{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T07:42:32Z","timestamp":1774942952787,"version":"3.50.1"},"reference-count":94,"publisher":"Association for Computing Machinery (ACM)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>\n            Modern\n            <jats:italic>Stream Processing Engines<\/jats:italic>\n            (SPEs) process large data volumes under tight latency constraints. Many SPEs execute processing pipelines using message passing on shared-nothing architectures and apply a partition-based\n            <jats:italic>scale-out<\/jats:italic>\n            strategy to handle high-velocity input streams. Furthermore, many state-of-the-art SPEs rely on a Java Virtual Machine to achieve platform independence and speed up system development by abstracting from the underlying hardware.\n          <\/jats:p>\n          <jats:p>\n            In this paper, we show that taking the underlying hardware into account is essential to exploit modern hardware efficiently. To this end, we conduct an extensive experimental analysis of current SPEs and SPE design alternatives optimized for modern hardware. Our analysis highlights potential bottlenecks and reveals that state-of-the-art SPEs are not capable of fully exploiting current and emerging hardware trends, such as multi-core processors and high-speed networks. Based on our analysis, we describe a set of design changes to the common architecture of SPEs to\n            <jats:italic>scale-up<\/jats:italic>\n            on modern hardware. We show that the single-node throughput can be increased by up to two orders of magnitude compared to state-of-the-art SPEs by applying specialized code generation, fusing operators, batch-style parallelization strategies, and optimized windowing. This speedup allows for deploying typical streaming applications on a single or a few nodes instead of large clusters.\n          <\/jats:p>","DOI":"10.14778\/3303753.3303758","type":"journal-article","created":{"date-parts":[[2019,2,27]],"date-time":"2019-02-27T14:57:56Z","timestamp":1551279476000},"page":"516-530","source":"Crossref","is-referenced-by-count":82,"title":["Analyzing efficient stream processing on modern hardware"],"prefix":"10.14778","volume":"12","author":[{"given":"Steffen","family":"Zeuch","sequence":"first","affiliation":[{"name":"German Research Center for Artificial Intelligence"}]},{"given":"Bonaventura Del","family":"Monte","sequence":"additional","affiliation":[{"name":"German Research Center for Artificial Intelligence"}]},{"given":"Jeyhun","family":"Karimov","sequence":"additional","affiliation":[{"name":"German Research Center for Artificial Intelligence"}]},{"given":"Clemens","family":"Lutz","sequence":"additional","affiliation":[{"name":"German Research Center for Artificial Intelligence"}]},{"given":"Manuel","family":"Renz","sequence":"additional","affiliation":[{"name":"German Research Center for Artificial Intelligence"}]},{"given":"Jonas","family":"Traub","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t Berlin"}]},{"given":"Sebastian","family":"Bre\u00df","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t Berlin and German Research Center for Artificial Intelligence"}]},{"given":"Tilmann","family":"Rabl","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t Berlin and German Research Center for Artificial Intelligence"}]},{"given":"Volker","family":"Markl","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t Berlin and German Research Center for Artificial Intelligence"}]}],"member":"320","published-online":{"date-parts":[[2019,1]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Apache Storm Trident. URL storm.apache.org\/releases\/1.1.1\/Trident-tutorial.html.  Apache Storm Trident. URL storm.apache.org\/releases\/1.1.1\/Trident-tutorial.html."},{"key":"e_1_2_1_2_1","volume-title":"Retrieved","author":"Disni","year":"2018","unstructured":"Disni : Direct storage and networking interface, 2017 . Retrieved March 30, 2018 , from https:\/\/github.com\/zrlio\/disni. Disni: Direct storage and networking interface, 2017. Retrieved March 30, 2018, from https:\/\/github.com\/zrlio\/disni."},{"key":"e_1_2_1_3_1","volume-title":"t. association. infiniband roadmap","author":"I.","year":"2017","unstructured":"I. t. association. infiniband roadmap , 2017 . Retrieved October 30, 2017, from http:\/\/www.infinibandta.org\/. I. t. association. infiniband roadmap, 2017. Retrieved October 30, 2017, from http:\/\/www.infinibandta.org\/."},{"key":"e_1_2_1_4_1","unstructured":"Package java.util.concurrent 2017. Retrieved October 30 2017 from https:\/\/docs.oracle.com\/javase\/8\/docs\/api\/java\/util\/concurrent\/package-summary.html.  Package java.util.concurrent 2017. Retrieved October 30 2017 from https:\/\/docs.oracle.com\/javase\/8\/docs\/api\/java\/util\/concurrent\/package-summary.html."},{"key":"e_1_2_1_5_1","volume-title":"Retrieved","author":"A","year":"2017","unstructured":"A ReaderWriter queue which shows superior performance in benchmarks, 2017 . Retrieved October 30, 2017 , from https:\/\/github.com\/cameron314\/readerwriterqueue\/tree\/master\/benchmarks. A ReaderWriter queue which shows superior performance in benchmarks, 2017. Retrieved October 30, 2017, from https:\/\/github.com\/cameron314\/readerwriterqueue\/tree\/master\/benchmarks."},{"key":"e_1_2_1_6_1","unstructured":"Sparkrdma shufflemanager plugin 2017. Retrieved March 30 2018 from https:\/\/github.com\/Mellanox\/SparkRDMA.  Sparkrdma shufflemanager plugin 2017. Retrieved March 30 2018 from https:\/\/github.com\/Mellanox\/SparkRDMA."},{"key":"e_1_2_1_7_1","volume-title":"Retrieved","author":"A","year":"2017","unstructured":"A SPSC implementation from Facebook, 2017 . Retrieved October 30, 2017 , from https:\/\/github.com\/facebook\/folly\/tree\/master\/folly. A SPSC implementation from Facebook, 2017. Retrieved October 30, 2017, from https:\/\/github.com\/facebook\/folly\/tree\/master\/folly."},{"key":"e_1_2_1_8_1","volume-title":"Retrieved","author":"A","year":"2017","unstructured":"A SPSC queue implemented by the Boost library, 2017 . Retrieved October 30, 2017 , from www.boost.org\/doc\/libs\/1\\_64\\_0\/doc\/html\/boost\/lockfree\/queue.html. A SPSC queue implemented by the Boost library, 2017. Retrieved October 30, 2017, from www.boost.org\/doc\/libs\/1\\_64\\_0\/doc\/html\/boost\/lockfree\/queue.html."},{"key":"e_1_2_1_9_1","volume-title":"Retrieved","author":"A","year":"2017","unstructured":"A SPSC queue implemented by the TBB library, 2017 . Retrieved October 30, 2017 , from https:\/\/www.threadingbuildingblocks.org\/docs\/doxygen\/a00035.html. A SPSC queue implemented by the TBB library, 2017. Retrieved October 30, 2017, from https:\/\/www.threadingbuildingblocks.org\/docs\/doxygen\/a00035.html."},{"key":"e_1_2_1_10_1","unstructured":"Hotspot virtual machine garbage collection tuning guide 2018. Retrieved November 2 2018 from https:\/\/docs.oracle.com\/en\/java\/javase\/11\/gctuning\/available-collectors.html.  Hotspot virtual machine garbage collection tuning guide 2018. Retrieved November 2 2018 from https:\/\/docs.oracle.com\/en\/java\/javase\/11\/gctuning\/available-collectors.html."},{"key":"e_1_2_1_11_1","first-page":"277","volume-title":"CIDR","volume":"5","author":"Abadi D. J.","year":"2005","unstructured":"D. J. Abadi , Y. Ahmad , M. Balazinska , U. Cetintemel , M. Cherniack , J.-H. Hwang , W. Lindner , A. Maskey , A. Rasin , E. Ryvkina , The design of the borealis stream processing engine . In CIDR , volume 5 , pages 277 -- 289 , 2005 . D. J. Abadi, Y. Ahmad, M. Balazinska, U. Cetintemel, M. Cherniack, J.-H. Hwang, W. Lindner, A. Maskey, A. Rasin, E. Ryvkina, et al. The design of the borealis stream processing engine. In CIDR, volume 5, pages 277--289, 2005."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-003-0095-z"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536222.2536229"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824076"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-014-0357-y"},{"key":"e_1_2_1_16_1","first-page":"480","volume-title":"VLDB","author":"Arasu A.","year":"2004","unstructured":"A. Arasu , M. Cherniack , E. Galvez , D. Maier , A. S. Maskey , E. Ryvkina , M. Stonebraker , and R. Tibbetts . Linear road: a stream data management benchmark . In VLDB , pages 480 -- 491 . 2004 . A. Arasu, M. Cherniack, E. Galvez, D. Maier, A. S. Maskey, E. Ryvkina, M. Stonebraker, and R. Tibbetts. Linear road: a stream data management benchmark. In VLDB, pages 480--491. 2004."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/1316689.1316720"},{"key":"e_1_2_1_18_1","volume-title":"Making apache spark the fastest open source streaming engine","author":"Armbrust M.","year":"2017","unstructured":"M. Armbrust . Making apache spark the fastest open source streaming engine , 2017 . Databricks Engineering Blog , URL https:\/\/databricks.com\/blog\/2017\/06\/06\/simple-super-fast-streaming-engine-apache-spark.html. M. Armbrust. Making apache spark the fastest open source streaming engine, 2017. Databricks Engineering Blog, URL https:\/\/databricks.com\/blog\/2017\/06\/06\/simple-super-fast-streaming-engine-apache-spark.html."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807128.1807148"},{"key":"e_1_2_1_20_1","volume-title":"Esper: Event stream processing and correlation","author":"Bernhardt T.","year":"2007","unstructured":"T. Bernhardt and A. Vasseur . Esper: Event stream processing and correlation , 2007 . T. Bernhardt and A. Vasseur. Esper: Event stream processing and correlation, 2007."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807291"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/2904483.2904485"},{"key":"e_1_2_1_23_1","first-page":"225","volume-title":"CIDR","volume":"5","author":"Boncz P.","year":"2005","unstructured":"P. Boncz , M. Zukowski , and N. Nes . MonetDB\/X100: hyper-pipelining query execution . In CIDR , volume 5 , pages 225 -- 237 , 2005 . P. Boncz, M. Zukowski, and N. Nes. MonetDB\/X100: hyper-pipelining query execution. In CIDR, volume 5, pages 225--237, 2005."},{"key":"e_1_2_1_24_1","volume-title":"Design and implementation of the maxstream federated stream processing architecture","author":"Botan I.","year":"2009","unstructured":"I. Botan , Y. Cho , R. Derakhshan , N. Dindar , L. Haas , K. Kim , C. Lee , G. Mundada , M.-C. Shan , N. Tatbul , Design and implementation of the maxstream federated stream processing architecture . 2009 . I. Botan, Y. Cho, R. Derakhshan, N. Dindar, L. Haas, K. Kim, C. Lee, G. Mundada, M.-C. Shan, N. Tatbul, et al. Design and implementation of the maxstream federated stream processing architecture. 2009."},{"key":"e_1_2_1_25_1","first-page":"75","volume-title":"VLDB","author":"Botan I.","year":"2007","unstructured":"I. Botan , D. Kossmann , P. M. Fischer , T. Kraska , D. Florescu , and R. Tamosevicius . Extending xquery with window functions . In VLDB , pages 75 -- 86 . 2007 . I. Botan, D. Kossmann, P. M. Fischer, T. Kraska, D. Florescu, and R. Tamosevicius. Extending xquery with window functions. In VLDB, pages 75--86. 2007."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2742783"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2882936"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2754169.2754180"},{"key":"e_1_2_1_29_1","volume-title":"Apache Flink: Stream and batch processing in a single engine. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 36(4)","author":"Carbone P.","year":"2015","unstructured":"P. Carbone , A. Katsifodimos , S. Ewen , V. Markl , S. Haridi , and K. Tzoumas . Apache Flink: Stream and batch processing in a single engine. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 36(4) , 2015 . P. Carbone, A. Katsifodimos, S. Ewen, V. Markl, S. Haridi, and K. Tzoumas. Apache Flink: Stream and batch processing in a single engine. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 36(4), 2015."},{"key":"e_1_2_1_30_1","volume-title":"Apache flink: Stream and batch processing in a single engine","author":"Carbone P.","year":"2015","unstructured":"P. Carbone , A. Katsifodimos , S. Ewen , V. Markl , S. Haridi , and K. Tzoumas . Apache flink: Stream and batch processing in a single engine . IEEE Data Engineering Bulletin , 36(4), 2015 . P. Carbone, A. Katsifodimos, S. Ewen, V. Markl, S. Haridi, and K. Tzoumas. Apache flink: Stream and batch processing in a single engine. IEEE Data Engineering Bulletin, 36(4), 2015."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983807"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2465282"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/1806596.1806638"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735496.2735503"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/872757.872857"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335432"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/1951365.1951426"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2016.138"},{"key":"e_1_2_1_39_1","volume-title":"Benchmarking streaming computation engines at yahoo","author":"Chintapalli S.","year":"2015","unstructured":"S. Chintapalli , D. Dagit , B. Evans , R. Farivar , T. Graves , M. Holderbaugh , Z. Liu , K. Nusbaum , K. Patil , B. J. Peng , and P. Poulosky . Benchmarking streaming computation engines at yahoo , 2015 . S. Chintapalli, D. Dagit, B. Evans, R. Farivar, T. Graves, M. Holderbaugh, Z. Liu, K. Nusbaum, K. Patil, B. J. Peng, and P. Poulosky. Benchmarking streaming computation engines at yahoo, 2015."},{"key":"e_1_2_1_40_1","unstructured":"R. Consortium. RDMA Protocol Verbs Specification (Version 1.0). April 2003.  R. Consortium. RDMA Protocol Verbs Specification (Version 1.0). April 2003."},{"key":"e_1_2_1_41_1","volume-title":"Intel\u00ae 64 and IA-32 Architectures Software Developer's Manual. Number 325462-044US","author":"I. Corporation","year":"2012","unstructured":"I. Corporation . Intel\u00ae 64 and IA-32 Architectures Software Developer's Manual. Number 325462-044US . August 2012 . I. Corporation. Intel\u00ae 64 and IA-32 Architectures Software Developer's Manual. Number 325462-044US. August 2012."},{"key":"e_1_2_1_42_1","first-page":"10","volume-title":"OSDI","author":"Dean J.","year":"2004","unstructured":"J. Dean and S. Ghemawat . Mapreduce: Simplified data processing on large clusters . In OSDI , pages 10 -- 10 . USENIX Association , 2004 . J. Dean and S. Ghemawat. Mapreduce: Simplified data processing on large clusters. In OSDI, pages 10--10. USENIX Association, 2004."},{"key":"e_1_2_1_43_1","unstructured":"S. Ewen. Off-heap memory in apache flink and the curious jit compiler 2015. Retrieved November 2 2018 from https:\/\/flink.apache.org\/news\/2015\/09\/16\/off-heap-memory.html.  S. Ewen. Off-heap memory in apache flink and the curious jit compiler 2015. Retrieved November 2 2018 from https:\/\/flink.apache.org\/news\/2015\/09\/16\/off-heap-memory.html."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2094114.2094126"},{"key":"e_1_2_1_45_1","first-page":"49","volume-title":"2014 USENIX Annual Technical Conference (USENIX ATC 14)","author":"Fernandez R. C.","year":"2014","unstructured":"R. C. Fernandez , M. Migliavacca , E. Kalyvianaki , and P. Pietzuch . Making state explicit for imperative big data processing . In 2014 USENIX Annual Technical Conference (USENIX ATC 14) , pages 49 -- 60 , Philadelphia, PA , 2014 . USENIX Association. R. C. Fernandez, M. Migliavacca, E. Kalyvianaki, and P. Pietzuch. Making state explicit for imperative big data processing. In 2014 USENIX Annual Technical Conference (USENIX ATC 14), pages 49--60, Philadelphia, PA, 2014. USENIX Association."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/1739041.1739068"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2194"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/1645953.1646061"},{"key":"e_1_2_1_49_1","volume-title":"Extending the yahoo! streaming benchmark","author":"Grier J.","year":"2016","unstructured":"J. Grier . Extending the yahoo! streaming benchmark , 2016 . J. Grier. Extending the yahoo! streaming benchmark, 2016."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2933267.2933304"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-87779-0_24"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2013.2243535"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2528412"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142522"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.5555\/3026959.3027000"},{"key":"e_1_2_1_56_1","first-page":"49","volume-title":"EDBT","author":"Kipf A.","year":"2017","unstructured":"A. Kipf , V. Pandey , J. B\u00f6ttcher , L. Braun , T. Neumann , and A. Kemper . Analytics on fast data: Main-memory database systems versus modern streaming systems . In EDBT , pages 49 -- 60 . 2017 . A. Kipf, V. Pandey, J. B\u00f6ttcher, L. Braun, T. Neumann, and A. Kemper. Analytics on fast data: Main-memory database systems versus modern streaming systems. In EDBT, pages 49--60. 2017."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2882906"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2010.5447892"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807290"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142543"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2610507"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/2933349.2933352"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/1058150.1058158"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/1516360.1516398"},{"key":"e_1_2_1_65_1","first-page":"383","volume-title":"OSDI","author":"Lion D.","year":"2016","unstructured":"D. Lion , A. Chiu , H. Sun , X. Zhuang , N. Grcevski , and D. Yuan . Don't get caught in the cold, warm-up your jvm: Understand and eliminate jvm warm-up overhead in data-parallel systems . In OSDI , pages 383 -- 400 , 2016 . D. Lion, A. Chiu, H. Sun, X. Zhuang, N. Grcevski, and D. Yuan. Don't get caught in the cold, warm-up your jvm: Understand and eliminate jvm warm-up overhead in data-parallel systems. In OSDI, pages 383--400, 2016."},{"key":"e_1_2_1_66_1","first-page":"617","volume-title":"2017 USENIX Annual Technical Conference (USENIX ATC 17)","author":"Miao H.","year":"2017","unstructured":"H. Miao , H. Park , M. Jeon , G. Pekhimenko , K. S. McKinley , and F. X. Lin . Streambox: Modern stream processing on a multicore machine . In 2017 USENIX Annual Technical Conference (USENIX ATC 17) , pages 617 -- 629 , Santa Clara, CA , 2017 . USENIX Association. H. Miao, H. Park, M. Jeon, G. Pekhimenko, K. S. McKinley, and F. X. Lin. Streambox: Modern stream processing on a multicore machine. In 2017 USENIX Annual Technical Conference (USENIX ATC 17), pages 617--629, Santa Clara, CA, 2017. USENIX Association."},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.5555\/2535461.2535475"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/2517349.2522738"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.14778\/2002938.2002940"},{"key":"e_1_2_1_70_1","first-page":"293","volume-title":"NSDI","author":"Ousterhout K.","year":"2015","unstructured":"K. Ousterhout , R. Rasti , S. Ratnasamy , S. Shenker , and B.-G. Chun . Making sense of performance in data analytics frameworks . In NSDI , pages 293 -- 307 . USENIX Association , 2015 . K. Ousterhout, R. Rasti, S. Ratnasamy, S. Shenker, and B.-G. Chun. Making sense of performance in data analytics frameworks. In NSDI, pages 293--307. USENIX Association, 2015."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920959"},{"key":"e_1_2_1_72_1","volume-title":"Do we need distributed stream processing?","author":"Pietzuch P.","year":"2018","unstructured":"P. Pietzuch , P. Garefalakis , A. Koliousis , H. Pirk , and G. Theodorakis . Do we need distributed stream processing? , 2018 . P. Pietzuch, P. Garefalakis, A. Koliousis, H. Pirk, and G. Theodorakis. Do we need distributed stream processing?, 2018."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824043"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.14778\/2856318.2856319"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDEW.2013.6547428"},{"key":"e_1_2_1_76_1","volume-title":"with a vengeance","author":"Schneider T.","year":"2015","unstructured":"T. Schneider . Analyzing 1.1 billion nyc taxi and uber trips , with a vengeance , 2015 . T. Schneider. Analyzing 1.1 billion nyc taxi and uber trips, with a vengeance, 2015."},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3093742.3093925"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.14778\/2752939.2752940"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2595641"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2595641"},{"key":"e_1_2_1_82_1","volume-title":"22nd International Conference on Extending Database Technology (EDBT)","author":"Traub J.","year":"2019","unstructured":"J. Traub , P. Grulich , A. R. Cu\u00e9llar , S. Bre\u00df , A. Katsifodimos , T. Rabl , and V. Markl . Efficient window aggregation with general stream slicing . In 22nd International Conference on Extending Database Technology (EDBT) , 2019 . J. Traub, P. Grulich, A. R. Cu\u00e9llar, S. Bre\u00df, A. Katsifodimos, T. Rabl, and V. Markl. Efficient window aggregation with general stream slicing. In 22nd International Conference on Extending Database Technology (EDBT), 2019."},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00135"},{"key":"e_1_2_1_84_1","volume-title":"USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 16)","author":"Trivedi A.","year":"2016","unstructured":"A. Trivedi , P. Stuedi , J. Pfefferle , R. Stoica , B. Metzler , I. Koltsidas , and N. Ioannou . On the {ir}relevance of network performance for data processing . In USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 16) . USENIX Association , 2016 . A. Trivedi, P. Stuedi, J. Pfefferle, R. Stoica, B. Metzler, I. Koltsidas, and N. Ioannou. On the {ir}relevance of network performance for data processing. In USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 16). USENIX Association, 2016."},{"key":"e_1_2_1_85_1","volume-title":"URL software.intel.com\/en-us\/articles\/single-producer-single-consumer-queue","author":"Vyukov D.","year":"2015","unstructured":"D. Vyukov . Single-producer\/single-consumer queue. Intel Developer Zonw , URL software.intel.com\/en-us\/articles\/single-producer-single-consumer-queue , 2015 . D. Vyukov. Single-producer\/single-consumer queue. Intel Developer Zonw, URL software.intel.com\/en-us\/articles\/single-producer-single-consumer-queue, 2015."},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2015.7113328"},{"key":"e_1_2_1_87_1","volume-title":"Project tungsten: Bringing apache spark closer to bare metal","author":"Xin R.","year":"2015","unstructured":"R. Xin and J. Rosen . Project tungsten: Bringing apache spark closer to bare metal , 2015 . Retrieved November 2, 2018, from URL https:\/\/databricks.com\/blog\/2015\/04\/28\/project-tungsten-bringing-spark-closer-to-bare-metal.html. R. Xin and J. Rosen. Project tungsten: Bringing apache spark closer to bare metal, 2015. Retrieved November 2, 2018, from URL https:\/\/databricks.com\/blog\/2015\/04\/28\/project-tungsten-bringing-spark-closer-to-bare-metal.html."},{"key":"e_1_2_1_88_1","unstructured":"B. Yavuz. Benchmarking structured streaming on databricks runtime against state-of-the-art streaming systems 2017.  B. Yavuz. Benchmarking structured streaming on databricks runtime against state-of-the-art streaming systems 2017."},{"key":"e_1_2_1_89_1","first-page":"2","volume-title":"NSDI","author":"Zaharia M.","year":"2012","unstructured":"M. Zaharia , M. Chowdhury , T. Das , A. Dave , J. Ma , M. McCauley , M. J. Franklin , S. Shenker , and I. Stoica . Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing . In NSDI , pages 2 -- 2 . 2012 . M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. J. Franklin, S. Shenker, and I. Stoica. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In NSDI, pages 2--2. 2012."},{"key":"e_1_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1145\/2517349.2522737"},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934664"},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-12098-5_15"},{"key":"e_1_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402752"},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.119"},{"key":"e_1_2_1_95_1","volume-title":"et al. Vectorwise: Beyond column stores","author":"Zukowski M.","year":"2012","unstructured":"M. Zukowski , P. A. Boncz , et al. Vectorwise: Beyond column stores . IEEE Data Eng. Bull ., 2012 . M. Zukowski, P. A. Boncz, et al. Vectorwise: Beyond column stores. IEEE Data Eng. Bull., 2012."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3303753.3303758","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T09:27:40Z","timestamp":1672219660000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3303753.3303758"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1]]},"references-count":94,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["10.14778\/3303753.3303758"],"URL":"https:\/\/doi.org\/10.14778\/3303753.3303758","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2019,1]]}}}