Please log in to watch this conference skillscast.

In this talk Laura will briefly explain several probabilistic data structures used for approximate query answering, such as a Bloom Filter, HyperLogLog and Count-Min Sketch, as well as present canonical examples of use at Twitter for each of the structures mentioned. You will learn how these structures are implemented in Twitter’s open-source abstract algebra library Algebird and how they are used by Summingbird - an open-source domain-specific language (implemented in Scala) that integrates online and batch MapReduce computations in a single framework. 

Queries such as whether a user has seen a tweet, the number of unique users who have favourited a tweet, or the number of times a tweet was seen over a period of time, are all hard to answer exactly due to having huge datasets and tight time constraints. However, the exact number is often not necessary as long as the errors are bounded. Certain algebraic data structures enable approximate answers to the queries mentioned, owing to distributed computations performed over them that are guaranteed to be correct because of their algebraic properties.

Take a look at Algebird and Summingbird.

YOU MAY ALSO LIKE:

• Damjan Vujnovic's Advanced JavaScript Workshop (in London on 4th - 6th November 2019)
• Lightbend Akka for Scala - Professional (in London on 11th - 12th November 2019)
• Scala eXchange London 2019 (in London on 12th - 13th December 2019)
• Scalax2gether Community Day 2019 (in London on 14th December 2019)
• Code Kata: Yilin Wei - Optics with Monocle (in London on 22nd October 2019)
• Don’t keep it to yourself - openness and honesty in the workplace (in London on 30th October 2019)
• Abstract Data Types In The Region Of Abysmal Pain, And How To Navigate Them (SkillsCast recorded in September 2019)
• The Last Frontier and Beyond (SkillsCast recorded in August 2019)