AURORA SQL VS MYSQL UPDATE

The above Bash script simply connects to a MySQL host and performs an update on a single row with a timeout of 1 second on both Bash and mysql client commands. 'UPDATE sbtest.sbtest1 SET k = $num WHERE id = 1' > /dev/null 2> /dev/null' Timeout 1 bash -c 'mysql -u$ -connect-timeout=1 -disable-reconnect -A -Bse Num=$(od -A n -t d -N 1 /dev/urandom |tr -d ' ') To measure the failover time, we are going to run a simple MySQL connect-update test, with a loop to count the SQL statement status that connect to a single database endpoint. This is where the most up-to-date slave takes over the master role in the cluster to resume the database service.

The type of failover that we are going to do is slave promotion in case that the master goes down. In this blog post, we are going to compare automatic failover times for Amazon Aurora, Amazon RDS for MySQL, and a MySQL Replication setup deployed and managed by ClusterControl. ClusterControl understands the database topology and is capable of performing automatic recovery, topology management, and many more advanced features to take control of your database. It also supports a number of other cloud platforms like Google Cloud Platform and Microsoft Azure. There are a number of database engine offerings for RDS like MySQL, MariaDB, PostgreSQL, Microsoft SQL Server and Oracle Server.ĬlusterControl 1.7.3 acts similarly to RDS as it supports database cluster deployment, management, monitoring, and scaling on the AWS platform.

It provides cost-effective and resizable capacity while automating time-consuming administration tasks such as hardware provisioning, database setup, patching, and backups. If your IT infrastructure is running on AWS, you have probably heard about Amazon Relational Database Service (RDS), an easy way to set up, operate, and scale a relational database in the cloud.

Aurora sql vs mysql