Performance and scaling for Amazon Aurora PostgreSQL
The following section discusses managing performance and scaling for an Amazon Aurora PostgreSQL DB cluster. It also includes information about other maintenance tasks.
Topics
Scaling Aurora PostgreSQL DB instances
You can scale Aurora PostgreSQL DB instances in two ways, instance scaling and read scaling. For more information about read scaling, see Read scaling.
You can scale your Aurora PostgreSQL DB cluster by modifying the DB instance class for each DB instance in the DB cluster. Aurora PostgreSQL supports several DB instance classes optimized for Aurora. Don't use db.t2 or db.t3 instance classes for larger Aurora clusters of size greater than 40 terabytes (TB).
Note
We recommend using the T DB instance classes only for development and test servers, or other non-production servers. For more details on the T instance classes, see DB instance class types.
Scaling isn't instantaneous. It can take 15 minutes or more to complete the change to a different DB instance class. If you use this approach to modify the DB instance class, you apply the change during the next scheduled maintenance window (rather than immediately) to avoid affecting users.
As an alternative to modifying the DB instance class directly, you can minimize downtime by using the high availability features of Amazon Aurora. First, add an Aurora Replica to your cluster. When creating the replica, choose the DB instance class size that you want to use for your cluster. When the Aurora Replica is synchronized with the cluster, you then failover to the newly added Replica. To learn more, see Aurora Replicas and Fast failover with Amazon Aurora PostgreSQL.
For detailed specifications of the DB instance classes supported by Aurora PostgreSQL, see Supported DB engines for DB instance classes.
Maximum connections to an Aurora PostgreSQL DB instance
An Aurora PostgreSQL DB cluster allocates resources based on the DB instance class and its
available memory. Each connection to the DB cluster consumes incremental amounts of
these resources, such as memory and CPU. Memory consumed per connection varies based on
query type, count, and whether temporary tables are used. Even an idle connection
consumes memory and CPU. That's because when queries run on a connection, more
memory is allocated for each query and it's not released completely, even when
processing stops. Thus, we recommend that you make sure your applications aren't
holding on to idle connections: each one of these wastes resources and affects
performance negatively. For more information, see Resources consumed
by idle PostgreSQL connections
The maximum number of connections allowed by an Aurora PostgreSQL DB instance is
determined by the max_connections parameter value specified in the
parameter group for that DB instance. The ideal setting for the
max_connections parameter is one that supports all the client
connections your application needs, without an excess of unused connections, plus at
least 3 more connections to support AWS automation. Before modifying the
max_connections parameter setting, we recommend that you consider the
following:
-
If the
max_connectionsvalue is too low, the Aurora PostgreSQL DB instance might not have sufficient connections available when clients attempt to connect. If this happens, attempts to connect usingpsqlraise error messages such as the following:psql: FATAL: remaining connection slots are reserved for non-replication superuser connections -
If the
max_connectionsvalue exceeds the number of connections that are actually needed, the unused connections can cause performance to degrade.
The default value of max_connections is derived from the following
Aurora PostgreSQL LEAST function:
LEAST({DBInstanceClassMemory/9531392},5000).
If you want to change the value for max_connections, you need to create a
custom DB cluster parameter group and change its value there. After applying your custom
DB parameter group to your cluster, be sure to reboot the primary instance so the new
value takes effect. For more information, see Amazon Aurora PostgreSQL
parameters and Creating a DB cluster parameter group in Amazon Aurora.
Tip
If your applications frequently open and close connections, or keep a large number of long-lived connections open, we recommend that you use Amazon RDS Proxy. RDS Proxy is a fully managed, highly available database proxy that uses connection pooling to share database connections securely and efficiently. To learn more about RDS Proxy, see Amazon RDS Proxy for Aurora.
For details about how Aurora Serverless v2 instances handle this parameter, see Maximum connections for Aurora Serverless v2.
Temporary storage limits for Aurora PostgreSQL
Aurora PostgreSQL stores tables and indexes in the Aurora storage subsystem. Aurora PostgreSQL
uses separate temporary storage for non-persistent temporary files. This includes files
that are used for such purposes as sorting large data sets during query processing or
for index build operations. For more information, see the article How can I
troubleshoot local storage issues in Aurora PostgreSQL-Compatible
instances?
These local storage volumes are backed by Amazon Elastic Block Store and can be extended by using a larger DB instance class. For more information about storage, see Amazon Aurora storage. You can also increase your local storage for temporary objects by using an NVMe enabled instance type and Aurora Optimized Reads-enabled temporary objects. For more information, see Improving query performance for Aurora PostgreSQL with Aurora Optimized Reads.
Note
You might see storage-optimization events when scaling DB instances,
for example, from db.r5.2xlarge to db.r5.4xlarge.
The following table shows the maximum amount of temporary storage available for each Aurora PostgreSQL DB instance class. For more information on DB instance class support for Aurora, see Amazon Aurora DB instance classes.
| DB instance class | Maximum temporary storage available (GiB) |
|---|---|
| db.x2g.16xlarge | 1829 |
| db.x2g.12xlarge | 1606 |
| db.x2g.8xlarge | 1071 |
| db.x2g.4xlarge | 535 |
| db.x2g.2xlarge | 268 |
| db.x2g.xlarge | 134 |
| db.x2g.large | 67 |
| db.r8g.48xlarge | 3072 |
| db.r8g.24xlarge | 1536 |
| db.r8g.16xlarge | 998 |
| db.r8g.12xlarge | 749 |
| db.r8g.8xlarge | 499 |
| db.r8g.4xlarge | 250 |
| db.r8g.2xlarge | 125 |
| db.r8g.xlarge | 63 |
| db.r8g.large | 31 |
| db.r7g.16xlarge | 1008 |
| db.r7g.12xlarge | 756 |
| db.r7g.8xlarge | 504 |
| db.r7g.4xlarge | 252 |
| db.r7g.2xlarge | 126 |
| db.r7g.xlarge | 63 |
| db.r7g.large | 32 |
| db.r7i.48xlarge | 3072 |
| db.r7i.24xlarge | 1500 |
| db.r7i.16xlarge | 1008 |
| db.r7i.12xlarge | 748 |
| db.r7i.8xlarge | 504 |
| db.r7i.4xlarge | 249 |
| db.r7i.2xlarge | 124 |
| db.r7i.xlarge | 62 |
| db.r7i.large | 31 |
| db.r6g.16xlarge | 1008 |
| db.r6g.12xlarge | 756 |
| db.r6g.8xlarge | 504 |
| db.r6g.4xlarge | 252 |
| db.r6g.2xlarge | 126 |
| db.r6g.xlarge | 63 |
| db.r6g.large | 32 |
| db.r6i.32xlarge | 1829 |
| db.r6i.24xlarge | 1500 |
| db.r6i.16xlarge | 1008 |
| db.r6i.12xlarge | 748 |
| db.r6i.8xlarge | 504 |
| db.r6i.4xlarge | 249 |
| db.r6i.2xlarge | 124 |
| db.r6i.xlarge | 62 |
| db.r6i.large | 31 |
| db.r5.24xlarge | 1500 |
| db.r5.16xlarge | 1008 |
| db.r5.12xlarge | 748 |
| db.r5.8xlarge | 504 |
| db.r5.4xlarge | 249 |
| db.r5.2xlarge | 124 |
| db.r5.xlarge | 62 |
| db.r5.large | 31 |
| db.r4.16xlarge | 960 |
| db.r4.8xlarge | 480 |
| db.r4.4xlarge | 240 |
| db.r4.2xlarge | 120 |
| db.r4.xlarge | 60 |
| db.r4.large | 30 |
| db.t4g.large | 16.5 |
| db.t4g.medium | 8.13 |
| db.t3.large | 16 |
| db.t3.medium | 7.5 |
Note
NVMe enabled instance types can increase the temporary space available by up to the total NVMe size. For more information, see Improving query performance for Aurora PostgreSQL with Aurora Optimized Reads.
You can monitor the temporary storage available for a DB instance with the
FreeLocalStorage CloudWatch metric,
-->
described in Amazon CloudWatch metrics for Amazon Aurora. (This doesn't apply to
Aurora Serverless v2.)
For some workloads, you can reduce the amount of temporary storage by allocating more
memory to the processes that are performing the operation. To increase the memory
available to an operation, increasing the values of the work_mem
Huge pages for Aurora PostgreSQL
Huge pages are a memory management feature that reduces overhead when a DB instance is working with large contiguous chunks of memory, such as that used by shared buffers. This PostgreSQL feature is supported by all currently available Aurora PostgreSQL versions.
Huge_pages parameter is turned on by default for all DB instance classes
other than t3.medium,db.t3.large,db.t4g.medium,db.t4g.large instance classes. You can't
change the huge_pages parameter value or turn off this feature in the
supported instance classes of Aurora PostgreSQL.
On Aurora PostgreSQL DB instances that don't support the huge pages memory feature, specific process memory usage might increase without corresponding workload changes.
The system allocates shared memory segments like the buffer cache during server startup. When huge memory pages aren't available, the system doesn't charge these allocations to the postmaster process. Instead, it includes the memory in the process that first accessed each 4KB page in the shared memory segment.
Note
Active connections share allocated memory as needed, regardless of how shared memory usage is tracked across processes.
