Compression has a lot of benefits in the data context. It reduces the size of stored data, so you will save some space and also have less data to transfer across the network in the case of a data processing pipeline. And if you use Bzip2, you can process the compressed data in parallel. In this post, I will try to explain how does it happen. Continue Reading →
Apache Spark 2.4.0 brought a lot of internal changes but also some new features exposed to the end users, as already presented high-order functions. In this post, I will present another new feature, or rather 2 actually, because I will talk about 2 new SQL functions. Continue Reading →
Some time ago I was thinking whether Apache Spark provides the support for auto-incremented values, so hard to implement in distributed environments After some research, I almost found what I was looking for - monotonically increasing id. In this post, I will try to explain why "almost". Continue Reading →
In November 2018 bithw1 pointed out to me a feature that I haven't used yet in Apache Spark - custom optimization. After some months consacred to learning Apache Spark GraphX, I finally found a moment to explore it. This post begins a new series about Apache Spark customization and it covers the basics, i.e. the 2 available methods to add the custom optimizations. Continue Reading →
In the previous post in GraphFrames category I mentioned the motifs finding feature and promised to write a separate post about it. After several weeks dedicated to the Apache Spark 2.4.0 features, I finally managed to find some time to explore the motifs finding in GraphFrames. Continue Reading →
Despite the recent critics (cf. "Serverless Computing: One Step Forward, Two Steps Back" link in the Read also section), serverless movement gains the popularity. Databricks proposes a serverless platform for running Apache Spark workflows, Google Cloud Platform comes with a similar service reserved to Dataflow pipelines and Amazon Web Services, ... In this post, I will summarize the good and bad sides of my recent experiences with AWS Lambda applied to the data processing. Continue Reading →
When we come to Scala and see the sealed keyword, we often wonder "why". After all, having all subclasses defined in one or more files shouldn't be a big deal. For us, programmers, it's not but for the compiler, it has an importance. In this post, I will try to show the sealed class use cases. Continue Reading →
Last time we've discovered the INTERSECT operator. To recall it quickly, it returns all rows that are defined in the combined datasets. Today we'll discover another operator, doing the opposite and called depending on the vendor: MINUS or EXCEPT. Continue Reading →
Some time ago I was asked by Sunil whether it was possible to load the initial state in Apache Spark Structured Streaming like in DStream-based API. Since the response was not obvious, I decided to investigate and share the findings through this post. Continue Reading →
The Futures appear as the first element to learn of Scala's asynchronous world. They're quite simple and probably exist in the languages you have been working on before. But they're not the single asynchronous types in Scala because they are accompanied by Promises covered in this post. Continue Reading →
Key-value stores have the advantage of being a kind of distributed and high-available memory cache. But even though they're quite easy to manipulate thanks to the key-based access, they also have some complicated tasks. One of them is the strategy of picking a good key. Continue Reading →
Some time ago I wrote a post about the graph data processing with streams. That article was based on X-Stream framework proposed by the searchers of EPFL research institute. At this occasion, I also mentioned the existence of newer alternative for X-Stream, adapted for distributed workloads, called Chaos. I voluntary omitted the explanation of Chaos in the previous post. Putting it aside of X-Stream would introduce too many new concepts. But now, after some weeks of graph processing discoveries, I would like to return to the successor of X-Stream and present it more in details. Continue Reading →
When I was working with Java and Spring framework, the annotations were my daily friend. When I have started to work with Scala, I haven't seen them a lot. It was quite surprising at the beginning. But with every new written line of code, I have started to see them more and more. After that time it's a good moment to summarize the experience and focus on the Scala annotations. Continue Reading →
In previous posts about memory in Apache Spark, I've been exploring memory behavior of Apache Spark when the input files are much bigger than the allocated memory. After that it's a good moment to sum up that in the post dedicated to classes involved in memory using tasks. Continue Reading →
For a long time, I've wanted to make a small real-time data visualization application with the use of websockets and some fancy JavaScript visualization framework. And the moment went when I was preparing the execution schemas to illustrate distributed graph algorithms covered in Graph algorithms in distributed world - part 1 post. I used there static images combined together but it was quite painful. Because of that, I decided to check whether it's possible to do in a more programmatic way. Continue Reading →