on linkedin

CODE:#!/usr/bin/python #-*-Coding:utf-8-*-"Created on 2014-8-26@author:guaguastd@name:linkedin_network_clusters.py" ' Import osimport sysimport jsonfrom urllib2 import httperrorfrom cluster import kmeansclustering, centroid# A helper funct Ion to munge data and build up an XML treesys.path.append (Os.path.join (OS.GETCWD (), "E:", "Eclipse", "LinkedIn", "Dfile")) From mykml import createkmlk = 3# get geo codefrom Geo Import Geo_from_bingg = geo_from_b

From: http://blog.sina.com.cn/s/blog_6c9b83990101bbzq.html 1. About databus LinkedIn opened up its low-Latency Change Data Capture System databus in February 26. This system can capture data on MySQL and Oracle Data sources. Currently, LinkedIn only opens up the connector on Oracle. Databus, as a consistency assurance component in the LinkedIn ecosystem, remain

SNS websites are not popular on the Internet outside China. Every name is well-known: Facebook, Twitter, YouTube, MySpace, Flickr and LinkedIn. Recently, I have been engaged in the development of overseas mobile phone apps for these websites, including Facebook, Twitter, and YouTube. I was looking for some domestic materials, but I really couldn't. There were few (possible reasons for being banned ). This is because

kafka[Is LinkedIn (a company) for log processing of distributed Message Queuing, LinkedIn's log data capacity is large, but the reliability requirements are not high, its log data mainly includes user behavior (login, browse, click, Share, like) and system running log (CPU, memory, disk, network, System and process status).Many of the current Message Queuing services provide reliable delivery guarantees, and the default is instant consumption (not sui

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 Director Vicente Silveira told his blog that the website password has indeed been leaked. The full text of the statement is as follows: This morning, some reports reported that the password was stolen and we hope to provide some up-to-date information. We have confirmed that some LinkedIn accounts and passwords have been stolen. We are investigating. The following measures are taken against the s

the crawler program, and then we can authenticate it. However, the problem is still not as simple as it is. However, I noticed that the above csrtfToken parameter has different results for each request. Remember one thing: cookie. Then, use firebug to analyze what cookies have written. To ensure security, linkedin checks whether the cookie on the client is the same as that of the original client for each request. If it denies the request, it is cons

Last week, LinkedIn CEO Jeff Weiner interviewed Pando daily in San Francisco. In medium, Li Jiang, an investor and writer, summarized three important courses he learned. There are suggestions that can be heard frequently in Silicon Valley, and some ideas that are hardly mentioned by anyone. I would like to share them with you here. 1. Focus Focus. Yes, you must have heard of this before. "Focus" is almost the mantra of Silicon Valley people. To start

method. Be aware of the change's forward compatibility (backward compatibility).2, (Java only) if there are multiple synchronized methods within a class, be careful about the deadlock problem. It is best to avoid sleeping in the synchronized method. If you can, use synchronized block instead.In addition to this, some LinkedIn design on the process of deploying services are observed. Although most technology companies may be like this, but for my litt

CODE:#!/usr/bin/python #-*-Coding:utf-8-*-"Created on 2014-8-18@author:guaguastd@name:linkedin_connection_ retrieve.py ' # import loginfrom login import linkedin_login# import jsonimport jsonfrom prettytable import prettytable# A ccess to LinkedIn Apilinkedin_api = linkedin_login () connections = Linkedin_api.get_connections () Connections_data = R ' E : \eclipse\linkedin\dfile\linkedin_connections.json ' #

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 Cubert Installation Guide · Understanding Cubert Concepts (i) partitioned blocks Understanding Cubert Concepts (ii) co-partitioned Blocks Original articles, reproduced please specify: Reprinted from: Oopsoutofmemory Shengli's blog, oopsoutofmemory This article link address: http://blog.csdn.net/oopsoom/article/details/46707733 Note: This document is based on the attribution-NonCommercial use-prohibition of the deduction of the

aggregator developedCloudera. It supports extensible "Pipes" and "sinks", and makes streaming log data very flexible. it also has more integrated distributed support. however, most of those systems are built for consuming the log data offline, and often expose Implementation Details unnecessarily (e.g. "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" mod

CODE:#!/usr/bin/python #-*-Coding:utf-8-*-"Created on 2014-8-19@author:guaguastd@name:job_title_standard.py" ' Import Osimport csvfrom Collections Import counterfrom operator import itemgetterfrom prettytable import prettytable# Specify CSV Directorycsv_file = Os.path.join (r "E:", "\ \", "Eclipse", "LinkedIn", "Dfile", "My_connections.csv") # define a set of Tran Sforms that converts the first item# to the second itemtransforms = [(' Sr. ', ' Senior

CODE:#!/usr/bin/python #-*-Coding:utf-8-*-"Created on 2014-8-19@author:guaguastd@name:job_title_standard.py" ' Import Osimport csvfrom Collections Import counterfrom operator import itemgetterfrom prettytable import prettytable# Specify CSV Directorycsv_file = Os.path.join (r "E:", "\ \", "Eclipse", "LinkedIn", "Dfile", "My_connections.csv") # define a set of Tran Sforms that converts the first item# to the second itemtransforms = [(' Sr. ', ' Senior

CODE:#!/usr/bin/python #-*-Coding:utf-8-*-"Created on 2014-8-20@author:guaguastd@name:geocode_connection_bing.py" From geopy import geocodersimport Jsongeo_app_key = "G = geocoders. Bing (Geo_app_key) # access to LinkedIn apifrom login Import Linkedin_loginlinkedin_api = Linkedin_login () connections = Lin Kedin_api.get_connections () Connections_data = R ' E:\eclipse\LinkedIn\dfile\linkedin_connections.jso

communication channel for other programs on the same machine. In Linux, the virtual file under/dev is the interface of the device driver, so you can deal with the USB port or even the GPU in this area. The virtual file under/proc is the kernel API, but it exists as a file, and you can use the same tool to access it in a normal file format.Even a program that connects to another machine via TCP is also a file descriptor, although the BSD Socket API (most commonly used to establish a TCP connecti

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.

CODE:#!/usr/bin/python #-*-Coding:utf-8-*-"Created on 2014-8-19@author:guaguastd@name:company_suffix_normalize.py" # import Jsonimport Osimport csvfrom collections Import counterfrom operator import itemgetterfrom prettytable import Pret tytable# Specify CSV directorycsv_file = Os.path.join (r "E:", "\ \", "Eclipse", "LinkedIn", "Dfile", "My_connections.csv") # define a set of transforms that converts the first item# to the second itemtransforms = [('