Analysis of the usa election of 2016 with apache spark graphx and neo4j _ articles about programming on mkdev

Almost right before the election started, I decided that it might have been interesting to analyse what people think and, more importantly, say on this topic. H2 database file Because, as you know, this election had promised to be an extraordinary one.

This is when I came up with an idea to utilise Twitter’s streaming API to continuously save vox populi onto disk for its further analysis. R studio data recovery serial key So I had been doing this during a period that spans four days – starting from 7th and up to 10th of November with occasional breaks. Database query languages When I thought that I already have enough of data for some experiments, an ETL task had been performed and the tweets (which were stored as plain text files) had been turned into a DataFrame compressed with Parquet.


P d database This DataFrame has around ten different fields. Database 101 But for the purpose of this article only these three were used:

Now we have data to play around with. M power database And as the title of the article suggests I will be using Apache Spark GraphX functionality for this purpose. Data recovery from external hard drive GraphX is one of four components built on top of Spark Core engine and it provides API for processing graphs.

So what can we conclude from these results? I suppose, it is that there were more people who were mad about Trump’s victory than Clinton’s defeat. Database join types Keep in mind, however, that twitter’s streaming API only outputs 1% of all the tweets that are posted. Section 8 database On top of that, I’ll remind you that there were times when I stopped the streaming process for some time and I’m afraid I missed the most interesting part of those days.

Finally, let’s use a more suitable algorithm for counting most popular tweets – PageRank. Icd 9 database This is an algorithm that was initially invented by founders of Google back when they were students to improve the relevance of search results in a new type of search engine they were working on. Database xampp The idea behind it is that a document is more important the more times it is referred to in other documents both directly and indirectly (through other intermediate documents). Database administrator jobs This video explains it in detail.

popularInDegrees . Data recovery joondalup zip ( popularPageRank ) // zip the results that we have got from two approaches . Database of genomic variants foreach { case (( l , _ ), ( r , _ )) => println ( s “$l $r ${if (l == r) “” else ” ! “}” ) // print out ids of the tweets and append an ! if they are not equal } Output : 796315640307060738 796315640307060738 780683171520212992 780683171520212992 796182637622816768 796182637622816768 796169187882369024 796169187882369024 795954831718498305 795954831718498305 796219890239733760 796219890239733760 796394920051441664 796394920051441664 796259238590824449 796259238590824449 796377820091981824 796377820091981824 795737237329743873 795737237329743873 796425920378765313 796425920378765313 796317753749729280 796317753749729280 795743568736481280 795743568736481280 796227455082233858 796227455082233858 796141495573155840 796171467943710720 ! 796171467943710720 796141495573155840 ! 796220439542439936 796220439542439936 796188203300442112 796188203300442112 796463632972267520 796463632972267520 796171852645355522 796171852645355522

Why is there so few differences in the two approaches? The reason behind this is that number of second level replies (reply to a reply to a tweet) in the data set being used is extremely insignificant (again, because of 1% limit that Twitter outputs in their free stream). Database viewer Furthermore, the top level tweets they reply to are also unpopular (have at most 2 replies) thus having no impact on the actual most popular tweets, which have hundreds of replies:

val replies = englishTweetsRDD // take tweets that are themselves replies: . H data recovery registration code free download filter (! _ . Database hardware _3 . Database roles isEmpty ) // get their ids: . B tree database management system map ( _ . Database file _1 ) . Data recovery near me collect () . Database job description toSet val repliesWithRepliesIds = englishTweetsRDD // get tweets that reply to replies: . Data recovery 94fbr filter { case ( _ , _ , inReplyToStatusId ) => replies ( inReplyToStatusId . Database foreign key toString ) } // get ids of first level replies: . Database as a service map ( _ . Iphone 6 data recovery _3 . Database google drive toLong ) . Data recovery geek squad collect () . Database recovery pending toSet graph // get a collection of tuples (VertexId, NumberIncomingEdges): . Data recovery prices inDegrees // get only replies with replies: . Database sharding filter { case ( id , _ ) => repliesWithRepliesIds ( id ) } // sort by number of incoming edges: . Database keys with example sortBy ( getCount , descending ) // take top 20 and print them out: . Data recovery xfs take ( 20 ) . Database management systems 3rd edition foreach ( println ) Output ( id , number of replies ) : ( 796188651583655942 , 2 ) ( 796222914823618561 , 2 ) ( 796226781980356608 , 2 ) ( 796627237340520448 , 1 ) ( 796591040501157888 , 1 ) ( 796622837540855808 , 1 ) ( 796141062997819396 , 1 ) ( 796572627531890689 , 1 ) ( 796177092077559808 , 1 ) ( 796447414961967104 , 1 ) ( 796428863568019460 , 1 ) ( 796512841918480384 , 1 ) ( 795739009616121856 , 1 ) ( 796596354709671936 , 1 ) ( 796459909814554628 , 1 ) ( 796500472911851520 , 1 ) ( 796420030367989761 , 1 ) ( 796491488691621892 , 1 ) ( 796154228934778880 , 1 ) ( 796478276663382016 , 1 )

In conclusion, it’s just a small and, hopefully, an interesting fraction of what can be done with GraphX. Database engineer salary It also includes other algorithms for operations on graphs comprised of large data sets and I hope that we will see more such stuff in future articles. Jstor database But that’s it for today. E m database More similar

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