Parallel Computing Framework What Apache Beam does
As a parallel computing framework, Apache Beam enables the processing of almost unlimited amounts of data. The open source software offers the definition of batch pipelines or pipelines for streaming data processing via various SDKs and supports Hadoop, Spark and other engines.
Apache Beam can be called a programming model for distributed computing. The model is able to process data types and data frames via APIs.
Apache Beam is an open source tool that can be used to process large amounts of data via pipelines via various SDKs. The Parallel Computing Framework provides SDKs that can be used to implement the Dataflow Model introduced by Google in 2015.
Basically, it is about coordinating correctness, latency and costs in massive and unlimited data processing. The data flow model is used when, above all, huge amounts of data are to be analyzed as quickly as possible, but also as accurately as possible.
Developing with Java and Python, processing with Hadoop, Spark and other systems
Apache Beam can be called a programming model for distributed computing. The model is able to process data types and data frames via APIs. Here you can create batch pipelines and streaming pipelines with Apache Beam. In this context, the pipelines are responsible for reading, processing and storing the data.
After a pipeline is created, Apache Beam can use a worker to define where the pipeline should run. Examples of this are Hadoop or Spark. This is one of the biggest advantages of Apache Beam: developers do not run the risk of falling victim to a vendor lock-in when using it. Since Apache Beam supports different programming languages and also different execution engines, there are very flexible processing options that can also be constantly changed.
For this purpose, Apache Beam provides various SDKs for programming languages, which can then be used to create pipelines. Java and Python are often used in this area. However, the active community is constantly expanding the possibilities of Beam and introducing further SDKs for more programming languages into the system.
The focus of the object is the decoupling of the processing logic of data from the executing processing engine. As a result, once created pipelines with their execution logic can run on different execution engines.
Data Flow Modeling with Apache Beam
Google introduced the Data Flow Model in 2015, and offers this processing of data as Google Dataflow in the Google Cloud Platform (GCP). Apache Beam allows you to create applications that work on the same model as the Data Flow Model, but also support other execution engines, for example, Hadoop or Spark.
When it comes to processing a data flow, it is almost never possible to determine exactly when all data is available, which must then be processed quickly and correctly. If a system starts data processing early, the speed is high, but the data processing is not correct because not all the data to be analyzed is yet available. If a system waits longer for processing, the amount of data increases, the processing gives better results due to the wide data layer, but the latency also gets worse. Here it is important to find an optimal compromise
Independence from language and execution engine
The interesting thing about the data flow model that Apache Beam offers with its SDKs is the independence from the executing engine, which is supposed to process the data. Apache Beam enables the creation of pipelines in almost any programming language via its SDKs. These include, for example, Java, Python, Go, Scala and other languages. The execution engines can also be freely selected. Currently, Google Dataflow, Apache Flink, Apache Spark or Hadoop are mainly used here.
The strength of Apache Beam is not only that the pipeline created with Apache Beam is executed in one of the supported execution engines, but the system tries to use the strengths of all engines in parallel. It is therefore no problem to use Flink, Spark and Hadoop in parallel for a pipeline. The pipelines and the created workers can execute different code almost language-neutral.In addition, the developers can work with the SDK of Beam in their preferred programming language, for example the SDK of Java or Python. The data can then be processed by various execution engines via the APIs in Apache Beam.
In parallel, there is also a separate SDK worker for each SDK. Each of the different SDK workers in Apache Beam is able to execute the same code. This significantly increases the independence of the developers from the language used and from the execution engine.
Apache Beam also allows batch Procession as a streaming system
In the data flow model, the data streams can be divided into various logical “Windows” that summarize certain actions in a timeline, for example, the clicks of visitors to a website on different subpages. As a result, a stream becomes a collection of various “Windows”, that is, sections or parts of an entire stream. How the Windows are divided with Apache Beam is ultimately up to the respective developer or data scientist. The Data Flow Model allows different, different forms for Windows.
Apache Beam distinguishes between “Event Time” and “Processing Time”. “Processing Time” is the time during which a user performs an action, for example a click. In turn, the “Processing Time” is the time when the action is received in the data processing system and processing begins. In an ideal scenario, “Event Time” and “Processing Time” are identical. However, if “Processing Time” is located only after the “Event Time”, this means a certain latency in the data processing, since the system only notices and can process the action after the actual execution.
In parallel, triggers are also used in the Data Flow Model. These are in close connection with the “Windows”. Triggers allow developers to specify when to output the output results for a particular Windows. Trigger and Windows are therefore a team that will be considered as part of the development of data flow models with Apache Beam.
Installing and testing Apache Beam
If you want to take a first look at Apache Beam, you can create a test environment on the project’s website with “Run in Colab”, for example in Python, Java or Go. On the page “Data Pipelines with Apache Beam” you can find an English-language and very detailed example of how to build pipelines in different languages with Apache Beam.