CS计算机代考程序代写 AWS Java scheme database flex algorithm concurrency SQL hbase data structure file system assembly COSC2406/2407 Database Systems Course Introduction

COSC2406/2407 Database Systems Course Introduction
Xiangmin (Emily) Zhou
RMIT University Room : 14.11.04
Online Consultation via Collaborate Ultra(no appointment required): 10:20-11.20am Thursdays
Email : xiangmin.zhou@rmit.edu.au
Course Overview
Xiangmin (Emily) Zhou (RMIT University)
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Acknowledgement of Country
Ngarara Place, RMIT City campus
RMIT University acknowledges the Kulin Nations as the Traditional Custodians of the land on which the University stands. The University respectfully recognises Elders both past and present. RMIT also acknowledges the Traditional Custodians of lands across Australia where it conducts its business, their Elders, Ancestors, cultures and heritage.
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Emergency Procedure Information
Before starting our session today, RMIT wishes to make you aware of the Emergency Procedures that are in place for your safety. Please look around and familiarize yourself with the emergency exits in this room and do this for each new venue in which you attend classes. Evacuation plans are located in all corridors.
• In the unlikely event of an evacuation, a “beep, beep” tone will sound. On this alert signal you should prepare yourself to evacuate the building.
• If the “whoop, whoop” tone sounds, please evacuate in an orderly fashion to the assembly area indicated on the evacuation plans located in the foyer of this building. Do not use lifts when evacuating the building.
• Please also obey any instructions provided by fire wardens in attendance, by RMIT Security or by emergency services. At the end of the evacuation, you will be advised when it is safe to return to the building.
• RMIT thanks you for your cooperation.
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Why you should take this course?
• Databases are still a hot field.
• DBMS developers are in demand and there are many challenging
unsolved problems in data management and processing.
• If you are good enough to write code for a DBMS, then you can write code on almost anything else.
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Pre-requisite Courses, Assumed Knowledge and Capabilities
• Fundamental knowledge of relational database concepts, including relational modelling and design, relational algebra, SQL, and conceptual modelling, equivalent to ISYS1055/1057 Database Concepts.
• Extensive programming skills in Java language and advanced knowledge of data structures and algorithms, equivalent to COSC1285/2123 Algorithms and Analysis.
Warning: If you have NOT passed both Database Concepts AND Algorithms and Analysis (or the equivalent elsewhere in previous studies) then you should WITHDRAW from this course NOW!
The assignment work in this course assumes knowledge of database concepts and will require advanced programming skills to implement complex data structures and algorithms.
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Changing Courses
Important dates for course change in Semester 1, 2021 are:
• 8 March, 2021 – last day to add a course/s.
• 31 March, 2021 – census date. This is the last day to drop
course/s without financial penalty and closing date for refunds for
cancelled enrolments.
• 15 March, 2021- myTimetable closes for allocation/adjustment –
Last day to allocate or adjust classes (by 5pm)
• 30 April 2021 – last day to drop course/s without academic penalty
(fail grade). Financial penalty still applies.
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Course Aims
After completing COSC2406/2407, you should be able to:
• Explain the implementation of the internals of database systems
• Evaluate and compare alternative designs for data models, file
structures, index schemes, and query evaluation
• Analyse techniques used for transactions, concurrency control,
and recovery
• Design and implement significant software components of a
database system (such as file structures and index schemes)
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Course Overview
Lecture Topic
1 Introduction to course. Structured, semi-structured & unstructured data
2 Disks and files
3 Files, pages and records
4 File organisations and indexing
5 Tree index structures
6 Hashed indexes
7 Index construction and sorting
8 Query evaluation
9 Query optimisation
10 Transactions,concurrencyandrecovery
11 Informationretrieval
12 Revisionandreview
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Weekly Lectures, Tutorials and Laboratory Classes
Classes each week
– lectorials start in week 1
– tutorials and laboratory classes start in week 2
• 1 hour online Lectorial
• 1 hour online Tutorial (sign up using the online myTimetable):
it is assumed that you will have attempted the tutorial questions before each workshop, and that you will be prepared to discuss your answers.
• 1 hour Laboratory class (sign up using the online myTimetable)
• 1 hour online consultation.
• myTimetable https://mytimetable.rmit.edu.au/even/student
• If you have problems with your timetable, contact RMIT Connect https://rmit.service-now.com/connect/ online, by phone.
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Linux machines in Amazon AWS cloud
• From Week 2 onwards we will be using Linux machines in the Amazon AWS cloud as our main teaching environment
• Every student in the course will be assigned an individual machine on which to install database systems and implement database components in Java
• You will be given a key that will grant you root access to your specific machine
• You will be responsible for securely maintaining your own key and your machine
• Keys MUST NOT be shared between people
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Assessment
Assessment Tasks:
• Weekly tasks – Formative weekly online exercises (15%) to be
completed by 11:59pm on Tuesday in weeks 3 to 10 (6 of 8 best results
will be counted for final marks.)
• Assignment 1 (20%):
• on a linux VM you will evaluate and compare alternative implementations of database systems and use Java to implement a Heap File
• released in early week 2 (Monday)
• final submission due 11.59pm on Sunday 4 April 2021
• Assignment 2 (45%):
• building on your solution to Assignment 1: design, implement, critically analyse and report on the indexing component of database systems
• released in week 6
• final submission due 11.59pm on Sunday 23 May 2021
• demo and code walkthrough in week 12
• Assignment 3 (take-home 24 hour exercise in Week 14) (20%) covering all aspects of the course
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Academic integrity – avoiding plagiarism
The following resources are available online:
• Study skills and plagiarism resource booklet – advice for those beginning a program of study at the School of Computer Science and IT
• Avoiding plagiarism for code – Examples of how to reference computer code correctly
• Academic integrity workshop slides http://www1.rmit.edu.au/browse;ID=r7h4nkq7a9pg
Read more about academic integrity here:
https://www.rmit.edu.au/students/student-essentials/ rights-and-responsibilities/academic-integrity
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Successful Course Completion
• See someone immediately if you have any difficulties that may prevent you from completing the course
• Be aware of RMIT special consideration process: https://www.rmit.edu.au/students/student-essentials/ assessment-and-exams/assessment/special-consideration
• Let the lecturer know if you may be delayed in submitting an assignment or other assessable material (we will be much more sympathetic if given warning—expect a cold reception if you contact us a day or two before the due date)
• Give us feedback about tutors/lab assistants, lab sessions, tutorials and lectures
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Online course materials
Available through canvas: https://rmit.instructure.com/
• Learning Resources
• Readings for each week
• A preliminary version of the complete course notes prepared prior
to the start of the semester
• Course notes for each individual lecture (which may include some
revisions) will be made available weekly
• Tutorial questions
• Laboratory exercises
• Assessment Tasks (to appear) – Formative weekly online exercises available each Friday (starting Friday in week 3), and assignment specifications
• Announcements
• Discussion Forum – be sure to read these forums regularly and
ask questions and contribute to the discussion (but do not post
solutions to assessment tasks).
• Actions from student surveys (to appear)
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Online readings for each week
• Weekly (pre-lecture) reading material will be available online, you should read this before attempting the weekly formative weekly online exercises
• The following textbooks covers many of the topics in this course: • R. Ramakrishnan and J. Gehrke, Database Management Systems,
McGraw-Hill, 2003 (3rd Edition).
• H. Garcia-Molina, J. Ullman, and J. Widom, Database Systems:
The Complete Book, Prentice Hall, 2008 (2nd edition).
• R. Elmasri and S.B. Navathe, Database Systems, Pearson, 2011
(6th edition).
• P.J. Sadalage and M. Fowler, NoSQL Distilled: A Brief Guide to the
Emerging World of Polygot Persistence, Addison-Wesley, 2013.
• References will be given to each of these books, though we will be mostly following Ramakrishnan and Gehrke.
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Key Contacts
• Lecturer (Xiangmin Zhou)
• Building 14, Level 11, Room 04
• Consultation: 10:20-11.20am Thursdays
• email: xiangmin.zhou@rmit.edu.au
• RMIT Connect is where you access student administration and
support, plus work and study opportunities.
• Online https://rmit.service-now.com/connect/
• Phone 9925 5000 (Monday to Thursday: 9am-5pm and Friday:
10am-5pm)
• Visit Building 10, Level 4
• College Academic Student Services (Science team) support the student lifecycle
• Building 10, Level 9
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Three ways to get help with IT
• Online https://rmit.service-now.com/serviceandsupport/, if you have a problem with your AWS Linux VM make sure you include in the Subject field:
“COSC2406 – Database Systems – Student VM Issue –
• Call ITS on +61 3 992 58888 (8am to 8pm Mon-Fri, 8.30am to 4.30pm)
• Visit a walk up support area on campus, including:
• Swanston library, Building 10 Level 6 – Monday to Friday 10.00 am
to 6.00 pm
• Swanston Academic Building (SAB) – Building 80 Level 3 – Monday
to Friday 8.00 am to 8.00 pm
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Does it sometimes feel like you’re the only girl in your classes?
We know what that’s like!
Join the RMIT Society for Women in Information TeCHnology (SWITCH) and become part of a supportive and welcoming community of female students studying and interested in all things tech!
email RMITSWITCH@gmail.com to join
All female and genderqueer students including international, domestic, undergrad and postgrad students are welcome to join!
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Acknowledgements
Material in this lecture series is collected from various sources, including:
• Lecture materials from: Falk Scholer, James Thom, Hugh Williams, and Justin Zobel, and minor changed by Xiangmin (Emily) Zhou.
• Additional material from: Bodo von Billerbeck, Steffen Heinz, M.V. Ramakrishna and Santha Sumanasekara, Audrey Tam.
• Supplementary material from: Raghu Ramakrishnan and Johannes Gehrke.
• Input from other staff.
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COSC2406/2407 Database Systems
NoSQL databases
Structured, semi-structured and unstructured data
Xiangmin (Emily) Zhou
RMIT University Room : 14.11.04
Online Consultation via Collaborate Ultra(no appointment required): 10:20-11.20am Thursdays
Email : xiangmin.zhou@rmit.edu.au
Lecture 1
Reference: Pramod J. Sadalage and Martin Fowler,
NoSQL: A Brief Guide to the Emerging World of Polygot Persistence, Addison-Wesley, 2013. Chapters 1, 2, 3, and 9.
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Data models
Many different data models for database systems, including: • network
• hierarchical
• relational
• deductive
• nested relational • objected-oriented
From the 1980s, relational data model became the dominant model.
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Benefits of Relational data model
• Persistence: Database systems are all about managing persistence and using a relational database makes it easy to get at and manage small bits of persistent data within large amount of persistent data (compared with using a file system)
• Concurrency: difficult to program, relational databases provide transactions which makes it easier to get correct
• Integration: allows multiple application programs to easily share the same data
• Standard model: (well mostly standard) data model and query language (SQL)
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Impedance Mismatch
Impedance mismatch refers to difference between • relational model
• in-memory record structures
See Sadalage and Fowler, page 6, Figure 1.1.
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Interoperability: application and integration databases
With rise of web services – integration and interoperability shifts from database to web services (“service oriented architecture”), and from SQL to HTTP (either XML or JSON).
• integration database – database acts as integration mechanism for multiple applications (integrity must be built-in to database)
• application database – database acccessed only by a single application (integrity can be maintained by application programs)
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Clusters
With clusters relational databases:
• not designed to run on clusters, even though some products such as Oracle RAC or Microsoft SQL Server provide cluster solutions – but with single point of failure
• could split data with sharding, but problems with querying, referential integrity, transactions and consistency across shards
Alternative to use clusters to deal with large volumes of data and traffic:
• e.g. Amazon and Google
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Big Data
Big Data (3 key Vs):
• Volume — amount of data
• Velocity — new data/bandwidth required
• Variety — a lot of data is now unstructured (or semi-structured)
Other Vs:
• Validity — only draw correct inferences from data (correlation vs causation)
• Veracity — how to assess accuracy of (say) a Twitter feed
• Value — from a business perspective what are costs and benefits
• Visibility — technology to make data from disparate sources visible
http://rob-livingstone.com/2013/06/big-data-or-black-hole/
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NoSQL
NoSQL has no single definition or data model
• NoSQL = “not only SQL” (one common interpretation)
• refers to one current direction of databases and database technology
Two main reasons for considering NoSQL instead of relational database
• scale of data requires clusters
• reduce effort mapping between database records and in-memory data structures
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NoSQL
Polyglot persistence:
• relational databases are not the only storage option
• can consider alternative storage options provided by different types of NoSQL databases that include:
• Document Store
• Key-value Store
• Column-family Store • Graph Store
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Aggregate-oriented data models
Used in Document Stores, Key-value Stores, and Column-family Store
• relational model stores records corresponding to rows in relational table
• aggregate-orientation recognizes need to operate on data with complex structure
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Aggregate-oriented data models
JSON example, from page 16, Sadalage and Fowler:
// in customers
{
“id”:1,
“name”:”Martin”,
“billingAddress”:[{“city”:”Chicago”}]
}
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Aggregate-oriented data models
JSON example continued, from page 16, Sadalage and Fowler:
//in orders
{
“id”:99,
“customerId”:1,
“orderItems”:[
{
“productId”:27,
“price”: 32.45,
“productName”: “NoSQL Distilled”
} ],

}
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Aggregate-oriented data model
Consequences (See Section 2.1.2)
• relational database are aggregate-ignorant
• aggregates good for running on clusters
• but don’t support ACID transaction
• but do support atomic consistency of a single aggregate
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Document store
Use ID to get to aggregate data, but can also query and index content of aggregate.
Use Cases:
• event logging
• content management systems, blogging platforms • web analytics
• e-commerce applications
When not to use:
• complex transactions spanning different structures • queries against varying aggregate structures
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Key-value store
Pairs of keys (usually strings) and values (which can be strings or primitives such as integers).
Access to aggregate value is via key, but content of aggregate is “opaque” to database.
Use Cases:
• storing session information • user profiles, preferences • shopping carts
When not to use:
• relationships among data
• multi operation transactions • query by data
• set operations
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Column-family stores
Can store tabular data (especially sparse tables), e.g. Google Bigtable, Apache HBase, Cassandra.
A Bigtable cluster serves a set of tables, which are sparse, distributed persistent data with dimensions:
• Rows (unit of “transaction” consistency)
• Columns (organised in column families, usually of same type, also
unit of access control)
• Time (each cell can have multiple versions, index by timestamp)
Use Cases:
• event logging
• content management systems, blogging platforms
• webcounters
When not to use:
• ACID transactions • aggregate functions
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Graph data
• Support data represented as graph (e.g. social networking) • SPARQL
• e.g. IBM RDF GraphStore
See Secion 3.2 Use Cases:
• connected data
• routing, dispatch, location-based services • recommender systems
When not to use:
• when need to update many items • large scale
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Schemaless Databases
NoSQL databases are schemaless
• database changes are more flexible • easier to deal with non-uniform data
• but still need some form of implicit schema to write programs to access the data
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MongoDB – a document store
Compare terminology of MongoDB (with Oracle) • MongoDB instance (= database instance)
• database (= schema)
• collection (= table)
• document (=row) • _id (=rowid)
• DBRef (= join)
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References
• Pramod J. Sadalage and Martin Fowler, NoSQL: A Brief Guide to the Emerging World of Polygot Persistence, Addison-Wesley, 2013.
• NoSQL – Wikipedia
• Chang et al. “Bigtable: A distributed storage system for structured data”, ACM Transactions on Computer Systems, Vol 26, No 2, June 2008.
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