Monitoring metrics in an Amazon Aurora cluster - Amazon Aurora
Monitoring planPerformance baselinePerformance guidelines

Monitoring metrics in an Amazon Aurora cluster

Amazon Aurora uses a cluster of replicated database servers. Typically, monitoring an Aurora cluster requires checking the health of multiple DB instances. The instances might have specialized roles, handling mostly write operations, only read operations, or a combination. You also monitor the overall health of the cluster by measuring the replication lag. This is the amount of time for changes made by one DB instance to be available to the other instances.

Topics

Monitoring plan

Before you start monitoring Amazon Aurora, create a monitoring plan. This plan should answer the following questions:

  • What are your monitoring goals?

  • Which resources will you monitor?

  • How often will you monitor these resources?

  • Which monitoring tools will you use?

  • Who will perform the monitoring tasks?

  • Whom should be notified when something goes wrong?

Performance baseline

To achieve your monitoring goals, you need to establish a baseline. To do this, measure performance under different load conditions at various times in your Amazon Aurora environment. You can monitor metrics such as the following:

  • Network throughput

  • Client connections

  • I/O for read, write, or metadata operations

  • Burst credit balances for your DB instances

We recommend that you store historical performance data for Amazon Aurora. Using the stored data, you can compare current performance against past trends. You can also distinguish normal performance patterns from anomalies, and devise techniques to address issues.

Performance guidelines

In general, acceptable values for performance metrics depend on what your application is doing relative to your baseline. Investigate consistent or trending variances from your baseline. The following metrics are often the source of performance issues:

  • High CPU or RAM consumption – High values for CPU or RAM consumption might be appropriate, if they're in keeping with your goals for your application (like throughput or concurrency) and are expected.

  • Disk space consumption – Investigate disk space consumption if space used is consistently at or above 85 percent of the total disk space. See if it is possible to delete data from the instance or archive data to a different system to free up space.

  • Network traffic – For network traffic, talk with your system administrator to understand what expected throughput is for your domain network and internet connection. Investigate network traffic if throughput is consistently lower than expected.

  • Database connections – If you see high numbers of user connections and also decreases in instance performance and response time, consider constraining database connections. The best number of user connections for your DB instance varies based on your instance class and the complexity of the operations being performed. To determine the number of database connections, associate your DB instance with a parameter group where the User Connections parameter is set to a value other than 0 (unlimited). You can either use an existing parameter group or create a new one. For more information, see Parameter groups for Amazon Aurora.

  • IOPS metrics – The expected values for IOPS metrics depend on disk specification and server configuration, so use your baseline to know what is typical. Investigate if values are consistently different than your baseline. For best IOPS performance, make sure that your typical working set fits into memory to minimize read and write operations.

When performance falls outside your established baseline, you might need to make changes to optimize your database availability for your workload. For example, you might need to change the instance class of your DB instance. Or you might need to change the number of DB instances and read replicas that are available for clients.