1. COMP9321 Web Application Engineering Semester 2, 2017
Dr. Amin Beheshti
Service Oriented Computing Group, CSE, UNSW Australia
Week 6
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2. We are Generating Vast Amounts of Data !!
Remote patient monitoring
Product sensors
Healthcare
Manufacturing …
books, music, videos, etc.
Social media
Real time location data
Retail
Digitalization of Artefacts
Location-Based Services
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3. We are Generating Vast Amounts of Data !!
Air Bus A380:
generate 10 TB every 30 min
Twitter:
Generate approximately 12 TB of data per day.
Facebook:
Facebook data grows by over 500 TB daily.
New York Stock:
Exchange 1TB of data everyday.
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4. Challenge How do we store and access this data over the web ?
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5. Challenge How do we store and access this data over the web ?
E-Commerce website
Data operations are mainly transactions (Reads and Writes)
Operations are mostly on-line
Response time should be quick but important to maintain security and reliability of transactions.
ACID properties are important
COMP9321, 17s2, Week 6

6. Challenge How do we store and access this data over the web ?
E-Commerce website
Data operations are mainly transactions (Reads and Writes)
Operations are mostly on-line
Response time should be quick but important to maintain security and reliability of transactions.
ACID properties are important
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7. Challenge How do we store and access this data over the web ?
Image serving website
Data operations are mainly fetching large files (Reads)
ACID requirements can be relaxed
Operations are mainly on-line
High bandwidth requirement
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8. Challenge How do we store and access this data over the web ?
Search Website
Data operations are mainly reading index files for answering queries (Reads)
ACID requirements can be relaxed
Index compilation is performed off-line due to the large size of source data (the entire Web)
Response times must be as fast as possible.
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9. Persistence
(Hibernate, pp.5-29)
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10. Persistence Persistence is:
“the continuance of an effect after its cause is removed”
In the context of storing data in a computer system, this means that:
“the data survives after the process with which
it was created has ended”
In other words, for a data store to be considered persistent:
“it must write to non-volatile storage”
(Hibernate, pp.5-29)
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11. Persistence
Persistence is a fundamental concept in application development.
In an object-oriented applications, persistence allows an object to outlive the process that created it.
The state of the object may be stored to disk and an object with the same state re-created at some point in the future.
Sometimes entire graphs of interconnected objects may be made persistent and later re-created in a new process.
(Hibernate, pp.5-29)
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12. Persistence Not all objects are persistent:
some (transient objects) will have a limited lifetime that is bounded by the life of the process that instantiated it.
Almost all Java applications contain a mix of persistent and transient Objects.
This means we need a subsystem that manages our persistent objects.
(Hibernate, pp.5-29)
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13. Data Persistence
(Hibernate, pp.5-29)
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14. Data Persistence
When we talk about persistence in Java, we normally mean storing data in a relational database using SQL.
Relational technology:
is a common denominator for many disparate systems and technology platforms.
provides a way of sharing data across different applications or technologies that form part of the same application.
The relational data model is often the common enterprise wide presentation of business entities.
(Hibernate, pp.5-29)
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15. Data Persistence
When you work with a relational database in a Java application, the Java code issues SQL statements to the database via the JDBC API.
The Java Database Connectivity (JDBC) API provides universal data access from the Java programming language.
Using the JDBC API, you can access virtually any data source, from relational databases to spreadsheets and flat files. 
JDBC API:
(Hibernate, pp.5-29)
COMP9321, 17s2, Week 6

16. Data Persistence
When you work with a relational database in a Java application, the Java code issues SQL statements to the database via the JDBC API.
The Java Database Connectivity (JDBC) API provides universal data access from the Java programming language.
Using the JDBC API, you can access virtually any data source, from relational databases to spreadsheets and flat files. 
JDBC API:
(Hibernate, pp.5-29)
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17. Relational Databases
(Hibernate, pp.5-29)
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18. Relational Databases
Data is stored as a collection of tuples that groups attributes e.g. (student-id, name, birthdate, courses).
Data is visualized as tables, where the tuples are the rows and the attributes form the columns.
Tables can be related to each other through specific columns.
Each row in a table has at least one unique attribute.
(Hibernate, pp.5-29)
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19. Structured Query Language (SQL)
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20. Structured Query Language (SQL)
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21. Database Concepts
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22. Database Concepts
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23. Accessing DB from an Application (JDBC)
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24. Accessing DB from an Application
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25. Java DataBase Connectivity
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26. JDBC Interfaces
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27. Typical JDBC Scenario
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28. PreparedStatement object
A more realistic case is that the same kind of SQL statement is processed over and over (rather than a static SQL statement).
In PreparedStatement, a place holder (?) will be bound to an incoming value before execution (no recompilation).
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29. Transaction Management
Committing after each update can be suboptimal in terms of performance.
It is also not suitable if you want to manage a series of operations as a logical single operation (i.e., transaction).
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30. Data Access Objects (DAO)
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31. Data Access Objects (DAO)
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32. Data Access Objects (DAO)
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33. Data Access Objects (DAO)
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34. Data Access Objects (DAO)
Example: Cars Database
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35. Data Access Objects (DAO)
Example: Cars Database
DTO (Data Transfer Object)
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36. Data Access Objects (DAO)
Example: Cars Database
DTO (Data Transfer Object) carries the actual data ...
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37. Data Access Objects (DAO)
Example: Cars Database
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38. Data Access Objects (DAO)
Example: Cars Database
COMP9321, 17s2, Week 6

39. Data Access Objects (DAO)
Example: Cars Database
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40. Data Access Objects (DAO)
Example: Cars Database
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41. Object-Relational Impedance Mismatch Problems
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42. Object-Relational Impedance Mismatch Problems
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43. Object-Relational Impedance Mismatch Problems
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44. Object-Relational Impedance Mismatch Problems
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45. Impedance (or Paradigm) Mismatch Problem
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46. Impedance (or Paradigm) Mismatch Problem
Granularity
(Hibernate, pp.5-29) The problem of granularity
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47. Impedance (or Paradigm) Mismatch Problem
Granularity
The granularity of data refers to the size in which data fields are sub-divided.
For example, a postal address can be recorded, with:
1- Coarse Granularity, as a single field:
address = 200 2nd Ave. South #358, St. Petersburg, FL USA
(Hibernate, pp.5-29) The problem of granularity
COMP9321, 17s2, Week 6

48. Impedance (or Paradigm) Mismatch Problem
Granularity
The granularity of data refers to the size in which data fields are sub-divided.
For example, a postal address can be recorded, with:
1- Coarse Granularity, as a single field:
address = 200 2nd Ave. South #358, St. Petersburg, FL USA
2- Fine Granularity, as multiple fields:
street address = 200 2nd Ave. South #358
city = St. Petersburg
postal code = FL
country = USA
(Hibernate, pp.5-29) The problem of granularity
COMP9321, 17s2, Week 6

49. Impedance (or Paradigm) Mismatch Problem
Granularity
The granularity of data refers to the size in which data fields are sub-divided.
For example, a postal address can be recorded, with:
1- Coarse Granularity, as a single field:
address = 200 2nd Ave. South #358, St. Petersburg, FL USA
2- Fine Granularity, as multiple fields:
street address = 200 2nd Ave. South #358
city = St. Petersburg
postal code = FL
country = USA
or even Finer Granularity:
street = 2nd Ave. South
address number = 200
suite/apartment number = #358
city = St. Petersburg
state = FL
postal-code = 33701
postal-code-add-on = 4313
country = USA
(Hibernate, pp.5-29) The problem of granularity
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50. Impedance (or Paradigm) Mismatch Problem
Granularity
Observation:
Classes in your OO-based model come in a range of different levels of granularity (coarse-grained entity classes like User, finer-grained classes like Address, simple String class like Postcode)
Just two levels of granularity in RDB: Tables and Columns with scalar types (i.e., not as flexible as Java type system)
Coarse grained means a single call will do more work, fine grained means it might take several calls to get the same work done.
Coarse grained is often better in distributed systems because calls between distributed components can be expensive and time consuming.
(Hibernate, pp.5-29) The problem of granularity
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51. Impedance (or Paradigm) Mismatch Problem
Subtypes
(Hibernate, pp.5-29) The problem of subtypes
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52. Impedance (or Paradigm) Mismatch Problem
Identity
(Hibernate, pp.5-29) The problem of identity
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53. Impedance (or Paradigm) Mismatch Problem
Identity
While on the subject of identity … Modern object persistence solutions recommend using surrogate key.
A surrogate key in a database is a unique identifier for either an entity in the modelled world or an object in the database.
The surrogate key is not derived from application data, unlike a natural (or business) key which is derived from application data.
(Hibernate, pp.5-29) The problem of identity
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54. Impedance (or Paradigm) Mismatch Problem
Association
(Hibernate, pp.5-29) The problem of association
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55. Impedance (or Paradigm) Mismatch Problem
Association
(Hibernate, pp.5-29) The problem of association
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56. Impedance (or Paradigm) Mismatch Problem
Object Graph Navigation
(Hibernate, pp.5-29) The problem of object graph navigation
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57. Impedance (or Paradigm) Mismatch Problem
Object Graph Navigation
Considering the following example:
(Hibernate, pp.5-29) The problem of object graph navigation
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58. Impedance (or Paradigm) Mismatch Problem
N+1 selects problem:
The N+1 query problem is a common performance issue. It looks like this:
Assuming load_cats() has an implementation that boils down to:
..and load_hats_for_cat($cat) has an implementation something like this:
..you will issue "N+1" queries when the code executes, where N is the number of cats:
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59. Impedance (or Paradigm) Mismatch Problem
The cost of mismatch problems:
The DAO pattern helps isolate the mismatch problems by separating the interfaces from implementation, but someone (usually application developers) still has to provide the implementation classes !!
(Hibernate, pp.5-29) The cost of mismatch problems
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60. Object-Relational Mapping (ORM)
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61. Object-Relational Mapping (ORM)
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62. Hibernate
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63. Hibernate
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64. Continuing with the Cars example ...
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65. Continuing with the Cars example ...
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66. To use Hibernate, you need:
Hibernate packages (hibernate*.jar)
A set of mapping (between a table and an object) les
A Hibernate configuration file (e.g., database connection details)
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67. Hibernate Example
See course material, week 6
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68. NoSQL
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69. What is NoSQL? Stands for No-SQL or Not Only SQL??
Class of non-relational data storage systems
E.g. BigTable, Dynamo, PNUTS/Sherpa, ..
Usually do not require a fixed table schema nor do they use the concept of joins
Distributed data storage systems
All NoSQL offerings relax one or more of the ACID properties (will talk about the CAP theorem)
Chapter 19: Distributed Databases
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70. RDBMS NoSQL vs Scale Up Scale Out Structured Data
Semi-/Un-Structured Data
Atomic Transactions
Eventual Consistency
Impedance Mismatch
Relaxed..
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71. CAP Theorem Three properties of a (distributed computer) system:
Consistency (all copies have same value)
Availability (system can run even if parts have failed) Via replication.
Partitions (network can break into two or more parts, each with active systems that can’t talk to other parts)
Brewer’s CAP “Theorem”: You can have at most two of these three properties for any system.
Very large systems will partition at some point.
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72. Why NoSQL?
NoSQL Data storage systems makes sense for applications that need to deal with very large semi-structured data :
e.g. Social Networking Feeds
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73. Why NoSQL? share, comment, review, crowdsource, etc.
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74. Types and examples of NoSQL databases
Column:
HBase , Accumulo, Cassandra, Druid, Vertica
Document:
MongoDB, Apache CouchDB, Clusterpoint, DocumentDB,
Key-value:
Dynamo, Aerospike, Couchbase, FairCom c-treeACE, FoundationDB, HyperDex, MemcacheDB, MUMPS, Oracle NoSQL Database, OrientDB, Redis, Riak, Berkeley DB
Graph:
Neo4J, AllegroGraph, InfiniteGraph, Giraph, MarkLogic, OrientDB, Virtuoso
Multi-model:
Alchemy Database, ArangoDB, CortexDB, FoundationDB, MarkLogic, OrientDB
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75. Types and examples of NoSQL databases
Column (Data Store):
It is a tuple (a key-value pair) consisting of three elements:
Unique name: Used to reference the column
Value: The content of the column
Timestamp: The system timestamp used to determine the valid content.
In relational databases, a column is a part of a relational table that can be seen in each row of the table. This is not the case in distributed data stores.
Example (In JSON-like notation): {
street: {name: "street", value: "1234 x street", timestamp: }, }
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76. Types and examples of NoSQL databases
Document-oriented database:
Designed for storing, retrieving, and managing document-oriented information, also known as semi-structured data.
XML databases are a subclass of document-oriented databases that are optimized to work with XML documents.
Document databases store all information for a given object in a single instance in the database, and every stored object can be different from every other.
Example:
MongoDB, a free and open-source cross-platform document-oriented database.
MongoDB avoids the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas.
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77. Types and examples of NoSQL databases
Key-value database:
A key-value store, or key-value database, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary or hash.
Dictionaries contain a collection of objects, or records, which in turn have many different fields within them, each containing data.
These records are stored and retrieved using a key that uniquely identifies the record, and is used to quickly find the data within the database.
Example:
Amazon DynamoDB, created to help address some scalability issues and is used to power parts of the Amazon Web Services, such as S3 (Simple Storage Service: provides developers and IT teams with secure, durable, highly-scalable cloud storage.).
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78. Graph Database Netflix Wiki Collaborative Filtering Social Network
User
Movie
Netflix
Collaborative Filtering
Social Network
Probabilistic Analysis
Docs
Words
Wiki
Text Analysis
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79. Graph Database Netflix Wiki Collaborative Filtering Social Network
User
Movie
Netflix
Collaborative Filtering
Social Network
…Beheshti, et al. “Large Scale Graph Processing Systems: Survey and An Experimental Evaluation”, Cluster Computing Journal, 2015
…Beheshti, et al. “On Characterizing the Performance of Distributed Graph Computation Platforms”. TPCTC Conference, 2014.
…,Beheshti S.M.R. et al. "DREAM: Distributed RDF Engine with Adaptive Query Planner and Minimal Communication", VLDB (2015)
Probabilistic Analysis
Docs
Words
Wiki
Text Analysis
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80. Graph Stores Use a graph structure Example Neo4j
Labeled, directed, attributed multi-graph
Label for each edge
Directed edges
Multiple attributes per node
Multiple edges between nodes
Relational DBs can model graphs, but an edge requires a join which is expensive
Example Neo4j
neo4j.com/
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81. Advantages of NoSQL Cheap, easy to implement
Data are replicated and can be partitioned
Easy to distribute
Don't require a schema
Can scale up and down
Quickly process large amounts of data
Relax the data consistency requirement (CAP)
Can handle web-scale data, whereas Relational DBs cannot
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82. Disadvantages of NoSQL
New and sometimes buggy
Data is generally duplicated, potential for inconsistency
No standardized schema
No standard format for queries
No standard language
Difficult to impose complicated structures
Depend on the application layer to enforce data integrity
No guarantee of support
Too many options, which one, or ones to pick
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83. More? Search NoSQL Documents:
Elasticsearch can be used to search all kinds of documents.
Elasticsearch uses Lucene (an indexing and search library) and tries to make all its features available through the JSON and Java API.
Database Service:
Dozens of new DBs! how do we choose which DB to use?
Solution:
Manage multiple database technologies and weave them together at the app layer..
Make this service accessible through a single API.
Example:
Developers will automatically have access to: a flexible key-value store that works with time-ordered events, graph relationships, and geospatial data..
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84. CoreDB: a Data Lake Service
CoreDB - an open source data lake service – offers researchers and developers a single REST API to organize, index and query their data and metadata. CoreDB manages multiple database technologies and offers a built-in design for security and tracing.
Paper: Beheshti, Benatallah, et al., “CoreDB: a Data Lake Service”, CIKM 2017
Open Source:
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85. References
(Hibernate) Hibernate In Action, Christian Bauer and Gavin King, Manning Publications
(HibernateDOC) docs/reference/en/html/
Some examples are originated from Dr. David Edmond from School of Information Systems, QUT, Brisbane and S. Sudarshan from IIT Bombay.
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86. COMP9321, 17s2, Week 6