A Survey on the Evolution of Stream Processing Systems (2008.00842v2)

Abstract: Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and numerous worldwide open-source communities. This survey provides a comprehensive overview of fundamental aspects of stream processing systems and their evolution in the functional areas of out-of-order data management, state management, fault tolerance, high availability, load management, elasticity, and reconfiguration. We review noteworthy past research findings, outline the similarities and differences between early ('00-'10) and modern ('11-'22) streaming systems, and discuss recent trends and open problems.

• The paper offers a comprehensive timeline of stream processing evolution, detailing the shift from first-generation to second-generation systems.
• It analyzes critical advancements in managing data order, state persistence, fault tolerance, and elasticity in distributed architectures.
• The study emphasizes practical implications for real-time analytics and outlines future research directions integrating machine learning and modern cloud systems.

A Survey on the Evolution of Stream Processing Systems

The research paper "A Survey on the Evolution of Stream Processing Systems" offers a comprehensive review of the evolution of stream processing technologies over the past two decades, categorized into "first-generation" and "second-generation" systems. The paper identifies key trends, challenges, and advancements in the domains of out-of-order data management, state management, fault tolerance, high availability, load management, elasticity, and reconfiguration that underpin these systems.

Historical Context and Evolution

The paper provides a historical account of the development of stream processing, tracing back to prototype systems such as Tapestry and Aurora, which introduced the foundational concepts of streaming queries and continuous data processing. These initial systems were oriented mainly towards scale-up architectures, focusing on ordered event processing with an intent to provide approximate results.

With the advent of MapReduce and the rise of cloud computing, there was a paradigm shift that led to the emergence of second-generation systems designed for distributed, data-parallel processing. These systems—such as Apache Flink, Apache Storm, and Spark Streaming—emphasize scale-out architectures on commodity hardware, enabling massive parallel processing of unbounded and unordered data streams.

Key Developments and System Features

1. Data Order and Timeliness: The paper describes the shift from in-order processing systems, which required buffering and reordering of tuples, to out-of-order systems that instead adopt low-watermarks and other mechanisms to manage disorder without the need for reordering. This transition is crucial for high-throughput, low-latency processing required by modern applications.
2. State Management: The management of state has transformed from in-memory summaries in early systems to robust, distributed state management in modern systems. Apache Flink, for example, allows for persistent state stored in efficient on-disk data structures, offering exactly-once state semantics amidst failures.
3. Fault Tolerance and High Availability: The paper details the evolution from active replication methods in earlier systems for high availability to predominantly passive replication approaches, facilitated by the durable storage offered by cloud infrastructure. The contemporary systems emphasize exactly-once semantics for state, although output commit problems manifest uniquely due to the distributed nature of these systems.
4. Load Management and Elasticity: Initial systems focused extensively on load shedding techniques under overload, whereas newer systems are equipped with elasticity mechanisms that allow dynamic resource scaling parallel to the workload, often influenced by heuristic or predictive policies. Flow control techniques, including back-pressure, are now integral parts of the architecture to regulate data flow and prevent overload.

Implications and Future Prospects

The advancements outlined in the paper have been pivotal in shaping modern data architectures and prominently figure in real-time analytics, real-time fraud detection, dynamic pricing, and operational stream processing scenarios among others. The ability to manage large-scale, unbounded data streams with guarantees of correctness and stateful computation underpins the capabilities of contemporary stream processing platforms.

Looking forward, the paper suggests that future research will focus on addressing the complexities introduced by the integration of machine learning workflows, graph analytics, and cloud-based microservices architectures with stream processing. Further, there is a growing emphasis on exploiting novel computing infrastructures and hardware accelerations to optimize and run low-latency and resource-efficient stream processing applications.

In conclusion, this survey provides a structured roadmap of the developments in stream processing systems, underscoring essential architectural shifts, technological alignments, associated benefits, and lingering challenges in the evolving landscape of data-driven technologies. The implications span not only advanced research but also practical applications in distributed systems, real-time data processing, and next-generation information systems.