Jepsen Tests

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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Wrapping Up: Jepsen Test Results for YugabyteDB 1.2 Webinar

Wrapping Up: Jepsen Test Results for YugabyteDB 1.2 Webinar

If you missed the April 30 webinar with Kyle Kingsbury, the creator of Jepsen and Karthik Ranganathan, CTO at YugaByte, you can check out the playback here and download the slides here.

Kyle did a great deep-dive into his testing approach, research methodology and what he found when applied to YugabyteDB. If you are looking for an unvarnished and honest assessment of YugabyteDB’s consistency and transactional claims,

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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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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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