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2016ccf_bdci_sougou icon 2016ccf_bdci_sougou

【源码以及PPT分享】2016CCF大数据与计算智能大赛:精准营销中搜狗用户画像挖掘

alibaba-mom icon alibaba-mom

2015 ali中间件比赛项目,1. 通信模块: 涉及到了消息架构设计,每一个主题分配1-2个队列,这个是可以横向增加,考虑到了一个主题有多个消费者订阅,而且还有过滤条件,为了负载均衡,对应的队列投递消息是根据随机数对队列数取膜完成的。以上是内存队列。 2. 持久化系统:因为考虑到机器坏掉,broker不可用等等原因导致的消息丢失,就需要每次收到的消息必须及时落盘,这个就是比赛优化的重中之重。为此设计了一个小型的文件系统,借鉴阿里的rocketmq. 用一个大的日志文件,一般1G大小每个,和内存队列的结构映射到实体队列。整个日志文件是顺序文件,每条消息固定大小,里面有对应的队列号,消息号,还有长度等等。这个方便恢复消息到实体队列用的。 同步刷盘,异步刷盘,持久化必须可靠,所以一般是同步刷盘到日志文件,日志文件定时异步刷盘到实体队列,实体队列里面是索引,消息的本体在日志文件里面。为了加快大面积的写入速度,用了内存映射:内存映射文件首先将外存上的文件映射到内存中的一块连续区域,被当成一个字节数组进行处理,读写操作直接对内存进行操作,而后再将内存区域重新映射到外存文件,这就节省了中间频繁的对外存进行读写的时间,大大降低了读写时间

ccc icon ccc

一个可爱的演示仓库

ccfbdc2014 icon ccfbdc2014

Data and code repository for CCF Big Data Challenge, 2014.

learn_jupyter icon learn_jupyter

This is a jupyter practical tutorial. Welcome to edit together!

state-of-the-art-result-for-machine-learning-problems icon state-of-the-art-result-for-machine-learning-problems

This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.

stolon icon stolon

PostgreSQL cloud native High Availability and more.

stopword_userdict icon stopword_userdict

Stopwords,User Dict,Sentiment lexicon(mainly focus on Danmaku,Danmu,TSC,bullet-comment,time-sync comments) 弹幕数据挖掘使用的停用词、用户词典、情感词典

tensorflow icon tensorflow

Computation using data flow graphs for scalable machine learning

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