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Cackle: Analytical Workload Cost and Performance Stability With Elastic Pools

Summary: Cackle blends fast, scalable but costly cloud functions with slow-start, inexpensive VMs for analytical workloads. It delivers stable latency and cost across diverse workloads by blending these tiers to avoid provisioning cliffs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6735
Venue
SIGMOD
Year
2023
Pagerank
4.5368524e-05
Overall Rank
8,361 | 41.84%
DOI
10.1145/3626720

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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

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

Rank Cited Paper Year Venue Pagerank
167 The Snowflake Elastic Data Warehouse 2016 SIGMOD 0.00039180521
426 Amazon Redshift and the Case for Simpler Data Warehouses 2015 SIGMOD 0.00023594359
659 The Making of TPC-DS 2006 VLDB 0.00018500853
1,284 Amazon Redshift Re-invented 2022 SIGMOD 0.00012837822
1,326 Starling: A Scalable Query Engine on Cloud Functions 2020 SIGMOD 0.00012576952
1,501 P-Store: An Elastic Database System with Predictive Provisioning 2018 SIGMOD 0.00011664869
2,424 Lambada: Interactive Data Analytics on Cold Data Using Serverless Cloud Infrastructure 2020 SIGMOD 8.8380822e-05
2,545 POLARIS: The Distributed SQL Engine in Azure Synapse 2020 VLDB 8.5725413e-05
3,844 The evolution of Amazon Redshift (extended abstract) 2021 VLDB 6.7076451e-05
5,441 Using Cloud Functions as Accelerator for Elastic Data Analytics 2023 SIGMOD 5.5028093e-05
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