1. New PRS in RDBMS Platform and Nget
Study of existing IT Systems of Indian Railways running in CRIS
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2. Outline of Presentation
Introduction to PRS System & NGeT
Achievements and Limitations of Present System
Network Issues related to Present System
RDBMS Platforms for New PRS
Solutions other than RDBMS
No SQL
New SQL
Conclusion
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3. Introduction to PRS System And NGeT
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4. Brief History
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5. CONCERT
CONCERT - COUNTRY-WIDE NETWORK FOR COMPUTERIZED ENHANCED RESERVATION AND TICKETING
CONCERT, a fully automated passenger reservation system
It allows passengers to do a booking from anywhere for a journey in any train in any class from anywhere to anywhere across counter.
It handles reservations, modifications, cancellation/ refunds, 39 supervisory on-line functions ,and 30 on-line enquiries.
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6. CONCERT(cont.)
It handles 265 concessions, 165 coach types and 40 types of quotas
Currently PRS network has more than 2600 POPs (points of presence i.e. booking locations) spread across the country with around 9100 POS (points of sale i.e. terminals).
Easy fare calculation and accounting
Easy Enquiry
MIS system is associated with PRS data to generate predefined MIS reports
Accounting MIS
Charting MIS
Data Warehouse
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7. CONCERT(cont.) Based on the distributed Architecture.
4 Sites (Delhi, Mumbai, Chennai, Kolkata)
RTR as Middleware for transaction routing and reliability.
Reservation based on the TDRC (Train, date, Route, Class)
ARP Rules
Different Quota & Classes
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8. CONCERT Interconnect Network
PRS APPLICATION ARCHITECTURE
IRCTC
Internet Booking
System
Indian Rail website
139
TOUCH SCREEN
Firewall
Enquiry
Charting
WEB
Server
Internet Client
Reservation
CONCERT Interconnect Network
FRONT-END SYSTEMS
BACK-END SYSTEMS
DELHI DATA CENTRE
CHENNAI DATA CENTRE
STORAGE
FRONTEND
SERVERS
BACKEND
SERVERS
MUMBAI DATA CENTRE
KOLKATA DATA CENTRE
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9. Booking Flow Fill forms Validate PNR generation Fare Calculation
Start Txn
Allocation
PNR update
Commit
Display Details
Seat /berth
Fare
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10. Proceed/Flush Proceed Flush Flush Release berth PNR Flush Proceed
Accounting
Print Ticket
yes
Non Issue
Success No
Return to Form
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11. Introduction to NGeT Next Generation e-Ticketing System
It is one client to PRS
Can book ticket from any location at any time
Easy to generate reports for NGeT transactions
Paperless ticket
Complete interface software between the IRCTC front-end server and the back end Alpha server.
Complete e-reservation and enquiries back end servers. 
Ticket printing and reset facilities in existing client.
Accounting reports for the IRCTC transactions.
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12. The Technology used PRS integration Layer & Centralised Server NGeT
OS: OpenVMS
Database: Flat File System
Application in C & Fortran language
RTR as middleware to fetch the result of enquiry
NGeT
OS: RHEL
Database: Oracle 11g
Java (JSF framework)
JRTR to communicate with PRS
GemFire as Cache
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13. Achievements of Present System
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14. Achievements of PRS System
PRS application is capable of handling 200 Booking Transaction(Tickets) Per Second per site .
Overall TPS of 800 (based on performance testing done for the system in year 2011-during implementation stage of Itanium Migration).
This is equivalent to 28.8 lakh tickets per hour. 
Number of passengers booked per day: 15 Lakhs
Transactions per second:
a. Tickets Booked per second (peak): 550
b. Enquiries served per second (peak): 5600
Point of sale: 9100
Number of Reserved Trains: 1500
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15. Transaction volume Un Reserved Reserved
Average number of tickets booked per day
75 Lakhs
10 Lakhs
Average number of passengers booked per day
200 Lakhs
15 Lakhs
Average earning per day
Rs. 50 Crores
Rs 77 Crores
Total number of terminals
10,408
9,100
Total number of booking locations
5,676
2,639
Transaction Type
Number of Transactions per day
Bookings
10 Lakhs
Cancellations
2.5 Lakhs
Enquiries
110 Lakhs
Special Transactions
0.5 Lakhs
Total
123 Lakhs
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16. MILESTONES ACHIEVED BY NGeT
Number of maximum concurrent sessions on nget.irctc.co.in was 1,33,710 on 13th August, 2014. 
Ticket Booking Statistic
Maximum booking per day on 27th August’ ,24,855
Maximum booking in on 27th August’ ,35,561
Maximum booking in on 16th August’ ,27,489
Maximum booking per minute on 27th August,'14- 10,554
Maximum booking per sec. on 18th August’
Average transaction response time for user to book ticket on NGeT excluding payment is sec.
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17. Issues
Ad-hoc query on the online database is not available in PRS system
Data is maintained in flat-file format and extraction of any information from the database in flat-file format requires development of routines to do the same.
There are peripheral systems available from which ad- hoc queries are answered to some extend (GUIDBA & Data Warehouse)
Migration of Accounting and Charting MIS on RDBMS is required to facilitate the system in providing response to all types of ad-hoc queries.
End of support for OpenVMS by HP.
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18. Network Issues Related to Present System
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19. PRS Network It Consist of Backbone network and UTN network
Backbone Network Inter-connects all PRS datacenters and CRIS datacenter with each other
UTN Network is Multi-tier hierarchical Network connected to corresponding PRS DC
Datacenters- NDLS, MAS, NKG, CSTM & Chanakyapuri
Centrally managed from NOC in CRIS, Chanakyapuri till Tier-1 Location
Implemented in 1999 for supporting PRS (CONCERT) networked transactions in year 1999
Full Mesh Topology in Backbone
UTN Network - Inverted Tree with Partial Mesh
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20. PRS Design Considerations
High Availability (>99.9% upto Tier-1, 99.8% overall)
Scalability
Lower Round Trip Time (<150 ms)
Lower routing convergence time
Route Diversity / Service Provider Diversity.
Better Manageability
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21. Backbone Network Kolkata Delhi Chennai CRIS Mumbai Secunderabad
Eastern Railway
North Eastern Railway
South Eastern Railway
Northern Railway
NKG
East Coast Railway
North Central Railway
South East Central Railway
North Western Railways
East Central Railway
North Frontier Railway
Kolkata
Delhi
Chennai
MAS
Backbone Network
RIDC
NGeT DC
Southern Railway
CRIS
South Western Railway
Mumbai
DR DC at SC
Secunderabad
Central Railway
South Central Railway
West Central Railway
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Western Railway
CSTM

22. UTN Network architecture
PRS Servers
Zonal Router
Area-1
Area-3
Area-2
Tier 2/3 Location routers
different Service Providers
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Tier-1 Locations with Routers in 1:1
High Availability (HA) Mode
Links to other locations

23. Network Failure in PRS
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24. RDBMS
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25. Relational Model Primary data model for data storage and processing.
Based on first order predicate logics.
Data is stored in format of table representing some entity.
Set of tuples(rows) representing relational instance, no duplicate row.
Columns represents attributes of the stored record .
Tables can relate each other, relation is depicted using Entity-Relational diagram.
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26. Relational Model Centre For Railway Information Systems Train Info
Date
Route
Class
12345
A-B 1
23456
C-D 2
Seat Allocation
Train
Date
Route
Class
Seat
PNR
12345
A-B 1 10
23456
C-D 2 12
Passenger Information
Passenger_Name
Age
Sex
PNR
ABCD 20 M
PQRS 22 F
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27. DBMS
DBMS- Set of programs designed to store, manage and process database.
Convenient and efficient way to store and retrieve database information.
Interface between database and application program.
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28. DBMS Architecture
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29. DBMS Characteristics Multiple Views (Data Abstraction)
Isolation of data and application (Program data independency)
Logical Data
Independence
Physical Data
Independence
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30. DBMS Characteristics Cont.
Relation-based tables
Less Redundancy
Normalization of data.
Consistency
Database transaction changes affected data only in allowed ways.
Query language
Transaction Management
Multiuser and concurrent access.
Recovery System
Security
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31. How RDBMS can serve PRS its needs
Structured Data
High Degree of organization
Readily searchable by simple SQL engines on key value.
Programming language support.
ODBC(Open Database Connectivity)
JDBC(Java Database Connectivity)
SQL (Structured Query Language)
PRS generates several type of MIS reports each one having its own program.
SQL allows to query database for the conditions specified.
No need for separate schedule creation.
Reduced development cost.
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32. How RDBMS can serve PRS its needs
Transaction Management
Transaction must satisfy ACID properties.
Database Transaction Management
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33. How RDBMS can serve PRS its needs
Transaction Management
In PRS a transaction updates the berth based on TDRC in one file and update Berth to PNR relation in another.
These two updates need to be done atomically.
Atomicity and durability are done by keeping track of each update made by transaction.
In case of failure DBMS undoes all the updates made by the transaction, thus ensuring atomicity.
In case of successful transaction DBMS commits the updates.
Database Transaction Management
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34. How RDBMS can serve PRS its needs
The commit could be done using two phase commit to ensure both commit are successful.
Database Transaction Management
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35. How RDBMS can serve PRS its needs
Isolation among transaction is handled by locking.
Locking is record level (TDRC).
Prevents other transactions from reading/updating the same record.
Releases lock after completion of two phase commit.
Consistency:  Any data written to the database must be valid according to all defined rules,.
DBMS check if X is following all rules specified.
Verifies Referential integrity constraints are satisfied.
Database Transaction Management
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36. How RDBMS can serve PRS its needs
Indexing
Access path for faster database record access.
Indexed access of record on key.
RDBMS support B/B+ tree index
Automatic Re- organizes with changes.
Time required to search is order of logpN.
Reduced read/write latency.
PRS uses RMS service in OpenVMS for record access
Database Access Methods
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37. How RDBMS can serve PRS its needs
B+ tree indexing
Database Access Methods
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38. How RDBMS can serve PRS its needs
Hashing
TDRC records are numbered for random access.
Hashing in RDBMS can provide random access to database records.
Database Access Methods
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39. Database Partitioning/Sharding
Database Sharding/Partitioning
Database Sharding can be done to improve performance when tables get large in size.
Sharding is partitioning database horizontally.
Index size reduces.
Different shards can be deployed on deferent servers.
Manage parallel access in the application
Partition tables map keys to nodes
Application decides where to route storage or lookup requests
Scales well for both reads and writes
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40. Database Partitioning/Sharding
Limitations of Database Partitioning
Not transparent
application needs to be partition-aware
Increase database complexity
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41. How RDBMS can serve PRS its needs
Database Compression
Saves disk space, thus reducing storage cost significantly.
Reduces memory use in the database buffer cache
Significantly speed query execution during reads.
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42. How RDBMS can serve PRS its needs
Security mechanism
Identification and Authentication
Authorization and Access Control
Encryption
Auditing
Role-Based Access Control for Multilevel Security
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43. Limitations of RDBMS Scalability Parallelism Performance
The major constraint with RDBMS is that relational databases are not largely scalable.
Parallelism
Not capable of providing high level of parallelism.
Increasing number of nodes increases database complexity to unmanageable levels.
Performance
If size of tables is large tables themselves effect performance in responding to SQL queries.
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44. Different RDBMS Solutions and Their Performance Comparison
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45. Base of different solutions
Each RDBMS have different features like
Interface (GUI, SQL, others)
Language Support (C, C#,C++, Java, Ruby, Objective C, and many more)
Operating Systems (Windows, Linux, Solaris, HP-UX, OS X, z/OS, AIX, FreeBSD, Solaris)
Licensing (Proprietary, Open source, Proprietary)
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46. Some Promising RDBMSs Oracle MySQL MemSQL SQLServer Postgre SQL DB2
Maintainer
First Public Release Date
Latest Stable Version
Latest Release Date
License
Oracle
Oracle Corporation
12cRelease 1
Proprietary
MySQL
5.6.26
GPL v2 or Proprietary
MemSQL
1.8
SQLServer
Microsoft
1989
2014
Postgre SQL
PostgreSQL Global Developmnet Group
9.4.3
PostgreSQL License
DB2
IBM
1983
10.5
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47. MySQL
MySQL is the most popular one of all the large-scale database servers
Feature rich
Stand-alone database server
open-source
Powers a lot of web-sites and applications online.
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48. MySQL Advantage Disadvantage Easy to work with Feature Rich Secure
Scalable and Powerful
Speedy
Known Limitations
Reliability Issues
Stagnated Development
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49. MySQL When to use When not to use Distributed Operations High Security
Web-sites and Web Applications
Custom solutions
SQL compliances
Concurrency
Lack of Features
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50. PostgreSQL Advanced, open-source [object]-RDBMS
the main goal of being standards-compliant and extensible
tries to adopt the ANSI/ISO SQL standards together with the revisions.
it support highly required and integral object-oriented and/or relational database functionality, such as the complete support for reliable transactions, i.e. ACID.
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51. PostgreSQL extremely capable of handling many tasks very efficiently
Support for concurrency is achieved without read locks thanks to the implementation of Multi version Concurrency Control (MVCC), which also ensures the ACID compliance.
highly programmable, and therefore extendible, with custom procedures that are called "stored procedures".
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52. PostgreSQL Disadvantage Advantage
An open-source SQL standard compliant RDBMS
Strong community
Strong third-party support
Extensible
Objective
Performance
Popularity
Hosting
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53. PostgreSQL When to use When not to use Data integrity
Complex, custom procedures
Integration
Complex designs
Speed
Simple set ups
Replication
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54. Operating System Support
Windows
OS X
Linux
BSD
UNIX
Oracle
Yes No
MySQL
memSQL
SQL Server
PostgreSQL
DB2
YEs NO
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55. Fundamental Features ACID Referential Integrity Transactions
Fine-grained Locking
Unicode
Interface
Oracle
Yes
Yes except for DDL
Yes(Row-Level locking)
API & GUI & SQL
MySQL
Yes-except for DDL
Yes (Row-Level Locking)
GUI & SQL
SQL Server
PostgreSQL
GUI & SQL & API
DB2
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56. Benchmarking results
According to a benchmarking of PostgreSQL 8.3 and Oracle 10g the PostgreSQL handled roughly 16,000 transactions per second
Latency incurred was 0.9ms at 95th percentile, and Oracle was comparable (Oracle licensing does not permit disclosing benchmark results).
The benchmarking was done on a pair of HP blade servers with 2x 2.4GHz quad-core Opterons and 8GB each, and one 320GB FusionIO PCIe SSD module each.
The transactions comprised of read-write operations with roughly around 10 queries per transaction.
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57. Benchmarking results
The test was made using Grinder, a multithreaded Java load-testing framework on a machine as powerful as the DB servers, with about 200 simultaneous connections.
Our current PRS system handles about 500 to 800 transactions per second ( around 200 per site).
Moreover the performance of the application depends on many other factors beside the Database engine.
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58. Solutions Other Than RDBMS
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59. Centre For Railway Information Systems

60. NoSQL means Not Only SQL
Implying that when designing a software solution or product, there are more than one storage mechanism that could be used based on the needs.
Not using the relational model
Running well on clusters
Mostly open-source
Built for the 21st century web estates
Schema-less
No fixed schema (formally described structure)
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61. NoSQL means Not Only SQL(Contd.)
No joins (typical in databases operated with SQL)
Expensive operation for combining records from two or more tables into one set
Joins require strong consistency and fixed schemas
Database Scaling
RDBMS are "scaled up" by adding hardware processing power
NoSQL is "scaled out" by spreading the load
Partitioning (sharding) / replication
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62. NoSQL in Present world Google (BigTable, LevelDB) LinkedIn (Voldemort)
Facebook (Cassandra)
Twitter (Hadoop/Hbase, FlockDB,
Cassandra)
Netflix (SimpleDB, Hadoop/HBase,
CERN (CouchDB)
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63. Features of NoSQL Elastic scaling Bigger Data Handling Capability
Maintaining NoSQL Servers is Cheaper
Lesser Server Cost
No Schema or Fixed Data model
Integrated Caching Facility
To improve programmer productivity by using a database that better matches an application's needs.
To improve data access performance via some combination of handling larger data volumes, reducing latency, and improving throughput.
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64. Comparison between sql and nosql
S.No
RDBMS
Nosql 1
Table based 
Document based, key-value pairs, graph databases  2
Predefined schema
Dynamic schema for unstructured data 3
Vertically scalable
Horizontally scalable 4
Structured query language
Unstructured Query Language 5
We can classify SQL databases as either open-source or close-sourced from commercial vendors.
NoSQL databases can be classified on the basis of way of storing data as graph databases, key-value store databases, document store databases, column store database and XML databases. 6
Not best fit for hierarchical data storage
 Fits better for the hierarchical data storage 7
 Excellent support are available
Rely on community support 8
 Emphasizes on ACID properties
 Emphasizes CAP theorem 9
Exampe-MySql, Oracle, Sqlite, Postgres .
Example-MongoDB, BigTable, Redis, RavenDb, Cassandra, Hbase, Neo4j and CouchDb.
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65. Some graphical comparision
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66. Performance
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67. Insert operation
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68. Types of NoSQL Databases:
Key-Value databases
Document databases
Column family stores
Graph Databases
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69. Key-Value databases
 Examples: Riak, Redis , Memcached and its flavors, Berkeley DB, HamsterDB (especially suited for embedded use), Amazon DynamoDB (not open-source), Project Voldemort and Couchbase.
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70. Document databases
 Examples: MongoDB, CouchDB , Terrastore, OrientDB, RavenDB,
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71. Column family stores
 Examples: Cassandra, HBase, Hypertable, and Amazon DynamoDB
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72. Graph Databases Examples: Neo4J, Infinite Graph, OrientDB, or FlockDB
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73. Analytics and business intelligence
Challenges of NoSQL
Maturity
Support
Analytics and business intelligence
Administration
Expertise
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74. NoSQL - Conclusion
NoSQL databases are becoming an increasingly important part of the database landscape, and when used appropriately, can offer real benefits.
However, enterprises should proceed with caution with full awareness of the legitimate limitations and issues that are associated with these databases
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75. Centre For Railway Information Systems

76. New SQL
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
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77. New SQL Categories
NewSQL systems can be loosely grouped into three categories: 
New architectures
The first type of NewSQL systems are completely new database platforms. These are designed to operate in a distributed cluster of shared-nothing nodes, in which each node owns a subset of the data. These databases are often written from scratch with a distributed architecture in mind, and include components such as distributed concurrency control, flow control, and distributed query processing. Example systems in this category are Google Spanner, Clustrix, VoltDB, MemSQL,Pivotal's SQLFire and GemFire XD, SAP HANA, FoundationDB, NuoDB, and Trafodion.
SQL engines
The second category are highly optimized storage engines for SQL. These systems provide the same programming interface as SQL, but scale better than built-in engines, such as InnoDB. Examples of these new storage engines include MySQL Cluster, Infobright, TokuDB and the now defunct InfiniDB.
Transparent sharding
These systems provide a sharding middleware layer to automatically split databases across multiple nodes. Examples of this type of system includes dbShards andScaleBase.
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78. Comparison between three Database strategy
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79. A typical NewSQL variant:
MemSQL is a high-performance, in-memory database that combines the horizontal scalability of distributed systems with the familiarity of SQL.
In-Memory Performance
Using memory, MemSQL concurrently reads and writes data on a distributed system, enabling access to billions of records in seconds. MemSQL also includes a disk-based column store.
Horizontal Scalability
By horizontally scaling on commodity hardware, MemSQL is easy to set-up, maintain and scale either on premises or in the cloud—reducing both your up-front investment and long-term maintenance costs.
Advanced SQL Analytics
Anything that can be expressed in SQL statements becomes available as quickly as data is captured. This enables easy integration with existing applications without costly query rewrites or custom connectors.
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80. Features : Centre For Railway Information Systems ANSI SQL Support
Multi-Statement Transactions
Compiled Queries
Fully-distributed JOINs
Full Durability to Disk
In-Memory Tables
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81. Features : (cont...) On-Disk Tables Massively Parallel Execution
Lock Free Data Structures
Geospatial Support
JSON Support
Cluster Management
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82. Centre For Railway Information Systems

83. Conclusion
Present PRS System(CONCERT) is very well designed and comprehensively developed for it’s problem requirements.
It’s upgradation is not a very urgent need today but in order to keep its technology up to date and to cater Indian Railways passenger quality service with faster, smarter, better software solution, it might be redesigned using one or more of these Next Generation Database solutions which suits it more.
However this upgradation may help to improve performance for communicating with RDBMS based PRS wings NGeT and DW.
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84. Centre For Railway Information Systems