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Posts Tagged ‘scala’

Getting started with Akka Stream Kafka – Using Kafka the reactive streams way

19 September 2016 7 comments

A few days ago my eyes fell on a new release of Akka Stream Kafka. Since I’m doing a lot with Kafka currently and I really wanted to get my hands dirty with Akka this sounded very good. Also a good opportunity to see if an upgrade to Kafka 0.10.0.1 (from 0.8.2.2) is worth while (since older versions of Kafka are not supported in Akka Stream Kafka 0.11).

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Categories: English, java, work Tags: , ,

Finding the link between heart rate and running pace with Spark ML – Fitting a linear regression model

2 September 2016 1 comment

Besides crafting software I’m an avid runner and cyclist. Firstly for my health and secondly because of all the cool gadgets there are available. Recently I started with a Coursera course on Machine Learning and with that knowledge I combined the output of my running watch with Spark ML. In this article I discuss how to load gps and heart rate data to a linear regression model and ultimately get a formula with heart rate as input and running pace as output.

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At-least-once delivery with Kafka and Spark – Improve the reliability of your job

The default behaviour of a Spark-job is losing data when it explodes. This isn’t a bad choice per se, but at my current project we need higher reliability. In this article I’ll talk about at least once delivery with the Spark write ahead log. This article is focussed on Kafka, but can also be applied to other Receivers.

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Spark Streaming Backpressure – finding the optimal rate is now done automatically

One of my complaints about Spark was that it wasn’t possible to set a dynamic maximum rate. This is a problem in many jobs since the maximum throughput isn’t always linear with the output rate. Another issue is with local testing. You have to set the rate to extremely low values and experiment a lot to make a Spark job usable on a local machine.
But all these problems are in the past with the introduction of backpressure (I believe it’s spelled as back pressure, but I’ll stick to the Spark notation).

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Understanding Spark parameters – A step by step guide to tune your Spark job

15 February 2015 1 comment

After using Spark for a few months we thought we had a pretty good grip on how to use it. The documentation of Spark appeared pretty decent and we had acceptable performance on most jobs. On one Job we kept hitting limits which were much lower than with that Jobs predecessor (Storm). When we did some research we found out we didn’t understand Spark as good as we thought.
My colleague Jethro pointed me to an article by Gerard Maas and I found another great article by Michael Noll. Combined with the Spark docs and some Googlin’ I wrote this article to help you tune your Spark Job. We improved our throughput by 600% (and then the elasticsearch cluster became the new bottle neck)

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