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<\/p>\nThe positive and negative impact of Flipkart’s much publicized Big Billion Sale is a classing example of the growing activity in e-commerce. Other reported inconsistencies in the ‘100 million dollar worth sales in 10 hours’ aside, the server downtime was a major source of disappointment for the users who deserved a fair access to the purchase options on the given day.<\/p>\n
In a way, downtime is good news for any e-commerce company. It indicates tremendous success in engaging the customers and other stakeholders including vendors, back end IT teams and marketing units. This means the business is heading in the right direction. Only the performance of the IT systems has to be constantly reinforced to match the ‘difficult to predict’ user activity. Fail Safe Performance Testing has become a must for any business environment which thrives on heavy volume transactions 24\/7 or during peak seasons.<\/p>\n
It is evident that the demand for highly scalable and dependable system is increasing exponentially for IT driven verticals especially e-retail, e-Learning, healthcare etc. In addition, customers are getting less tolerant and excessively vocal on social networks by sharing poor buying experience with screenshots.<\/p>\n
When it comes to e-commerce, performance testing assumes multiple dimensions. Performance testing of such a complex system should be done in a layered approach that is both manageable and delivers comprehensive coverage. Big distributed systems can’t be fully tested on UAT environment. There are several levels of testing stretching over a range of speeds, resources, and fidelity to a production system.<\/p>\n
For example, a typical large system might consist of thousands of various servers, front-end Web applications, REST API servers, internal services, caching systems, and various databases. Such a system might processes several terabytes of data every day and its storage is measured in petabytes. It is constantly hit by countless clients and users. It is difficult to replicate all this on a UAT environment.<\/p>\n
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Testing of large scale distributed systems is hard and there is much to test beyond traditional testing methods. Performance testing, load testing, and error testing must all be undertaken with realistic usage patterns and extreme loads.<\/p>\n
Traditionally performance testing approach usually follows Identification of Key Scenarios, Setting up the Load Environment, Designing the Scripts, Generating load, Monitoring and at last Analysis and Reporting. It works for most of the system but it is completely a different ball game when conducting performance testing of large scale distributed system.<\/p>\n
Business leaders and technology stakeholders need to look at performance from a fresh perspective.<\/p>\n
The following table describes some of the characteristics of the common test scenarios associated with large scale distributed systems:<\/p>\n
Key Characteristics<\/strong><\/td>\n<\/tr>\n| High Volume<\/td>\n | \u00bb Terabyte of transactional records in database | \n\u00bb Network throughput in gigabits per second<\/td>\n<\/tr>\n High Transactions<\/td>\n | \u00bb Millions of transactions per second from end users | \n\u00bb Millions of transactions in database due to few triggers (e.g. large report generation due to batch processing)<\/td>\n<\/tr>\n High Concurrency<\/td>\n | \u00bb Huge user base accessing simultaneously (e.g. Facebook)<\/td>\n<\/tr>\n | Geographically Distributed<\/td>\n | \u00bb Traffic from all over the world<\/td>\n<\/tr>\n | High availability<\/td>\n | \u00bb Huge revenue loss and complaining customers due to unavailability<\/td>\n<\/tr>\n | Huge Data Analytics<\/td>\n | \u00bb Big data, data warehousing<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n | The following table describes performance testing solutions to address the business problems associated with a large scale distributed system:<\/p>\n
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