Run-Time Operator State Spilling for Memory Intensive Long-Running Queries
Summary: Proposes run-time state spilling for memory-heavy, long-running queries with multiple stateful operators; shows inter-operator dependencies necessitating plan-level spill strategies. Introduces bottom-up operator-level spilling and partition-level strategies (local/global output, with penalty), implemented in D-CAPE; experiments favor global-output approaches. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Bin Liu
- 2. Yali Zhu
- 3. Elke A. Rundensteiner
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,226 | Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management | 2013 | SIGMOD | 0.00013168869 |
| 2,339 | Samza: Stateful Scalable Stream Processing at LinkedIn | 2017 | VLDB | 9.0029559e-05 |
| 4,152 | Query Suspend and Resume | 2007 | SIGMOD | 6.4009383e-05 |
| 6,871 | State-Slice: New Paradigm of Multi-query Optimization of Window-based Stream Queries | 2006 | VLDB | 4.8966771e-05 |
| 6,880 | E-Cube: Multi-Dimensional Event Sequence Analysis Using Hierarchical Pattern Query Sharing | 2011 | SIGMOD | 4.8934688e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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