byte string or as PubsubMessage objects. This behavior is controlled by the `with_attributes` parameter. + +See [PubSub IO](https://beam.apache.org/releases/pydoc/current/apache_beam.io.gcp.pubsub.html) transform documentation for more details. + +For a common pattern for configuring pipeline options see here [Pipeline option pattern](https://beam.apache.org/documentation/patterns/pipeline-options/). Review Comment: For a common pattern for configuring pipeline options, see here [Pipeline option pattern](https://beam.apache.org/documentation/patterns/pipeline-options/). ########## learning/prompts/code-generation/03_io_bigquery.md: ########## @@ -0,0 +1,45 @@ +Prompt: +Write the python code to read data from BigQuery. +Response: +You can read data from BigQuery using the `ReadFromBigQuery` transform. The following python code reads data from a BigQuery table. The table is provided as a command line argument `table`. The data is logged to the console: + +```python +import logging + +import apache_beam as beam +from apache_beam.io import ReadFromBigQuery +from apache_beam.options.pipeline_options import PipelineOptions + +class BigQueryReadOptions(PipelineOptions): +""" +Configure pipeline options for BigQuery read transform. +""" + @classmethod + def _add_argparse_args(cls, parser): + parser.add_argument( + "--table", + required=True, + help="BigQuery table to read from.") + +def read_table(): + """Read from BigQuery table function.""" + + #parse pipeline options + #streaming=True is required for a streaming pipeline + options = BigQueryReadOptions(streaming=True) + + with beam.Pipeline(options=options) as p: + #this pipeline reads from a BigQuery table and logs the data to the console + (p | "Read BigQuery table" >> ReadFromBigQuery(table=options.table) + | "Format data" >> Map(lambda row: f"Received row:\n{row}\n") + | Map(logging.info)) + +if __name__ == '__main__': + logging.getLogger().setLevel(logging.INFO) + read_table() +``` + +See [PubSub IO](https://beam.apache.org/releases/pydoc/current/apache_beam.io.gcp.pubsub.html) transform documentation for more details. + +For a common pattern for configuring pipeline options see here [Pipeline option pattern](https://beam.apache.org/documentation/patterns/pipeline-options/). Review Comment: For a common pattern for configuring pipeline options, see here [Pipeline option pattern](https://beam.apache.org/documentation/patterns/pipeline-options/). ########## learning/prompts/documentation-lookup/07_basic_runners.md: ########## @@ -0,0 +1,22 @@ +Prompt: +What is a Runner in Apache Beam? +Response: +Apache Beam Runners are the execution engines that execute the pipelines. They are responsible for translating or adapting the pipeline into a form that can be executed on a massively parallel big data processing system, such as Apache Flink, Apache Spark, Google Cloud Dataflow, and more. + +Choosing a runner is an important step in the pipeline development process. The runner you choose determines where and how your pipeline runs. See the [capabilities matrix](https://beam.apache.org/documentation/runners/capability-matrix/) for more information on awailable runners and their capabilities. + +Runner is specified using the `--runner` flag when executing the pipeline. For example, to run the WordCount pipeline on Google Cloud Dataflow, you would run the following command: + +```python +python -m apache_beam.examples.wordcount \ + --region DATAFLOW_REGION \ + --input gs://dataflow-samples/shakespeare/kinglear.txt \ + --output gs://STORAGE_BUCKET/results/outputs \ + --runner DataflowRunner \ + --project PROJECT_ID \ + --temp_location gs://STORAGE_BUCKET/tmp/ + ``` + +A [Direct Runner](https://beam.apache.org/documentation/runners/direct/) stands out as a special runner that executes the pipeline on the local machine. It is useful for testing and debugging purposes. Review Comment: A [Direct Runner](https://beam.apache.org/documentation/runners/direct/) stands out as a special runner that executes the pipeline on the local machine. It is helpful for testing and debugging purposes. ########## learning/prompts/documentation-lookup/08_basic_windowing.md: ########## @@ -0,0 +1,28 @@ +Prompt: +What is Windowing in Apache Beam? +Response: +Windowing is a key concept in stream processing, as it allows you to divide streams of data into logical units for efficient and correct parallel processing. +With an unbounded data set, it is impossible to collect all of the elements, since new elements are constantly being added. In the Beam model, any PCollection (including unbounded PCollections) can be subdivided into [logical windows](https://beam.apache.org/documentation/programming-guide/#windowing-basics). Grouping transforms then consider each PCollection’s elements on a per-window basis. Review Comment: With an unbounded data set, collecting all of the elements is impossible since new elements are constantly being added. In the Beam model, any PCollection (including unbounded PCollections) can be subdivided into [logical windows](https://beam.apache.org/documentation/programming-guide/#windowing-basics). Grouping transforms then consider each PCollection’s elements on a per-window basis. ########## learning/prompts/code-explaination/02_io_pubsub.md: ########## @@ -0,0 +1,34 @@ +Prompt: +What does this code do? +```python +pipeline_options = PipelineOptions(pipeline_args) + +with beam.Pipeline(options=options) as p: + (p | "Read" >> ReadFromPubSub(subscription=input_subscription) + | "Process" >> ParDo(ProcessMessage()) + | "Write" >> WriteToPubSub(topic=output_topic)) +``` + +Response: +This code reads messages from a [PubSub](https://cloud.google.com/pubsub) subscription `input_subscription` using the `ReadFromPubSub`, processes them and writes the resulting collection to a PubSub topic `output_topic` using the `WriteToPubSub` transform. + +PubSub is currently supported only in streaming pipelines. + +Reading messages directly from a topic is also supported. In this case a temporary subscription will be created automatically. + +`ReadFromPubSub` produces a `PCollection` of `PubsubMessage` objects or a `PCollection` of byte sequences. Behavior is controlled by the `with_attributes` parameter with byte sequences being the default. +For more on PCollections see the [Beam Programming Guide](https://beam.apache.org/documentation/basics/#pcollection). + +Processing of the messages is done by the `ProcessMessage` class. This class is a subclass of the `DoFn` class. +Simplest implementation of ProcessMesageclass could be something like: + +```python +class ProcessMessage(beam.DoFn): + def process(self, element): + yield element +``` +More on `DoFn` class can be found [here](https://beam.apache.org/documentation/programming-guide/#dofn). + +See [PubSub IO](https://beam.apache.org/releases/pydoc/current/apache_beam.io.gcp.pubsub.html) transform documentation for more details. + +For a common pattern for configuring pipeline options see here [Pipeline option pattern](https://beam.apache.org/documentation/patterns/pipeline-options/). Review Comment: For a common pattern for configuring pipeline options, see here [Pipeline option pattern](https://beam.apache.org/documentation/patterns/pipeline-options/). ########## learning/prompts/code-explaination/02_io_pubsub.md: ########## @@ -0,0 +1,34 @@ +Prompt: +What does this code do? +```python +pipeline_options = PipelineOptions(pipeline_args) + +with beam.Pipeline(options=options) as p: + (p | "Read" >> ReadFromPubSub(subscription=input_subscription) + | "Process" >> ParDo(ProcessMessage()) + | "Write" >> WriteToPubSub(topic=output_topic)) +``` + +Response: +This code reads messages from a [PubSub](https://cloud.google.com/pubsub) subscription `input_subscription` using the `ReadFromPubSub`, processes them and writes the resulting collection to a PubSub topic `output_topic` using the `WriteToPubSub` transform. + +PubSub is currently supported only in streaming pipelines. + +Reading messages directly from a topic is also supported. In this case a temporary subscription will be created automatically. + +`ReadFromPubSub` produces a `PCollection` of `PubsubMessage` objects or a `PCollection` of byte sequences. Behavior is controlled by the `with_attributes` parameter with byte sequences being the default. +For more on PCollections see the [Beam Programming Guide](https://beam.apache.org/documentation/basics/#pcollection). + +Processing of the messages is done by the `ProcessMessage` class. This class is a subclass of the `DoFn` class. Review Comment: The `ProcessMessage` class processes the messages. This class is a subclass of the `DoFn` class. ########## learning/prompts/code-generation/03_io_bigquery.md: ########## @@ -0,0 +1,45 @@ +Prompt: +Write the python code to read data from BigQuery. Review Comment: should be specific to Beam\Dataflow? Such as 'Write the Apache Beam pipeline in Python to read data from BigQuery" ########## learning/prompts/code-explaination/03_io_bigquery.md: ########## @@ -0,0 +1,34 @@ +Prompt: +What does this code do? +```python +pipeline_options = PipelineOptions(pipeline_args) + +with beam.Pipeline(options=options) as p: + (p | "Read" >> ReadFromPubSub(subscription=input_subscription) + | "Process" >> ParDo(ProcessMessage()) + | "Write" >> WriteToPubSub(topic=output_topic)) +``` + +Response: +This code reads messages from a [PubSub](https://cloud.google.com/pubsub) subscription `input_subscription` using the `ReadFromPubSub`, processes them and writes the resulting collection to a PubSub topic `output_topic` using the `WriteToPubSub` transform. Review Comment: This code reads messages from a [PubSub](https://cloud.google.com/pubsub) subscription `input_subscription` using the `ReadFromPubSub`, processes them, and writes the resulting collection to a PubSub topic `output_topic` using the `WriteToPubSub` transform. ########## learning/prompts/documentation-lookup/10_basic_metrics.md: ########## @@ -0,0 +1,24 @@ +Prompt: +What are Metrics in Apache Beam? +Response: +[Metrics](https://beam.apache.org/documentation/programming-guide/#metrics) in Apache Beam provide a way to get insight into the current state of a user pipeline including during pipeline execution. + +Metrics are named and scoped to a specific step in the pipeline. They may be dynamically created during pipeline execution. If a runner doesn’t support some part of reporting metrics, the fallback behavior is to drop the metric updates rather than failing the pipeline. Review Comment: Metrics are named and scoped to a specific step in the pipeline. They may be dynamically created during pipeline execution. If a runner doesn't support some part of reporting metrics, the fallback behavior is to drop the metric updates rather than fail the pipeline. ########## learning/prompts/documentation-lookup/08_basic_windowing.md: ########## @@ -0,0 +1,28 @@ +Prompt: +What is Windowing in Apache Beam? +Response: +Windowing is a key concept in stream processing, as it allows you to divide streams of data into logical units for efficient and correct parallel processing. Review Comment: Please also add empty line to separate the paragraph ########## learning/prompts/documentation-lookup/10_basic_metrics.md: ########## @@ -0,0 +1,24 @@ +Prompt: +What are Metrics in Apache Beam? +Response: +[Metrics](https://beam.apache.org/documentation/programming-guide/#metrics) in Apache Beam provide a way to get insight into the current state of a user pipeline including during pipeline execution. Review Comment: [Metrics](https://beam.apache.org/documentation/programming-guide/#metrics) in Apache Beam provides a way to get insight into the current state of a user pipeline, including during pipeline execution. ########## learning/prompts/documentation-lookup/17_advanced_ai_ml.md: ########## @@ -0,0 +1,18 @@ +Prompt: +What are AI and ML capabilities in Apache Beam? +Response: +Apache Beam has a number of built-in [AI and ML capabilities](https://beam.apache.org/documentation/ml/overview/) that enable you to: +- Process large datasets for both preprocessing and model inference. +- Conduct exploratory data analysis and smoothly scale up data pipelines in production as part of your MLOps ecosystem. +- Run your models in production with varying data loads, both in batch and streaming + +See [here](https://beam.apache.org/documentation/patterns/ai-platform/) for common AI Platform integration patterns in Apache Baam. Review Comment: See [here](https://beam.apache.org/documentation/patterns/ai-platform/) for common AI Platform integration patterns in Apache Beam. ########## learning/prompts/code-generation/03_io_bigquery.md: ########## @@ -0,0 +1,45 @@ +Prompt: +Write the python code to read data from BigQuery. +Response: +You can read data from BigQuery using the `ReadFromBigQuery` transform. The following python code reads data from a BigQuery table. The table is provided as a command line argument `table`. The data is logged to the console: Review Comment: You can read data from BigQuery using the `ReadFromBigQuery` transform. The following Python code reads data from a BigQuery table. The table is provided as a command line argument `table.` The data is logged to the console: ########## learning/prompts/documentation-lookup/09_basic_triggers.md: ########## @@ -0,0 +1,30 @@ +Prompt: +What are Triggers in Apache Beam? +Response: + +Beam uses triggers to determine when to emit the aggregated results of each [window](https://beam.apache.org/documentation/programming-guide/#windowing) (referred to as a pane) + +Triggers provide two additional capabilities compared to [outputting at the end of a window](https://beam.apache.org/documentation/programming-guide/#default-trigger): allow early results to be output before the end of the window or allow late data to be handled after the end of the window + +This allows you to control the flow of your data and balance between completenes, latency and cost. + +You set the triggers for a PCollection by setting the `trigger` parameter of the WindowInto transform. + +```python + pcollection | WindowInto( + FixedWindows(1 * 60), + trigger=AfterProcessingTime(1 * 60), + accumulation_mode=AccumulationMode.DISCARDING) +``` + +When a trigger fires, it emits the current contents of the window as a pane. Since a trigger can fire multiple times, the accumulation mode determines whether the system accumulates the window panes as the trigger fires, or discards them. This behavior is controlled by the [window accumulation mode](https://beam.apache.org/documentation/programming-guide/#window-accumulation-modes) parameter of the WindowInto transform. + + +Beam provides a number of [built-in triggers](https://beam.apache.org/documentation/programming-guide/#triggers) that you can use to determine when to emit the results of your pipeline’s windowed computations: Review Comment: Beam provides several [built-in triggers](https://beam.apache.org/documentation/programming-guide/#triggers) that you can use to determine when to emit the results of your pipeline's windowed computations: ########## learning/prompts/code-generation/02_io_pubsub.md: ########## @@ -0,0 +1,51 @@ +Prompt: +Write the python code to read messages from a PubSub subscription. Review Comment: should be specific to Beam\Dataflow? Such as 'Write the Apache Beam pipeline in Python to read messages from a PubSub subscription' ########## learning/prompts/documentation-lookup/17_advanced_ai_ml.md: ########## @@ -0,0 +1,18 @@ +Prompt: +What are AI and ML capabilities in Apache Beam? +Response: +Apache Beam has a number of built-in [AI and ML capabilities](https://beam.apache.org/documentation/ml/overview/) that enable you to: +- Process large datasets for both preprocessing and model inference. +- Conduct exploratory data analysis and smoothly scale up data pipelines in production as part of your MLOps ecosystem. +- Run your models in production with varying data loads, both in batch and streaming + +See [here](https://beam.apache.org/documentation/patterns/ai-platform/) for common AI Platform integration patterns in Apache Baam. + +The recommended way to implement inference in Apache Beam is by using the [RunInference API](https://beam.apache.org/documentation/sdks/python-machine-learning/). See [here](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_pytorch_tensorflow_sklearn.ipynb) for more details of how to use RunInference for PyTorch, scikit-learn, and TensorFlow. + +Using pre-trained models in Apache Beam is also supported with [PyTorch](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_pytorch.ipynb), [Scikit-learn](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_sklearn.ipynb), and [Tensorflow](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb). Running inference on [custom models](https://beam.apache.org/documentation/ml/about-ml/#use-custom-models) is also supported. + +Apache Beam also supports automatic model refresh, which allows you to update models, hot swapping them in a running streaming pipeline with no pause in processing the stream of data, avoiding downtime. See [here](https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh) for more details. Review Comment: Apache Beam also supports automatic model refresh, which allows you to update models, hot-swapping them in a running streaming pipeline with no pause in processing the data stream, avoiding downtime. See [here](https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh) for more details. More on Apache Beam ML innovations for production can be found [here](https://cloud.google.com/blog/products/ai-machine-learning/dataflow-ml-innovations-on-apache-beam/). ########## learning/prompts/code-explaination/03_io_bigquery.md: ########## @@ -0,0 +1,34 @@ +Prompt: +What does this code do? +```python +pipeline_options = PipelineOptions(pipeline_args) + +with beam.Pipeline(options=options) as p: + (p | "Read" >> ReadFromPubSub(subscription=input_subscription) + | "Process" >> ParDo(ProcessMessage()) + | "Write" >> WriteToPubSub(topic=output_topic)) +``` + +Response: +This code reads messages from a [PubSub](https://cloud.google.com/pubsub) subscription `input_subscription` using the `ReadFromPubSub`, processes them and writes the resulting collection to a PubSub topic `output_topic` using the `WriteToPubSub` transform. + +PubSub is currently supported only in streaming pipelines. + +Reading messages directly from a topic is also supported. In this case a temporary subscription will be created automatically. Review Comment: Reading messages directly from a topic is also supported. In this case, a temporary subscription will be created automatically. ########## learning/prompts/code-generation/02_io_pubsub.md: ########## @@ -0,0 +1,51 @@ +Prompt: +Write the python code to read messages from a PubSub subscription. +Response: +You can read messages from a PubSub subsription or topic using the `ReadFromPubSub` transform. PubSub is currently supported only in streaming pipelines. Review Comment: You can read messages from a PubSub subscription or topic using the `ReadFromPubSub` transform. PubSub is currently supported only in streaming pipelines. ########## learning/prompts/code-explaination/03_io_bigquery.md: ########## @@ -0,0 +1,34 @@ +Prompt: +What does this code do? +```python Review Comment: Isn't it the same as the previous example? ########## learning/prompts/code-explaination/02_io_pubsub.md: ########## @@ -0,0 +1,34 @@ +Prompt: +What does this code do? +```python +pipeline_options = PipelineOptions(pipeline_args) + +with beam.Pipeline(options=options) as p: + (p | "Read" >> ReadFromPubSub(subscription=input_subscription) + | "Process" >> ParDo(ProcessMessage()) + | "Write" >> WriteToPubSub(topic=output_topic)) +``` + +Response: +This code reads messages from a [PubSub](https://cloud.google.com/pubsub) subscription `input_subscription` using the `ReadFromPubSub`, processes them and writes the resulting collection to a PubSub topic `output_topic` using the `WriteToPubSub` transform. + +PubSub is currently supported only in streaming pipelines. + +Reading messages directly from a topic is also supported. In this case a temporary subscription will be created automatically. + +`ReadFromPubSub` produces a `PCollection` of `PubsubMessage` objects or a `PCollection` of byte sequences. Behavior is controlled by the `with_attributes` parameter with byte sequences being the default. Review Comment: `ReadFromPubSub` produces a `PCollection` of `PubsubMessage` objects or a `PCollection` of byte sequences. Behavior is controlled by the `with_attributes` parameter, with byte sequences being the default. ########## learning/prompts/code-explaination/02_io_pubsub.md: ########## @@ -0,0 +1,34 @@ +Prompt: +What does this code do? +```python +pipeline_options = PipelineOptions(pipeline_args) + +with beam.Pipeline(options=options) as p: + (p | "Read" >> ReadFromPubSub(subscription=input_subscription) + | "Process" >> ParDo(ProcessMessage()) + | "Write" >> WriteToPubSub(topic=output_topic)) +``` + +Response: +This code reads messages from a [PubSub](https://cloud.google.com/pubsub) subscription `input_subscription` using the `ReadFromPubSub`, processes them and writes the resulting collection to a PubSub topic `output_topic` using the `WriteToPubSub` transform. + +PubSub is currently supported only in streaming pipelines. + +Reading messages directly from a topic is also supported. In this case a temporary subscription will be created automatically. Review Comment: Reading messages directly from a topic is also supported. In this case, a temporary subscription will be created automatically. ########## learning/prompts/documentation-lookup/04_basic_pcollections.md: ########## @@ -0,0 +1,22 @@ +Prompt: +Wahat is a PCollection in Apache Beam? +Response: +A `PCollection` in Apache Beam is a core abstractions representing a distributed, multi-element data set or data stream. It's the primary data structure used in Apache Beam pipelines to handle large-scale data processing, both in batch and streaming modes. Review Comment: A PCollection in Apache Beam is a core abstraction representing a distributed, multi-element data set or data stream. It’s the primary data structure used in Apache Beam pipelines to handle large-scale data processing in batch and streaming modes. ########## learning/prompts/documentation-lookup/11_basic_state.md: ########## @@ -0,0 +1,20 @@ +Prompt: +What is a State in Apache Beam? +Response: +[State](https://beam.apache.org/documentation/basics/#state-and-timers) in Apache Beam is an API that allows for managing per-key state in element-wise operations like [ParDo](https://beam.apache.org/documentation/programming-guide/#pardo) or Map. It enables fine-grained control over aggregations, particularly in situations where [windowing](https://beam.apache.org/documentation/programming-guide/#windowing) and [triggers](https://beam.apache.org/documentation/programming-guide/#triggers) are insufficient. + +State is [scoped](https://beam.apache.org/documentation/programming-guide/#state-and-timers) to the current processing key within a window, meaning each key has its own state. It's particularly useful for complex aggregations across multiple elements. + +Beam provides several [types of state](https://beam.apache.org/documentation/programming-guide/#types-of-state) that can be used for different use cases including scalar and collection values: Review Comment: Beam provides several [types of state](https://beam.apache.org/documentation/programming-guide/#types-of-state) that can be used for different use cases, including scalar and collection values: ########## learning/prompts/code-explaination/02_io_pubsub.md: ########## @@ -0,0 +1,34 @@ +Prompt: +What does this code do? +```python +pipeline_options = PipelineOptions(pipeline_args) + +with beam.Pipeline(options=options) as p: + (p | "Read" >> ReadFromPubSub(subscription=input_subscription) + | "Process" >> ParDo(ProcessMessage()) + | "Write" >> WriteToPubSub(topic=output_topic)) +``` + +Response: +This code reads messages from a [PubSub](https://cloud.google.com/pubsub) subscription `input_subscription` using the `ReadFromPubSub`, processes them and writes the resulting collection to a PubSub topic `output_topic` using the `WriteToPubSub` transform. + +PubSub is currently supported only in streaming pipelines. + +Reading messages directly from a topic is also supported. In this case a temporary subscription will be created automatically. + +`ReadFromPubSub` produces a `PCollection` of `PubsubMessage` objects or a `PCollection` of byte sequences. Behavior is controlled by the `with_attributes` parameter with byte sequences being the default. +For more on PCollections see the [Beam Programming Guide](https://beam.apache.org/documentation/basics/#pcollection). + +Processing of the messages is done by the `ProcessMessage` class. This class is a subclass of the `DoFn` class. +Simplest implementation of ProcessMesageclass could be something like: Review Comment: The simplest implementation of ProcessMesageclass could be something like this: ########## learning/prompts/documentation-lookup/12_basic_timers.md: ########## @@ -0,0 +1,12 @@ +Prompt: +What is a Timer in Apache Beam? +Response: +In Apache Beam a [Timer](https://beam.apache.org/documentation/basics/#state-and-timers) is a per-key timer callback API enabling delayed processing of data stored using the [State API](https://beam.apache.org/documentation/programming-guide/#state-and-timers) + +Apache Beam provides two [types of timers](https://beam.apache.org/documentation/programming-guide/#timers) - processing time timers and event time timers. Processing time timers are based on the system clock and event time timers are based on the timestamps of the data elements. Review Comment: Apache Beam provides two [types of timers](https://beam.apache.org/documentation/programming-guide/#timers) - processing time timers and event time timers. Processing time timers are based on the system clock, and event time timers are based on the timestamps of the data elements. ########## learning/prompts/documentation-lookup/09_basic_triggers.md: ########## @@ -0,0 +1,30 @@ +Prompt: +What are Triggers in Apache Beam? +Response: + +Beam uses triggers to determine when to emit the aggregated results of each [window](https://beam.apache.org/documentation/programming-guide/#windowing) (referred to as a pane) + +Triggers provide two additional capabilities compared to [outputting at the end of a window](https://beam.apache.org/documentation/programming-guide/#default-trigger): allow early results to be output before the end of the window or allow late data to be handled after the end of the window + +This allows you to control the flow of your data and balance between completenes, latency and cost. Review Comment: This allows you to control the flow of your data and balance between completeness, latency, and cost. ########## learning/prompts/documentation-lookup/11_basic_state.md: ########## @@ -0,0 +1,20 @@ +Prompt: +What is a State in Apache Beam? +Response: +[State](https://beam.apache.org/documentation/basics/#state-and-timers) in Apache Beam is an API that allows for managing per-key state in element-wise operations like [ParDo](https://beam.apache.org/documentation/programming-guide/#pardo) or Map. It enables fine-grained control over aggregations, particularly in situations where [windowing](https://beam.apache.org/documentation/programming-guide/#windowing) and [triggers](https://beam.apache.org/documentation/programming-guide/#triggers) are insufficient. + +State is [scoped](https://beam.apache.org/documentation/programming-guide/#state-and-timers) to the current processing key within a window, meaning each key has its own state. It's particularly useful for complex aggregations across multiple elements. + +Beam provides several [types of state](https://beam.apache.org/documentation/programming-guide/#types-of-state) that can be used for different use cases including scalar and collection values: +- ValueState +- BagState +- SetState +- MapState +- OrderedListState +- CombiningState + +Per-key state needs to be garbage collected, or eventually the increasing size of state may negatively impact performance. See [here](https://beam.apache.org/documentation/programming-guide/#garbage-collecting-state) for more info on common strategies for garbage collecting state. + +See community blogpost on [Stateful Processing](https://beam.apache.org/blog/stateful-processing/) for more information. Review Comment: See the community blogpost on [Stateful Processing](https://beam.apache.org/blog/stateful-processing/) for more information. ########## learning/prompts/documentation-lookup/11_basic_state.md: ########## @@ -0,0 +1,20 @@ +Prompt: +What is a State in Apache Beam? +Response: +[State](https://beam.apache.org/documentation/basics/#state-and-timers) in Apache Beam is an API that allows for managing per-key state in element-wise operations like [ParDo](https://beam.apache.org/documentation/programming-guide/#pardo) or Map. It enables fine-grained control over aggregations, particularly in situations where [windowing](https://beam.apache.org/documentation/programming-guide/#windowing) and [triggers](https://beam.apache.org/documentation/programming-guide/#triggers) are insufficient. + +State is [scoped](https://beam.apache.org/documentation/programming-guide/#state-and-timers) to the current processing key within a window, meaning each key has its own state. It's particularly useful for complex aggregations across multiple elements. + +Beam provides several [types of state](https://beam.apache.org/documentation/programming-guide/#types-of-state) that can be used for different use cases including scalar and collection values: +- ValueState +- BagState +- SetState +- MapState +- OrderedListState +- CombiningState + +Per-key state needs to be garbage collected, or eventually the increasing size of state may negatively impact performance. See [here](https://beam.apache.org/documentation/programming-guide/#garbage-collecting-state) for more info on common strategies for garbage collecting state. Review Comment: The per-key state needs to be garbage collected, or eventually, the increasing size of the state may negatively impact performance. See [here](https://beam.apache.org/documentation/programming-guide/#garbage-collecting-state) for more info on common garbage collection strategies. ########## learning/prompts/documentation-lookup/14_advanced_pipeline_patterns.md: ########## @@ -0,0 +1,13 @@ +Prompt: +What pipeline patterns exist in Apache Beam? +Response: +Beam pipeline patterns are a set of best practices for building Beam pipelines. They are based on real-world Beam deployments and are designed to help you build resilient, flexible, and portable Beam pipelines. + +Here are some of the most common pipeline patterns: +- [File processing patterns](https://beam.apache.org/documentation/patterns/file-processing/) +- [Pipeline options patterns](https://beam.apache.org/documentation/patterns/pipeline-options/) +- [Side input patterns](https://beam.apache.org/documentation/patterns/side-inputs/) +- [Windowing patterns](https://beam.apache.org/documentation/patterns/custom-windows/) +- [State and Timers patterns](https://beam.apache.org/documentation/patterns/grouping-elements-for-efficient-external-service-calls/) + +For more information see the [Pipeline Patterns](https://beam.apache.org/documentation/patterns/overview/) section of the Beam programming guide. Review Comment: For more information, see the [Pipeline Patterns](https://beam.apache.org/documentation/patterns/overview/) section of the Beam programming guide. ########## learning/prompts/documentation-lookup/16_advanced_pipeline_lifecycle.md: ########## @@ -0,0 +1,36 @@ +Prompt: +What is a pipeline development lifecycle in Apache Beam? +Response: + +The Apache Beam pipeline development lifecycle is an iterative process that usually involves the following steps: + +- Design your pipeline. +- Develop your pipeline code. +- Test your pipeline. +- Deploy your pipeline. + +On each iteration, you may need to go back and forth between the different steps to refine your pipeline code and to fix any bugs that you find. Review Comment: On each iteration, you may need to go back and forth between the different steps to refine your pipeline code and fix any bugs you find. ########## learning/prompts/documentation-lookup/12_basic_timers.md: ########## @@ -0,0 +1,12 @@ +Prompt: +What is a Timer in Apache Beam? +Response: +In Apache Beam a [Timer](https://beam.apache.org/documentation/basics/#state-and-timers) is a per-key timer callback API enabling delayed processing of data stored using the [State API](https://beam.apache.org/documentation/programming-guide/#state-and-timers) + +Apache Beam provides two [types of timers](https://beam.apache.org/documentation/programming-guide/#timers) - processing time timers and event time timers. Processing time timers are based on the system clock and event time timers are based on the timestamps of the data elements. + +Beam also supports dynamically setting a timer tag using TimerMap in the Java SDK. This allows for setting multiple different timers in a DoFn and allowing for the timer tags to be dynamically chosen - e.g. based on data in the input elements + +See community blogpost on [Timely Processing](https://beam.apache.org/blog/timely-processing/) for more information. Review Comment: See the community blogpost on [Timely Processing](https://beam.apache.org/blog/timely-processing/) for more information. ########## learning/prompts/documentation-lookup/13_advanced_splittable_dofn.md: ########## @@ -0,0 +1,18 @@ +Prompt: +What is Splittable DoFn in Apache Beam? +Response: +Splittable DoFn (SDF) is a generalization of [DoFn](https://beam.apache.org/documentation/programming-guide/#pardo) that lets you process elements in a non-monolithic way. Splittable DoFn makes it easier to create complex, modular I/O connectors in Beam. +When you apply a splittable DoFn to an element, the runner has the option of splitting the element’s processing into smaller tasks. You can checkpoint the processing of an element, and you can split the remaining work to yield additional parallelism. + +At a high level, an SDF is responsible for processing element and restriction pairs. A restriction represents a subset of work that would have been necessary to have been done when processing the element. + +Executing an [Splittable DoFn](https://beam.apache.org/documentation/programming-guide/#splittable-dofns) follows the following steps: +1. Each element is paired with a restriction (e.g. filename is paired with offset range representing the whole file). +2. Each element and restriction pair is split (e.g. offset ranges are broken up into smaller pieces). +3. The runner redistributes the element and restriction pairs to several workers. +4. Element and restriction pairs are processed in parallel (e.g. the file is read). Within this last step, the element and restriction pair can pause its own processing and/or be split into further element and restriction pairs. + +See Tour of Beam [Splittable DoFn module](https://tour.beam.apache.org/tour/python/splittable-dofn/splittable) for practical example. Review Comment: See Tour of Beam [Splittable DoFn module](https://tour.beam.apache.org/tour/python/splittable-dofn/splittable) for a practical example. ########## learning/prompts/documentation-lookup/08_basic_windowing.md: ########## @@ -0,0 +1,28 @@ +Prompt: +What is Windowing in Apache Beam? +Response: +Windowing is a key concept in stream processing, as it allows you to divide streams of data into logical units for efficient and correct parallel processing. +With an unbounded data set, it is impossible to collect all of the elements, since new elements are constantly being added. In the Beam model, any PCollection (including unbounded PCollections) can be subdivided into [logical windows](https://beam.apache.org/documentation/programming-guide/#windowing-basics). Grouping transforms then consider each PCollection’s elements on a per-window basis. Review Comment: With an unbounded data set, collecting all of the elements is impossible since new elements are constantly being added. In the Beam model, any PCollection (including unbounded PCollections) can be subdivided into [logical windows](https://beam.apache.org/documentation/programming-guide/#windowing-basics). Grouping transforms then consider each PCollection’s elements on a per-window basis. ########## learning/prompts/documentation-lookup/16_advanced_pipeline_lifecycle.md: ########## @@ -0,0 +1,36 @@ +Prompt: +What is a pipeline development lifecycle in Apache Beam? +Response: + +The Apache Beam pipeline development lifecycle is an iterative process that usually involves the following steps: + +- Design your pipeline. +- Develop your pipeline code. +- Test your pipeline. +- Deploy your pipeline. + +On each iteration, you may need to go back and forth between the different steps to refine your pipeline code and to fix any bugs that you find. + +Desiging a pipeline addresses the following questions: +- Where my data is stored? Review Comment: - Where is my data stored? ########## learning/prompts/documentation-lookup/15_advanced_xlang.md: ########## @@ -0,0 +1,15 @@ +Prompt: +What is a multi-language pipeline in Apache Beam? +Response: +Beam lets you combine transforms written in any supported SDK language (currently, [Java](https://beam.apache.org/documentation/programming-guide/#1311-creating-cross-language-java-transforms) and [Python](https://beam.apache.org/documentation/programming-guide/#1312-creating-cross-language-python-transforms)) and use them in one multi-language pipeline. For example, a pipeline that reads from a Python source, processes the data using a Java transform, and writes the data to a Python sink is a multi-language pipeline. + +For example, the [Apache Kafka connector](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/kafka.py) and [SQL transform](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/transforms/sql.py) from the Java SDK can be used in Python pipelines. + +See quickstart examples for [Java](https://beam.apache.org/documentation/sdks/java-multi-language-pipelines) and [Python](https://beam.apache.org/documentation/sdks/python-multi-language-pipelines) to learn how to create a multi-language pipeline. + +Depending on the SDK language of the pipeline, you can use a high-level SDK-wrapper class, or a low-level transform class to access a cross-language transform. See [Using cross-language transforms](https://beam.apache.org/documentation/programming-guide/#use-x-lang-transforms) section of Apache Beam Documentation. + +Developing a cross-language transform involves defining a Uniform Resourse Name(URN) for registering the transform with an expansion service. See [Defining a URN](https://beam.apache.org/documentation/programming-guide/#1314-defining-a-urn) for additional information. and examples. Review Comment: Developing a cross-language transform involves defining a Uniform Resourse Name(URN) for registering the transform with an expansion service. See [Defining a URN](https://beam.apache.org/documentation/programming-guide/#1314-defining-a-urn) for additional information and examples. ########## learning/prompts/documentation-lookup/17_advanced_ai_ml.md: ########## @@ -0,0 +1,18 @@ +Prompt: +What are AI and ML capabilities in Apache Beam? +Response: +Apache Beam has a number of built-in [AI and ML capabilities](https://beam.apache.org/documentation/ml/overview/) that enable you to: +- Process large datasets for both preprocessing and model inference. +- Conduct exploratory data analysis and smoothly scale up data pipelines in production as part of your MLOps ecosystem. +- Run your models in production with varying data loads, both in batch and streaming + +See [here](https://beam.apache.org/documentation/patterns/ai-platform/) for common AI Platform integration patterns in Apache Baam. + +The recommended way to implement inference in Apache Beam is by using the [RunInference API](https://beam.apache.org/documentation/sdks/python-machine-learning/). See [here](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_pytorch_tensorflow_sklearn.ipynb) for more details of how to use RunInference for PyTorch, scikit-learn, and TensorFlow. Review Comment: The recommended way to implement inference in Apache Beam is by using the [RunInference API](https://beam.apache.org/documentation/sdks/python-machine-learning/). See [here](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_pytorch_tensorflow_sklearn.ipynb) for more details on how to use RunInference for PyTorch, scikit-learn, and TensorFlow. ########## learning/prompts/documentation-lookup/13_advanced_splittable_dofn.md: ########## @@ -0,0 +1,18 @@ +Prompt: +What is Splittable DoFn in Apache Beam? +Response: +Splittable DoFn (SDF) is a generalization of [DoFn](https://beam.apache.org/documentation/programming-guide/#pardo) that lets you process elements in a non-monolithic way. Splittable DoFn makes it easier to create complex, modular I/O connectors in Beam. +When you apply a splittable DoFn to an element, the runner has the option of splitting the element’s processing into smaller tasks. You can checkpoint the processing of an element, and you can split the remaining work to yield additional parallelism. Review Comment: When you apply a splittable DoFn to an element, the runner can split the element’s processing into smaller tasks. You can checkpoint the processing of an element, and you can split the remaining work to yield additional parallelism. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
