Managed Query Processing within the SAP HANA Database Platform

The SAP HANA database extends the scope of traditional database engines as it supports data models beyond regular tables, e.g. text, graphs or hierarchies. Moreover, SAP HANA also provides developers with a more fine-grained control to define their database application logic, e.g. exposing specific operators which are difficult to express in SQL. Finally, the SAP HANA database implements efficient communication to dedicated client applications using more effective communication mechanisms than available with standard interfaces like JDBC or ODBC. These features of the HANA database are complemented by the extended scripting engine–an application server for server-side JavaScript applications–that is tightly integrated into the query processing and application lifecycle management. As a result, the HANA platform offers more concise models and code for working with the HANA platform and provides superior runtime performance. This paper describes how these specific capabilities of the HANA platform can be consumed and gives a holistic overview of the HANA platform starting from query modeling, to the deployment, and efficient execution. As a distinctive feature, the HANA platform integrates most steps of the application lifecycle, and thus makes sure that all relevant artifacts stay consistent whenever they are modified. The HANA platform also covers transport facilities to deploy and undeploy applications in a complex system landscape

SAP HANA Database: Data Management for Modern Business Applications

The SAP HANA database is positioned as the core of the SAP HANA Appliance to support complex business analytical processes in combination with transactionally consistent operational workloads. Within this paper, we outline the basic characteristics of the SAP HANA database, emphasizing the distinctive features that differentiate the SAP HANA database from other classical relational database management systems. On the technical side, the SAP HANA database consists of multiple data processing engines with a distributed query processing environment to provide the full spectrum of data processing -- from classical relational data supporting both row- and column-oriented physical representations in a hybrid engine, to graph and text processing for semi- and unstructured data management within the same system. From a more application-oriented perspective, we outline the specific support provided by the SAP HANA database of multiple domain-specific languages with a built-in set of natively implemented business functions. SQL -- as the lingua franca for relational database systems -- can no longer be considered to meet all requirements of modern applications, which demand the tight interaction with the data management layer. Therefore, the SAP HANA database permits the exchange of application semantics with the underlying data management platform that can be exploited to increase query expressiveness and to reduce the number of individual application-to-database round trips

A Service Oriented Framework for Analysing Social Network Activities

AbstractAnalysing and monitoring Social Networking activities raise multiple challenges for the evolution of Service Oriented Systems Engineering. This is particularly evident for event detection in social networks and, more in general, for large-scale Social Analytics, which require continuous processing of data. In this paper we present a service oriented framework exploring effective ways to leverage the opportunities coming from innovations and evolutions in computational power, storage, and infrastructures, with particular focus on modern architectures including in-memory database technology, in-database computation, massive parallel processing, Open Data Services, and scalability with multi-node clusters in Cloud. A prototype of this system was experimented in the contest of a specific kind of social event, an art exhibition of sculptures, where the system collected and analyzed in real-time the tweets issued in an entire region, including exhibition sites, and continuously updated analytical dashboards placed in one of the exhibition rooms

Generative AI-Driven for Sap Hana Analytics

During the course of a year, a large organization that utilizes a complex information technology system such as SAP ERP typically receives hundreds of thousands of requests from its help desk. It is possible to make these requests either over the phone or online through the use of Service Manager (SM) or from the Service Desk. "Enterprise resource planning" (ERP) software automates procedures pertaining to technology, services, and human resources through a network of interconnected applications. It is a form of software used for business process management. An intelligent system that can provide user assistance for SAP ERP is suggested as a solution by this research study. Consumers are able to obtain automatic responses to their support requests, which not only results in a reduction in the amount of time spent on the investigation and resolution of issues, but also increases the level of responsiveness to end users. Classifying multiclass text for the purpose of efficient query interpretation is accomplished by the system through the utilization of machine learning methods. The evidence is retrieved by the system through the utilization of a customized framework, which enables the most effective response. The capabilities of conversational artificial intelligence make it possible for the framework to construct chatbots that enable different groups of people to work together simultaneously

Efficient Transaction Processing in SAP HANA Database: The End of a Column Store Myth

The SAP HANA database is the core of SAP's new data management platform. The overall goal of the SAP HANA database is to provide a generic but powerful system for different query scenarios, both transactional and analytical, on the same data representation within a highly scalable execution environment. Within this paper, we highlight the main features that differentiate the SAP HANA database from classical relational database engines. Therefore, we outline the general architecture and design criteria of the SAP HANA in a first step. In a second step, we challenge the common belief that column store data structures are only superior in analytical workloads and not well suited for transactional workloads. We outline the concept of record life cycle management to use different storage formats for the different stages of a record. We not only discuss the general concept but also dive into some of the details of how to efficiently propagate records through their life cycle and moving database entries from write-optimized to read-optimized storage formats. In summary, the paper aims at illustrating how the SAP HANA database is able to efficiently work in analytical as well as transactional workload environments

SAP HANA Data Volume Management

Today information technology is a data-driven environment. The role of data is to empower business leaders to make decisions based on facts, trends, and statistical numbers. SAP is no exception. In modern days many companies use business suites like SAP on HANA S/4 or ERP or SAP Business Warehouse and other non-SAP applications and run those on HANA databases for faster processing. While HANA is an extremely powerful in-memory database, growing business data has an impact on the overall performance and budget of the organization. This paper presents best practices to reduce the overall data footprint of HANA databases for three use cases like SAP Business Suite on HANA, SAP Business Warehouse, and Native HANA database

O Paradigma "Code Push-Down"

A SAP é um dos maiores e mais bem-conceituados fornecedores de sistemas ERP. Tal como a maioria dos sistemas ERP, estes também têm estado em constante evolução. Desde a disponibilização da sua base de dados SAP High-Speed Analytical Appliance (HANA), a SAP tem tentado persuadir os seus clientes a adotar esta base de dados. Em 2018, a SAP anunciou que iria acabar o suporte do seu ERP, SAP ECC, favorecendo a adoção do seu novo ERP, SAP S/4 HANA, que apenas suporta o uso de bases de dados SAP HANA. O suporte estava previsto acabar em 2025, no entanto foi adiado para 2027 a pedido dos seus clientes. O fim deste suporte significa que uma porção significativa dos clientes da SAP irão migrar para o ERP SAP S/4 HANA (+ SAP HANA) e, como recomendado pela SAP, provavelmente também irão adotar o paradigma de desenvolvimento “Code Push-Down”, que se foca em empurrar lógica aplicacional para a camada/nível da base de dados. Apesar desta mudança no paradigma de desenvolvimento poder, supostamente, trazer benefícios significativos de desempenho, também pode ter consequências no que toca às outras qualidades do software desenvolvido. Este trabalho tem como objetivo analisar o paradigma de desenvolvimento “Code PushDown”, descobrir possíveis desvantagens/limitações e tentar elaborar um guião geral de como aplicar o paradigma de forma a tentar mitigá-las. E talvez, ao suceder nos seus objetivos, também incentivar a realização de mais trabalhos sobre o tema.SAP is one of the biggest and most well-established ERP system providers. Like most ERP systems, their ERP systems and surrounding ecosystems have been in constant evolution. Since the introduction of their SAP High-Speed Analytical Appliance (HANA) database, they have been pushing their clients towards its adoption. In 2018, they announced the end of support for their SAP ECC ERP in favor of the new SAP S/4 HANA ERP, which only supports SAP HANA. This end of support was to take place in 2025 but, due to requests by their customers, it has since been extended to 2027. This end of support means a significant portion of SAP’s clients are migrating to SAP S/4 HANA (+ SAP HANA) and, as recommended by SAP, will most likely also adopt their “Code PushDown” development paradigm, which is based around pushing application logic down to the database tier/layer. Although this shift in development paradigms can, supposedly, bring significant gains in performance, it may also have consequences when it comes to other qualities of the developed software. This work aims to analyze the “Code Push-Down” development paradigm, discover possible downsides/tradeoffs and try to provide general guidelines on how to apply it in order to possibly mitigate them. And perhaps, by succeeding in meeting the objectives, to incentivize further work about this topic

Implications of non-volatile memory as primary storage for database management systems

Traditional Database Management System (DBMS) software relies on hard disks for storing relational data. Hard disks are cheap, persistent, and offer huge storage capacities. However, data retrieval latency for hard disks is extremely high. To hide this latency, DRAM is used as an intermediate storage. DRAM is significantly faster than disk, but deployed in smaller capacities due to cost and power constraints, and without the necessary persistency feature that disks have. Non-Volatile Memory (NVM) is an emerging storage class technology which promises the best of both worlds. It can offer large storage capacities, due to better scaling and cost metrics than DRAM, and is non-volatile (persistent) like hard disks. At the same time, its data retrieval time is much lower than that of hard disks and it is also byte-addressable like DRAM. In this paper, we explore the implications of employing NVM as primary storage for DBMS. In other words, we investigate the modifications necessary to be applied on a traditional relational DBMS to take advantage of NVM features. As a case study, we have modified the storage engine (SE) of PostgreSQL enabling efficient use of NVM hardware. We detail the necessary changes and challenges such modifications entail and evaluate them using a comprehensive emulation platform. Results indicate that our modified SE reduces query execution time by up to 40% and 14.4% when compared to disk and NVM storage, with average reductions of 20.5% and 4.5%, respectively.The research leading to these results has received funding from the European Union’s 7th Framework Programme under grant agreement number 318633, the Ministry of Science and Technology of Spain under contract TIN2015-65316-P, and a HiPEAC collaboration grant awarded to Naveed Ul Mustafa.Peer ReviewedPostprint (author's final draft