![]() Once this completes a new failover replica is created and the old master is deleted and a new failover replica is created.ĭuring this transition, requests to the Cloud SQL cluster can fail. The instance name and IP address move to the failover replica. The failover replica is promoted to the primary instance role (master). If a Google Cloud SQL High Availability master instance becomes unresponsive, Cloud SQL automatically switches to the failover replica. This can be in a random or round-robin fashion. In the examples below, we load balance read requests on the master, failover replica and read replica.Ĭonnection management can distribute read-type requests between the master, failover replica and the read replicas. You can have more than one read replica.Ī failover replica can also be used as a read replica. Read replicas can be a different machine type than the master instance. ![]() Read replicas do not provide high availability as a master cannot fail over to a read replica. Load balancing is implemented by adding Read Replicas.Ī read replica provides a read-only copy of the master. Write requests must be sent to the master instance. For requests that update (write) the database, load balancing is not supported. Google Cloud SQL supports load balancing for read-type requests. Google Cloud SQL for MySQL uses semisynchronous replication for data synchronization from the master to the slave and asynchronous replication between the master and the read-only replicas (also called slaves). The master and slaves can be located in different zones to improve high availability. This configuration consists of a primary instance (master) and a failover replica (slave). The Google Cloud SQL High Availability configuration, which requires creating an additional instance, provides data redundancy. Connection management can detect failed connections and reopen each connection with a database replica server improving high availability. Limiting the number of connections created can detect code bugs that fail to release connections and can help prevent self-inflicted DoS of your database resources. Sharing connections improves connection latency and performance. Cloud SQL for PostgreSQL connection limitsīy implementing connection management, connections can be shared, reused and quantity limited.Cloud SQL for MySQL Second Generation connection limits.Failing to close connections can result in a Denial-of-service (DoS) for your own services.įor container-based deployments (App Engine, Cloud Functions, Cloud Run, Containers on GCE, Kubernetes), connection management or the lack of, can affect cold-start times (waiting for a new database connection) and/or limit the number of containers that can be simultaneously deployed (hitting connection limits blocks new containers from getting new connections). ![]() Database servers usually impose a limit on the number of simultaneous connections. Opening connections to a database server is typically considered an expensive operation. Connection ManagementĪ typical application opens and closes connections as required. I created a GitHub repository with the files in this article. Then we will analyze this example, find issues and explore improved solutions. ![]() In this article, we will start with a simple example, written in Node.js. Incorrectly implementing one of these areas can affect the other areas, usually negatively. There are several factors to consider regarding security, performance, fault tolerance and availability. Designing an application that incorporates Google Cloud SQL requires some thought. ![]()
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