Data processing articles

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Apache Beam Apache Spark Apache Spark GraphFrames Apache Spark GraphX Apache Spark SQL Apache Spark Streaming Apache Spark Structured Streaming PySpark

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Nested loop join in Apache Spark SQL

In programming a simple is often the synonymous of understandable and maintainable. However it doesn't always mean efficient. One of examples of this thesis is nested loop join that is also present in Apache Spark SQL.

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Query metrics in Apache Spark Structured Streaming

One of important points for long-living queries is the tracking. It's always important to know how the query performs. In Structured Streaming we can follow this execution thanks to special object called ProgressReporter.

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Fault tolerance in Apache Spark Structured Streaming

The Structured Streaming guarantees end-to-end exactly-once delivery (in micro-batch mode) through the semantics applied to state management, data source and data sink. The state was more covered in the post about the state store but 2 other parts still remain to discover.

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Continuous execution in Apache Spark Structured Streaming

During the years Apache Spark's streaming was perceived as working with micro-batches. However, the release 2.3.0 tries to change this and proposes a new execution model called continuous. Even though it's still in experimental status, it's worthy to learn more about it.

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Stateful transformations with mapGroupsWithState

Streaming stateful processing in Apache Spark evolved a lot from the first versions of the framework. At the beginning was updateStateByKey but some time after, judged inefficient, it was replaced by mapWithState. With the arrival of Structured Streaming the last method was replaced in its turn by mapGroupsWithState.

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Stateful aggregations in Apache Spark Structured Streaming

Recently we discovered the concept of state stores used to deal with stateful aggregations in Structured Streaming. But at that moment we didn't spend the time on these aggregations. As promised, they'll be described now.

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Output modes in Apache Spark Structured Streaming

Structured Streaming introduced a lot of new concepts regarding to the DStream-based streaming. One of them is the output mode.

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StateStore in Apache Spark Structured Streaming

During my last Spark exploration of the RPC implementation one class caught my attention. It was StateStoreCoordinator used by the state store that is an important place in Structured Streaming pipelines.

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Triggers in Apache Spark Structured Streaming

Some last weeks I was focused on Apache Beam project. After some readings, I discovered a lot of similar concepts between Beam and Spark Structured Streaming (or inversely?). One of this similarities are triggers.

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Apache Spark Structured Streaming and watermarks

The idea of watermark was firstly presented in the occasion of discovering the Apache Beam project. However it's also implemented in Apache Spark to respond to the same problem - the problem of late data.

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RPC in Apache Spark

The communication in distributed systems is an important element. The cluster members rarely share the hardware components and the single solution to communicate is the exchange of messages in the client-server model.

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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.

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Fanouts in Apache Beam's combine transform

Uneven load is one of problems in distributed data processing. How to ensure that the any of nodes becomes a straggler ? Apache Beam proposes a solution for that in the form of fanout mechanism applicable in Combine transform.

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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.

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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.

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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.

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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.

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Triggers in Apache Beam

Another important point of windowing in Apache Beam concerns triggers. Thanks to them we can freely control when the window results are computed.

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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.

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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.

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