This page contains a list of the most frequent issues you might run into when working with Google Cloud SQL instances as well as steps you can take to address them. If the information here does not solve your issue, see the Support Overview for getting further help.
Verify that you are authorized to connect
If your connections are failing, check that you are authorized to connect:
- If you are having trouble connecting using an IP address, for example, you are connecting from your on-premises environment with the psql client, then make sure that the IP address you are connecting from is authorized to connect to the Cloud SQL instance. Here's your current IP address.
- Try the
gcloud sql connectto connect to your instance. This command authorizes your IP address for a short period of time. You can run this in an environment with Cloud SDK and psql client installed. You can also run this command in Google Cloud Shell, which is available in the Google Cloud Platform Console and has Cloud SDK and the psql client pre-installed. Cloud Shell provides a Compute Engine instance that you can use to connect to Cloud SQL.
- Temporarily allow all IP addresses to connect to an instance by
0.0.0.0/0. This confirms that your client can connect.
Understand connection limits
There are no QPS limits for Google Cloud SQL instances. However, there are connection, size, and App Engine specific limits in place.
Cloud SQL instances limits
|Limit||MySQL First Generation instances||MySQL Second Generation instances||PostgreSQL instances||Notes|
|Concurrent connections||Determined by tier||4,000||100|
|Enqueued connection requests||100||N/A||N/A||First Generation instances only. Incoming connection requests are briefly queued before the connection is established. The queue can accept only 100 incoming connection requests.|
|Storage size||250 GB||Up to 10,230 GB, depending on machine type||Up to 10,230 GB, depending on whether the instance has dedicated or shared vCPUs.||It is possible to increase individual First Generation instance limits up to 500 GB for customers with a Silver or higher Google Cloud support package.|
Google App Engine Limits
All database requests must finish within 24 hours.
Google App Engine applications are also subject to additional App Engine quotas and limits as discussed on the Quotas page.
To learn more about managing connections, see the FAQ How should I manage connections?.
Connections from Compute Engine
If you expect that connections between your Compute Engine instance and your Cloud SQL instance will include long-lived unused connections, then you should be aware that connections with a Compute Engine instance time out after 10 minutes of inactivity. For more information, see Networking and Firewalls in the Google Compute Engine documentation.
To keep long-lived unused connections alive, you can set the TCP keepalive. The following commands set the TCP keepalive value to one minute and make the configuration permanent across instance reboots.
# Display the current tcp_keepalive_time value. cat /proc/sys/net/ipv4/tcp_keepalive_time # Set tcp_keepalive_time to 60 seconds and make it permanent across reboots. echo 'net.ipv4.tcp_keepalive_time = 60' | sudo tee -a /etc/sysctl.conf # Apply the change. sudo /sbin/sysctl --load=/etc/sysctl.conf # Display the tcp_keepalive_time value to verify the change was applied. cat /proc/sys/net/ipv4/tcp_keepalive_time
Disk spaceIf your instance reaches the maximum storage amount allowed, writes to the database fail. If you delete data, for example, by dropping a table, the space is usually freed, but it is not reflected in the reported Storage Used of the instance. You can run the
VACUUM FULLcommand to recover unused space; note that write operations are blocked while the vacuum command is running. Learn more.
There are a number of reasons why Google Cloud SQL may suspend an instance, including:
For example, if the credit card for the project's billing account has expired, the instance may be suspended. You can check the billing information for a project by going to the Google Cloud Platform Console billing page, selecting the project, and viewing the billing account information used for the project.
For example, a violation of the Google Cloud Platform Acceptable Use Policy may cause the instance to be suspended. For more information, see "Suspensions and Removals" in the Google Cloud Platform Terms of Service.
For example, if an instance is stuck in a crash loop, i.e., it crashes while starting or just after starting, Google Cloud SQL may suspend it.
While an instance is suspended, you can continue to view information about it or you can delete it, if the suspension was triggered by billing issues.
Cloud SQL users with Platinum, Gold, or Silver support packages can contact our support team directly about suspended instances. All users can use the guidance above along with the google-cloud-sql forum.
Keep a reasonable number of database schemas and tables
Database schemas and tables consume system resources. A very large number can affect instance performance.
Make sure that your instance is not constrained on memory or CPU. For performance-intensive workloads, your instance should have at least 60 GB of memory.
For slow database inserts, updates, or deletes, check the locations of the writer and database; sending data a long distance introduces latency.
For slow database selects, consider the following:
- Caching is extremely important for read performance. Check the various
blks_hit / (blks_hit + blks_read)ratios from the PostgreSQL Statistics Collector. Ideally, the ratio should be above 99%. If this is not the case, consider increasing the size of your instance's RAM.
- If your workload consists of CPU intensive queries (sorting, regexes, other complex functions), your instance might be throttled; add vCPUs.
- Check the location of the reader and database - latency will affect read performance even more than write performance.
- Investigate non-Cloud SQL specific performance improvements, such as adding appropriate indexing, reducing data scanned, and avoiding extra round trips.
EXPLAINto identify where to add indexes to tables to improve query performance. For example, make sure every field that you use as a JOIN key has an index on both tables.