A long-running Spark Streaming job, once submitted to the YARN cluster should run forever until it’s intentionally stopped. Any interruption introduces substantial processing delays and could lead to data loss or duplicates. Neither YARN nor Apache Spark have been designed for executing long-running services. But th...
When I tried to deploy my first Spark application on a YARN cluster, I realized that there was no clear and concise instruction how to prepare the application for deployment. This blog post could be treated as missing manual on how to build Spark application written in Scala to get deployable binary.
In the world beyond batch, streaming data processing is a future of dig data. Despite of the streaming framework using for data processing, tight integration with replayable data source like Apache Kafka is often required. The streaming applications often use Apache Kafka as a data source, or as a destination for ...
I published post on the allegro.tech blog, how to integrate Spark Streaming and Kafka. In the blog post you will find how to avoid java.io.NotSerializableException exception when Kafka producer is used for publishing results of the Spark Streaming processing.
When you develop a distributed system, it’s crucial to make it easy to test. Execute tests in a controlled environment, ideally from your IDE. Long develop-test-develop cycle for complex systems could kill your productivity. Below you find my testing strategy for Spark and Spark Streaming applications.
Books
A few days ago I participated in quick presentation of significant e-commerce platform. The custom platform implemented mostly in PHP and designed as scalable and distributed system. And I was really impressed! Below you can find a short summary of chosen libraries, frameworks and tools.
Overview
Overview
Recently I read excellent book Release It! written by Michael Nygard. The book is 7 years old and I don’t know how I could miss the book until now.