VLDB 2026 Research / reviewers in the wild / expert
Kapil Bajaj
dblp:212/2242
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 87% Cloud and datacenter computing · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems › storage hierarchy
tiered storage |
0.8 | 1 | 2024 | Goku: A Schemaless Time Series Database for Large Scale Monitoring at Pinterest · Proc. VLDB Endow. 2024 |
Storage systems › data management
time series database |
0.8 | 1 | 2024 | Goku: A Schemaless Time Series Database for Large Scale Monitoring at Pinterest · Proc. VLDB Endow. 2024 |
Methods — techniques the papers use, named apart from their topics
tiered storage · 0.8replication · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Goku: A Schemaless Time Series Database for Large Scale Monitoring at PinterestabstractEngineers rely heavily on observability tools to monitor their business and system metrics and set up alerting on it. A reliable and efficient monitoring system is very important for development velocity. In this paper, we introduce Goku, a time series database (TSDB) we built from the ground up at Pinterest. Over the years, we have studied user patterns and common requests to constantly evolve Goku to store and serve the use cases at Pinterest with high efficiency and reduced costs. At its core, Goku uses tiered storage to store new and frequently queried metrics data in memory while leveraging solid state drive (SSD) and hard disks (HDD) for older data. Goku aggregates metrics data at write time while also rolling up datapoints with lower time granularity to low latency to certain use cases. Goku also supports modifying configurations on metrics data like time to live (TTL), rollup granularity, backfilling capability, etc. Using multiple replicas and AWS S3 as backup, Goku is highly available and fault tolerant. Monil Mukesh Sanghavi, Ming-May Hu, Zhenxiao Luo, Kapil Bajaj |
Proc. VLDB Endow. | 5 |