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Google sql vs mysql8/7/2023 ![]() In a production environment, you’ll want to set this to static so it won’t change. It’s important to note that the Striim VM currently has a dynamic external IP address. Open the Google Console and go back to the Deployment Manager, and click “Visit site”. Striim also needs a checkpoint table to keep the state in the event of failures, so create that table structure using the following: To do this, open the Google Cloud shell, log into the MySQL server, and run the SQL to create the table structure. ![]() We also need to create the database structure for the data we imported into the local MySQL instance. Then open the database instance and take note of the IP address. Open the instance and copy the instance connection name for use later. Once again, we’ll use the defaults for a basic Google MySQL instance. Select the SQL option from the side menu in Google Cloud and create a new MySQL instance. While we wait for the Striim server to deploy, let’s create a Google SQL database to which we’ll migrate our database. In production, however, you’d need to size appropriately depending on your load. For this tutorial, we’ll use the basic defaults for a Striim server. Select this option and start the deployment process by pressing the deploy button at the bottom of this screen. Go to the marketplace and search for Striim.Ī number of options should return, but the one we’re after is the first item, which allows integration of real-time data to GCP. Open your Google Cloud console and open or start a new project. ![]() Now that we have an on-premises data set in MySQL, let’s set up a new Striim application on Google Cloud Platform to act as the migration service. This will allow us to insert a lot of data after Striim has been configured to show how powerful Change Data Capture is. Rather than importing all the data this data set contains, I’ve excluded the load_salaries2.dump and load_salaries3.dump files. The data set is pretty large, which is perfect for our purposes, and contains a dummy set of employee information, including salaries. For the purpose of this post, the dataset we have loaded data from a GitHub source ( ) in a local MySQL instance. Let’s walk through an example of connecting an on-premises instance of MySQL to Google Cloud SQL for MySQL.īefore we dive into Striim, we are assuming you have an on-premises MySQL instance already configured and containing relevant data. Migrate one or more on-premises application (with data) to the cloud for production testing with almost zero impact on the existing application. There are a number of benefits in migrating applications this way, such as being able to:Īdd a new, client-facing cloud application by synchronizing an existing, traditionally on-premises application’s data set. This is where real-time ETL tools like Striim shine. Traditional Extract, Translate, and Load (ETL) tools require multiple passes and, potentially, significant downtime to handle data migration activities. The older and bigger the application, the more difficult that migration becomes. One of the major hurdles when migrating applications, whether you’re changing the technology or moving to the cloud, is migrating your data. In this blog post we are going to use a database technology called Change Data Capture to synchronize data from MySQL into a Google Cloud SQL instance. Existing applications built on top of on-premises deployments of databases like MySQL. But, moving your existing on-premises applications to the cloud can be a challenge. Migrating from MySQL to Google Cloud SQL opens up cloud services that offer a wealth of capabilities with low management overhead and cost. ![]()
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