linkedin id

CODE:#!/usr/bin/python #-*-Coding:utf-8-*-' Created on 2014-8-18@author:guaguastd@name:job_position_display.py ' ' # Import loginfrom Login Import linkedin_login# import jsonimport json# access to LinkedIn Apilinkedin_api = Linkedin_login ( ) # Load connections data from disk Fileconnections_data = R ' E:\eclipse\LinkedIn\dfile\linkedin_connections.json ' Connections = json.loads (open (Connections_data). r

of the subscription. Each consumer group is associated with a temporary owner registry and a persistent offset registry. For each partition that is subscribed to contains an owner registry, the content is the consumer ID of the subscription to this partition, and an offset registry containing the offset of the last subscription. Message Delivery Assurance Kafka does not have strict requirements for the repetition, loss, error, and s

CODE:#!/usr/bin/python #-*-Coding:utf-8-*-' Created on 2014-8-18@author:guaguastd@name:job_position_display.py ' ' # Import loginfrom Login Import linkedin_login# import jsonimport json# access to LinkedIn Apilinkedin_api = Linkedin_login ( ) # Load connections data from disk Fileconnections_data = R ' E:\eclipse\LinkedIn\dfile\linkedin_connections.json ' Connections = json.loads (open (Connections_data). r

LinkedIn was a year and a half ago, and a former customer wanted me to send an invitation. I think I was the first person in China to contact LinkedIn. I have benefited from disdain, experimentation, confusion, research, and love, now I am writing this blog and sharing some tips with LinkedIn. LinkedIn has provided me

. "minute Files") to the consumer. Most of them use a "push" model in which the broker forwards data to consumers. at LinkedIn, we find the "pull" model more suitable for our applications since each consumer can retrieve the messages at the maximum rate it can sustain andAvoid being floodedBy messages pushed faster than it can handle. Why should we use pull instead of push? consumer's hunger is only known by consumer, so it is reasonable for the br

At the JavaOne 2008 meeting, the developer of the famous social networking site LinkedIn made 2 presentations on the architecture of the LinkedIn website:Linkedin-a Professional Social Network Built with Java Technologies and Agile practicesLinkedIn Communication ArchitectureTake a look at the basics of the LinkedIn website:LinkedIn world's top-level traffic22 mi

How Linkedin engineers optimize their Java code, linkedinjava Link to the original article: how Linkedin engineers optimized their Java code Recently, I found a very good blog on Linkedin's technical blog when I was refreshing technical blogs of major companies. This blog post introduces the middle layer of the Linkedin information stream, Feed Mixer, which prov

LinkedIn (www.linkedin.com) is currently the world's largest professional networkCommunityThe number of registered users worldwide exceeds 75 million. When you join LinkedIn, you can not only keep in touch with former colleagues and classmates, but also communicate, discuss, and exchange opinions with professionals from all walks of life in the community, more job-seeking and business cooperation opportunit

The original link:How LinkedIn engineers optimize their Java codeWhen I recently brushed the technical blogs of major companies, I found a very good blog post on LinkedIn's technology blog. This blog post describes the LinkedIn information middle tier feed Mixer, which provides support for multiple distribution channels such as LinkedIn's web home page, University homepage, company homepage, and client (as

I. Overall Architecture Zoie is a real-time search engine system implemented by linkedin Based on Lucene. Its official wiki is described as follows: Http://snaprojects.jira.com/wiki/display/ZOIE/Overview Zoie is a realtime indexing and search system, and as such needs to have relatively close coupling between the logically distinct Indexing and Searching subsystems: as soon as a document made available to be indexed, it must be immediately searchable.

According to the KDD Conference, he was very impressed with the lectures by LinkedIn's chief scientist posse, not only from his French accent, but also from LinkedIn's meticulous data analysis. Title and abstract are as follows: Key lessons learned building recommender systems for large-scale social networksChristian posse LinkedIn Inc. Abstract By helping members to connect, discover and share relevant content or find a new career Opportunity, recomm

Search for SNS by Friendster, LinkedIn, Orkut, and liring Author: hhqTime: 09:48:01Original load: www.hhq.pim.cn Font SNS research ColumnSince Friendster launched the social network service (SNS), it has set off a boom in Social Network Services (SNS) around the world. The Social Network Service (SNS) site wants to change the Internet through the record of Interpersonal Network relationships. Traditional portal websites have