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Monarch: Google’s Planet-Scale In-Memory Time Series Database

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Manage episode 487366651 series 3670304
Content provided by The Binary Breakdown. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Binary Breakdown or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This technical paper details the architecture and design of Monarch, a planet-scale in-memory time series database developed at Google. Monarch is used to monitor the performance and availability of massive, globally distributed systems like YouTube, Google Maps, and Gmail. The paper discusses the system's novel features, including its regionalized architecture, expressive query language, and support for sophisticated data types, such as distributions and exemplars. It also presents a comprehensive evaluation of Monarch's scalability, highlighting its ability to handle trillions of time series, ingest terabytes of data per second, and serve millions of queries per second. Finally, the paper outlines key lessons learned from the development and operation of Monarch over a decade.

https://www.vldb.org/pvldb/vol13/p3181-adams.pdf

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43 episodes

Artwork
iconShare
 
Manage episode 487366651 series 3670304
Content provided by The Binary Breakdown. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Binary Breakdown or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This technical paper details the architecture and design of Monarch, a planet-scale in-memory time series database developed at Google. Monarch is used to monitor the performance and availability of massive, globally distributed systems like YouTube, Google Maps, and Gmail. The paper discusses the system's novel features, including its regionalized architecture, expressive query language, and support for sophisticated data types, such as distributions and exemplars. It also presents a comprehensive evaluation of Monarch's scalability, highlighting its ability to handle trillions of time series, ingest terabytes of data per second, and serve millions of queries per second. Finally, the paper outlines key lessons learned from the development and operation of Monarch over a decade.

https://www.vldb.org/pvldb/vol13/p3181-adams.pdf

  continue reading

43 episodes

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