Name
Abstract | ||
|---|---|---|
AbstractGoogle services continuously generate vast amounts of application data. This data provides valuable insights to business users. We need to store and serve these planet-scale data sets under the extremely demanding requirements of scalability, sub-second query response times, availability, and strong consistency; all this while ingesting a massive stream of updates from applications used around the globe. We have developed and deployed in production an analytical data management system, Napa, to meet these requirements. Napa is the backend for numerous clients in Google. These clients have a strong expectation of variance-free, robust query performance. At its core, Napa's principal technologies for robust query performance include the aggressive use of materialized views, which are maintained consistently as new data is ingested across multiple data centers. Our clients also demand flexibility in being able to adjust their query performance, data freshness, and costs to suit their unique needs. Robust query processing and flexible configuration of client databases are the hallmark of Napa design.Most of the related work in this area takes advantage of full flexibility to design the whole system without the need to support a diverse set of preexisting use cases. In comparison, a particular challenge we faced is that Napa needs to deal with hard constraints from existing applications and infrastructure, so we could not do a "green field" system, but rather had to satisfy existing constraints. These constraints led us to make particular design decisions and also devise new techniques to meet the challenges. In this paper, we share our experiences in designing, implementing, deploying, and running Napa in production with some of Google's most demanding applications. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.14778/3476311.3476377 | Hosted Content |
DocType | Volume | Issue |
Journal | 14 | 12 |
ISSN | Citations | PageRank |
2150-8097 | 0 | 0.34 |
References | Authors | |
0 | 44 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ankur Agiwal | 1 | 0 | 0.34 |
| Kevin Lai | 2 | 0 | 0.34 |
| Gokul Nath Babu Manoharan | 3 | 1 | 1.04 |
| Indrajit Roy | 4 | 11 | 1.13 |
| Jagan Sankaranarayanan | 5 | 0 | 0.34 |
| Hao Zhang | 6 | 0 | 0.34 |
| Tao Zou | 7 | 0 | 0.34 |
| Jim Chen | 8 | 3 | 4.46 |
| Min Chen | 9 | 0 | 0.34 |
| Ming Dai | 10 | 0 | 0.34 |
| Thanh Do | 11 | 0 | 1.35 |
| Haoyu Gao | 12 | 0 | 0.34 |
| Haoyan Geng | 13 | 0 | 0.34 |
| Raman Grover | 14 | 0 | 0.34 |
| Bo Huang | 15 | 0 | 0.34 |
| Yanlai Huang | 16 | 0 | 0.34 |
| Adam Li | 17 | 0 | 0.68 |
| Jianyi Liang | 18 | 0 | 0.68 |
| Tao Lin | 19 | 0 | 0.34 |
| Li Liu | 20 | 0 | 0.34 |
| Yao Liu | 21 | 0 | 0.34 |
| Xi Mao | 22 | 0 | 0.34 |
| Maya Meng | 23 | 0 | 0.34 |
| Prashant Mishra | 24 | 0 | 0.34 |
| Jay Patel | 25 | 0 | 0.34 |
| Rajesh Sr | 26 | 0 | 0.34 |
| Vijayshankar Raman | 27 | 0 | 0.34 |
| Sourashis Roy | 28 | 0 | 0.68 |
| Mayank Singh Shishodia | 29 | 0 | 0.68 |
| Tianhang Sun | 30 | 0 | 0.68 |
| Justin Tang | 31 | 0 | 0.68 |
| Jun Tatemura | 32 | 0 | 0.68 |
| Sagar Trehan | 33 | 0 | 0.34 |
| Ramkumar Vadali | 34 | 0 | 0.68 |
| Prasanna Venkatasubramanian | 35 | 0 | 0.34 |
| Joey Zhang | 36 | 0 | 0.34 |
| Kefei Zhang | 37 | 0 | 0.34 |
| Yupu Zhang | 38 | 0 | 0.68 |
| Zeleng Zhuang | 39 | 0 | 0.68 |
| Goetz Graefe | 40 | 0 | 0.34 |
| Divy Agrawal | 41 | 0 | 0.68 |
| Jeffrey F. Naughton | 42 | 8363 | 1913.71 |
| Sujata Kosalge | 43 | 0 | 0.34 |
| Divyakant Agrawal | 44 | 8201 | 1674.75 |