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Darwin: Scale-In Stream Processing
Summary: Propose “scale-in”: compile-tailored stream processing that maximizes utilization of existing single- and multi-node hardware and supports recoverable larger-than-memory state, avoiding the scale-up vs availability tradeoff. Darwin prototype implements this and reports ~10× speedups vs scale-out systems while matching scale-up throughput.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 450
- Venue
- CIDR
- Year
- 2022
- Pagerank
- 4.2503199e-05
- Overall Rank
- 9,925 | 31.03%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 59 |
Efficiently Compiling Efficient Query Plans for Modern Hardware |
2011 |
VLDB |
0.0006445664 |
| 892 |
Faster: A Concurrent Key-Value Store with In-Place Updates |
2018 |
SIGMOD |
0.00015522869 |
| 1,097 |
Trill: A High-Performance Incremental Query Processor for Diverse Analytics |
2015 |
VLDB |
0.00014083973 |
| 3,050 |
Viper: An Efficient Hybrid PMem-DRAM Key-Value Store |
2021 |
VLDB |
7.6491863e-05 |
| 3,771 |
SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures |
2016 |
SIGMOD |
6.7738535e-05 |
| 4,269 |
Maximizing Persistent Memory Bandwidth Utilization for OLAP Workloads |
2021 |
SIGMOD |
6.2950779e-05 |
| 4,449 |
DFI: The Data Flow Interface for High-Speed Networks |
2021 |
SIGMOD |
6.1740574e-05 |
| 4,490 |
Analyzing Efficient Stream Processing on Modern Hardware |
2019 |
VLDB |
6.137834e-05 |
| 5,433 |
Enabling Low Tail Latency on Multicore Key-Value Stores |
2020 |
VLDB |
5.5086304e-05 |
| 5,670 |
BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures |
2019 |
SIGMOD |
5.3799054e-05 |
| 6,218 |
Charting the Design Space of Query Execution using VOILA |
2021 |
VLDB |
5.1462707e-05 |
| 6,649 |
Grizzly: Efficient Stream Processing Through Adaptive Query Compilation |
2020 |
SIGMOD |
4.9724735e-05 |
| 7,371 |
Hazelcast Jet: Low-latency Stream Processing at the 99.99th Percentile |
2021 |
VLDB |
4.744863e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 1,546 |
Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark |
2018 |
SIGMOD |
0.00011418993 |
| 5,044 |
Massive Scale-out of Expensive Continuous Queries |
2011 |
VLDB |
5.7338982e-05 |
| 6,649 |
Grizzly: Efficient Stream Processing Through Adaptive Query Compilation |
2020 |
SIGMOD |
4.9724735e-05 |
| 7,658 |
Scalable Delivery of Stream Query Result |
2009 |
VLDB |
4.6817213e-05 |
| 10,043 |
Accelerating Stream Processing Engines via Hardware Offloading |
2026 |
SIGMOD |
4.1905499e-05 |
| 2,777 |
Quickstep: A Data Platform Based on the Scaling-Up Approach |
2018 |
VLDB |
8.1346418e-05 |
| 11,471 |
Klink: Progress-Aware Scheduling for Streaming Data Systems |
2021 |
SIGMOD |
4.1905499e-05 |
| 4,797 |
Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines |
2020 |
SIGMOD |
5.9101178e-05 |
| 1,226 |
Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management |
2013 |
SIGMOD |
0.00013168869 |
| 4,490 |
Analyzing Efficient Stream Processing on Modern Hardware |
2019 |
VLDB |
6.137834e-05 |