This blog covers the function and implementation compactions, focusing on those details and configurations that are relevant to YugabyteDB. The second half focuses on the behavior and prioritization of background compactions.
Welcome to the December 2022 edition of the YugabyteDB Tips and Tricks series, where we recap distributed SQL database questions from around the Internet! This month we will focus on the differences between table partitioning and sharding in a distributed SQL deployment, whether or not the scaling limitations in RocksDB has an impact on YugabyteDB, and tools for visualizing the data stored in the Yugabyte database.
This blog post was co-authored by Mikhail Bautin and Kannan Muthukkaruppan
As described in our previous post “How We Built a High Performance Document Store on RocksDB?”, YugabyteDB’s distributed document store (DocDB) uses RocksDB as its per-node storage engine. We made multiple performance and data density related enhancements to RocksDB in the course of embedding it into DocDB’s document storage layer (figure below).
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This blog post was co-authored by Mikhail Bautin and Kannan Muthukkaruppan
RocksDB is a popular embeddable persistent key-value store. First open sourced by Facebook in 2012 as a fork of the Google LevelDB project, it has been adapted over the years to a wide range of workloads including database storage engines and application data caching.
In this post, we explain our rationale for selecting RocksDB as a foundational building block for YugabyteDB.
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