The Apache Beam model provides useful abstractions that insulate you from low-level details of distributed processing, such as coordinating individual workers, sharding datasets, and other such tasks. Dataflow fully manages these low-level details.
This section contains summaries of fundamental concepts. On the Apache Beam website, the Apache Beam Programming Guide walks you through the basic concepts of building pipelines using the Apache Beam SDKs.
- A pipeline encapsulates the entire series of computations involved in reading
input data, transforming that data, and writing output data. The input source
and output sink can be the same or of different types, allowing you to convert
data from one format to another. Apache Beam programs start by constructing a
Pipelineobject, and then using that object as the basis for creating the pipeline's datasets. Each pipeline represents a single, repeatable job.
PCollectionrepresents a potentially distributed, multi-element dataset that acts as the pipeline's data. Apache Beam transforms use
PCollectionobjects as inputs and outputs for each step in your pipeline. A
PCollectioncan hold a dataset of a fixed size or an unbounded dataset from a continuously updating data source.
- A transform represents a processing operation that transforms data. A
transform takes one or more
PCollections as input, performs an operation that you specify on each element in that collection, and produces one or more
PCollections as output. A transform can perform nearly any kind of processing operation, including performing mathematical computations on data, converting data from one format to another, grouping data together, reading and writing data, filtering data to output only the elements you want, or combining data elements into single values.
ParDois the core parallel processing operation in the Apache Beam SDKs, invoking a user-specified function on each of the elements of the input
ParDocollects the zero or more output elements into an output
ParDotransform processes elements independently and possibly in parallel.
- Pipeline I/O
- Apache Beam I/O connectors let you read data into your pipeline and write output data from your pipeline. An I/O connector consists of a source and a sink. All Apache Beam sources and sinks are transforms that let your pipeline work with data from a number of different data storage formats. You can also write a custom I/O connector.
- Aggregation is the process of computing some value from multiple input elements. The primary computational pattern for aggregation in Apache Beam is to group all elements with a common key and window. Then, it combines each group of elements using an associative and commutative operation.
- User Defined Functions (UDFs)
- Some operations within Apache Beam allow executing user-defined code as a
way of configuring the transform. For
ParDo, user-defined code specifies the operation to apply to every element, and for
Combine, it specifies how values should be combined. A pipeline might contain UDFs written in a different language than the language of your runner. A pipeline might also contain UDFs written in multiple languages.
- Runners are the software that accepts a pipeline and executes it. Most runners are translators or adapters to massively parallel big-data processing systems. Other runners exist for local testing and debugging.
- Event time
- The time a data event occurs, determined by the timestamp on the data element itself. This is contrasted with the time the actual data element gets processed at any stage in the pipeline.
- Windowing enables grouping operations over unbounded collections by dividing the collection into windows of finite collections according to the timestamps of the individual elements. A windowing function tells the runner how to assign elements to an initial window, and how to merge windows of grouped elements. Apache Beam lets you define different kinds of windows or use the predefined windowing functions.
- Apache Beam tracks a watermark, which is the system's notion of when all data in a certain window can be expected to have arrived in the pipeline. Apache Beam tracks a watermark because data is not guaranteed to arrive in a pipeline in time order or at predictable intervals. In addition, there are no guarantees that data events will appear in the pipeline in the same order that they were generated.
- Triggers determine when to emit aggregated results as data arrives. For bounded data, results are emitted after all of the input has been processed. For unbounded data, results are emitted when the watermark passes the end of the window, indicating that the system believes all input data for that window has been processed. Apache Beam provides several predefined triggers and lets you combine them.
For detailed explanations, see the Apache Beam Programming Guide on the Apache Beam website.