The document discusses the evolution and challenges of modern database systems, focusing on relational models, ACID rules, distributed systems, and the CAP theorem. It also covers NoSQL databases, their advantages, limitations, and the emergence of NewSQL, which seeks to combine scalability with ACID guarantees. Key types of databases, including key-value, column-based, document-based, and graph-based stores, are outlined along with considerations for choosing the appropriate type based on data consistency and scalability needs.

1. Modern Databases
AND ITS CHALLENGES

2. Outline
The objective of databases
The Relational Model and ACID rules
The Distributed Systems and CAP Theorem
The NoSQL Databases
The NewSQL Databases

3. Why need databases
Availability : data are made available to wide variety of users
Integrity : the data available in the database is a reliable data
Security : only authorized users can access the data
Independence : users deal with data in efficient manner
, “abstract view” of how the data is stored in the database.

4. The Relational model
The current traditional Databases such as (SQL Server,
Oracle, MySQL …. etc.).
The Idea of relational model come at 1970
Is very stable architecture
The most popular model for storing data in the web and
business applications till now
has fixed structure for database schema
Relational model databases depend on Transactions and it’s
ACID rules

5. ACID Rules
Atomic : A transaction is a logical unit of work which must be
either completed with all of its data modifications, or none of
them is performed.
Consistent : At the end of the transaction, all data must be left in
a consistent state.
Isolated : Modifications of data performed by a transaction must
be independent of another transaction. Unless this happens, the
outcome of a transaction may be erroneous.
Durable : When the transaction is completed, effects of the
modifications performed by the transaction must be permanent
in the system.

6. The problem with Relational Model
Some datatypes not suitable for traditional
database (Graph , unstructured)
Scalability problem : and it’s Cost
Agility challenges that face modern applications
(Schema Free)

7. Distributed Systems
A distributed system consists of multiple computers and
software components that communicate through a
computer network
A distributed system can consist of any number of possible
configurations, such as mainframes, workstations, personal
computers, and so on.
The computers interact with each other and share the
resources of the system to achieve a common goal.

8. CAP Theorem
CAP theorem states that there are three basic requirements which exist in a special relation
when designing applications for a distributed architecture
Consistency - This means that the data in the database remains consistent after the execution of
an operation. For example after an update operation all clients see the same data.
Availability - This means that the system is always on (Every request receives response), no
downtime.
Partition Tolerance - This means that the system continues to function even the communication
among the servers is unreliable (nodes are up, but can't communicate)
Ex: if the network stops delivering messages between two sets of servers, will the system
continue to work correctly?

9. CAP Theorem
In theoretically it is impossible to fulfill all 3
requirements
Relational Databases achieve
◦ Availability
Every request receives a (non-error) response
◦ Consistency
All clients always have the same view of data
◦ No Partition Tolerance

10. What is NoSQL
non-relational database management systems
It is designed for distributed data stores where
very large scale of data storing needs
Scale Horizontally
Schema Free

11. Why NoSQL
The huge contents generated every time by
humans, devices, machines… etc.
Certain types of data
Agility challenges that face modern
applications

12. History of NoSQL
The term NoSQL was coined by Carlo Strozzi in the year 1998. He
used this term to name his database which did not have an SQL
interface.
In the early 2009, in an event on open-source distributed
databases the term reused to refer databases which are non-
relational, distributed, and does not conform to ACID.
In the same year, the "no:sql(east)" conference held in Atlanta,
USA, NoSQL was discussed.
And then, discussion and practice of NoSQL got a momentum,
and NoSQL saw an unprecedented growth.

13. NoSQL Consistency
According to CAP Theorem it is impossible to fulfill all 3
requirements
NoSQL Databases achieves
◦ No Consistency
may some clients have different views of data
◦ Availability
all nodes are always in contact
◦ Partition Tolerance
system continues to function even the communication among the
servers is unreliable

14. NoSQL Consistency
In 2007, Amazon discovered that every 100ms of latency on the Amazon website cost 1% in
sales. At the time their annual sales were around $14.7 billion. And 1% of $14.7 billion is a lot of
sales to lose.
they outlined an approach for a new kind of database. One that guaranteed Availability and
Partition tolerance at the expense of Consistency.
They rely on Eventual Consistency, where data would be consistent in the end (after some of
time).
For a bank where transactions have to be consistent, that just wouldn’t work. For companies
like Google, it’s acceptable.

15. Types of NoSQL Databases
Key-Value Store
It has a Big Hash Table of keys & values {Riak,
Amazon S3 (Dynamo)}
Column-based Store
Each storage block contains data from only one
column, {HBase, Cassandra}
Document-based Store
It stores documents made up of tagged elements.
{CouchDB, MongoDB}
Graph-based
A network database that uses edges and nodes to
represent and store data. {Neo4J}

16. Advantages and Disadvantages
Advantages Disadvantages
High scalability No standardization
Distributed Computing Limited query capabilities (so far)
Lower cost Eventual consistent
Schema flexibility, semi-structure data Less support and tools compared to
relational databases
No complicated Relationships
Very Easy software development

17. Example of NoSQL (MongoDB , C#)
1 2
3

18. Example of NoSQL (MongoDB)
Mongo Query
•db.Student.find({"FirstName":"Mohamed"})
SQL Query
•Select * From Student Where
FirstName=‘Mohamed’

19. The NewSQL
is a class of modern relational
database management systems that
seek to provide
the same scalable performance of
NoSQL systems for online
transaction processing (OLTP) read-
write workloads while still
maintaining the ACID guarantees of
a traditional database system.
How?
◦ Minimize the Locking
◦ Avoid Table or Row level locking
◦ Heavily depend on Memory

20. NuoDB Architecture
Transaction Engines (TEs): The transaction processing layer is
made up of in-memory process nodes that coordinate with
each other and the storage management layer. Nodes can be
easily added or removed to align with transaction volume.
Storage Managers (SMs): The storage management layer consists
of process nodes that have both in-memory and on-disk storage
components. SMs provide on-disk data durability guarantees, and
multiple SMs can be used to increase data redundancy.
Multi-version
concurrency
Management
Tire

21. How to choose
It depends
◦ The degree of consistency and integrity
◦ The scalability you need
◦ The complexity of data model
◦ the query complexity
◦ Your team knowledge

22. How about hybrid
SQL
NoSQL
Integration
Application

23. References
https://www.red-gate.com/simple-talk/opinion/opinion-pieces/does-nosql--
nodba/?utm_source=simpletalk&utm_medium=weblink&utm_content=sombrero&utm_campai
gn=magazine
https://www.w3resource.com/mongodb/nosql.php
https://www.mongodb.com/nosql-explained?jmp=footer
https://en.wikipedia.org/wiki/NewSQL
https://en.wikipedia.org/wiki/CAP_theorem
http://galaldev.blogspot.com.eg/2017/02/sql-server-transaction.html