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Category: How It Works

Java Development: How Applications Litter Beyond the Heap

Java Development: How Applications Litter Beyond the Heap

In Java development, garbage collection is a routine task. Applications generate garbage all the time. And that garbage is meticulously cleaned out by CMS, G1, Azul C4 and other types of collectors. Basically, our applications are born to bring value to this world, but nothing is perfect—including our applications that leave litter in the Java heap.

However, the story doesn’t end with the Java heap. In fact, it only starts there. Let’s take the example of a basic Java application that uses a relational database—such as PostgreSQL—and solid state drives (SSDs) as a storage device.

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Distributed SQL Sharding: How Many Tablets, and at What Size?

Distributed SQL Sharding: How Many Tablets, and at What Size?

The first answer to this question is the usual “it depends“. The second answer, thanks to YugabyteDB’s auto-splitting feature and distributed SQL sharding principles, is “don’t worry, this is managed automatically.

However, it’s still important to understand how sharding works, how to handle corner cases correctly, and how to split tablets to save resources. In this post, we’ll explore how sharding works in YugabyteDB by defining the initial quantity and size of tablets.

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YugabyteDB and Red Hat OpenShift: Resilient Kubernetes Workloads at Scale

YugabyteDB and Red Hat OpenShift: Resilient Kubernetes Workloads at Scale

Kubernetes has become widely adopted in the Fortune 500. Many companies are now using the platform to run stateless and stateful applications on-premises or as hybrid cloud deployments in production. Of course, with any new technology, there are growing pains when running resilient Kubernetes workloads. But most executives and developers agree that the benefits far outweigh the challenges.

Data on the Kubernetes ecosystem is evolving rapidly with the rise of stateful applications. However,

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How YugabyteDB Scales Temenos’ Cloud Platform to 100K Business Transactions Per Second

How YugabyteDB Scales Temenos’ Cloud Platform to 100K Business Transactions Per Second

Temenos could not continue to rely on monolithic databases for manufacturing business operations. They needed a high-availability, scale-out transactional database, so they turned to YugabyteDB. They recently announced that the Temenos Banking Cloud is achieving 100,000 business transactions per second. Learn how that performance benchmark was achieved.

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Change Data Capture (CDC) From YugabyteDB to Elasticsearch

This post walks through how to send data from YugabyteDB to Elasticsearch using YugabyteDB’s Change Data Capture (CDC) feature.

Background

YugabyteDB CDC is a pull-based approach to CDC introduced in YugabyteDB 2.13 that reports changes from the database’s write-ahead-log (WAL). More specifically, the detailed CDC architecture is mentioned in YugabyteDB’s documentation.

Elasticsearch is a search engine based on the Lucene library. It also provides a distributed,

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Distributed SQL Deep Dive: Inside YugabyteDB’s Two-Layer Architecture

Distributed SQL Deep Dive: Inside YugabyteDB’s Two-Layer Architecture

YugabyteDB is a 100% open source, distributed SQL database system. This single phrase expresses two distinct notions: a SQL database system, and a distributed database system. Historically, these notions were mutually exclusive. But current technology allows a single system to implement both notions. YugabyteDB does this with its two-layer architecture: an extensible query processing layer and a distributed document store.

In this blog post, we explain how YugabyteDB’s two-layer architecture works and compare it against other popular databases.

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Cloud Native Java: Integrating YugabyteDB with Spring Boot, Quarkus, and Micronaut

Cloud Native Java: Integrating YugabyteDB with Spring Boot, Quarkus, and Micronaut

Java is the quintessential language runtime for enterprise applications built on monoliths, microservices, and modular architecture patterns. But when it comes to “Enterprise Java,” Spring is the de facto framework of choice.

The Spring ecosystem—with the simplicity of Spring Boot—has grown to provide integration touchpoints to a majority of the Java ecosystem. For starters, it offers a clean abstraction and “glue” code to build cohesive enterprise applications. However,

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Linux Performance Tuning: Dealing with Memory and Disk IO

Linux Performance Tuning: Dealing with Memory and Disk IO

Linux is a general purpose operating system. This means it’s created to generally do what is right, instead of having specific code paths to perform what is right for a single specific task—and potentially be wrong for others.

Linux does not have tunable parameters for reserving memory for caching disk pages (the page cache), like operating systems such as HPUX (dbc_min_pct, dbc_max_pct) or AIX (minperm%, maxperm%). Instead,

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