Notes about Cutting-Edge Technologies and Everything

The Problem (part 1) Cassandra is a highly-distributable NoSQL database with tunable consistency. What makes it highly distributable makes it also, in part, vulnerable: the whole deployment must run on synchronized clocks. It’s quite surprising that, given how crucial this is, it is not covered sufficiently in literature. And, if it is, it simply refers to installation of a NTP daemon on each node which – if followed blindly – leads to really bad consequences. You will find blog posts by users who got burned by clock drifting. In the first installment of this two part series, it is covered how important clocks are and how bad clocks can be in virtualized systems (like Amazon EC2) today. Details: https://blog.rapid7.com/2014/03/14/synchronizing-clocks-in-a-cassandra-cluster-pt-1-the-problem/ Solutions (part 2) Some disadvantages of off-the-shelf NTP installations, and how to overcome them. Details: https://blog.rapid7.com/2014/03/17/synchronizing-clocks-in-a-cassandra-cluster...

Rather than using configured constant timeouts, systems can continually measure response times and their variability, and automatically adjust timeouts according to the observed response time distribution. This can be done with a Phi Accrual failure detector, which is used by Cassandra and Akka. http://ternarysearch.blogspot.ca/2013/08/phi-accrual-failure-detector.html

Joins are prevalent operations in many data processing applications. Most data processing systems feature APIs that make joining data sets very easy. However, the internal algorithms for join processing are much more involved – especially if large data sets need to be efficiently handled. Therefore, join processing serves as a good example to discuss the salient design points and implementation details of a data processing system. In this blog post, we cut through Apache Flink’s layered architecture and take a look at its internals with a focus on how it handles joins. Specifically, I will: show how easy it is to join data sets using Flink’s fluent APIs, discuss basic distributed join strategies, Flink’s join implementations, and its memory management, talk about Flink’s optimizer that automatically chooses join strategies, show some performance numbers for joining data sets of different sizes, and finally briefly discuss joining of co-located and pre-sorted data sets. Details:...