LightSaber: Efficient Window Aggregation on Multi-core Processors

Theodorakis, Georgios; Koliousis, Alexandros; Pietzuch, Peter; Pirk, Holger

doi:10.1145/3318464.3389753

Cited by 27 publications

(30 citation statements)

References 42 publications

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“…STREAMBOX [10] makes use of lock-based primitives to protect scheduling phases of batches onto threads, and indeed exhibited limited scalability compared with WINDFLOW. A recent work still adopting morsel-driven parallelism has been described in [11]. It advocates a code generation approach to improve performance by fusing in a single tight loop several operators in pipeline.…”

Section: Related Workmentioning

confidence: 99%

“…Threads execute entire pipelines of operators as a tight loop on the batch elements, until a pipeline breaker (e.g., a keyby distribution) is reached. Recent research works (e.g., STREAMBOX [10] and others [11]) successfully adopt this approach, and are able to implement applications composed of relational algebra operators exchanging structured records of data. However, most of their optimizations (e.g., automatic code generation) are hard to be extended to more general application domains.…”

Section: Introductionmentioning

confidence: 99%

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WindFlow: High-Speed Continuous Stream Processing With Parallel Building Blocks

Mencagli

Torquati

Cardaci

et al. 2021

IEEE Trans. Parallel Distrib. Syst.

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Nowadays, we are witnessing the diffusion of Stream Processing Systems (SPSs) able to analyze data streams in near realtime. Traditional SPSs like STORM and FLINK target distributed clusters and adopt the continuous streaming model, where inputs are processed as soon as they are available while outputs are continuously emitted. Recently, there has been a great focus on SPSs for scale-up machines. Some of them (e.g., BRISKSTREAM) still use the continuous model to achieve low latency. Others optimize throughput with batching approaches that are, however, often inadequate to minimize latency for live-streaming applications. Our contribution is to show a novel software engineering approach to design the runtime system of SPSs targeting multicores, with the aim of providing a uniform solution able to optimize throughput and latency. The approach has a formal nature based on the assembly of components called building blocks, whose composition allows optimizations to be easily expressed in a compositional manner. We use this methodology to build a new SPS called WINDFLOW. Our evaluation showcases the benefits of WINDFLOW: it provides lower latency than SPSs for continuous streaming, and can be configured to optimize throughput, to perform similarly and even better than batch-based scale-up SPSs.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

WindFlow: High-Speed Continuous Stream Processing With Parallel Building Blocks

Mencagli

Torquati

Cardaci

et al. 2021

IEEE Trans. Parallel Distrib. Syst.

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“…AStream [20] shares computation and resources among several queries executed in Flink [4]. Several approaches focus on sharing optimizations given different predicates, grouping, or window clauses [10,17,18,24,25,42,46]. However, these approaches evaluate Select-Project-Join queries with windows and aggregate single events.…”

Section: Related Workmentioning

confidence: 99%

To Share, or not to Share Online Event Trend Aggregation Over Bursty Event Streams

Poppe¹,

Lei²,

Ma³

et al. 2021

Preprint

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Complex event processing (CEP) systems continuously evaluate large workloads of pattern queries under tight time constraints. Event trend aggregation queries with Kleene patterns are commonly used to retrieve summarized insights about the recent trends in event streams. Stateof-art methods are limited either due to repetitive computations or unnecessary trend construction. Existing shared approaches are guided by statically selected and hence rigid sharing plans that are often sub-optimal under stream fluctuations. In this work, we propose a novel framework HAMLET that is the first to overcome these limitations. HAMLET introduces two key innovations.First, HAMLET adaptively decides whether to share or not to share computations depending on the current stream properties at run time to harvest the maximum sharing benefit. Second, HAMLET is equipped with a highly efficient shared trend aggregation strategy that avoids trend construction. Our experimental study on both real and synthetic data sets demonstrates that HAMLET consistently reduces query latency by up to five orders of magnitude compared to the state-of-the-art approaches.

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“…LightSaber [50] is a recent stream processing engine, optimized for window aggregation queries. Lightsaber focuses on single server setups with a large number of cores and a large amount of shared memory.…”

Section: Related Workmentioning

confidence: 99%

ROME: All Overlays Lead to Aggregation, but Some Are Faster than Others

Blöcher

Coppa

Kleber

et al. 2021

ACM Trans. Comput. Syst.

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Aggregation is common in data analytics and crucial to distilling information from large datasets, but current data analytics frameworks do not fully exploit the potential for optimization in such phases. The lack of optimization is particularly notable in current “online” approaches which store data in main memory across nodes, shifting the bottleneck away from disk I/O toward network and compute resources, thus increasing the relative performance impact of distributed aggregation phases. We present ROME, an aggregation system for use within data analytics frameworks or in isolation. ROME uses a set of novel heuristics based primarily on basic knowledge of aggregation functions combined with deployment constraints to efficiently aggregate results from computations performed on individual data subsets across nodes (e.g., merging sorted lists resulting from top- k ). The user can either provide minimal information which allows our heuristics to be applied directly, or ROME can autodetect the relevant information at little cost. We integrated ROME as a subsystem into the Spark and Flink data analytics frameworks. We use real world data to experimentally demonstrate speedups up to 3 × over single level aggregation overlays, up to 21% over other multi-level overlays, and 50% for iterative algorithms like gradient descent at 100 iterations.

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LightSaber: Efficient Window Aggregation on Multi-core Processors

Cited by 27 publications

References 42 publications

WindFlow: High-Speed Continuous Stream Processing With Parallel Building Blocks

WindFlow: High-Speed Continuous Stream Processing With Parallel Building Blocks

To Share, or not to Share Online Event Trend Aggregation Over Bursty Event Streams

ROME: All Overlays Lead to Aggregation, but Some Are Faster than Others

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