1. VICTORIA UNIVERSITY OF WELLINGTON Te Whare Wananga o te Upoko o te Ika a Maui SWEN 432 Advanced Database Design and Implementation Exam and Lecture Overview Lecturer : Dr. Pavle Mogin

2. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 1 Plan for Final Lecture Evaluation of Teaching and the Course What you may expect to be asked in the Exam What we have learned

3. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 2 Exam - General Exam questions will cover all the important topics we learned in lectures and applied in assignments 180 marks, 180 minutes There is an Appendix containing all supporting documents and some statements, methods, commands Have a look at Appendix first To make preparation, use: –Lecture Notes, –Assignments and their Model Solutions (Assignment 5 Model Solution will be published before the exam), and –Previous exams

4. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 3 Exam – Structure and Content Exam structure will follow the structure of lectures: –Cloud Databases Common features of Cloud databases Cassandra MongoDB Dynamo –XML Data Model and XML Databases Except for common theoretical questions, the content of the of the exam will follow the content of assignments: –Short description of a “real life application”: Design the database Populate the database Query the database So, it is the most important what we have learned in lectures

5. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 4 Cloud Databases Basic features –Scalability –Availability, –Shared nothing, –Data partitioning and replication, –Data versioning, –Gossip protocol Trade-offs in cloud databases –CAP theorem –BASE Range of consistency levels Availability and consistency trade-offs

6. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 5 Cloud Databases – General Features Basic features –Scalability –Availability, –Shared nothing, –Data partitioning and replication, Consistent Hashing, Master – Slave, Membership changes –Data versioning, –Gossip protocol Trade-offs in cloud databases –CAP theorem –BASE Range of consistency levels Availability and consistency trade-offs

7. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 6 Cassandra (1) Network communication: –Gossip protocol Data partitioning and replication: –Consistent hashing Versioning: –Time stamps Data model: –Column families with a CQL abstraction layer CQL: –Keyspace (column families having the same replication factor), –Tables (abstracting column families) (has non default features), –Query syntax: very close to the relational SQL, but not that powerful

8. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 7 Cassandra(2) Data modeling: –For (almost) each query a separate table (materialized view) –An accent on using physical database characteristics to achieve good performance: Counter type columns, Primary key structure ((partitioning key), clustering key) Tables sorted according to the clustering key Using leveled data compaction for frequently updated tables Consistency levels: –Spread from strong to eventual –Adjustable per a DML statement Light weight transactions: –To avoid overwrite other people’s work

9. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 8 Cassandra Cluster Manager (CCM)

10. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 9 MongoDB (1) Data Model: –Data representation by documents –Document implementation by: Embedding or Referencing – A rich query language: –Shell methods: find(), insert(), update() Simple aggregation (count(), distinct()), Pipelined Aggregation with several stages Data partitioning by sharding, Master – Slave mode of replication, No data versioning

11. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 10 MongoDB (2) No definite answer which of two data modelling technique is better –The answer depends on the database content and queries Database Querying: –The find() shell method: Two parameters: selection object and projection object Query modifiers (sort(), limit(), skip()) Issues: data redundancy and long dot separated field names (in the case of embedding) –Single aggregation methods: easy to apply, but of a limited power –Pipelined Aggregation: Several stages, each having several expressions, Complex to define, Powerful

12. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 11 XML Databases(1) eXtendible Markup Language (XML) Basics, –Well formed documents XML Meta Languages –Document Type Definition (DTD) Validation –XML Schema Identity constraints –Mapping an XML Schema into a DTD and vice versa XML Query Languages –XPath, –XQuery, and –XUpdate

13. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 12 XML Databases (2) XQuery –FLOWER expressions for and let (followed by an XPath expression) where (optional) order by (optional) return (contains exactly one element constructor) –Joins and inverted queries –Aggregates and aggregates of aggregates Aggregate functions are applied on sequences bound to variables defined by the let clause Aggregates of aggregates (e.g. max(sum($n)) ) should have the (approximate structure) structure let $n: = (FLOWER expression defining the set of strings) return {max(sum($n))} –Conditional expressions if () then () else ()

14. SWEN 432 Advanced Database Design and Implementation 2014 Final Lecture 13 XML Databases (3) Storage Techniques for XML Databases –Native XML Databases –Coexistence of XML and Relational Databases Mapping and storing XML in RDM databases Mapping DTD to a relational schema Publishing RDM databases as XML documents