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Author: Sid Choudhury

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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Best Practices for Deploying   Confluent Kafka, Spring Boot & Distributed SQL Based Streaming Apps on Kubernetes

Best Practices for Deploying Confluent Kafka, Spring Boot & Distributed SQL Based Streaming Apps on Kubernetes

In our previous post “Develop IoT Apps with Confluent Kafka, KSQL, Spring Boot & Distributed SQL”, we highlighted how Confluent Kafka, KSQL, Spring Boot and YugabyteDB can be integrated to develop an application responsible for managing Internet-of-Things (IoT) sensor data. In this post, we will review the challenges and best practices associated with deploying such a stateful streaming application on Kubernetes.

Challenges of Streaming Apps

Streaming apps are inherently stateful in nature given the large volume of data managed and that too continuously.

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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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YugabyteDB Engineering Update – June 4, 2019

YugabyteDB Engineering Update – June 4, 2019

We are pleased to announce that YugabyteDB 1.2.10 is live! You can read the release notes of this and previous versions here. This release is shipping with foreign key support plus 16 new features, enhancements and bug fixes. Here’s a few highlights from the 1.2.10 release:

YSQL Updates

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

  • Basic support for FOREIGN KEY constraints is now available!

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Spring Data REST Services Powered By Distributed SQL – A Hands-on Lab

Spring Data REST Services Powered By Distributed SQL – A Hands-on Lab

The Spring application development framework is arguably the most popular framework among Java developers. However, given its extensive breadth and depth, it can be difficult to learn for new users. As the name suggests, Spring Boot makes it easy to `boot up` with the Spring framework. It shortens development time by taking an opinionated view of the framework and the associated third-party libraries. Annotation configuration and default codes are two examples of such a view.

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Develop IoT Apps with Confluent Kafka, KSQL, Spring Boot & Distributed SQL

Develop IoT Apps with Confluent Kafka, KSQL, Spring Boot & Distributed SQL

In our previous post “5 Reasons Why Apache Kafka Needs a Distributed SQL Database”, we highlighted why Kafka-based data services need a distributed SQL database like YugabyteDB as their highly scalable, long-term persistent data store. In this post, we show how Confluent Kafka, KSQL, Spring Boot and YugabyteDB can be integrated to develop an application for managing Internet-of-Things (IoT) sensor data.

The Scenario – IoT-Enabled Fleet Management

A trucking company wants to track its fleet of IoT-enabled vehicles that are delivering shipments across the country.

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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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YugabyteDB 2.0: A Distributed SQL Roadmap

YugabyteDB 2.0: A Distributed SQL Roadmap

The YugabyteDB community is eagerly looking forward to v2.0, the next major release expected in Summer 2019. Needless to say, we are very excited about the depth and breadth of features in this release. Here’s an overview of the various areas we expect the new release to cover.

Distributed SQL

YSQL is YugabyteDB’s PostgreSQL wire-compatible distributed SQL API. Thanks to our design decision to use the existing PostgreSQL query layer as a starting point,

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Why Distributed SQL Beats Polyglot Persistence for Building Microservices?

Why Distributed SQL Beats Polyglot Persistence for Building Microservices?

Today’s microservices rely on data with different models and read/write access patterns. Polyglot persistence, first introduced in 2008, states that each such data model should be powered by an independent database that is purpose-built for that model. This post highlights the loss of agility that microservices development and operations suffer when adopting polyglot persistence. We review how distributed SQL serves as an alternative approach that doesn’t compromise this agility.

E-Commerce Example

Polyglot Persistence in Action at an E-Commerce App (Source: Martin Fowler)

Breaking down monolithic applications into smaller,

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Distributed SQL on Google Kubernetes Engine (GKE) with YugabyteDB’s Helm Chart

Distributed SQL on Google Kubernetes Engine (GKE) with YugabyteDB’s Helm Chart

The glory days of the heavy-weight hypervisor are slowly fading away, and in the last few years, containerization of applications and services is the new reality. With containerization, enterprises can prototype, deploy, and meet scale demands more quickly. To systematically and efficiently manage these large-scale deployments, enterprises have bet on technologies like Kubernetes (aka k8s), a powerful container orchestrator, to get the job done. Kubernetes was originally developed by Google, but it has been open sourced since 2014 and is today developed by a large community of contributors.

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