Although all cloud service providers offer some kind of built-in instrumentation and graphs, they're mostly rather conservative in this are not to consume extra server resources and not to overflow and confuse beginners with too much information. So for advanced troubleshooting it might make sense to gather some additional metrics on your own, especially given that you can also easily add custom business metrics to pgwatch using plain SQL, for example to track the amount of incoming sales orders. Also with pgwatch / Grafana you have more freedom on the visual representation side and access to around 30 prebuilt dashboards and a lot of freedom creating custom alerting rules.

The common denominator for all managed cloud services is that they remove / disallow dangerous or potentially dangerous functionalities like file system access and untrusted PL-languages like Python - so you'll lose a small amount of metrics and "helper functions" compared to a standard on-site setup described in the previous chapter <preparing_databases>. This also means that you will get some errors displayed on some preset dashboards like "DB overview" and thus will be better off using a dashboard called "DB overview Unprivileged" tailored specially for such a use case.

pgwatch has been tested to work with the following managed database services:

Google Cloud SQL for PostgreSQL

• No Python / OS helpers possible. OS metrics can be integrated in Grafana though using the Stackdriver data source.
• pg_monitor system role available.
• pgwatch default preset name: gce.
• Documentation: https://cloud.google.com/sql/docs/postgres

To get most out pgwatch on GCE you need some additional clicks in the GUI / Cloud Console "Flags" section to enable some common PostgreSQL monitoring parameters like track_io_timing and track_functions.

Amazon RDS for PostgreSQL

• No Python / OS helpers possible. OS metrics can be integrated in Grafana though using the CloudWatch data source

• pg_monitor system role available.

• pgwatch default preset names: rds, aurora

• Documentation:

  https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_PostgreSQL.html https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.AuroraPostgreSQL.html

Note that the AWS Aurora PostgreSQL-compatible engine is missing some additional metrics compared to normal RDS.

Azure Database for PostgreSQL

• No Python / OS helpers possible. OS metrics can be integrated in Grafana though using the Azure Monitor data source
• pg_monitor system role available.
• pgwatch default preset name: azure
• Documentation: https://docs.microsoft.com/en-us/azure/postgresql/

Surprisingly on Azure some file access functions are whitelisted, thus one can for example use the wal_size metrics.

Note

By default Azure has pg_stat_statements not fully activated by default, so you need to enable it manually or via the API. Documentation link here.

Aiven for PostgreSQL

The Aiven developer documentation contains information on how to monitor PostgreSQL instances running on the Aiven platform with pgwatch.