Massively Distributed Database Systems大规模分布式数据库系统
dbs数据库名词解释
dbs数据库名词解释- DBS：数据库系统（Database System），是指由软件、硬件和数据组成的，用于存储、管理和检索大量有组织的数据的系统。
数据库系统可以分为关系型数据库系统（RDBMS）和非关系型数据库系统（NoSQL）等不同类型。
- 数据库（Database）：是指将数据按照一定的结构和规则组织起来，并存储在计算机系统中的数据集合。
它可以被认为是一个组织数据的仓库，可以存储和管理大量结构化、半结构化和非结构化数据。
- 数据库管理系统（Database Management System，简称DBMS）：是一种管理数据库的软件，它提供了对数据库的管理和操作功能。
数据库管理系统可以用来创建、修改和删除数据库中的数据、定义和管理数据库模式、处理数据的查询和事务等操作。
- 数据库模式（Database Schema）：是指数据库的逻辑结构和组织方式，在数据库中定义了表、表之间的关系、属性和约束等。
数据库模式确定了数据库中数据的存储方式和访问方式。
- 数据表（Table）：是数据库模式中的一种对象，它由多个列和行组成。
每个列描述了一个属性，每行表示一个记录。
数据表用于存储实体或对象的数据，每个表都有一个唯一的名称，并且可以定义各种约束和索引等。
- 数据列（Column）：也称为字段或属性，是数据表中的一个垂直方向的数据集合，它定义了表中每个记录的一个属性的数据类型和约束。
- 数据行（Row）：也称为记录或元组，是数据表中的一个水平方向的数据集合，它包含了表中的每个属性对应的具体值。
- 数据库索引（Database Index）：是一种数据结构，用于加快数据库中数据的检索速度。
索引可以基于一个或多个列，它提供了一种类似于书的目录的功能，可以根据指定条件快速定位到数据。
- 数据库查询语言（Database Query Language，简称DQL）：是一种用于在数据库中执行查询操作的语言。
常见的数据库查询语言包括结构化查询语言（SQL）和NoSQL数据库的查询语言（如MongoDB的查询语言等）。
04-常见的攻击技术介绍——DDoS攻击
大量ACK冲击服务器
受害者资源消耗
查表 回应ACK/RST ACK Flood流量要较大才会对 服务器造成影响
攻击者
DDoS攻击介绍——Connection Flood
Connection Flood 攻击原理
攻击表象
攻击者
大量tcp connect
正常tcp connect 正常tcp connect
NETBIOS/SMB 微软专利的组网协议多年来一直存在着各种各样的问题，
他们的某些缓冲区溢出漏洞可以导致Dos攻击和NetBILS名字重叠攻击，遭到 攻击的Windows系统将无法接入自己所属的局域网。
DOS工具包 因为没有耐心去一种一种地尝试哪种攻击手段可以奏效，所
以有些黑客干脆利用脚本把自己收集到的攻击工具打包在一起去攻击目标系统。
最新的DDOS变体普遍依赖IRC的“机器人”功能来管理他们的攻击活动。在各种 “机器人”软件中，流传最广的是Agobot/Gaobot系列。
针对DOS攻击的防范措施
因为DOS攻击具有难以追查其根源（思考为什么难于追查）的特点， 所以DOS要比其他任何攻击手段都应该采用有抵御、监测和响应等多种 机制相结合的多重防线来加以防范。单独使用这些机制中的任何一种都 不能保证百分之百的安全，但如果把它们结合起来，就可以把网络财产 所面临的风险控制在一个适当的水平。
伪造地址进行SYN 请求
就是让 你白等
SYN （我可以连接吗？） 不能建立正常的连接！
为何还 没回应
受害者
攻击表象
SYN_RECV状态 半开连接队列
遍历，消耗CPU和内存 SYN|ACK 重试 SYN Timeout：30秒~2分钟 无暇理睬正常的连接请求—拒 绝服务
关于分布式数据库系统的计算机英语
关于分布式数据库系统的计算机英语1. Introduction to Distributed Database Systems2. Key Concepts in Distributed Database Systems2.2 Data Replication: Data replication is the process of creating multiple copies of data and storing them at different sites in the network. Replication enhances fault tolerance and availability of data by allowing access to the nearest replica when a site or a network link fails.2.3 Data Consistency: Ensuring data consistency is a major challenge in a distributed database system. Consistency refers to the correctness and integrity of data across different sites. Various techniques, such as distributed transaction management and replica synchronization, are used to maintain data consistency.2.4 Data Transparency: Data transparency refers to the ability of users and applications to access and manipulate data without being aware of its distribution and location in the network. Transparency is achieved through the use of a distributed query processor that handles the distribution and retrieval of data.3.1 Data Fragmentation and Allocation: Data fragmentation involves dividing the database into smaller parts, called fragments, which are distributed across different sites. Theallocation process determines which fragment is stored at which site, based on factors such as data access patterns and network bandwidth.3.4 Replica Management: Replica management involves the creation, maintenance, and coordination of replicas in a distributed database system. This includes replica synchronization, consistency management, and fault detection and recovery.4. Advantages and Challenges of Distributed Database Systems4.1 Advantages of Distributed Database Systems- Improved performance and scalability: Distributed database systems can handle large amounts of data and provide high performance by distributing the workload across multiple nodes.- Fault tolerance and high availability: Data replication and distributed nature of the system make it resilient to failures, ensuring that data is available even if a site or a network link fails.- Cost-effective: Distributed database systems can utilize existing hardware and network infrastructure, minimizing the need for additional resources.4.2 Challenges of Distributed Database Systems- Data consistency: Ensuring consistency across multiple sites is challenging, especially in the presence of concurrent transactions and replication.- Network latency: Network latency and bandwidth constraints can impact the performance of distributed database systems.- Security and privacy: Distributed database systems need to address security concerns such as access control, encryption, and authentication, to protect data from unauthorized access.5. Conclusion。
分布式数据库系统DDS基本概念、原理和优化问题DistributedDatabaseSystems
Page 1.13
分布处理，如果不分程度，则到处都 有，即便是单处理器的计算机系统中 也有分布处理。其实，计算机发展的 过程就是一个不断将处理分布化的过 程，例如，将CPU和I/O功能分开就是 一个分布处理的样例。不过，现在我 们讲的分布处理则要复杂得多，单处 理器系统不包括在内。
Page 1.14
分布计算系统（distributed computing system）
定义为一组通过计算机网络 互联的、自主的处理单元（不 一定同构），协同工作，完成 指派的任务。所谓的计算单元 ，指的是可以在其上面执行程 序的计算设施。
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分布式数据库的产生与发展
比较成熟的数据库系统出现于20世纪六十年代末和七十年代。 以IMS为代表的层次型数据库系统于1968年问世。 20世纪七十年代初，美国CODASYL的数据库任务组的提出了 有名的网络数据库模型DBTG。 E. F. Codd于20世纪七十年代中期提出了关系数据库。 七十年代，计算机科学技术的发展与飞速发展的现代通信技术 相结合，导致了计算机网络的出现。这个时期，世界上先后建 成了许多规模巨大的、全国性的广域计算机网络对经济、国防 、情报、科学技术和社会生活产生了深刻的影响。随着微型计 算机的广泛应用，又自然地提出了这样的新问题，为了加强和 扩大微型计算机处理数据的功能，要求将许多分布在不同地点 上的微型计算机互连起来，共同工作。这样，进入了分布式数 据库时代。
利用并行计算机系统提供的并行处理能力，通 过并行地使用多个CPU和硬盘来提高处理速度 和I/O速度，从而加速数据库的活动。
主要研究内容 • 并行数据库的物理组织 • 并行数据操作算法的设计、分析与实现 • 并行数据库查询优化
GBase MPP数据库产品介绍
数据库产品介绍
GBase 8a MPP Cluster
目录
1
GBase 8a MPP 产品简介及技术分析 GBase 8a MPP 应用场景及行业典型案例 GBase 8a MPP 平台稳定性及运维支撑体系
2
3
大数据≠任何单一的数据处理技术
Hadoop
NoSQL，互联网、 非结构化
合适的技术解决针对的问题
NewSQL
传统数据库
OldSQL，交易、 联机事务
MPP数据库
NewSQL,分析应 用、结构化行业 大数据
OldSQL
＋
NoSQL
大数据平台 混搭架构
大数据
多种数据处理技术的组合
One Size Doesn’t Fit All！
GBase 8a MPP Cluster 产品简介
分布式任务
Parser Optimizer Coordinator
• GCWare：
• GNode：
GCWare 用于各节点GCluster 实例 间共享信息，以及控制多副本数据 分布式 操作时，提供可操作节点，并在多 数据管理层 副本操作中，控制各节点数据一致 性状态。
GNode 是GCluster 中最基本的存 储和计算单元。GNode 负责集群数 据在节点上的实际存储，并从 分布式 GCluster 接收和执行经分解的SQL 集群管理层 执行计划，执行结果返回给 GCluster。
应用平台
混 搭 结 构பைடு நூலகம்数 据 平 台
统一接入管理
关系模型 存储过程 SQL 星型模型 ACID 雪花模型 数据 交换
HBase
智慧工地管理方案及技术措施18
智慧工地管理方案及技术措施18智慧工地是一种利用信息化手段进行精确设计和施工模拟的工程项目管理方法。
通过三维设计平台实现施工过程管理，建立互联协同、智能生产、科学管理的施工项目信息化生态圈。
在虚拟现实环境下，将数据与物联网采集到的工程信息进行数据挖掘分析，提供过程趋势预测及专家预案，实现工程施工可视化智能管理。
智慧工地将更多人工智慧、传感技术、虚拟现实等高科技技术植入到建筑、机械、人员穿戴设施、场地进出关口等各类物体中，形成“物联网”，再与“互联网”整合在一起，实现工程管理干系人与工程施工现场的整合。
智慧建造整体架构可以分为三个层面。
第一个层面是终端层，利用物联网技术和移动应用提高现场管控能力。
通过RFID、传感器、摄像头、手机等终端设备，实现对项目建设过程的实时监控、智能感知、数据采集和高效协同，提高作业现场的管理能力。
第二层就是平台层，通过云平台进行高效计算、存储及提供服务，让项目参建各方更便捷的访问数据，协同工作，使得建造过程更加集约、灵活和高效。
第三层就是应用层，核心内容应始终围绕以提升工程项目管理这一关键业务为核心，因此PM项目管理系统是工地现场管理的关键系统之一。
BIM的可视化、参数化、数据化的特性让建筑项目的管理和交付更加高效和精益，是实现项目现场精益管理的有效手段。
要实现智慧建造，就必须要做到不同项目成员之间、不同软件产品之间的信息数据交换。
建立一个公开的信息交换标准，才能使所有软件产品通过这个公开标准实现互相之间的信息交换，才能实现不同项目成员和不同应用软件之间的信息流动。
这个基于对象的息交换标准格式包括定义信息交换的格式、定义交换信息、确定交换的信息和需要的信息是同一个东西三种标准。
2、BIM技术在建筑物使用寿命期间可以有效地进行运营维护管理。
它拥有空间定位和记录数据的能力，可以快速准确地定位建筑设备组件，进行可接入性分析，选择可持续性材料，并制定行之有效的维护计划。
结合RFID技术，将建筑信息导入资产管理系统，可以实现建筑物的资产管理。
分布式数据库如何工作 Distributed Database IT英语作文论文
分布式数据库如何工作Distributed Database Howdoes it workHow does Distributed Database work?A distributed database is considered as a database in which two or more files are located in two different places. However, they are either connected through the same network or lies in a completely different network. It is a single huge database in which portions of the data are stored in multiple physical locations and processing system is done by distributing the data among various nodes of the database. It is a system in which a huge database is settled down in a distributed manner in several physicallocations to avoid any kind of confusions while dealing with that database.The distributed database system is managed in a centralized manner by connecting the data logically. This helps in managing the bulk data in a manner as if it was all stored in one single place. In such a centralized database it is seen that the data are synchronized in such a manner that deletes or updates done in one location is automatically upgraded in other parts of the data. This is the concept of a distributed database in making the management of bulk data easy. Now we will tell you more with the help of an infographic.How Does Distributed Database Work?Definition of NetworkThe network is defined as a system that helps in connecting multiple devices together that helps them to communicate effectively. Networks can be small or it can consist of billions of devices that are connected to each other. Networking is of various types and each has some role or the other to perform. Two major types of networks are LAN and WAN. The first type is a local area network that allows for forming a network to a specific and personalized area such as home, office and campus.Within this also there is single or large network depending on the space of the area. On the other hand, WAN is a wide area network that is not limited to a single area and spread over multiple locations. WAN is seen to consist of multiple LAN system and these LANs are connected with the help of internet. Moreover,WAN allows limiting the access to the network with the help of authentication, firewalls and other security systems.The network is also defined according to characteristics that help in categorizing different types of networks such as typology, protocol and architecture and forms an integral part in the distributed database system.The typology is the geometric arrangement of the network in a system in the form of a ring, star, bus and others.The protocol is another characteristic that defines a set of rules and signals that help the networks use to communicate with each other. For example, the protocol for LAN is Ethernet.Architecture is another network characteristics that show the design or form of the network such as peer to peer or server architecture.The characteristics of the networks play an important role in a distributed database because it helps in connecting data in different location effectively and in a secured manner.Features of Distributed DatabaseIn a collection or group, it is seen that a distributed database is logically connected to each other and is often described under a single database. This means that a distributed database is not kept in a spread manner and is represented in a collaborative form.This interdependency of the database on each other from a different location is done with the help of a processor. The processors in a site connect with another site with the help of the network and do not have any kind of multiprocessing configuration. However, there are misconceptions that the distributed database system is loosely connected to each other in a file.In reality, it is not so because the entire process of a distributed database system is a complicated one. Based on these facts, the distributed database has various types of features that help defines them clearly, such as:Location independentDistributed query processingReliability of safety and reduction in data lossThe internal and external security systemCost-effective by reducing the bandwidth pricesEase of access to the data even if a failure occurs in umbrella networkEasy integration of more nodes to the databaseThe efficiency of speed and resourcesThere are some concerns connected to a distributed database system such as it should be kept up-to-date and there should be consistency while using the data that is remotely stored.Advantages of Distributed Database systemA distributed database is capable of offering various types of advantages to the business in the maintenance of large size data in a simpler and systematic form. This type of database is able to make modular development which means that a system can easily be expanded by connected new computers or local data to a site. Then the site is connected to the distributed system without much interruption.The distributed database also offers advantages over a centralized database system by preventing the system to stop working completely. In a time of failure, it is seen that a centralized database system stops completely, while in a distributed database in case of failure the system becomes slow and continue to perform until the error is fixed completely. Thisallows the user of the database from stopping their work completed in a time of failure.In addition to the above benefits, it is also seen that the distributed database system helps in offering lower communication costs to the admin. The admin can access the data effectively if is located close to where it is extracted the most. This facility helps in reducing the cost of the database admins. This is because communication becomes easier in this system by locating the data closer to the point of use.The response rate for the extraction of particular information or data is done at a faster rate with the help of the distributed database system. This is because the data is distributed in such a manner that it is kept close to the users in a particular site and they can use the data anytimethey want from the site. These are some of the advantages that the distributed database offers to the user for handling large and complex data.The environment in which Distributed Database WorksThe ability to create a distributed version of a database has been existing since the 1980s. This is done based on various types of distributed database environment that are widely categorized as homogenous and heterogeneous database.This shows that the process of distributed database system does not work in a single type of system and is spread over sites. This means that multiple computers and networks are involved in the process. This has led to thecategorization of the environment of the database in two different categories.Homogenous database–environment helps the sites to store the database identically. This type of environment works in a way in which the structures are the same in all the sites such as operating system, database management system and data structures. This environment further works under two environment that is autonomous and non-autonomous.Autonomous–in this each DBMS works in an independent manner by passing messages back and forth and helps in sharing data updates.Non-autonomous–in this environment the central database management system worksand coordinates database access across sites and update other nodes.Heterogeneous Database–in this environment different sites use different types of software to reach the problems of query processing and transactions. In such type of environment, the distributed database is stored in different sites in such a way that one site is unaware of what is having in another site. In such a process, the company uses different data models for storing the database and hence translation has to be done to connect from one model to another.In a heterogeneous environment, it is seen that a distributed database system works in a much complex manner and involves various steps, unlike the homogeneous database. There are two broad categories of nodes such as systemsand gateway. The system helps in supporting one or all the functionality of the logical database. Gateway, on the other hand, helps in creating paths for other databases without creating many benefits for one single logical database.Options for Distributing a DatabaseDistribution of a database in a site in a number of forms depending on the characteristics of the data. There are four basic strategies adopted by the Distribution Database system to distribute the data across multiples sites.The types of strategies that distributed database can use in its process are data replication, horizontal partitioning, vertical partitioning and combination of the above. The characteristicsand the processes involved in each of these options can be explained with the help of relational databases. Now we will tell you about the Data replication.Data ReplicationIn this type of option, it is seen that the entire data relation is stored in two or more number of sites. In this type of processes, it is seen that the database is distributed or stored in copies in different systems entirely. This is a way distributed database system will allow for fault tolerance capacity by storing a copy of all data in a number of sites.Such type of processes in common in an information system organization in which the database is removed from a centralized positionand moved to location specific server so that it is kept close to the user. This type of method help in using either synchronous or asynchronous distributed database technologies. Thus, replication is a copied version of the entire database stored in every site that the organization use to access.Advantages of replication are huge due to the ease of usage and highly secured process. Some of the advantages of using the replication process of the distributed database are:Reliability- this means that one site containing the relation database fails then another site can be approached easily to get a copy of the database. The available copies can then be uploaded after the transaction takes place andfailed nodes can be updated once they are repaired and return to service.Fast response- this process allows for fast response of the database in case of need because the data is stored near to the user to be processed quickly.Node decoupling- is another benefit of the replication process for distributing database because in this each transaction may move without coordinating with another network as each site has access to the entire database.Data Replication process also faces various kinds of disadvantages such as space for storage requirement as the database is huge and also complexities and cost attached toupdating the database because each site has to be updated about any new relation.Horizontal PartitioningThis is yet another process that is used in a distributed database in which some of the rows in a relation are put in one site and other rows are put under a base relation in another site. It is done in a horizontal or base form as the name suggests and the rows of the database are distributed in a number of sites.This can be seen with the help of an example that is customer relations in which the rows are located in home branches. In this system in case the transaction is made in the home branch then the transaction is processed locally and response time is reduced. In case the customermakes a transaction in another branch then the data is sent to the home branch for processing and then send back to the initiating branch.This process of distributed database system also has various types of advantages and disadvantages from the efficiency it adds to data management. The advantages of using horizontal partitioning are:Efficiency- this means that the data in this system is stored close to the user and separated from other data that is used by some other users. This reduces the chances of confusion and improved efficiencies to a great extent.Local optimization- data is stored in such a way that it can help in improving the performance of local access.Security- it is the biggest advantage of using this process because all types of data are not available in one place and data that is not relevant is kept separately without any kind of distraction.The use of horizontal partitioning also has various kinds of disadvantages attached to it such as inconsistent access speed, which means that the data is required from various points and this increases the access time. Moreover, there is a backup vulnerability, which means that due to lack of replication of similar kinds of data when one type of data become damaged in one site then it is completely lost and cannot be updated.Vertical PartitioningVertical partitioning is yet another form of distributed database process in which the data is partitioned column-wise. Some of the columns of the data or relations are projected in one site and other columns are projected under a base relation in another site.In this type of process, the distributed database system works in a separate manner as it works in horizontal partitioning system. The data or relations that are shared in each of the sites are connected to each other with the help of a common domain so that it can be extracted easily.Vertical partition of the database also has some advantages and disadvantages to being used and getting destroyed. The advantages of vertical partitioning are similar to that of thehorizontal partition system because in this process as well data are kept separately without much replication. The only exception that in vertical partition the combination of the data is many complications difficult to make compared to horizontal partitions.。
数据库系统概念(database system concepts)英文第六版 第一章
Relational Model
n R e l a t i o n a l model (Chapter 2)
• Columns
n Example of t a b u l a r d a t a i n t h e r e l a t i o n a l model
n Two c l a s s e s of languages l Procedural – u s e r s p e c i f i e s what d a t a i s r e q u i r e d and how t o get those data l Declarative (nonprocedural) – user specifies what data i s r e q u i r e d without s p e c i f y i n g how t o g e t those data
l Difficulty in accessing data 4 Need t o w r i t e a new program t o c a r r y out each new t a s k
l Data i s o l a t i o n — multiple f i l e s and formats l Integrity problems
l Concurrent access by multiple users 4 Concurrent access needed f o r performance 4 Uncontrolled concurrent accesses can lead to inconsistencies – Example: Two people reading a balance (say 100) and updating i t by withdrawing money (say 50 each) a t the same time
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triangulation DT(P) such that no point in P is inside the circumcircle of any triangle in DT(P)
disk of (a, b)
7
RNG – Relatively Neighborhood Graph
• Graph RNG(V,E)
• V is a set of nodes n (id, p) where p is a point in Euclidean space • E is a set of edge (a, b) that two points a and b by an edge
whenever there is no third point c that is closer to both a and b than they are to each other (there is no other point within the intersection of the circles centered at a and b with radius the distance d(a, b))
Basic Concepts – in-network query processing
each node has - a local & tiny DB and - sensors
a query "find the nodes whan 35oC"
• No global network topology like TCP/IP • Network topology with its neighbors
local stateless routing algorithm
5
Unit-Disk Graph
• UDG:
Graph G(N,E) where N is the set of nodes (sensors) and E is set of edges whose length is less than 1 (unit)
10
Example
full UDG with 200 nodes GG with 200 nodes (subset of full UDG)
RNG with 200 nodes (subset of GG)
GG with 200 nodes over 2Km X 2Km where radio range is 250 m
8
Delaunay Triangulation Graph
• Graph DTG(V,E)
• V is a set of nodes n (id, p) where p is a point in Euclidean space • E is a set of edge e (a, b) where e is a side of triangle constructed by
• Types if UDG
• RNG • Gabriel Graph • Delaunay Graph
• Each node in V maintains the node IDs connected via edges in E
6
Gabriel Graph
• Graph GG(V,E)
• V is a set of nodes n (id, p) where p is a point in Euclidean space • E is a set of edge (a, b) that there is no other node within the closed
9
Routing - GPSR
• in Brad Karp and H.T. Kung in MobiCom 2000, pp.243-254 • GPSR (Greedy Perimeter Stateless Routing) • A node x
• broadcasts a query message with destination point D • the closest node y receives and forwards the message.
• Issue
• Query processing time determined by # of hops • Energy consumption
• Battery is normally limited • Energy consumption for communication is relatively high
How to process it?
2
Why in-network query processing ?
• scalable
• No need to store the entire DB • Interact with neighbor nodes • A node failure is not critical
• SQL-like query
3
Energy Consumption
in S. Banerjee, A. Misra, /~suman/pubs/winet03.pdf 4
Multi-hop instead of infrastructure network
Massively Distributed Database Systems
In-Network Query Processing (Ad-Hoc Sensor Network)
Fall 2015 Ki-Joune Li http://isel.cs.pusan.ac.kr/~lik Pusan National University