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Tag: Data Sharding

Distributed SQL Essentials: Sharding and Partitioning in YugabyteDB

Distributed SQL Essentials: Sharding and Partitioning in YugabyteDB

A distributed SQL database provides a service where you can query the global database without knowing where the rows are. You connect to any node, without having to know the cluster topology. You query your tables, and the database will determine the best access to your data, whether it’s close to your client or geographically distant.

The organization of data, whether co-located or partitioned, is the most important consideration for high performance,

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Distributed SQL Tips and Tricks – Feb 21, 2020

Distributed SQL Tips and Tricks – Feb 21, 2020

Welcome to this week’s tips and tricks blog where we recap some distributed SQL questions from around the Internet. We’ll also review upcoming events, new documentation, and blogs that have been published since the last post. Got questions? Make sure to ask them on our YugabyteDB Slack channel, Forum, GitHub, or Stackoverflow. Ok, let’s dive right in:

When should I use JSON vs JSONB data types?

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Four Data Sharding Strategies We Analyzed in Building a Distributed SQL Database

Four Data Sharding Strategies We Analyzed in Building a Distributed SQL Database

A distributed SQL database needs to automatically partition the data in a table and distribute it across nodes. This is known as data sharding and it can be achieved through different strategies, each with its own tradeoffs. In this post, we will examine various data sharding strategies for a distributed SQL database, analyze the tradeoffs, explain the rationale for which of these strategies YugabyteDB supports and what we picked as the default sharding strategy.

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How Data Sharding Works in a Distributed SQL Database

How Data Sharding Works in a Distributed SQL Database

Editor’s note: This post was updated August 28, 2020 to include new sharding features available starting in YugabyteDB 2.2

Enterprises of all sizes are embracing rapid modernization of user-facing applications as part of their broader digital transformation strategy. The relational database (RDBMS) infrastructure that such applications rely on suddenly needs to support much larger data sizes and transaction volumes. However, a monolithic RDBMS tends to quickly get overloaded in such scenarios.

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