标题:
Architecture and Practice for DAL (5) Data Sharding
Architecture and Practice for Data Access Layer (5) Data Sharding
联动优势数据访问层DAL架构和实践之五:分片数据分片
说明:
How to implement a dalet to access sharding databases.
和已有DAL软件(如许超前DAL手机之家、陈思儒Amoeba/贺贤懋Cobar等)不一样,在前端访问方式的选择上,抛弃JDBC方式,而是为同一个dalet数据服务,同时提供自定义TCP长连接和HTTP长连接两种接口。
因而通过抛弃JDBC可以获得多方面的好处——
1)可减少S端协议解析和查询分析的开销;
2)也简化C端编程。
3)后端存储就不再限于RDB了,而可以是任意NOSQL、文件、缓存、甚至是Tuxedo等在线服务。
4)可以实现无状态了,更容易横向扩展。
5)从接口上就可消除join等关键字的误用,避免引起服务端负担过重。
标题:
Architecture and Practice for DAL (5) Data Sharding
Architecture and Practice for Data Access Layer (5) Data Sharding
联动优势数据访问层DAL架构和实践之五:分片数据分片
说明:
How to implement a dalet to access sharding databases.
和已有DAL软件(如许超前DAL手机之家、陈思儒Amoeba/贺贤懋Cobar等)不一样,在前端访问方式的选择上,抛弃JDBC方式,而是为同一个dalet数据服务,同时提供自定义TCP长连接和HTTP长连接两种接口。
因而通过抛弃JDBC可以获得多方面的好处——
1)可减少S端协议解析和查询分析的开销;
2)也简化C端编程。
3)后端存储就不再限于RDB了,而可以是任意NOSQL、文件、缓存、甚至是Tuxedo等在线服务。
4)可以实现无状态了,更容易横向扩展。
5)从接口上就可消除join等关键字的误用,避免引起服务端负担过重。
Wrapper induction construct wrappers automatically to extract information f...George Ang
Wrapper induction is a technique to automatically generate wrappers to extract information from web sources. It involves learning extraction rules from labeled examples to construct a wrapper as a finite state machine or set of delimiters. Two main wrapper induction systems are WIEN, which defines wrapper classes including LR, and STALKER, which uses a more expressive model with extraction rules and landmarks to handle structure hierarchically. Remaining challenges include selecting informative examples, generating label pages automatically, and developing more expressive models.
This document summarizes a tutorial given by Bing Liu on opinion mining and summarization. The tutorial covered several key topics in opinion mining including sentiment classification at the document and sentence level, feature-based opinion mining and summarization, comparative sentence extraction, and opinion spam detection. The tutorial provided an overview of the field of opinion mining and abstraction as well as summaries of various approaches to tasks such as sentiment classification using machine learning methods and feature scoring.
The document provides an overview of Huffman coding, a lossless data compression algorithm. It begins with a simple example to illustrate the basic idea of assigning shorter codes to more frequent symbols. It then defines key terms like entropy and describes the Huffman coding algorithm, which constructs an optimal prefix code from the frequency of symbols in the data. The document discusses how the algorithm works, its advantages in achieving compression close to the source entropy, and some limitations. It also covers applications of Huffman coding like image compression.
Do not crawl in the dust different ur ls similar textGeorge Ang
The document describes the DustBuster algorithm for discovering DUST rules - rules that transform one URL into another URL that contains similar content. The algorithm takes as input a list of URLs from a website and finds valid DUST rules without requiring any page fetches. It detects likely DUST rules based on a large support principle and small buckets principle. It then eliminates redundant rules and validates the remaining rules using a sample of URLs to identify rules that transform URLs with similar content. Experimental results on logs from two websites show that DustBuster is able to discover DUST rules that can help improve crawling efficiency.
The document discusses techniques for optimizing front-end web performance. It provides examples of how much time is spent loading different parts of top websites, both with empty caches and full caches. The "performance golden rule" is that 80-90% of end-user response time is spent on the front-end. The document also outlines Yahoo's 14 rules for performance optimization, which include making fewer HTTP requests, using content delivery networks, adding Expires headers, gzipping components, script and CSS placement, and more.
3. 系统 / 存储
层
Web 应用 QQ Client 应用 Chat Room 应用
数据存取、操作 API
UDP File ServerHTTP File Server
User DB Item Info DB Item/Image Files
DB Cache Server File Cache Server图片处理
Server
接口层
应用层
数据维护 Daemon
各层细化的结构图及数据流各层细化的结构图及数据流