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Category: Databases

Relational Data Modeling with Foreign Keys in a Distributed SQL Database

Relational Data Modeling with Foreign Keys in a Distributed SQL Database

Note added on October 31, 2019

A lot has happened since this post was published in July 2019. Back then, the current YugabyteDB version was 1.2.10. And now, it’s 2.0.3. My original text included some caveats and comments like “Until this support is added in a future release…”. Now, no caveats are needed. I therefore revised my text and the companion downloadable code to remove all reference to those earlier, interim,

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YugabyteDB Engineering Update – June 27, 2019

YugabyteDB Engineering Update – June 27, 2019

We are pleased to announce that YugabyteDB 1.2.11 is live! You can read the release notes of this and previous versions here. This release is shipping with stored procedures support, plus 30 new features, enhancements and bug fixes. Here’s a few highlights from the 1.2.11 release.

What’s YSQL? It’s YugabyteDB’s PostgreSQL-compatible, distributed SQL API.

[#1155] Support for FUNCTION and PROCEDURE

Stored procedures allow developers to “bundle up” operations that would otherwise take several queries and round trips into a single function.

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GO-JEK’s Performance Benchmarking of CockroachDB, TiDB & YugabyteDB on Kubernetes

GO-JEK’s Performance Benchmarking of CockroachDB, TiDB & YugabyteDB on Kubernetes

Iqbal Farabi and Tara Baskara, Systems Engineers from GO-JEK Indonesia, recently presented the results of their benchmarking of cloud native databases on Kubernetes at KubeCon Europe in Barcelona. The three databases they benchmarked were CockroachDB, TiDB and YugabyteDB. This post brings their presentation (video recording) and slides (PDF) to the attention of our readers. It also highlights a few areas of collaboration between the GO-JEK team and YugabyteDB Engineering.

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Achieving Fast Failovers After Network Partitions in a Distributed SQL Database

Achieving Fast Failovers After Network Partitions in a Distributed SQL Database

In February of this year, Kyle Kingsbury of Jepsen.io was conducting formal testing of YugabyteDB for correctness under extreme and unorthodox conditions. Obviously, simulating all manner of network partitions is part of his testing methodology. As a result, during his testing he spotted the fact that although nodes would reliably come back after a failure, the recovery itself was taking roughly 25 seconds to occur. We certainly didn’t like the sound of that!

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6 Technical Challenges Developing a Distributed SQL Database

6 Technical Challenges Developing a Distributed SQL Database

You can join the discussion on HackerNews here.

We crossed the three year mark of developing the YugabyteDB database in February of 2019. It has been a thrilling journey thus far, but not without its fair share of technical challenges. There were times when we had to go back to the drawing board and even sift through academic research to find a better solution than what we had at hand.

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How to Achieve High Availability, Low Latency & GDPR Compliance in a Distributed SQL Database

How to Achieve High Availability, Low Latency & GDPR Compliance in a Distributed SQL Database

Today’s developers understand that the key requirement to converting and retaining customers is all about delivering fast and responsive experiences, while remaining resilient to failures and compliant with data governance regulations. YugabyteDB is purpose built for geo-distributed applications that require high availability, high performance and regulatory compliance. In this blog, we are going to “look under the hood,” to explore exactly how YugabyteDB distributes data across multiple clouds, regions and availability zones.

YugabyteDB is an open source,

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Distributed PostgreSQL on a Google Spanner Architecture – Query Layer

Distributed PostgreSQL on a Google Spanner Architecture – Query Layer

Our previous post dived into the details of the storage layer of YugabyteDB called DocDB, a distributed document store inspired by Google Spanner. This post focuses on Yugabyte SQL (YSQL), a distributed, highly resilient, PostgreSQL-compatible SQL API layer powered by DocDB. A follow-up post will highlight the challenges faced and lessons learned when engineering such a database.

YSQL, Distributed PostgreSQL Made Real

Yugabyte SQL (YSQL) is a distributed and highly resilient SQL layer,

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Distributed PostgreSQL on a Google Spanner Architecture – Storage Layer

Distributed PostgreSQL on a Google Spanner Architecture – Storage Layer

In this post, we’ll dive into the architecture of the distributed storage layer of YugabyteDB, which is inspired by Google Spanner’s design. Our subsequent post covers the Query Layer, where the storage layer meets PostgreSQL as the SQL API. Finally, here is a follow-up post that highlights the key technical challenges we faced while engineering a distributed SQL database like YugabyteDB.

Logical Architecture

YugabyteDB is comprised of two logical layers,

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Announcing YugabyteDB 1.2 and Company Update

Announcing YugabyteDB 1.2 and Company Update

The team at YugaByte is excited to announce that YugabyteDB 1.2 is officially GA! You can download the latest version from our Quick Start page.

New in 1.2: YugaByte SQL Beta 3

YugaByte SQL (YSQL) is our PostgreSQL v11 compatible, distributed SQL API. It is ideal for powering microservices that require low latency, internet scale, geographic data distribution and extreme resilience to failures but want the data modeling flexibility of SQL (joins,

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YugabyteDB High Availability & Transactions for PostgreSQL & MongoDB Developers

YugabyteDB High Availability & Transactions for PostgreSQL & MongoDB Developers

In the first post of our series comparing YugabyteDB with PostgreSQL and MongoDB, we mapped the core concepts in YugabyteDB to the two popular databases. This post is a deeper dive into the high availability and transactions architecture of these databases.

High Availability

Almost all databases including YugabyteDB use replication to ensure that the database remains highly available under failures. The basic idea is to keep copies of data on independent failure domains so that loss of one domain does not lead to data loss or data unavailability from the application client standpoint.

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