![]() ![]() For example, you can’t selectively backtrack a single table or a single data update.īacktracking isn’t supported with binary log (binlog) replication. The limit for a backtrack window is 72 hours.īacktracking affects the entire DB cluster. Currently, you can’t perform backtracking on DB clusters that were created with the Backtrack feature disabled. For DB clusters that were created with the Backtrack feature enabled, you can create a clone DB cluster with the Backtrack feature enabled. You can enable the Backtrack feature when you create a new DB cluster or restore a snapshot of a DB cluster. For more information, see Backtracking in Aurora.īacktracking is only available for DB clusters that were created with the Backtrack feature enabled. The following limitations apply to backtracking:īacktracking an Aurora DB cluster is available in certain Amazon Regions and for specific Aurora MySQL versions only. If you don’t have enough space in your backtrack window to store the table, the table might be removed from the backtrack change records eventually. It does this so that you can revert back to a time before you deleted the table. When backtracking is enabled for a DB cluster, and you delete a table stored in the DB cluster, Aurora keeps that table in the backtrack change records. When your actual backtrack window is smaller than your target, we send you a notification. Typically, the actual backtrack window is smaller than the target when you have extremely heavy workload on your DB cluster. However, in some cases, the DB cluster can’t store enough change records to backtrack the maximum amount of time, and your actual backtrack window is smaller than your target. In most cases, you can backtrack the maximum amount of time that you specified. You can think of your target backtrack window as the goal for the maximum amount of time you want to be able to backtrack your DB cluster. If your workload is heavy, you store more change records in your backtrack window than you do if your workload is light. The workload is the number of changes you make to your DB cluster in a given amount of time. Both the target backtrack window and the workload on your DB cluster determine the number of change records you store. ![]() Aurora retains change records for the target backtrack window, and you pay an hourly rate for storing them. The actual backtrack window is based on your workload and the storage available for storing information about database changes, called change records.Īs you make updates to your Aurora DB cluster with backtracking enabled, you generate change records. The actual backtrack window is the actual amount of time you can backtrack your DB cluster, which can be smaller than the target backtrack window. For example, you might specify a target backtrack window of 24 hours if you want to be able to backtrack the DB cluster one day. When you enable backtracking, you specify a target backtrack window. The target backtrack window is the amount of time you want to be able to backtrack your DB cluster. ![]() The following image shows the Backtrack section. To create a DB cluster, follow the instructions in Creating an Amazon Aurora DB cluster. When you create a new Aurora MySQL DB cluster, backtracking is configured when you choose Enable Backtrack and specify a Target Backtrack window value that is greater than zero in the Backtrack section. This allows for quick access and recovery times measured in seconds. Enabling the backtrack feature provisions a FIFO buffer in the cluster for storage of LSNs. In this case, the backtrack time is two hours before the original time.Īmazon Aurora uses a distributed, log-structured storage system (read Design Considerations for High Throughput Cloud-Native Relational Databases to learn a lot more) each change to your database generates a new log record, identified by a Log Sequence Number (LSN). For example, you can backtrack a DB cluster three hours and then backtrack forward in time one hour. You can repeatedly backtrack a DB cluster back and forth in time to help determine when a particular data change occurred. Backtracking a DB cluster doesn’t require a new DB cluster and rewinds the DB cluster in minutes. Restoring a DB cluster to a point in time launches a new DB cluster and restores it from backup data or a DB cluster snapshot, which can take hours. ![]() If you mistakenly perform a destructive action, such as a DELETE without a WHERE clause, you can backtrack the DB cluster to a time before the destructive action with minimal interruption of service. ![]()
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