{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:51:46Z","timestamp":1773481906785,"version":"3.50.1"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:p>Apache Spark is a widely deployed big data analytics framework that offers such attractive features as resiliency, load-balancing, and a rich ecosystem. However, there is still plenty of room for improvement in its performance. Although a data-parallel system in a native programming language significantly improves performance, it may require re-implementing many functionalities of Spark to become a full-featured system. It is desirable for native big data systems to just write a compute engine in native languages to ensure high efficiency, and reuse other mature features provided by Spark rather than re-implement everything. But the interaction between the JVM and the native world risks becoming a bottleneck.<\/jats:p>\n          <jats:p>This paper proposes Chukonu, a native big data framework that re-uses critical big data features provided by Spark. Owing to our novel DAG-splitting approach, the potential Spark integration overhead is alleviated, and its even outperforms existing pure native big data frameworks. Chukonu splits DAG programs into run-time parts and compile-time parts: The run-time parts are delegated to Spark to offload the complexities due to feature implementations. The compile-time parts are natively compiled. We propose a series of optimization techniques to be applied to the compile-time parts, such as operator fusion, vectorization, and compaction, to significantly reduce the Spark integration overhead. The results of evaluation show that Chukonu has a speedup of up to 71.58X (geometric mean 6.09X) over Apache Spark, and up to 7.20X (geometric mean 2.30X) over pure-native frameworks on six commonly-used big data applications. By translating the physical plan produced by SparkSQL into Chukonu programs, Chukonu accelerates Spark-SQL's TPC-DS performance by 2.29X.<\/jats:p>","DOI":"10.14778\/3503585.3503596","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T22:18:07Z","timestamp":1649974687000},"page":"872-885","source":"Crossref","is-referenced-by-count":4,"title":["Chukonu"],"prefix":"10.14778","volume":"15","author":[{"given":"Bowen","family":"Yu","sequence":"first","affiliation":[{"name":"Tsinghua University"}]},{"given":"Guanyu","family":"Feng","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Huanqi","family":"Cao","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Xiaohan","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Zhenbo","family":"Sun","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Haojie","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Xiaowei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Weimin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Wenguang","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]}],"member":"320","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"https:\/\/flink.apache.org\/. 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