Monarch: Google’s Planet-Scale In-Memory Time Series Database
Summary: Monarch is Google’s planet-scale, in-memory time-series DB for a multi-tenant service. It features a regionalized distributed architecture with global query and configuration planes, an expressive relational model, and novel reliability and flexibility mechanisms to ingest TB/s and serve millions of queries—plus decade-long operational lessons. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Colin Adams
- 2. Luis Alonso
- 3. Benjamin Atkin
- 4. John Banning
- 5. Sumeer Bhola
- 6. Rick Buskens
- 7. Ming Chen
- 8. Xi Chen
- 9. Yoo Chung
- 10. Qin Jia
- 11. Nick Sakharov
- 12. George Talbot
- 13. Adam Tart
- 14. Nick Taylor
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 210 | Gorilla: A Fast, Scalable, In-Memory Time Series Database | 2015 | VLDB | 0.0003404384 |
| 808 | Universality of Serial Histograms | 1993 | VLDB | 0.00016432772 |
| 1,805 | M4: A Visualization-Oriented Time Series Data Aggregation | 2014 | VLDB | 0.00010493299 |
| 2,267 | ModelarDB: Modular Model-Based Time Series Management with Spark and Cassandra | 2018 | VLDB | 9.1519895e-05 |
| 3,355 | F1 Query: Declarative Querying at Scale | 2018 | VLDB | 7.1829142e-05 |
| 4,649 | Window-Aware Load Shedding for Aggregation Queries over Data Streams | 2006 | VLDB | 6.0236001e-05 |
| 5,158 | Coconut: A Scalable Bottom-Up Approach for Building Data Series Indexes | 2018 | VLDB | 5.6588553e-05 |
| 6,123 | Data Ingestion for the Connected World | 2017 | CIDR | 5.1991194e-05 |
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