NEW SQL CONTENT WHAT IS NEW SQL

1. SUBMITTED BY,
B.VINITHAMANI,
II MSC cs.
OVERVIEW OF NEW
SQL,COMPARING SQL,NOSQL AND
NEWSQL

2. NEW SQL CONTENT
qINTRODUCING NEW SQL
qARCHITECTURE
qDRAWBACK OF NEWSQL

3. Ø New SQL is a class of modern relational database
management system
Ø The same Scalable performance of NOSQL system for
Online transaction processing
Ø (read-write)workloads while still maintaining the
ACID guarantees of a traditional database system
WHAT IS NEW SQL

4. 1. New architecture : volt DB,
2. SQL engine : Toku DB , Scale DB
3. Transparent Sharing : Scale base , db shards
WHY DO WE NEED NEWSQL:
qThe same scalable performance of NOSQL for OLTP ,
and still maintaining the ACID with relations and
SQL
NEW SQL CATEGORIES

5. qRead data without blocking update
qEach transaction keeps a snapshot
qBy reading the snapshot gets a consistent view of the
database
Time
MVCC
(multi version concurrency control)
Snapshot

6.  Provide concurrency control
 Traditional relational db concurrency control
2 phase looking
 New SQL DB concurrency control
 MVCC(multi version concurrency control)
1. Optimistic concurrency control
2. Basic timestamp concurrency control
3. t/o with partition-level locking
4. And others
EX:google spanner,voltDB,memSQL
ARCHITECTURE: NEW SQL

7.  Write latency
with the concurrency control , need more the time
to make sure the data is consistent
Can use in memory mechanism to help us reduce
latency , but restricted by memory size
DRAWBACK OF NEWSQL

8.  A database trends to watch
 New SQL is ACID complaint , SQLbased , scalable ,
distributed , highly available RDBMS system
 NEWSQL database are become more demanded due
to the rice of data-oriented industries
availability
consistency
Partition
tolerance

9.  SQL Standard
 SQL Characteristics
 SQL Database Examples
SQL DATABASES

10. The following is a short, incomplete history of the SQL
Standards – ISO/IEC 9075
 1987 – Initial ISO/IEC Standard
 1989 – Referential Integrity
 1992 – SQL2
 1995 SQL/CLI (ODBC)
 1996 SQL/PSM – Procedural Language extensions
 1999 – User Defined Types
 2003 – SQL/XML
 2008 – Expansions and corrections
 2011 (or 2012) System Versioned and Application Time
Period Tables
SQL STANDARDS

11.  Data stored in columns and tables
 Relationships represented by data
 Data Manipulation Language
 Data Definition Language
 Transactions
 Abstraction from physical layer
SQL CHARECTERISTICS

12.  Applications specify what, not how
 Query optimization engine
 Physical layer can change without modifying
applications
 Create indexes to support queries
 In Memory databases
SQL- PHYSICAL LAYER
ABSTRACTION

13.  Data manipulated with Select, Insert, Update, &
Delete statements
 Select T1.Column1, T2.Column2 …
From Table1, Table2 …
Where T1.Column1 = T2.Column1 …
 Data Aggregation
 Compound statements
 Functions and Procedures
 Explicit transaction control
DATA MINING LANGUAGE(DML)

14.  Schema defined at the start
 Create Table (Column1 Datatype1, Column2 Datatype
2, …)
 Constraints to define and enforce relationships
 Primary Key
 Foreign Key
 Etc.
 Triggers to respond to Insert, Update , & Delete
 Stored Modules
 Alter …
 Drop …
 Security and Access Control
DTA DEFINATION LANGUAGE

15.  Atomic – All of the work in a transaction completes
(commit) or none of it completes
 Consistent – A transaction transforms the database
from one consistent state to another consistent state.
Consistency is defined in terms of constraints.
 Isolated – The results of any changes made during a
transaction are not visible until the transaction has
committed.
 Durable – The results of a committed transaction
survive failures
TRANSACTIONS- ACID Properties

16.  Commercial
 IBM DB2
 Oracle RDMS
 Microsoft SQL Server
 Sybase SQL Anywhere
 Open Source (with commercial options)
 MySQL
 Ingres
Significant portions of the
world’s economy use SQL databases!
SQL DATABASE Examples

17. INTRODUCTION:
q They also use non-SQL languages and
mechanism to interact with data.
q NOSQL database system arose alongside major
internet companies , such as Google , Amazon and
face book .
NO SQL DATABASE

18. Strong consistency:
All clients see the same version of data.
Highly available:
Data always available , at least one copy of there
quested data even if one of the nodes is down.
Partition –Tolerance:
The total system keeps its characteristics even
when being deployed on different servers
CHARACTERISTICS OF NOSQL
DATABASE

19. ü Large scale data processing.
ü Exploratory analytics on semi-structure data
(export level).
ü Large volume data storage.
PRIMARY UESE OF NON SQL
DATABASE

20. CLASSIFICATION OF NOSQL
DATABASES
Key value databases
Column family
databases
Graphics databases
Documents databases

22. q This DMS store items as alpha-numeric
identifiers that refers to the key. Each key has
associated values.
q This value could be simple text strings or more
complex lists and sets.
q Search only performed against keys , and limited
to exact matches
KEY VALUE DATABASES

23. KEY VALUE DATA BASES:
Car
Key Attributes
1. Make: Nisan
Model: pathfinder
Color: green
Year: 2003
2. Make: Nisan
Model: pathfinder
Color: blue
Color: green
Year:2005
Transmission: auto

24. qDesigned to manage and store documents
qThese documents are encoded in a standard data
enhance format such as XML,JSON(java script option
notation) or BSON(binary JSON)
DOCUMENT DATABASES

25. Uses of documents:
Documents databases are good for storing
and managing Big Data-Size collections of literal
documents such as text documents email
messages
Collection of data

26. q It consist of a key-value pair where the value
consist of set of columns.
q The column family databases are represented in
tables , each key-value pair being a row.
q All the related data
can be grouped as one family.
COLUMN- FAMILY DATABASES

27. Uses of column family:
Large-scale , batch-oriented data processing.
Sorting , parsing , conversion:
- Conversions between hexadecimal , binary and
decimal code values
Exploratory and predictive analytics performed expert
statistics

28. 1234
name Martin
Billing address Data
payment data
Odr 1001 Data
Odr 1002 Data
Odr 1003 Data
Odr 1004 data
Column key Column value
Row key

29. Graph database are useful when are you more interested
in relationship between data then the data itself and it
perfectly for social network.
GRAPH DATABASES

31. DIFFERENCE FOR SQL & NOSQL

32. SQL NOSQL
Relational database No-relational , distributed databases
Relational model Model-less approach
Pre- defined schema Dynamic schema for unstructured data
Uses SQL Uses Un SQL (Unstructured query
language)
Not preferred for large databases Largely preferred for large datasets
Emphasis on ACLD properties Follows browser’s CAP theorem
Excellent support from vendors Relies heavily on community support
Supports complex querying and data
keeping needs
Does not have good support for complex
querying
EX:Oracle,DB2,MySQL,MS SQL ,
PostgreSQL
EX:MonogoDB,Hbase,Cassandra,Radis,Ne
o4j,CouchDb,Couchbase,Risk Etc….

33. THANKYOU