3. Read the following case study and answer the questions that follow:
Indian Railways: Distributed In-Memory Data Management Solution Improves the Capacity and Availability of New E-Ticketing System
Founded in 1853, Indian Railways is the world’s second-largest railway network with nearly 7,000 stations and 72,000 miles of track. Every day, the railway carries over 23 million passengers on more than 12,000 trains. Passengers have always been able to purchase reserved tickets at railway stations—often waiting in long lines. In 2002, The Indian Rail Catering and Tourism Corporation (IRCTC), designed an Internet-based ticketing system. Initially, tickets were delivered by courier to customers and in they moved to a completely web-based e-ticketing system where tickets could be printed using a web-based application. In the beginning, the e-ticketing system generated around 100 e-tickets a day, but over the years demand has grown to where the system generates approximately 500,000 tickets daily.
While the e-ticketing system definitely made it easier to buy reserved tickets, the experience was far from seamless. Indian Railways’ unique Tatkal ticketing system offers the ability to book last-minute trips one day before the date of travel starting at 10 a.m. In the past, the e-ticketing system would often crash at 10 a.m. because of Tatkal ticket demand, but the system experienced issues any time there were more than 40,000 concurrent users logged on to purchase tickets. Even when it didn’t crash, the system was so slow that it would take customers up to 15 minutes to buy a ticket—and often the system would time out and not allow customers to finish their purchases.
Solution
CRIS experts had already determined that simply adding new hardware would not solve performance issues. They designed a completely new application but realized they also needed to incorporate technology that would enable the new e-ticketing system to manage huge concurrent workloads, migrate 3 million users and provide dynamic load balancing to seamlessly manage demand at peak hours.
After evaluating a variety of options, CRIS IT leaders chose to base their application on Pivotal GemFire, a distributed in-memory database which is part of Pivotal Big Data Suite. The CRIS team designed their new e-ticketing system around the shared-nothing distributed architecture of GemFire to improve load balancing at the web and application tiers.
CRIS initiated a pilot of the new e-ticketing system in June 2014, successfully migrated all 3 million users and then launched the system officially on July 2014.
CRIS uses Pivotal GemFire to support high concurrent transactions for reservations and ticket purchases in the newly launched Indian Railways next-generation e-ticketing system. The scalability and resilience features in GemFire have helped to increase the capacity and availability of the service.
Benefits
In the past, the old system would crash if more than 40,000 users tried to access the system at one time. Now, thanks to the ability of GemFire to manage data in memory across a cluster of nodes, the new system can easily scale to more than 200,000 concurrent users without impacting performance—even at high demand times.
Due to performance issues, Indian Railways used to only be able to sell a maximum of 2,000 tickets every minute. Today, because of dynamic load balancing and improved performance, the system can manage surges in sales of more than 10,000 per minute, such as during holiday periods such as Diwali. And on an hourly basis, average online sales are now nearly two and a half times higher than they used to be, growing from 60,000 per hour in online ticket sales to 150,000 per hour.
Source: https://content.pivotal.io/case-studies/indian-railways
a) One example of a capacity
b) One example of availability management
c) One example of capacity management
d) One example of an availability issue or an availability factor
e) One example of the cost of unavailability of the old e-ticketing system