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The Mythical MySQL DBA

I’ve been getting more than my fair share of calls from recruiters of late. Even in this depressed economic climate where jobs are rarer than a cab at rush hour, it’s heartening to know that tech engineers are in great demand. And it’s even more heartening to think that the demand for MySQL DBAs has never been better.

In this article, I’m going to discuss the mythical MySQL DBA, the reason why MySQL DBA and target.com keep crashing and much more. So, let’s get started!

Why Target Keeps Crashing

My reckoning was confirmed by a Bloomberg news report about stalwart retailers suffering from a dearth of talented engineers. Bloomberg cited Target’s outage-prone e-commerce site as a symptom of, among other things the market’s shortage.

One of the challenges old-timers …

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Top MySQL DBA Interview Questions (Part 2)

Continuing from our Top MySQL DBA interview questions (Part 1) here are five more questions that test a MySQL DBA’s knowledge, with two that will help suss out some personality traits.

6. Disk I/O

Disk performance should be an ever-present concern to a DBA. So, although they don’t need to be a storage specialist, they should have a working knowledge. Ask them about RAID versions, mirroring versus striping, and so forth. Mirroring combines two disks as a unit. Every write is duplicated on both disks.

If you lose one disk, you have an immediate copy. Like a tandem truck that has spare tires running in parallel. Lose one, and you don’t have to pull over immediately to replace it. Striping spreads I/O over multiple disks so you on the one hand increase throughput linearly as you add disks.

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Easy MySQL Replication With Hotbackups

Setting up replication in MySQL is something we need to do quite often. Slaves die, replication fails, or tables and data get out of sync. Whenever we build a slave, we must start with a snapshot of all the data from the master database.

MySQLdump is the tried-and-true method of doing this, however it requires that we lock all the tables in the database. If we’re dumping a large database, this could be a significant period, where no writing can happen to our database for the duration of the backup. For many environments read-only is still an outage.

How to Create a Slave Using Xtrabackup

Enter hotbackups to the rescue. Percona comes with a tool that allows you to perform hotbackups of a running MySQL database, with no blocking. It’s able to do this because of Innodb & multi-version concurrency control (MVCC). Luckily we don’t need to dig into the guts to enjoy the benefits of this great technology.

Here’s …

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Migrating MySQL to Oracle

This article is from 2006. MySQL has come a long way since then. MySQL 5.5 is very robust and feature-rich, matching Oracle in many different areas including datatypes, stored procedures and functions, high availability solutions, ACID compliance and MVCC, hotbackups, cold backups and dumps, full text, and other index options, materialized views and much more.  Here’s a high level mysql feature guide.

What really separates the two technologies is culture. MySQL, rooted in the open-source tradition is much more do-it-yourself, leaning towards roll-your-own solutions in many cases. Meanwhile, Oracle provides named and proven paths to solve specific problems.

You might also check out:  …

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Top MySQL DBA Interview Questions (Part 1)

MySQL DBAs are in greater demand now than they’ve ever been. While some firms are losing the fight for talent, promising startups with a progressive bent are getting first dibs with the best applicants.

Whatever the case, interviewing for a MySQL DBA is a skill in itself so I thought I’d share a guide of top MySQL DBA interview questions to help with your screening process.
It’s long and detailed with some background to give context so I will be publishing this in two parts.

The History of The DBA As A Career

In the Oracle world of enterprise applications, the DBA has long been a strong career path. Companies building their sales staff required Peoplesoft or SAP, and those deploying the financial applications or e-business suite needed operations teams to manage those systems.

At the heart of that operations team were database administrators or DBAs, a catchall title that …

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Getting the most out of MySQL HeatWave Lakehouse with Autopilot

This blog post describes how to use MySQL Autopilot to automate many tasks in MySQL HeatWave Lakehouse, including schema inference, capacity estimation, load time estimation, load script generation, adaptive data sampling, parallel loading of data, and more.

MySQL ROUND Function Examples

The upcoming tutorial will focus on the ROUND function, which is used to round numeric values for precision and accuracy. Rounding is vital in databases to avoid discrepancies in calculations, particularly with financial and scientific data. The article will provide practical examples and insights into effectively using the ROUND function for reliable results in dbForge Studio for MySQL.

The post MySQL ROUND Function Examples appeared first on Devart Blog.

Testing MySQL Applications With Node.js and Testcontainers

Writing tests for code that interacts with a database can be daunting. In this post, we discuss how you can easily set up a testing environment using Testcontainers and Node.js and write some tests for code that interacts with a MySQL database.

Testing MySQL Applications With Node.js and Testcontainers

Testing code that interacts with a database can sometimes be a daunting task. Check out this post about using Testcontainers and Node.js to test code that interacts with a MySQL database.

Storing time series data in sharded MySQL to power Query Insights

Every day PlanetScale processes more than 10 billion of our customers’ queries. We need to collect, store, and serve telemetry data generated by these queries to power Insights, our built-in query performance tool. This post describes how we built a scalable telemetry pipeline using Apache Kafka and a sharded PlanetScale database. Insights requirements To show you Insights, we pull from the following datasets: Database-level time series data (e.g., queries per second across the entire database). Query pattern-level time series data (e.g., p95 for a single query pattern like SELECT email FROM users where id = %) Data on specific query executions for slow/expensive queries (the “slow query log”). The database-level data fits well into a time series database like Prometheus, but we run into several issues when trying to store the query-pattern level data in a time series database. On any given day, there are 10s of millions of unique query …

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