Well, that’s a wrap! Yugabyte would like to extend a special thanks to JD and Amanda from the Postgreconf.org team, and to all the speakers from Facebook, Google, Amazon, Pivotal, Salesforce, Narvar, Plume Design and others that presented at the first-ever Distributed SQL Summit on Sept 20, 2019.
If you couldn’t make it out to this year’s event, have no fear!
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It was a warm day in early October, and two large white tents occupied the lawn that sits between the Gates, Hewlett, and Packard buildings. Companies pasted the word “AI” in big bold letters across their banners, and students formed long lines, resumes in-hand, eager to learn about the Next Big Thing and how often the company cafeteria serves Poké.
Six months prior I had made the decision to leave my comfortable job as a Software Engineer in the networking industry and pursue a Masters degree in Computer Science at Stanford,
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Triggers are a basic feature that all monolithic SQL systems like Oracle, SQL Server and PostgreSQL have supported for many years. They are very useful in a variety of scenarios ranging from simple audit logging, to advanced tasks like updating remote databases in a federated cluster. In this blog, we’ll look at examples of INSERT, UPDATE and INSTEAD OF triggers in Yugabyte DB.
What’s Yugabyte DB? It is an open source, high-performance distributed SQL database built on a scalable and fault-tolerant design inspired by Google Spanner.
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This post addresses a concern raised about a benchmarking result we recently published comparing the performance of YugabyteDB, Amazon Aurora and CockroachDB. It was pointed out that we unfairly used the default isolation level for each database rather than use serializable isolation level in all databases (even though serializable level was not required for these workloads). In addition, we are also happy to share additional results with the workloads run at YugabyteDB’s serializable isolation level.
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YugabyteDB 2.0 is now available! This release features the production-ready Yugabyte SQL (YSQL) API. Discover new updates, including performance benchmarks, Jepsen test results, and a sneak peek at upcoming Beta features.
YugabyteDB 2.0 is now generally available, featuring the production readiness of the PostgreSQL compatible Yugabyte SQL API (YSQL). In addition to Jepsen testing results, new performance benchmarks, and ecosystem integrations, YugabyteDB 2.0 offers a high degree of PostgreSQL compatibility, with plans for future support.
Today, we are proud to announce that Blitzz and
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One of the flagship features of the Yugabyte distributed SQL database is the PostgreSQL-compatible YSQL API. Let’s take a look at the performance and scalability of YSQL and compare it to two other PostgreSQL-compatible databases—Amazon Aurora PostgreSQL and CockroachDB.
We are excited to congratulate the Rook community on the release on 1.1! We are also pleased to announce that the Rook operator for YugabyteDB is now available from rook.io and also on Github. This release extends the Rook storage operator as a custom resource, as well as provides an additional way to easily create, natively view and manage YugabyteDB within a Kubernetes cluster. In this blog we’ll summarize how to get started with the operator.
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YugabyteDB and Kubernetes have very complementary design principles because they both rely on an extensible and flexible API layer, as well as a scale-out architecture for performance and availability. In this blog post we’ll look at best practices and recommendations when choosing Kubernetes as the cluster foundation for a distributed SQL system. This will begin with a review of relevant architectural decisions of the YugabyteDB. Then we’ll walk you through how to handle the provisioning,
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