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Introduction to Spark 2.0 for Database Researchers

Summary: Introduces Spark 2.0 to database researchers, focusing on Catalyst, whole-stage code generation, and hands-on use of DataFrames, SQL, streaming, and ML pipelines. Emphasizes engine internals and a practical path to hack Spark by extending the query optimizer to accelerate distributed joins. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5208
Venue
SIGMOD
Year
2016
Pagerank
4.3218691e-05
Overall Rank
9,584 | 33.33%
DOI
10.1145/2882903.2912565

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
1,532 Data Management in Machine Learning: Challenges, Techniques, and Systems 2017 SIGMOD 0.00011472681
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
66 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00061639801
542 Shark: SQL and Rich Analytics at Scale 2013 SIGMOD 0.00020595648
3,535 Scaling Spark in the Real World: Performance and Usability 2015 VLDB 6.9992495e-05
6,784 SparkR: Scaling R Programs with Spark 2016 SIGMOD 4.9265155e-05
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