Microservice architectures have become a popular approach for designing scalable distributed applications. Despite their extensive use in industrial settings for over a decade, there is limited understanding of the data management challenges that arise in these applications. Consequently, it has been difficult to advance data system technologies that effectively support microservice applications. To fill this gap, we present Online Marketplace, a microservice benchmark that highlights core data management challenges that existing benchmarks fail to address. These challenges include transaction processing, query processing, event processing, constraint enforcement, and data replication. We have defined criteria for various data management issues to enable proper comparison across data systems and platforms. Through case studies with state-of-the-art data platforms, we discuss the issues encountered while implementing and meeting Online Marketplace's criteria. By capturing the overhead of meeting the key data management requirements that are overlooked by existing benchmarks, we gain actionable insights into the experimental platforms. This highlights the significance of Online Marketplace in advancing future data systems to meet the needs of microservice practitioners.

The actor model has gained increasing popularity. However, it lacks support for complex state management tasks, such as enforcing foreign key constraints and ensuring data replication consistency across actors. These are crucial properties in partitioned application designs, such as microservices. To fill this gap, we start by analyzing the key impediments in state-of-the-art actor systems. We find it difficult for developers to express complex data relationships across actors and reason about the impact of state updates on performance due to opaque state management abstractions. To solve this conundrum, we develop SmSa, a novel data management layer for actor systems, allowing developers to declare data dependencies that cut across actors, including foreign keys, data replications, and other dependencies. SmSa can transparently enforce the declared dependencies, reducing the burden on developers. Furthermore, SmSa employs novel logging and concurrency control algorithms to support transactional maintenance of data dependencies.

Microservice architectures emerged as a popular architecture for designing scalable applications. This architecture promotes the decomposition of an application into independently deployable small services each encapsulating a private state [1]. Data exchanges and communication among microservices are often achieved via asynchronous events. This architecture enables practitioners to reap benefits associated with loose coupling, fault isolation, higher data availability, independent schema evolution, and increased scalability [2].

Dependency Injection (DI) is a commonly applied mechanism to decouple classes from their dependencies in order to provide higher modularization. However, bad DI practices often lead to negative consequences, such as increasing coupling. Although white literature conjectures about the existence of DI anti-patterns, there is no evidence on their practical relevance, usefulness, and generality. The objective of this study is to propose and evaluate a catalog of DI anti-patterns and associated refactorings. We reviewed existing reported DI anti-patterns in order to analyze their completeness. The limitations found in literature motivated proposing a novel catalog of 12 DI anti-patterns. We developed a tool to statically analyze the occurrence level of the candidate DI anti-patterns in both open-source and industry projects. Next, we survey practitioners to assess their perception on the relevance, usefulness, and their willingness on refactoring anti-pattern instances of the catalog. Our static code analyzer tool showed a relative recall of 92.19% and high average precision. It revealed that at least 9 different DI anti-patterns appeared frequently in the analyzed projects. Besides, our survey confirmed the perceived relevance of the catalog and developers expressed their willingness to refactor instances of anti-patterns from source code. The catalog contains DI anti-patterns that occur in practice and that are perceived as useful. Sharing it with practitioners may help them to avoid such anti-patterns, thus improving source-code quality.

Microservices have become a popular architectural style for data-driven applications, given their ability to functionally decompose an application into small and autonomous services to achieve scalability, strong isolation, and specialization of database systems to the workloads and data formats of each service. Despite the accelerating industrial adoption of this architectural style, an investigation of the state of the practice and challenges practitioners face regarding data management in microservices is lacking. To bridge this gap, we conducted a systematic literature review of representative articles reporting the adoption of microservices, we analyzed a set of popular open-source microservice applications, and we conducted an online survey to cross-validate the findings of the previous steps with the perceptions and experiences of over 120 experienced practitioners and researchers. Through this process, we were able to categorize the state of practice of data management in microservices and observe several foundational challenges that cannot be solved by software engineering practices alone, but rather require system-level support to alleviate the burden imposed on practitioners. We discuss the shortcomings of state-of-the-art database systems regarding microservices and we conclude by devising a set of features for microservice-oriented database systems.

This paper reports on the results of an exploratory case study on a large-scale Big Data systems development project in the Oil&Gas domain within a non-profit organisation. The aim of this study was to investigate the RE practices and challenges in such projects, currently bereft in the scientific literature. This investigation was focused on: (a) RE practices; (b) sources and distribution of requirements; (c) the role of Big Data characteristics and technologies in RE and systems design; and (d) RE challenges in engineering Big Data Systems. The main results show that (a) there is a lack of specific project tailored RE practices, tools, and frameworks for elicitation, specification and modelling, analysis, and prioritisation of requirements; (b) 40% of the system's requirements are considered Big Data-related from which 75% are identified from internal sources; (c) Big Data characteristics and technologies play an important role in defining quality requirements and system's architecture; (d) five challenges in eliciting, documenting, and analysing Big Data related requirements were identified and discussed. The findings suggest academics and practitioners opportunities to engage in further research in this area.

Context: Data-intensive systems, a.k.a. big data systems (BDS), are software systems that handle a large volume of data in the presence of performance quality attributes, such as scalability and availability. Before the advent of big data management systems (e.g. Cassandra) and frameworks (e.g. Spark), organizations had to cope with large data volumes with custom-tailored solutions. In particular, a decade ago, Tecgraf/PUC-Rio developed a system to monitor truck fleet in real-time and proactively detect events from the positioning data received. Over the years, the system evolved into a complex and large obsolescent code base involving a costly maintenance process. Goal: We report our experience on replacing a legacy BDS with a microservice-based event-driven system. Method: We applied action research, investigating the reasons that motivate the adoption of a microservice-based event-driven architecture, intervening to define the new architecture, and documenting the challenges and lessons learned. Results: We perceived that the resulting architecture enabled easier maintenance and faultisolation. However, the myriad of technologies and the complex data flow were perceived as drawbacks. Based on the challenges faced, we highlight opportunities to improve the design of big data reactive systems. Conclusions: We believe that our experience provides helpful takeaways for practitioners modernizing systems with data-intensive requirements.

[Context] Data is being collected at an unprecedented scale. Data sets are becoming so large and complex that traditionally engineered systems may be inadequate to deal with them. While software engineering comprises a large set of approaches to support engineering robust software systems, there is no comprehensive overview of approaches that have been proposed and/or applied in the context of engineering big data systems. [Goal] This study aims at surveying existing research on big data software engineering to unveil and characterize the development approaches and major contributions. [Method] We conducted a systematic mapping study, identifying 52 related research papers, dated from 2011 to 2016. We classified and analyzed the identified approaches, their objectives, application domains, development lifecycle phase, and type of contribution. [Results] As a result, we outline the current state of the art and gaps on employing software engineering approaches to develop big data systems. For instance, we observed that the major challenges are in the area of software architecture and that more experimentation is needed to assess the classified approaches. [Conclusion] The results of this systematic mapping provide an overview on existing approaches to support building big data systems and helps to steer future research based on the identified gaps.