12/17/2022 0 Comments Sync uptodate offline![]() The instant after the transaction was committed the log send queue would be very large while the log generation rate was still showing very low. ![]() The database would have a very low write load until a 4 MB file was dropped in it. I once administered a system which used filestream. ![]() This calculation can be a bit misleading if your write load is inconsistent. Also, it is measuring data loss by time rather than quantity. Your first thought might be to look at the send rate rather than the generation rate but it is important to remember that we are not looking for how long it will take to synchronize, we are looking for what window of time will we lose data in. T data_loss = log_send_queue / log_generation_rate Each method has its own quirks which are important to understand and put into context. There are two methods of calculating data loss. If you would like to read more on the policy based management method, review this BOL post. I will be covering the dynamic management view (DMV) method because I find it is more versatile and very useful when creating custom alerts in various monitoring tools. Policy based management is one method of verifying that you can achieve your RTOs and RPOs. That same server staying up for the entire year does not mean that you can meet five nines as a service level agreement (SLA). Thomas Grohser once told me, “do not confuse luck with high-availability.” A server may stay online without ever failing or turning off for many years but if that server has no redundancy features then it is not highly-available. If the primary replica fails before the secondary replicas harden the log records, then the application will believe a transaction was committed but a failover would result in the loss of that data. This improves performance because the application does not have to wait for the log records to be transmitted but it opens up the AG to the potential of data loss. With asynchronous commit mode, the application receives confirmation that the transaction committed after the last log record is flushed (step 1) to the primary replica’s log file. Any transactions which were not hardened before the primary failed would be rolled back and an appropriate error would be bubbled up to the application for it to alert the user or perform its own error handling. This is how AGs can guarantee zero data loss. With synchronous commit mode, the application does not receive confirmation that the transaction committed until after the log records are hardened (step 5) on all synchronous secondary replicas. Both availability modes follow the same work flow, with one small yet critical difference. Selecting a mode is equivalent to selecting whether you want to favor data protection or transaction performance. There are two availability modes, synchronous commit and asynchronous commit. This becomes a maintenance concern because you either need to continue to expand your disk or you might run out of capacity entirely. ![]() If the log records cannot be truncated your log will likely begin to grow. Slow synchronization to even a single replica will prevent log truncation. The worse the performance, the more potential data loss will occur and the longer it can take for a failed over database to come back online.Īvailability Groups must retain all transaction log records until they have been distributed to all secondary replicas. When using Availability Groups (AGs), your RTO and RPO rely upon the replication of transaction log records between at least two replicas to be extremely fast. Data loss affects your ability to meet recovery point objectives (RPO) and downtime affects your recovery time objectives (RTO). With all of the high-availability (HA) and disaster recovery (DR) features, the database administrator must understand how much data loss and downtime is possible under the worst case scenarios.
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