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Scotty: General and Efficient Open-source Window Aggregation for Stream Processing Systems

Published: 27 March 2021 Publication History

Abstract

Window aggregation is a core operation in data stream processing. Existing aggregation techniques focus on reducing latency, eliminating redundant computations, or minimizing memory usage. However, each technique operates under different assumptions with respect to workload characteristics, such as properties of aggregation functions (e.g., invertible, associative), window types (e.g., sliding, sessions), windowing measures (e.g., time- or count-based), and stream (dis)order. In this article, we present Scotty, an efficient and general open-source operator for sliding-window aggregation in stream processing systems, such as Apache Flink, Apache Beam, Apache Samza, Apache Kafka, Apache Spark, and Apache Storm. One can easily extend Scotty with user-defined aggregation functions and window types. Scotty implements the concept of general stream slicing and derives workload characteristics from aggregation queries to improve performance without sacrificing its general applicability. We provide an in-depth view on the algorithms of the general stream slicing approach. Our experiments show that Scotty outperforms alternative solutions.

References

[1]
Tyler Akidau, Robert Bradshaw, et al. 2015. The dataflow model: A practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing. Proc. VLDB Endow. 8, 12 (2015), 1792--1803.
[2]
Alexander Alexandrov, Rico Bergmann, Stephan Ewen, et al. 2014. The Stratosphere platform for big data analytics. VLDB J. 23, 6 (2014), 939--964.
[3]
Apache Apex. 2018. Enterprise-grade unified stream and batch processing engine. Retrieved from https://apex.apache.org/.
[4]
Apache Beam. 2018. An advanced unified programming model. Retrieved from https://beam.apache.org/.
[5]
Arvind Arasu and Jennifer Widom. 2004. Resource sharing in continuous sliding-window aggregates. Proceedings of the International Conference on Very Large Data Bases (PVLDB’04). 336--347.
[6]
Michael Armbrust, Tathagata Das, Joseph Torres, Burak Yavuz, Shixiong Zhu, Reynold Xin, et al. 2018. Structured streaming: A declarative API for real-time applications in Apache Spark. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD’18). 601--613.
[7]
Ahmed Awad, Jonas Traub, and Sherif Sakr. 2019. Adaptive watermarks: A concept drift-based approach for predicting event-time progress in data streams. In Proceedings of the International Conference on Extending Database Technology (EDBT’19).
[8]
Cagri Balkesen and Nesime Tatbul. 2011. Scalable data partitioning techniques for parallel sliding window processing over data streams. In Proceedings of the International Workshop on Data Management for Sensor Networks (DMSN’11).
[9]
Lawrence Benson, Philipp M. Grulich, Steffen Zeuch, Volker Markl, and Tilmann Rabl. 2020. Disco: Efficient distributed window aggregation. In Proceedings of the International Conference on Extending Database Technology (EDBT’20).
[10]
Pramod Bhatotia, Umut A. Acar, Flavio P. Junqueira, and Rodrigo Rodrigues. 2014. Slider: Incremental sliding window analytics. In Proceedings of the International Middleware Conference. ACM, 61--72.
[11]
Brice Bingman. 2018. Poor performance with sliding time windows. In Flink Jira Issues. Retrieved from issues.apache.org/jira/browse/FLINK-6990.
[12]
Irina Botan, Roozbeh Derakhshan, Nihal Dindar, Laura Haas, Renée J. Miller, and Nesime Tatbul. 2010. SECRET: A model for analysis of the execution semantics of stream processing systems. Proc. VLDB Endow. 3, 1--2 (2010), 232--243.
[13]
Paris Carbone. 2018. Scalable and Reliable Data Stream Processing. Ph.D. Dissertation. KTH Stockholm.
[14]
Paris Carbone, Stephan Ewen, Gyula Fóra, Seif Haridi, Stefan Richter, and Kostas Tzoumas. 2017. State management in Apache Flink: Consistent stateful distributed stream processing. Proc. VLDB Endow. 10, 12 (2017), 1718--1729.
[15]
Paris Carbone, Gyula Fóra, Stephan Ewen, Seif Haridi, and Kostas Tzoumas. 2015. Lightweight asynchronous snapshots for distributed dataflows. Retrieved from https://arxiv.org/abs/1506.08603.
[16]
Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, Seif Haridi, and Kostas Tzoumas. 2015. Apache Flink: Stream and batch processing in a single engine. IEEE Data Eng. Bull. 38, 4 (2015), 28--38.
[17]
Paris Carbone, Jonas Traub, Asterios Katsifodimos, Seif Haridi, and Volker Markl. 2016. Cutty: Aggregate sharing for user-defined windows. In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM’16). 1201--1210.
[18]
Badrish Chandramouli, Jonathan Goldstein, Mike Barnett, Robert DeLine, Danyel Fisher, John C. Platt, James F. Terwilliger, and John Wernsing. 2014. Trill: A high-performance incremental query processor for diverse analytics. Proceedings of the International Conference on Very Large Data Bases (PVLDB’14) 8, 4 (2014), 401--412.
[19]
Sanket Chintapalli, Derek Dagit, Bobby Evans et al. 2016. Benchmarking streaming computation engines: Storm, Flink and Spark streaming. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW’16). 1789--1792.
[20]
Xenofontas Dimitropoulos, Paul Hurley, Andreas Kind, and Marc Ph Stoecklin. 2009. On the 95-percentile billing method. In Passive and Active Network Measurement. Springer, 207--216.
[21]
Buğra Gedik. 2014. Generic windowing support for extensible stream processing systems. Softw.: Pract. Exp. 44, 9 (2014), 1105--1128.
[22]
Thanaa M. Ghanem, Moustafa A. Hammad, Mohamed F. Mokbel, Walid G. Aref, and Ahmed K. Elmagarmid. 2007. Incremental evaluation of sliding-window queries over data streams. IEEE Trans. Knowl. Data Eng. 19, 1 (2007), 57--72.
[23]
Jim Gray, Surajit Chaudhuri, Adam Bosworth, Andrew Layman, Don Reichart, Murali Venkatrao, Frank Pellow, and Hamid Pirahesh. 1997. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Min. Knowl. Discov. 1, 1 (1997), 29--53.
[24]
Michael Grossniklaus, David Maier, James Miller, Sharmadha Moorthy, and Kristin Tufte. 2016. Frames: Data-driven windows. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS’16).
[25]
Philipp M. Grulich, Sebastian Breß, Jonas Traub, Tilmann Rabl, Janis von Bleichert, Zongxiong Chen, Steffen Zeuch, and Volker Markl. 2020. Grizzly: Efficient stream processing through adaptive query compilation. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM New York, NY.
[26]
Philipp M. Grulich, Jonas Traub, Sebastian Breß, Asterios Katsifodimos, Tilmann Rabl, and Volker Markl. 2019. Generating reproducible out-of-order data streams. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS’19). 256--257.
[27]
Shenoda Guirguis, Mohamed A. Sharaf, Panos K. Chrysanthis, and Alexandros Labrinidis. 2011. Optimized processing of multiple aggregate continuous queries. In Proceedings of the Conference on Information and Knowledge Management (CIKM’11). ACM, 1515--1524.
[28]
Shenoda Guirguis, Mohamed A. Sharaf, Panos K. Chrysanthis, and Alexandros Labrinidis. 2012. Three-level processing of multiple aggregate continuous queries. In Proceedings of the IEEE International Conference on Data Engineering (ICDE’12). 929--940.
[29]
Guenter Hesse, Christoph Matthies, Kelvin Glass, Johannes Huegle, and Matthias Uflacker. 2019. Quantitative impact evaluation of an abstraction layer for data stream processing systems. In Proceedings of the International Conference on Distributed Computing Systems (ICDCS’19). IEEE, 1381--1392.
[30]
Martin Hirzel, Henrique Andrade, Buğra Gedik, Vibhore Kumar, Giuliano Losa, Howard Nasgaard, Robert Soulé, and Kun-Lung Wu. 2009. SPL stream processing language specification. IBM Res. Report (2009). http://cs.yale.edu/homes/soule/pubs/rc24897.pdf.
[31]
Martin Hirzel, Scott Schneider, and Kanat Tangwongsan. 2017. Sliding-window aggregation algorithms: Tutorial. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS’17). 11--14.
[32]
Martin Hirzel, Robert Soulé, Scott Schneider, Buğra Gedik, and Robert Grimm. 2014. A catalog of stream processing optimizations. Comput. Surveys 46, 4 (2014), 46.
[33]
Kartik Hosanagar, John Chuang, Ramayya Krishnan, and Michael D. Smith. 2008. Service adoption and pricing of content delivery network (CDN) services. Manage. Sci. 54, 9 (2008), 1579--1593.
[34]
Ryan Huebsch, Minos Garofalakis, Joseph M. Hellerstein, and Ion Stoica. 2007. Sharing aggregate computation for distributed queries. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 485--496.
[35]
Julius Hülsmann, Jonas Traub, and Volker Markl. 2020. Demand-based sensor data gathering with multi-query optimization. Proc. VLDB Endow. 13, 12 (2020), 2801--2804.
[36]
Zbigniew Jerzak, Thomas Heinze, Matthias Fehr, Daniel Gröber, Raik Hartung, and Nenad Stojanovic. 2012. The DEBS 2012 grand challenge. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS’12). 393--398.
[37]
Uwe Jugel, Zbigniew Jerzak, Gregor Hackenbroich, and Volker Markl. 2014. M4: A visualization-oriented time series data aggregation. In Proceedings of the International Conference on Very Large Data Bases (PVLDB’14). 797--808.
[38]
Jeyhun Karimov, Tilmann Rabl, Asterios Katsifodimos, Roman Samarev, Henri Heiskanen, and Volker Markl. 2018. Benchmarking distributed stream processing engines. In IEEE Proceedings of the IEEE International Conference on Data Engineering (ICDE’18).
[39]
Alexandros Koliousis, Matthias Weidlich, Raul Castro Fernandez, Alexander L. Wolf, Paolo Costa, and Peter Pietzuch. 2016. SABER: Window-based hybrid stream processing for heterogeneous architectures. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD’16). 555--569.
[40]
Jay Kreps. 2016. Introducing Kafka streams: Stream processing made simple. Confluent Blog. Retrieved from https://www.confluent.io/blog/introducing-kafka-streams-stream-processing-made-simple/.
[41]
Sailesh Krishnamurthy, Michael J. Franklin, Jeffrey Davis, Daniel Farina, Pasha Golovko, Alan Li, and Neil Thombre. 2010. Continuous analytics over discontinuous streams. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD’10). 1081--1092.
[42]
Sailesh Krishnamurthy, Chung Wu, and Michael Franklin. 2006. On-the-fly sharing for streamed aggregation. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 623--634.
[43]
Jin Li, David Maier, Kristin Tufte, Vassilis Papadimos, and Peter A. Tucker. 2005. No pane, no gain: Efficient evaluation of sliding-window aggregates over data streams. SIGMOD Record 34, 1 (2005), 39--44.
[44]
Jin Li, David Maier, Kristin Tufte, Vassilis Papadimos, and Peter A. Tucker. 2005. Semantics and evaluation techniques for window aggregates in data streams. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD’05). 311--322.
[45]
Jin Li, Kristin Tufte, David Maier, and Vassilis Papadimos. 2008. AdaptWID: An adaptive, memory-efficient window aggregation implementation. IEEE Internet Comput. 12, 6 (2008), 22--29.
[46]
Jin Li, Kristin Tufte, Vladislav Shkapenyuk, Vassilis Papadimos, Theodore Johnson, and David Maier. 2008. Out-of-order processing: A new architecture for high-performance stream systems. Proc. VLDB Endow. 1, 1 (2008), 274--288.
[47]
Christopher Mutschler, Holger Ziekow, and Zbigniew Jerzak. 2013. The DEBS 2013 grand challenge. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS’13). 289--294.
[48]
Shadi A. Noghabi, Kartik Paramasivam, Yi Pan, Navina Ramesh, Jon Bringhurst, Indranil Gupta, and Roy H. Campbell. 2017. Samza: Stateful scalable stream processing at LinkedIn. Proc. VLDB Endow. 10, 12 (2017), 1634--1645.
[49]
OpenJDK. 2018. JMH benchmarking suite project website. Retrieved from http://openjdk.java.net/projects/code-tools/jmh/.
[50]
OpenJDK. 2018. Nashorn project, ObjectSizeCalculator. Retrieved from http://openjdk.java.net/projects/nashorn/.
[51]
Kostas Patroumpas et al. 2006. Window specification over data streams. In Proceedings of the International Conference on Extending Database Technology (EDBT’06).
[52]
David Salomon. 2007. Variable-length Codes for Data Compression. Springer.
[53]
Anatoli U. Shein, Panos K. Chrysanthis, and Alexandros Labrinidis. 2015. F1: Accelerating the optimization of aggregate continuous queries. In Proceedings of the Conference on Information and Knowledge Management (CIKM’15). 1151--1160.
[54]
Anatoli U. Shein, Panos K. Chrysanthis, and Alexandros Labrinidis. 2017. Flatfit: Accelerated incremental sliding-window aggregation for real-time analytics. In Proceedings of the International Conference on Scientific and Statistical Database Management.
[55]
Anatoli U. Shein, Panos K. Chrysanthis, and Alexandros Labrinidis. 2018. SlickDeque: High throughput and low latency incremental sliding-window aggregation. In Proceedings of the International Conference on Extending Database Technology (EDBT’18).
[56]
Leo Syinchwun. 2016. Lightweight event time window. In Flink Jira Issues. Retrieved from issues.apache.org/jira/browse/FLINK-5387.
[57]
Kanat Tangwongsan, Martin Hirzel, and Scott Schneider. 2017. Low-latency sliding-window aggregation in worst-case constant time. In Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS’17). 66--77.
[58]
Kanat Tangwongsan, Martin Hirzel, and Scott Schneider. 2019. Optimal and general out-of-order sliding-window aggregation. Proc. VLDB Endow. 12, 10 (2019), 1167--1180.
[59]
Kanat Tangwongsan, Martin Hirzel, Scott Schneider, and Kun-Lung Wu. 2015. General incremental sliding-window aggregation. Proceedings of the International Conference on Very Large Data Bases (PVLDB’15) 8, 7 (2015), 702--713.
[60]
Joseph Torres, Michael Armbrust, Tathagata Das, and Shixiong Zhu. 2018. Introducing low-latency continuous processing mode in structured streaming in Apache Spark 2.3. Databricks Blog. Retrieved from https://databricks.com/blog/2018/03/20/low-latency-continuous-processing-mode-in-structured-streaming-in-apache-spark-2-3-0.html.
[61]
Ankit Toshniwal, Siddarth Taneja, Amit Shukla, Karthik Ramasamy, Jignesh M. Patel, Sanjeev Kulkarni, Jason Jackson, Krishna Gade, Maosong Fu, Jake Donham, et al. 2014. Storm@twitter. In Proceedings of the ACM Special Interest Group on Management of Data (SIGMOD’14). 147--156.
[62]
Jonas Traub. 2019. Demand-based data stream gathering, processing, and transmission. TU Berlin, PhD Thesis.
[63]
Jonas Traub, Sebastian Breß, Tilmann Rabl, Asterios Katsifodimos, and Volker Markl. 2017. Optimized on-demand data streaming from sensor nodes. Proceedings of the Symposium on Cloud Computing (SoCC’17). 586--597.
[64]
Jonas Traub, Philipp M. Grulich, Alejandro Rodriguez Cuellar, Sebastian Breß, Asterios Katsifodimos, Tilmann Rabl, and Volker Markl. 2018. Scotty: Efficient window aggregation for out-of-order stream processing. In Proceedings of the IEEE International Conference on Data Engineering (ICDE’18).
[65]
Jonas Traub, Philipp M. Grulich, Alejandro Rodriguez Cuellar, Sebastian Breß, Asterios Katsifodimos, Tilmann Rabl, and Volker Markl. 2019. Efficient window aggregation with general stream slicing. In Proceedings of the International Conference on Extending Database Technology (EDBT’17).
[66]
Jonas Traub, Julius Hülsmann, Sebastian Breß, Tilmann Rabl, and Volker Markl. 2019. SENSE: Scalable data acquisition from distributed sensors with guaranteed time coherence. Retrieved from https://arxiv.org/abs/1912.04648.
[67]
Jonas Traub, Nikolaas Steenbergen, Philipp M. Grulich, Tilmann Rabl, and Volker Markl. 2017. I2: Interactive real-time visualization for streaming data. In Proceedings of the International Conference on Extending Database Technology (EDBT’17). 526--529.
[68]
Peter A. Tucker, David Maier, Tim Sheard, and Leonidas Fegaras. 2003. Exploiting punctuation semantics in continuous data streams. IEEE Trans. Knowl. Data Eng. 15, 3 (2003), 555--568.
[69]
Kostas Tzoumas et al. 2015. High-throughput, low-latency, and exactly-once stream processing with Apache Flink. Retrieved from data-artisans.com/blog/high-throughput-low-latency-and-exactly-once-stream-processing-with-apache-flink.
[70]
Mikhail Vorontsov. 2013. Memory consumption of popular Java data types - part 2. Java Performance Tuning Guide. Retrieved from http://java-performance.info/memory-consumption-of-java-data-types-2/.
[71]
Guozhang Wang. 2017. Enabling exactly-once in Kafka streams. Confluent Blog. Retrieved from https://www.confluent.io/blog/enabling-exactly-once-kafka-streams/.
[72]
Jark Wu. 2017. Improve performance of sliding time window with pane optimization. In Flink Jira Issues. Retrieved from issues.apache.org/jira/browse/FLINK-7001.
[73]
Yuan Yu, Pradeep Kumar Gunda, and Michael Isard. 2009. Distributed aggregation for data-parallel computing: Interfaces and implementations. In Proceedings of the ACM Special Interest Group on Operating Systems (SIGOPS’09). 247--260.
[74]
Matei Zaharia, Tathagata Das, Haoyuan Li, Scott Shenker, and Ion Stoica. 2012. Discretized streams: An efficient and fault-tolerant model for stream processing on large clusters. In Proceedings of the USENIX Conference on Hot Topics in Cloud Computing.
[75]
Matei Zaharia, Reynold S. Xin, Patrick Wendell, et al. 2016. Apache Spark: A unified engine for big data processing. Commun. ACM 59, 11 (2016), 56--65.
[76]
Steffen Zeuch, Ankit Chaudhary, Bonaventura Del Monte, et al. 2020. The NebulaStream Platform: Data and application management for the Internet of Things. In Proceedings of the Conference on Innovative Data Systems Research (CIDR’20).
[77]
Steffen Zeuch, Bonaventura Del Monte, Jeyhun Karimov, Clemens Lutz, Manuel Renz, Jonas Traub, Sebastian Breß, Tilmann Rabl, and Volker Markl. 2019. Analyzing efficient stream processing on modern hardware. In Proceedings of the International Conference on Very Large Data Bases (PVLDB’19).
[78]
Shuhao Zhang, Feng Zhang, Yingjun Wu, Bingsheng He, and Paul Johns. 2020. Hardware-conscious stream processing: A survey. ACM SIGMOD Record 48, 4 (2020), 18--29.

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cover image ACM Transactions on Database Systems
ACM Transactions on Database Systems  Volume 46, Issue 1
March 2021
143 pages
ISSN:0362-5915
EISSN:1557-4644
DOI:10.1145/3457891
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 27 March 2021
Accepted: 01 November 2020
Revised: 01 October 2020
Received: 01 March 2020
Published in TODS Volume 46, Issue 1

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Author Tags

  1. Apache Beam
  2. Apache Flink
  3. Apache Kafka Streams
  4. Apache Samza
  5. Apache Spark
  6. Apache Storm
  7. Scotty
  8. Window
  9. aggregate sharing
  10. aggregation
  11. open-source
  12. session window
  13. sliding-window
  14. stream processing
  15. tumbling window

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  • Research-article
  • Research
  • Refereed

Funding Sources

  • SFB 1404 FONDA, and the EU Horizon 2020 Opertus Mundi project
  • German Ministry for Education and Research as BIFOLD

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  • (2024)An Overview of Continuous Querying in (Modern) Data SystemsCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654679(605-612)Online publication date: 9-Jun-2024
  • (2024)Springald: GPU-Accelerated Window-Based Aggregates Over Out-of-Order Data StreamsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.343161135:9(1657-1671)Online publication date: 1-Sep-2024
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