Sometimes metadata is disregarded but very often it helps to retrieve the information easier and faster. One of such use cases are the headers of Apache Parquet where the stats about the column's content are stored. The reader can, without parsing all the lines, know whether what is he looking for is in the file or not. The metadata is also a part of one of Big Data patterns called automated processing metadata insertion. Continue Reading →
Some time ago I found a site listing Big Data patterns (link in "Read also" section). However, that site describes them from a very general point of view and it's not always obvious to figure out the what, why and how. That's why I decided to start a new series of posts where I will try to describe these patterns and give some more technical context. Continue Reading →
Two great features whose I experienced when I have been working with Dataflow were the serverless character and the auto-scalability. That's why when I first saw the Apache Spark on Kubernetes initiative, I was more than happy to write one day the pipelines automatically adapting to the workload. That also encouraged me to discover the horizontal scalability and this post is the first result of my recent research on that topic. Continue Reading →
During my exploration of Apache Spark configuration options, I found an entry called spark.scheduler.mode. After looking for its possible values, I ended up with a pretty intriguing concept called FAIR scheduling that I will detail in this post. Continue Reading →
In one of my previous posts I presented how to add a custom optimization to Apache Spark SQL. It was not a good moment to deep delve into the topic because of its complexity. That's why I will try to do a better job here by showing the API of native optimizations. Continue Reading →
March 29, 2019
SQL
Bartosz Konieczny
I have already described grouping sets feature in the context of Apache Spark. But natively they are a part of SQL standard and that's why I would like to extend the previous post here. After all, you don't need Big Data to use them - even though nowadays it's difficult to not to deal with it. Continue Reading →
One of data governance goals is to ensure data consistency across different producers. Unfortunately, very often it's only a theory and especially when the data format is schemaless. It's why the data exploration is an important step in the process of data pipeline definition. In this post I wanted to do a small exercise and check how Apache Spark SQL behaves with inconsistent data. Continue Reading →
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 →
February 28, 2019
SQL
Bartosz Konieczny
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 →