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Niko's Project Corner

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An efficient schema for hierarchical data on Elasticsearch

(20th November 2016)

Many busi­nesses gen­er­ate rich datasets from which valu­able in­sights can be dis­cov­ered. A ba­sic start­ing point is to an­alyze sep­arate events such as item sales, tourist at­trac­tion vis­its or movies seen. From these a time se­ries (to­tal sales / item / day, to­tal vis­its / tourist spot / week) or ba­sic met­rics (his­togram of movie rat­ings) can be ag­gre­gated. Things get a lot more in­ter­est­ing when in­di­vid­ual data points can be linked to­gether by a com­mon id, such as items be­ing bought in the same bas­ket or by the same house hold (iden­ti­fied by a loy­alty card), the spots vis­ited by a tourist group through out their jour­ney or movie rat­ings given by a speci­fic user. This richer data can be used to build rec­om­men­da­tion en­gi­nes, iden­tify sub­sti­tute prod­ucts or ser­vices and do clus­ter­ing anal­ysis. This ar­ti­cle de­scribes a schema for Elas­tic­search which sup­ports ef­fi­cient fil­ter­ing and ag­gre­ga­tions, and is au­to­mat­ically com­pat­ible with new data val­ues.

Languages: Python
Tags: Business Intelligence Databases Elasticsearch

Caching and perf. monitoring with Redis and Python

(10th October 2016)

When im­ple­ment­ing real-time APIs most of the time server load can greatly be re­duced by caching fre­quently ac­cessed and rarely mod­ified data, or re-us­able cal­cu­la­tion re­sults. Luck­ily Python has sev­eral fea­tures which make it easy to add new con­structs and wrap­pers to the lan­guage, for ex­am­ple thanks to *args, **kwargs func­tion ar­gu­ments, first-class func­tions, dec­ora­tors and so fort. Thus it doesn't take too much ef­fort to im­ple­ment a @cached dec­ora­tor with business-specific logic on cache invalidation. Redis is the perfect fit for the job thanks to its high performance, binary-friendly key-value store with TTL and different data eviction policies and support for other data structures which make it trivial to store additional key metrics there.

Languages: Python
Tags: Databases Redis

Scalable analytics with Docker, Spark and Python

(23rd December 2015)

Tra­di­tion­ally data sci­en­tists in­stalled soft­ware pack­ages di­rectly to their ma­chi­nes, wrote code, trained mod­els, saved re­sults to lo­cal files and ap­plied mod­els to new data in batch pro­cess­ing style. New data-driven prod­ucts re­quire rapid de­vel­op­ment of new mod­els, scal­able train­ing and easy in­te­gra­tion to other as­pects of the busi­ness. Here I am propos­ing one (per­haps al­ready well-known) cloud-ready ar­chi­tec­ture to meet these re­quire­ments.

Languages: Bash Python
Tags: Architecture Docker Spark Nginx GitHub JVM
GitHub: nikonyrh/docker-scripts

Very fuzzy searching with Elasticsearch

(21st October 2015)

I en­coun­tered an in­ter­est­ing ques­tion at Stack Over­flow about fuzzy search­ing of hashes on Elas­tic­search and de­cided to give it a go. It has na­tive sup­port for fuzzy text searches but due to per­for­mance rea­sons it only sup­ports an edit dis­tance up-to 2. In this con­text the max­imum al­lowed dis­tance was eight so an al­ter­na­tive so­lu­tion was needed. A so­lu­tion was found from lo­cal­ity-sen­si­tive hash­ing.

Languages: Python
Tags: Elasticsearch Databases GitHub Stack Overflow
GitHub: nikonyrh/stackoverflow-scripts