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What Do You Mean By a “Distributed Database?”

The earthly landscape we walk upon usually changes very slowly. It’s measurable in centimeters or inches per year. But the digital landscape, and specifically the distributed database landscape, is changing at a massive rate. You can read more about the tremendous changes currently occurring in the industry in our recent blog on this next tech cycle.

Before we can look at the way these changes are impacting the distributed database landscape, we have to define what we mean by the term “distributed database.” That’s the purpose of this article.

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Spotlight on CockroachDB

The construction, process, and usage of databases has evolved a lot over the last few decades. Traditional relational databases were enough to work with the data present at that time, but with the innate reliance on the Internet, the progression of cloud-native architecture, and the advancement of how businesses utilize and analyze data science, relational databases are not cutting it. What happens if a node fails in a traditional single machine of a relational database? Your database would go down along with any applications that depend on it. 

Over time as NoSQL databases were introduced—which are capable of handling a large amount of data in real-time—the risk of apps failing began to decrease but the risk of data inconsistencies increased. So, there has been a growing need for a better storage solution for data to cope with today’s dynamic cloud-native architecture. CockroachDB was specifically designed to solve and meet this need. 

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How to Geo-Partition Data in Distributed SQL

We are excited to announce the availability of row-level geo-partitioning in YugabyteDB, a feature heavily requested by our user community and enterprise customers alike. This feature allows fine-grained control over pinning data in a user table (at a per-row level) to geographic locations, thereby allowing the data residency to be managed at the database level.

Making the nodes of a multi-region database cluster aware of the location characteristics of the data they store allows conforming to regulatory compliance requirements such as GDPR by keeping the appropriate subset of data local to different regions, and is arguably the most intuitive way to eliminate the high latency that would otherwise get incurred when performing operations on faraway, remote regions.

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Distributed SQL Essentials

Distributed SQL databases combine the resilience and scalability of a NoSQL database with the full functionality of a relational database. In this Refcard, we explore the fundamentals of distributed SQL, including architecting for availability, handling schema design challenges, using JSON and columnar indexes, as well as assessing approaches to replication.
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Getting Started With Longhorn Distributed Block Storage and Cloud Native Distributed SQL

Longhorn is cloud native distributed block storage for Kubernetes that is easy to deploy and upgrade, 100 percent open source, and persistent.  Longhorn’s built-in incremental snapshot and backup features keep volume data safe, while its intuitive UI makes scheduling backups of persistent volumes easy to manage. Using Longhorn, you get maximum granularity and control, and can easily create a disaster recovery volume in another Kubernetes cluster and fail over to it in the event of an emergency.

Cloud Native Infrastructure Stack: Computing, deployment, administration, storage and database.

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Distributed SQL Change Management With Liquibase and YugabyteDB on GKE

Liquibase is an open source and extensible change management project that supports a variety of databases including Snowflake, MySQL, and PostgreSQL via JDBC. Liquibase allows users to easily define changes in SQL, XML, JSON, and YAML. These changes are then managed in a version control system so the changes can be documented, ordered, and standardized. For more information on the features and benefits of Liquibase, check out their documentation site.

In this blog post we’ll show you how to:

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Distributed SQL: An Evolution of the Database

As organizations transition to the cloud, they eventually find that the legacy relational databases that are behind some of their most critical applications simply do not take advantage of the promise of the cloud and are difficult to scale. It is the database that is limiting the speed and effectiveness of this transition. To address this, organizations want the reliability of a tested relational data store, such as Oracle, SQL Server, Postgres, and MySQL, but with the benefits of scale and global coverage that comes with the cloud

Some have turned to NoSQL stores to try to meet these requirements. These alternatives can typically meet the scale requirements but then fall short as a transactional database because they were not designed from the ground up to provide true consistency. Recently, some of the NoSQL solutions have offered “ACID transactions” but they’re full of caveats and fail at delivering isolation levels necessary for mission-critical workloads like a financial ledger, inventory control, and identity management.

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Getting Started With Distributed SQL

In recent years, NoSQL distributed databases have become common, as they are built from the ground up to be distributed. Yet they force difficult design choices, such as choosing availability over consistency, data integrity, and ease of query, to meet their applications’ need for scale. This Refcard serves as a reference to the key characteristics of distributed SQL databases, how functionality compares across database offerings, and the criteria for designing a proof of concept.
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Getting Started With Distributed SQL on Azure Kubernetes Service

Microsoft’s Azure Kubernetes Service (AKS) offers a highly available, secure, and fully managed Kubernetes service for developers looking to host their applications on containers in the cloud. AKS features elastic provisioning, an integrated developer experience for rapid application development, enterprise security features, and the most available regions of any cloud provider.

YugabyteDB is a natural fit for AKS because it was designed to support cloud native environments since its initial design.

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The Benefit of Partial Indexes in Distributed SQL Databases

Partial Indexes


If a partial index is used instead of a regular one, on a nullable column — where only a small fraction of the rows have not null values for this column—then the response time for inserts, updates, and deletes can be shortened significantly. As a bonus, the response times for single row selects shorten a little bit too. This post explains what a partial index is, shows how to create one, describes the canonical use case that calls for a partial index, describes some straightforward performance tests, and shows that the results justify the recommendation to use a partial index when you have the appropriate use case.