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The emergence of intelligent devices, mobility, social media, pervasive networking and analytics has created a digital economy that is fundamentally altering the traditional relationships amongst companies, partners and consumers. Higher growth of internal and external data sources has resulted in a dramatic rise in volume, velocity and variety of unstructured data, often referred to as Big Data. Processing this data effectively and quickly followed by distilling meaningful insights is critical for growth and leadership of companies.

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Capitalizing on the New Era of In-memory Computing

- Girish Khanzode

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With accelerating market dynamics, the window of decision making is narrowing. Users need split-second, correct answers to almost any question, using any data from anywhere. A rapid and analytics-driven response has become crucial when faced with internal and external events. Owing to technological innovation and changing consumer aspirations, managers cannot rely solely on experience or gut feel to arrive at the right decisions. Instead, they require real-time visibility into complete transactional data and analytics processing to take effective measures.

Business users expect to rely less on IT resources. There is increasing pressure to reduce IT costs. Customers now take for granted a satisfying end-user experience coupled with sophisticated self-service offerings.

Increasing unstructured data is also diminishing the effectiveness of traditional forecasting models. Online reviews and discussions on products now create a far deeper impact on revenues and product lifecycles than actual point-of-sale data.

In order to take on these new challenges, data must be analyzed within expected time windows for relevant decisions and actions. Consequently, new techniques of data processing have become extremely critical.

As predicted by Moore’s Law, memory prices continue to slide coupled with a rise in the number of processors on the chip. DRAM prices are dropping by half every couple of years. Despite these favorable advancements, the single point of latency still continues to be the hard disk even if we consider fast access disks like solid state hard drives (SSD). To overcome this bottleneck, next-generation servers are increasingly relying on directly accessing memory to service I/O requests that were earlier handled by primary storage. Multi-core CPUs are enabling high speed large-scale parallel processing of in-memory data. These factors are propelling in-memory computing since all the data that needs to be processed can now be stored in memory itself.

In-memory computing can process massive quantities of real-time events and data in the main memory of the server within seconds to detect correlations and patterns, displaying emerging opportunities and risks, thus expediting informed business decisions.

This new computing model has the potential to deeply impact existing IT processes and dramatically shorten batch process execution time, which earlier took days or weeks. It facilitates hosting of both transactional and analytical applications on a single server, requiring smaller disk storage capacities and fewer databases, contributing to IT cost savings. In-memory computing is also extremely effective when handling unstructured data like social media, video or machine data.

T H E R I S E O F I N - M E M O R Y C O M P U T I N G E R A

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AdvantagesIn-memory computing holds immense potential benefits for data processing. Extremely fast computational speeds ensure that results are up-to-date and hence more accurate. It enables analysis of more granular data which would otherwise be obscured in aggregation. IT costs and complexities are lowered when managing heterogeneous data types and large volumes. Data access speed becomes lightning fast since movement to main memory from disk storage is avoided, resulting in negligible data latency.

Transactional operations become significantly faster since an in-memory database requires 5 nanoseconds to access memory compared to 5 milliseconds for a disk read operation by a traditional database, making it a million times faster. Data loading from disk-based storage is circumvented, thus simplifying database structures and reducing software complexity while also requiring less CPU computing power. It is estimated that a typical database management system based on an in-memory model is around 15 times faster than the traditional on-disk one.

In-memory computing enables parallel processing in the database layer instead of the application layer, resulting in faster execution. Combining analytics and operations capabilities in a single system coupled with reduction in redundant data storage results in approximately 30% lower costs due to flattened IT infrastructure comprising leaner hardware and smaller systems. IT software support costs are lowered due to reduced extract, transform, and load (ETL) processes between systems. In addition, on-the-fly aggregation eliminates the need for manual data query tuning and data aggregation efforts.

Companies want to micro-segment customers, requiring the analysis of granular data as well as the factors that influence buying patterns, in order to improve marketing efficacy. However, traditional data warehousing methods focus on data aggregation and preparation prior to the data analysis process. The resulting loss in data granularity due to anticipated data usage assumptions made before preparation can result in ineffective decisions and missing patterns.

Relational databases must maintain up-to-date data maps hosted on disks, find the right volume when executing each element of a query, and also update the results in the system, necessitating storage tuning by database administrators to maintain optimal system levels. Keeping data in memory eliminates all these costly efforts and allows software to easily scale as demand and data volume rises.

In-memory databases do not have overheads of disk management like on-disk databases. Format changes are performed in memory without data restructuring and data need not be reallocated as it grows. Negligible I/O waiting time inside in-memory databases greatly improves and smoothens data throughput. With time, businesses experience growth in data volumes accompanied by increasing demand for timely access and analysis of the same. The in-memory approach becomes a superior alternative to disk-based systems in addressing these needs.

Big Data metadata is known for multi-dimensionality, flat-schemas, collections, derivation and recursion. This creates new challenges in traditional relational database environments, resulting in complex SQL joins and schemas that are difficult to process inside software program code or stored procedures. These challenges are mitigated in the in-memory approach with use of NoSQL processing techniques.

Relational databases store table rows as blocks on the disk along with indexed columns. In-memory databases, instead, operate on data grouped into columns that require fewer I/O operations. Columnar data is of the same type and can be easily and efficiently compressed, which makes it a highly optimal alternative for analytical workloads. This approach avoids data searches involving scanning of index entries and navigation of indirect references to find the disk pages where the data resides. In-memory databases scan columns and jump memory pointers to prepare the desired data. Thus, queries run significantly faster than on disk-based relational databases even though they use optimization techniques like caching of rows in memory after reading from the disk.

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ChallengesReal-time data access can increase its vulnerability or can introduce new risks, especially in the financial domain. For example, a stock trader may execute trades in real-time before a compliance system can kick in. Or a production analysis report that is used to make a decision might provide completely different conclusions when prepared again a few minutes later. Companies will need to set up different sets of policies for real-time analytical reporting involving what-if scenarios and for time-logged compliant production reports.

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Application AreasFollowing are some of the potential areas that can benefit immensely from in-memory technologies:

Personalized Incentives

Point-of-sale retailers and loyalty program distributors can offer more attractive real-time discounts to long-term customers based on their individual purchase history. Analytics of this nature requires a large amount of data mining with quick results. E-commerce sites can leverage in-memory databases and parallel processing algorithms to handle a far greater load with improved site response performance to enhance customer satisfaction and lure new customers, resulting in higher revenues.

Optimized Pricing

Large retail chains can advertise the most competitive rates for their goods by closely monitoring their inventory and carefully tracking consumption patterns and trends, to be able to optimally order items from suppliers. This process can be further streamlined by tracking truck routes and traffic data in real-time in order to meet dynamic demand. Software systems for this level of sophistication require absolutely latest data and the capability to process it in real-time in order to offer products at the lowest cost while avoiding overstocking or running out of fast moving goods.

High Frequency Transactions

Trading firms deploy sophisticated high-frequency trading platforms that automatically and swiftly make buy and sell decisions that are driven by price pattern changes in order to survive and make profits. A delay of even a few microseconds can degrade these systems’ performance. Analytical algorithms must scan long-term price points of hundreds of stocks to identify promising long/short selling stock

candidates and then execute these trades in seconds. If data is fetched from disks using traditional techniques, the system might give losses due to time delays. In-memory systems offer the speed and scalability critical to succeed in these operations.

Next Generation Analytics

In order to improve quality of decisions, data has to be processed at a higher frequency. Using traditional analytics, mining of terabytes of data will require days to identify useful results and trends. In-memory analytics can perform the same operation in hours, even minutes, making in-memory technology an integral part of high-performance analytics in future. Speedy execution will bring huge competitive advantage to businesses that need to make correct and rapid decisions driven by the latest trends.

Risk Management

Fraud detection is critical for companies engaged in high-value financial transactions. It is imperative to raise an alert when a transaction is about to happen, as delays could prove disastrous. Since detection algorithms must process terabytes of financial transactions in a few seconds, in-memory systems become essential.

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ConclusionIn-memory computing is all set to turn mainstream due to the host of benefits it offers and supportive factors, such as dropping memory prices, availability of more computing power and the growing need to leverage Big Data that requires new methods of processing unstructured information. Companies should use in-memory techniques while developing new analytics applications to take advantages of them and also consider re-engineering legacy systems to prepare them for new world of data, reduce complexity, improve scalability and speed.

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About the Author

Girish Khanzode Products & Platforms Innovator for Futuristic Technologies, Infosys Labs

Girish is a veteran in Enterprise Software Product design and development with more than 20 years of professional experience. He has built and led large product engineering teams to deliver highly complex products in multiple domains, covering entire product life cycle. Currently, he is engaged in innovating and building the next generation products and platforms in emerging new technology areas like Enterprise Data Security and Privacy, Collaboration technologies, Digital Workplace, Social Analytics, Smart Cities, Big Data and Internet of Things. Girish holds M. Tech. degree in Computer Engineering and a bachelor’s degree in Electrical Engineering.

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