On Rackspace, by our present monolithic structure, we have been managing giant clusters of cases working on MySQL. Every of Freedom Monetary Community’s enterprise items had one giant cluster of cases. Rackspace was managing these clusters, and thus taking work off our palms, however we had little or no management over these databases. Each small change resembling disk resizing would take a few weeks at the very least. Due to that, our database cases have been vastly overprovisioned and costly.
We noticed that Google Cloud may host and handle all of our databases, saving us worthwhile time and assets, and that Google Cloud SQL’s versatility would permit us to construct versatile, safe options that may meet the wants of our groups and our clients. We have been in a position to break down our clusters into many smaller cases that we are able to handle completely by automation with out including overhead.
A posh migration made simpler by Google Cloud
Our migration concerned the transformation of our monolithic structure to a microservices structure, deployed on Google Kubernetes Engine (GKE) and utilizing the Cloud SQL Proxy in a sidecar container sample or the Go proxy library to connect with Cloud SQL. Every microservice makes use of its personal schema and schemas could be grouped in shared cases or be hosted on devoted cases for larger load purposes.
We efficiently leveraged Google Cloud’s new Database Migration Service (DMS) emigrate our databases from Rackspace to Cloud SQL. We used it emigrate three separate manufacturing databases, with 5 whole schemas migrated and an general measurement of near 1 TB of information with lower than 15 minutes of downtime. Finally, the migration was profitable and largely painless. We’ve shut down our companies at Rackspace, and all of our databases are working on Google Cloud’s managed companies now. DMS was actually the one choice due to the dimensions of our databases. We estimated that doing a “dump and cargo” migration would have required software downtime in extra of 12 hours—to not point out the hours we’d have spent doing prep work.
Utilizing Cloud SQL as our database basis
Since finishing the migration, Cloud SQL has helped us meet our objectives round safety, scale, and adaptability. We now deploy a sturdy set of microservices and cases—following a latest resizing, we’ve got an estimated 180 cases consuming 350 CPUs, for 1300 gigs of RAM. Our microservice examples embrace every thing from easy use circumstances and software configuration databases to bigger, extra advanced databases that maintain info used steadily by enterprise groups. We save a lot time not having to handle 180 cases.
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