{"id":645574,"date":"2013-03-06T22:08:03","date_gmt":"2013-03-07T03:08:03","guid":{"rendered":"http:\/\/gigaom.com\/?p=617623"},"modified":"2013-03-06T22:08:03","modified_gmt":"2013-03-07T03:08:03","slug":"how-facebook-uses-numbers-to-show-people-places-and-things-with-graph-search","status":"publish","type":"post","link":"https:\/\/mereja.media\/index\/645574","title":{"rendered":"How Facebook uses numbers to show people, places and things with Graph Search"},"content":{"rendered":"

Facebook engineers posted more details Wednesday on the back end of its Graph Search function, showing how the social network assigns numbers to users, places and other reference points and then lets users form queries and find answers using those numbers.<\/p>\n

Unicorn, the software and search engine that makes Graph Search possible for at least hundreds of thousands of users, starts by giving every user a number. In the example cited by Facebook Engineering in a blog post<\/a>, a fictitious person named David has a number, or fbid, of 10003. His home, New York, is 111. And \u201cDownton Abbey,\u201d a television show David has liked on Facebook, is 222. Friends of David get called up with the search term \u201cfriend:10003.\u201d People who live in New York are at \u201clives-in:111,\u201d and people who like \u201cDownton Abbey\u201d live at \u201clike:222.\u201d Put those three strings together, and you\u2019ll get other friends of David who live in New York and like \u201cDownton Abbey.\u201d<\/p>\n

In each search string, sequence is important. The post states that Unicorn serves up quick results by quantifying the importance of each element of a search string and then sequencing those elements in order of importance. But at a whiteboard session last month at Facebook headquarters in Menlo Park, Calif., Facebook engineers said they want to automate the process of flipping around users\u2019 search strings to trigger better search results. It\u2019s one of a handful of things the engineers are looking to do to further improve Graph Search in order to live up to the company\u2019s lofty goals for it, as I reported <\/a> after attending the whiteboard session. <\/p>\n

New likes per day alone number more than 2.7 billion, according to Wednesday\u2019s blog post. At that rate, the number of possible fbids clearly will continue to grow, and the search strings will get longer, too. Turning out good search results in a couple of seconds could become more of a challenge.<\/p>\n

It\u2019s a good thing Facebook is innovating<\/a> on the hardware side through the Open Compute Project. That work could become a higher priority if Facebook grows at a faster clip, although at least a few users are quitting for a slew of reasons<\/a>.<\/p>\n

Wednesday\u2019s post does not mention advancements on Graph Search since the whiteboard session, even though the engineers said they would improve Graph Search in the months ahead.<\/p>\n

Facebook engineers work with big data sets in several other ways, often with Hadoop. Facebook\u2019s engineering manager of analytics infrastructure, Ravi Murthy, will moderate a panel on the future of Hadoop and business intelligence at GigaOM\u2019s Structure:Data conference<\/a> in New York in two weeks. <\/p>\n

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