Asynchronous modes of replication leave room for data loss, because transactions might be committed to the primary database but not to the secondary database(s). Data layers of that type typically have more resilience built into the applications, making for a better overall experience.įinally, be sure you understand the replication modes available in the database. Databases that are storing more transient and caching layers are better fits for Kubernetes. Next, consider the function that database is performing in the context of your application and business. Some open source projects provide custom resources and operators to help with managing the database. It will be easier to run a database on Kubernetes if it includes concepts like sharding, failover elections and replication built into its DNA (for example, ElasticSearch, Cassandra, or MongoDB). Since pods are mortal, the likelihood of failover events is higher than a traditionally hosted or fully managed database. When choosing to go down the Kubernetes route, think about what database you will be running, and how well it will work given the trade-offs previously discussed. Tips for running your database on Kubernetes Also, some of the more database-specific administrative tasks-backups, scaling, tuning, etc.-are different due to the added abstractions that come with containerization. That said, it is important to remember that pods (the database application containers) are transient, so the likelihood of database application restarts or failovers is higher. Running a database on Kubernetes is closer to the full-ops option, but you do get some benefits in terms of the automation Kubernetes provides to keep the database application running.
All of that can be a lot of work, but you have all the features and database flavors at your disposal.
#Dbvisualizer timesten full#
This might best be described as the full-ops option, where you take full responsibility for building your database, scaling it, managing reliability, setting up backups, and more. This also means you might not have access to the exact version of a database, extension, or the exact flavor of database that you want.ĭo-it-yourself on a VM. You just create a database, build your app, and let Google Cloud scale it for you. As a developer or operator, you don’t need to mess with them. This is the low-ops choice, since Google Cloud handles many of the maintenance tasks, like backups, patching and scaling. This includes Cloud Spanner, Cloud Bigtable and Cloud SQL, among others. In this blog, we’ll explore when and what types of databases can be effectively run on Kubernetes.īefore we dive into the considerations for running a database on Kubernetes, let’s briefly review our options for running databases on Google Cloud Platform (GCP) and what they’re best used for.įully managed databases. Operators want to use the same tools for databases and applications, and get the same benefits as the application layer in the data layer: rapid spin-up and repeatability across environments. However, the data layer is getting more attention, since many developers want to treat data infrastructure the same as application stacks. That makes it challenging to run a database in a distributed environment. So handling things like state (the database), availability to other layers of the application, and redundancy for a database can have very specific requirements. That’s not surprising, since containerized workloads inherently have to be resilient to restarts, scale-out, virtualization, and other constraints. Despite all that growth on the application layer, the data layer hasn’t gotten as much traction with containerization. Today, more and more applications are being deployed in containers on Kubernetes-so much so that we’ve heard Kubernetes called the Linux of the cloud.
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