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New Query Optimization Techniques in the Spark Engine of Azure Synapse

Summary: Novel exchange placement in Spark-based Azure Synapse reduces data shuffles and enables multi-consumer reuse. Push-downs (aggregates, semi-joins) and peephole tweaks target scale-out stateful ops, achieving 1.8x on TPC-DS vs Spark 3.0.1. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12963
Venue
VLDB
Year
2022
Pagerank
4.4957661e-05
Overall Rank
8,506 | 40.83%
DOI
10.14778/3503585.3503601

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
5,023 GenRewrite: Query Rewriting via Large Language Models 2026 SIGMOD 5.75363e-05
10,121 TQEx: Tensor-based Query Engine Enhanced by Bridging the Gap 2026 SIGMOD 4.1945683e-05
10,749 Scaling GPU-Accelerated Databases beyond GPU Memory Size 2025 VLDB 4.1945683e-05
11,267 Anser: Adaptive Information Sharing Framework of AnalyticDB 2023 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

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

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