Apache Beam articles

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Joins in Apache Beam

Dealing with joins in relational databases is quite straightforward thanks to underlying data structures (e.g. trees). However it's less convenient to work with them in data processing world where schemaless and denormalization rule. Continue Reading →

Side output in Apache Beam

The possibility to define several additional inputs for ParDo transform is not the single feature of this type in Apache Beam. The framework provides also the possibility to define one or more extra outputs through the structures called side outputs. Continue Reading →

Side input in Apache Beam

Very often dealing with a single PCollection in the pipeline is sufficient. However there are some cases, for instance when one dataset complements another, when several different distributed collections must be joined in order to produce meaningful results. Apache Spark deals with it through broadcast variables. Apache Beam also has similar mechanism called side input. Continue Reading →

Dealing with state lifecycle in Apache Beam

As we saw in the previous post, Apache Beam brings the possibility to deal with state. However, as we learned there, the state itself allows only to keep something in memory during the window duration. After that, the state is removed. But thanks to another Beam's feature called timers we can deal with the expiring state just before its removal from the state store. Continue Reading →

Stateful processing in Apache Beam

Real-time processing is most of the time somehow related to stateful processing. Either we need to solve some sessionization problem, count the number of visitors per minute etc. Not surprisingly Apache Beam comes with the API adapted to put in place the solutions to them. Continue Reading →

Late data in Apache Beam

Data, especially in streaming applications, can very often arrive on late to the processing pipeline. Despite of that, Apache Beam is able to handle this case pretty easily thanks to watermark mechanism. Continue Reading →

Windows in Apache Beam

As mentioned in one of the first posts about Apache Beam, the concept of window is a key element in its data processing logic. Even for bounded data a default window called global is defined. For the unbounded one the variety of windows is much bigger. Continue Reading →

Coders in Apache Beam

Since in distributed computing the data moves either locally (within single worker) or remotely (between several different workers), it must have a format understandable by the machine. And this format is guaranteed by the operation of serialization, also present in Apache Beam. Continue Reading →

Data partitioning in Apache Beam

The power of Big Data processing platforms resides mainly in the ability to parallelize processing on different nodes. Each framework has its own unit of parallelism. In Spark it's called partition. Apache Beam calls it bundle. Continue Reading →