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9 Techniques to Build Cloud-Native, Geo-Distributed SQL Apps with Low Latency

9 Techniques to Build Cloud-Native, Geo-Distributed SQL Apps with Low Latency

This post is an in-depth look at the various techniques that applications needing low latency and high availability can leverage while using a geo-distributed SQL database like YugabyteDB so that the negative impacts of a high-latency, unreliable Wide Area Network (WAN) are minimized.

Geo-Distributed SQL is the Future of RDBMS

Enterprises are increasingly moving to cloud native applications powered by microservices architecture. These applications run on elastic cloud infrastructure such as serverless frameworks and containers.

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Recapping My Internship at Yugabyte – Jayden Navarro

Recapping My Internship at Yugabyte – Jayden Navarro

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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Getting Started with PostgreSQL Triggers in a Distributed SQL Database

Getting Started with PostgreSQL Triggers in a Distributed SQL Database

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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PostgreSQL Compatibility in YugabyteDB 2.0

PostgreSQL Compatibility in YugabyteDB 2.0

The team at Yugabyte and members of the community were excited to announce the general availability of YugabyteDB 2.0 this week. One of the flagship features of the release was the production readiness of the PostgreSQL compatible, Yugabyte SQL API (YSQL). In other blogs we covered Jepsen testing results, new performance benchmarks and ecosystem integrations including the GraphQL projects Hasura and Prisma. In this post we’d like to take the opportunity to dive a little deeper into the PostgreSQL compatibility features to demonstrate what’s supported and what’s around the corner.

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Best Practices and Recommendations for Distributed SQL on Kubernetes

Best Practices and Recommendations for Distributed SQL on Kubernetes

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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YugabyteDB’s Distributed SQL API Jepsen Test Results

YugabyteDB’s Distributed SQL API Jepsen Test Results

Note: You can join the discussion on Hacker News here.

We are very excited to announce that the SQL API of YugabyteDB v1.3.1.2 passed Jepsen testing performed by Kyle Kingsbury [Edit] (with the exception of transactional DDL support, which almost no other distributed SQL database vendor supports, and we plan to support soon. The real-world impact of this open issue is minor as it is limited to cases where DML happens before DDL has fully completed).

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Low Latency Reads in Geo-Distributed SQL with Raft Leader Leases

Low Latency Reads in Geo-Distributed SQL with Raft Leader Leases

Note: This post contains interactive animations that explain how some of these complex algorithms work. Please view this post in a suitable media (at least 1000px by 600px screen resolution) for best results.

In this blog post, we are going to dive deep into the read performance of Raft – why read performance can take a hit and how it can be improved using leader leases. Additionally, we will also look at how to make the correctness guarantees around leader leases stronger.

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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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How to Handle Runaway Queries in a Distributed SQL Database

How to Handle Runaway Queries in a Distributed SQL Database

Runaway queries are queries that scan through a large set of data. Such queries consume vast amounts of I/O and CPU resources of the database in the background, even if the results appear as harmless timeouts to the end user or the client application. How do runaway queries get executed in the first place, anyway? Everyone who uses databases has at some point or another entered SELECT * from some_large_table, only to realize they forgot to add a LIMIT n clause.

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5 Reasons Why Apache Kafka Needs a Distributed SQL Database

5 Reasons Why Apache Kafka Needs a Distributed SQL Database

Modern enterprise applications must be super-elastic, adaptable, and running 24/7. However, traditional request-driven architectures entail a tight coupling of applications. For example, App 1 asks for some information from App 2 and waits. App 2 then sends the requested information to App 1. This sort of app-to-app coupling hinders development agility and blocks rapid scaling.

In event-driven architectures, applications publish events to a message broker asynchronously. They trust the broker to route the message to the right application,

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