The Distributed SQL Blog

TPC-C Benchmark: 10,000 Warehouses on YugabyteDB

TPC-C Benchmark: 10,000 Warehouses on YugabyteDB

We are excited to announce that the TPC-C benchmark implementation for YugabyteDB is now open source and ready to use! While this implementation is not officially ratified by the TPC organization, it closely follows the TPC-C v5.11.0 specification.

For those new to TPC-C, the aim of the benchmark is to test how a database performs when handling transactions generated by a real-world OLTP application. This blog post shows the results of running the TPC-C benchmark in addition to outlining our experience of developing and running a TPC-C benchmark against YugabyteDB.

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Distributed SQL Tips and Tricks for PostgreSQL and Oracle DBAs – July 24, 2020

Distributed SQL Tips and Tricks for PostgreSQL and Oracle DBAs – July 24, 2020

Welcome to this week’s tips and tricks blog where we explore both beginner and advanced YugabyteDB topics for PostgreSQL and Oracle DBAs. First things first, for those of you who might be new to either distributed SQL or YugabyteDB.

What is Distributed SQL?

Distributed SQL databases are becoming popular with organizations interested in moving data infrastructure to the cloud or cloud native environments. This is often motivated by the desire to reduce TCO or move away from the horizontal scaling limitations of monolithic RDBMS like Oracle,

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Porting Oracle to YugabyteDB

Porting Oracle to YugabyteDB

I am presenting an Introduction to SQL webinar at the end of the month–July 29 at 10am PT (1pm ET). In preparation for this, I needed a dataset. Because YugabyteDB is compatible with PostgreSQL, it seemed obvious to try out the PostgreSQL Tutorial site. However, I have never liked the “film database” example. I prefer the classic customers, orders, and products example. So instead I checked out the sister site the Oracle Tutorial.

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Getting Started with Longhorn Distributed Block Storage and Cloud Native Distributed SQL

Getting Started with Longhorn Distributed Block Storage and Cloud Native Distributed SQL

Editor’s note: This post was originally published on the Rancher blog, and has been cross-posted here and updated as of July 21, 2020 to account for new versions of software available.

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.

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Distributed SQL Tips and Tricks – July 17, 2020

Distributed SQL Tips and Tricks – July 17, 2020

Welcome to this week’s tips and tricks blog where we recap some distributed SQL questions from around the Internet. We’ll also review upcoming events, new documentation, and blogs that have been published since the last post. Got questions? Make sure to ask them on our YugabyteDB Slack channel, Forum, GitHub, or Stackoverflow. Ok, let’s dive right in:

How to identify a colocated database from the rest

One way to identify a colocated table in YugabyteDB is to look at the yb-master UI page (https://<yb-master-ip>:7000/tables).

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Announcing YugabyteDB 2.2 – Distributed SQL Made Easy

Announcing YugabyteDB 2.2 – Distributed SQL Made Easy

We are excited to announce the general availability of YugabyteDB 2.2! The highlight of this release is that YugabyteDB now supports fully-transactional distributed backups thus making mission-critical distributed SQL deployments operationally simple, even for the most demanding enterprise environments. This release also includes critical new features such as online index builds, colocated tables, and deferred constraints. The end result is that YugabyteDB continues to make distributed SQL easy. For those of you who are new to distributed SQL,

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GraphQL & Distributed SQL Tips and Tricks – July 10, 2020

GraphQL & Distributed SQL Tips and Tricks – July 10, 2020

Welcome to this week’s tips and tricks blog where we explore topics related to combining GraphQL and YugabyteDB to develop scalable APIs and services. We’ll also review upcoming events, new documentation, and blogs that have been published since the last tips and tricks post.

This next section is for those of you who might be new to either GraphQL or distributed SQL.

What’s GraphQL?

GraphQL is a query language (more specifically a specification) for your API,

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INSERT INTO Yugabyte (We’re Hiring, July 2020 Edition)

INSERT INTO Yugabyte (We’re Hiring, July 2020 Edition)

Recent Highlights and Happenings

It’s been another fast-moving month here at Yugabyte. Although it remains a difficult time nationally and globally, it’s our hope that sharing this information and our currently open positions may be some good news at a time when it’s needed most.

On the momentum front, we recently announced that we raised $30 million in an oversubscribed Series B funding round led by 8VC, with participation from Wipro Ventures and existing investors,

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Distributed SQL Tips and Tricks – June 29, 2020

Distributed SQL Tips and Tricks – June 29, 2020

Welcome to this week’s tips and tricks blog where we recap some distributed SQL questions from around the Internet. We’ll also review upcoming events, new documentation, and blogs that have been published since the last post. Got questions? Make sure to ask them on our YugabyteDB Slack channel, Forum, GitHub, or Stackoverflow. Ok, let’s dive right in:

Regular expression queries (or pattern matching queries)

YugabyteDB inherits PostgreSQL upper layer,

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Polymorphism in SQL part two – variadic functions

Polymorphism in SQL part two – variadic functions

YugabyteDB is an open source, high-performance distributed SQL database built on a scalable and fault-tolerant design inspired by Google Spanner. YugabyteDB uses its own special distributed document store called DocDB. But it provides SQL and stored procedure functionality by re-using the “upper half” of the standard PostgreSQL source code. This is explained in the two part blog post “Distributed PostgreSQL on a Google Spanner Architecture”: (1) Storage Layer; and (2) Query Layer.

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