IBM Streams Research Articles

A Survey of Distributed Data Stream Processing Frameworks

Big data processing systems are evolving to be more stream oriented where each data record is processed as it arrives by distributed and low-latency computational frameworks on a continuous basis. As the stream processing technology matures and more organizations invest in digital transformations, new applications of stream analytics will be identified and implemented across a wide spectrum of industries. One of the challenges in developing a streaming analytics infrastructure is the difficulty in selecting the right stream processing framework for the different use cases. With a view to addressing this issue, in this paper we present a taxonomy, a comparative study of distributed data stream processing and analytics frameworks, and a critical review of representative open source (Storm, Spark Streaming, Flink, Kafka Streams) and commercial (IBM Streams) distributed data stream processing frameworks. The study also reports our ongoing study on a multilevel streaming analytics architecture that can serve as a guide for organizations and individuals planning to implement a real-time data stream processing and analytics framework.

Challenges and experiences in building an efficient apache beam runner for IBM streams

This paper describes the challenges and experiences in the development of IBM Streams runner for Apache Beam. Apache Beam is emerging as a common stream programming interface for multiple computing engines. Each participating engine implements a runner to translate Beam applications into engine-specific programs. Hence, applications written with the Beam SDK can be executed on different underlying stream computing engines, with negligible migration penalty. IBM Streams is a widely-used enterprise streaming platform. It has a rich set of connectors and toolkits for easy integration of streaming applications with other enterprise applications. It also supports a broad range of programming language interfaces, including Java, C++, Python, Stream Processing Language (SPL) and Apache Beam. This paper focuses on our solutions to efficiently support the Beam programming abstractions in IBM Streams runner. Beam organizes data into discrete event time windows. This design, on the one hand, supports out-of-order data arrivals, but on the other hand, forces runners to maintain more states, which leads to higher space and computation overhead. IBM Streams runner mitigates this problem by efficiently indexing inter-dependent states, garbage-collecting stale keys, and enforcing bundle sizes. We also share performance concerns in Beam that could potentially impact applications. Evaluations show that IBM Streams runner outperforms Flink runner and Spark runner in most scenarios when running the Beam NEXMark benchmarks. IBM Streams runner is available for download from IBM Cloud Streaming Analytics service console.

Low-synchronization, mostly lock-free, elastic scheduling for streaming runtimes

We present the scalable, elastic operator scheduler in IBM Streams 4.2. Streams is a distributed stream processing system used in production at many companies in a wide range of industries. The programming language for Streams, SPL, presents operators, tuples and streams as the primary abstractions. A fundamental SPL optimization is operator fusion, where multiple operators execute in the same process. Streams 4.2 introduces automatic submission-time fusion to simplify application development and deployment. However, potentially thousands of operators could then execute in the same process, with no user guidance for thread placement. We needed a way to automatically figure out how many threads to use, with arbitrarily sized applications on a wide variety of hardware, and without any input from programmers. Our solution has two components. The first is a scalable operator scheduler that minimizes synchronization, locks and global data, while allowing threads to execute any operator and dynamically come and go. The second is an elastic algorithm to dynamically adjust the number of threads to optimize performance, using the principles of trusted measurements to establish trends. We demonstrate our scheduler's ability to scale to over a hundred threads, and our elasticity algorithm's ability to adapt to different workloads on an Intel Xeon system with 176 logical cores, and an IBM Power8 system with 184 logical cores.

SPL

Big data is revolutionizing how all sectors of our economy do business, including telecommunication, transportation, medical, and finance. Big data comes in two flavors: data at rest and data in motion. Processing data in motion is stream processing . Stream processing for big data analytics often requires scale that can only be delivered by a distributed system, exploiting parallelism on many hosts and many cores. One such distributed stream processing system is IBM Streams. Early customer experience with IBM Streams uncovered that another core requirement is extensibility, since customers want to build high-performance domain-specific operators for use in their streaming applications. Based on these two core requirements of distribution and extensibility, we designed and implemented the Streams Processing Language (SPL). This article describes SPL with an emphasis on the language design, distributed runtime, and extensibility mechanism. SPL is now the gateway for the IBM Streams platform, used by our customers for stream processing in a broad range of application domains.

Consistent regions

Guaranteed tuple processing has become critically important for many streaming applications. This paper describes how we enabled IBM Streams, an enterprise-grade stream processing system, to provide data processing guarantees. Our solution goes from language-level abstractions to a runtime protocol. As a result, with a couple of simple annotations at the source code level, IBM Streams developers can define consistent regions , allowing any subgraph of their streaming application to achieve guaranteed tuple processing. At runtime, a consistent region periodically executes a variation of the Chandy-Lamport snapshot algorithm to establish a consistent global state for that region. The coupling of consistent states with data replay enables guaranteed tuple processing.

IBM Streams Processing Language: Analyzing Big Data in motion

The IBM Streams Processing Language (SPL) is the programming language for IBM InfoSphere® Streams, a platform for analyzing Big Data in motion. By “Big Data in motion,” we mean continuous data streams at high data-transfer rates. InfoSphere Streams processes such data with both high throughput and short response times. To meet these performance demands, it deploys each application on a cluster of commodity servers. SPL abstracts away the complexity of the distributed system, instead exposing a simple graph-of-operators view to the user. SPL has several innovations relative to prior streaming languages. For performance and code reuse, SPL provides a code-generation interface to C++ and Java®. To facilitate writing well-structured and concise applications, SPL provides higher-order composite operators that modularize stream sub-graphs. Finally, to enable static checking while exposing optimization opportunities, SPL provides a strong type system and user-defined operator models. This paper provides a language overview, describes the implementation including optimizations such as fusion, and explains the rationale behind the language design.