Troubleshoot

Run the gcloud sql operations list command to list all operations for the given Cloud SQL instance.

Look in the logs and filter by text to find the user. You may need to use audit logs for private information. Relevant log files include:

• cloudsql.googleapis.com/postgres.log
• If Cloud Audit Logs is enabled and you have the required permissions to view them, cloudaudit.googleapis.com/activity may also be available.

After an instance is purged, no data recovery is possible. However, if the instance is restored, then its backups are also restored. For more information on recovering a deleted instance, see Recovery backups.

If you have done an export operation, create a new instance and then do an import operation to recreate the database. Exports are written to Cloud Storage and imports are read from there.

If you really need to cancel the operation, you can ask customer support to force restart the instance.

Create the database users before restoring the SQL dump.

To keep backups indefinitely, you can create an on-demand backup, as they are not deleted in the same way as automated backups. On-demand backups remain indefinitely. That is, they remain until they're deleted or the instance they belong to is deleted. Because that type of backup is not deleted automatically, it can affect billing.

• There could be too many open connections. Too many connections can result from errors that occur in the middle of a connection where there are no autovacuum settings to clean up dead connections.
• Cycling can occur if any custom code is using retry logic that doesn't stop after a few failures.
• There could be too much traffic. Use connection pooling and other best practices for connectivity.

Things to try:

1. Verify that the database is set up for autovacuum.
2. Check if there is any connection retry logic set up in custom code.
3. Turn down traffic until the database recovers and then slowly turn traffic back up.

CREATE UNLOGGED TABLE ....

These tables are not included in a restore from a backup:

• The contents of unlogged tables doesn't survive failover on an HA instance.
• Unlogged tables don't survive postgres crashes.
• Unlogged tables are not replicated to read replicas.
• Unlogged tables are automatically wiped during backup restore.

The solution is to avoid using unlogged tables if you want to restore those tables through a backup. If you're restoring from a database that already has unlogged tables, then you can dump the database to a file, and reload the data after modifying the dumped file to ALTER TABLE to SET LOGGED on those tables.

You're trying to cancel an import or export operation that's completed, failed, or cancelled. If the operation is running, you can cancel it.

Cloud SQL doesn't support the cancellation of the operation because it has an operation type other than IMPORT or EXPORT.

Cloud SQL can't cancel the import or export operation at this time. Try again in a few seconds. If the problem persists, contact Google Cloud Support.

Remove all Authorized Networks entries from the Cloud SQL instance if you can. Otherwise, create a replica without any Authorized Networks entries.

You're trying to use the Google Cloud console to clone an instance with a private IP address, but you didn't specify the allocated IP range that you want to use and the source instance isn't created with the specified range. As a result, the cloned instance is created in a random range.

Use gcloud to clone the instance and provide a value for the
--allocated-ip-range-name parameter. For more information, see Cloning an instance with a private IP.

• Networking instability.
• No response to TCP keep-alive commands (either the client or the server isn't responsive, possibly overloaded)
• The database engine connection lifetime was exceeded and the server ends the connection.

Applications must tolerate network failures and follow best practices such as connection pooling and retrying. Most connection poolers catch these errors where possible. Otherwise the application must either retry or fail gracefully.

For connection retry, we recommend the following methods:

1. Exponential backoff. Increase the time interval between each retry, exponentially.
2. Add randomized backoff also.

Combining these methods helps reduce throttling.

The client certificates have expired or the path to the certificates isn't correct.

Regenerate the certificates by recreating them.

Connect with the default user, then change users.

The host address does not match the address in the server certificate's alternative names.

If you are using Node.js with verify-full or its equivalent, please use the DNS name for the servername parameter. The DNS name can be found in the server certificate using openssl. For example, openssl x509 -in server-cert.pem -noout -text |grep 'DNS:'.

 ssl: {
  rejectUnauthorized: true,
  ca: fs.readFileSync("/path/to/server/CA"),
  servername: 'N-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxx.us-central1.sql.goog'
}

• The size of the allocated IP range for the private service connection is smaller than /24.
• The size of the allocated IP range for the private service connection is too small for the number of Cloud SQL instances.
• The requirement on the size of allocated IP range will be larger if instances are created in multiple regions. See allocated range size

To resolve this issue, you can either expand the existing allocated IP range or allocate an additional IP range to the private service connection. For more information, see Allocate an IP address range.

If you used the --allocated-ip-range-name flag while creating the Cloud SQL instance, you may only expand the specified IP range.

If you're allocating a new range, take care that the allocation doesn't overlap with any existing allocations.

After creating a new IP range, update the vpc peering with the following command:

gcloud services vpc-peerings update \
--service=servicenetworking.googleapis.com \
--ranges=OLD_RESERVED_RANGE_NAME,NEW_RESERVED_RANGE_NAME \
--network=VPC_NETWORK \
--project=PROJECT_ID \
--force
    

If you're expanding an existing allocation, take care to increase only the allocation range and not decrease it. For example, if the original allocation was 10.0.10.0/24, then make the new allocation at least 10.0.10.0/23.

In general, if starting from a /24 allocation, decrementing the /mask by 1 for each condition (additional instance type group, additional region) is a good rule of thumb. For example, if trying to create both instance type groups on the same allocation, going from /24 to /23 is enough.

After expanding an existing IP range, update the vpc peering with following command:

gcloud services vpc-peerings update \
--service=servicenetworking.googleapis.com \
--ranges=RESERVED_RANGE_NAME \
--network=VPC_NETWORK \
--project=PROJECT_ID
    

Use CSV exports to export only what you need.

Use export offloading. At a high level, in export offloading, instead of issuing an export on the source instance, Cloud SQL spins up an offload instance to perform the export. Export offloading has several advantages, including increased performance on the source instance and the unblocking of administrative operations while the export is running. With export offloading, total latency can increase by the amount of time it takes to bring up the offload instance. Generally, for reasonably sized exports, latency is not significant. However, if your export is small enough, then you may notice the increase in latency.

Edit the dump file to remove the references.

Do a manual export using the pg_dump tool.

You could build your own automated export system using Google Cloud products such as Cloud Scheduler, Pub/Sub, and Cloud Run functions, similar to this article on automating backups.

Connect to the database and set the database time zone to the one you want, either per user or per database.

In Cloud SQL for PostgreSQL, you can specify the following. These settings remain after a session is closed, mimicking a .conf configuration:

ALTER DATABASE dbname SET TIMEZONE TO 'timezone';
ALTER USER username SET TIMEZONE TO 'timezone';

These settings apply only to new connections to the database. To see the change to the time zone, disconnect from the instance and then reconnect to it.

Edit the instance to upgrade to a larger machine size to get more CPUs and memory.

Close unused operations. Check the CPU and memory usage of your Cloud SQL instance to make sure there are plenty of resources available. The best way to ensure maximum resources for the import is to restart the instance before beginning the operation.

A restart:

• Closes all connections.
• Ends any tasks that may be consuming resources.

Create the database users before importing.

There may be an unexpected disk usage after importing data. This usage may be because of using point-in-time recovery.

To resolve this, after you import data, disable point-in-time recovery if you want to delete logs and recover storage. Keep in mind that decreasing the storage used doesn't shrink the size of the storage provisioned for the instance.

This error message appears if you try to import a SQL dump file that's uploaded in Cloud Storage to a Cloud SQL database, and the import job has run for about four days.

ROLE_NAME is a custom database role defined in the source PostgreSQL database. The default cloudsqlsuperuser user imports the SQL dump file. However, this user might not belong to the ROLE_NAME role.

To resolve this issue, complete the following steps:

1. Create the ROLE_NAME role in the destination database where you're importing the SQL dump file.

2. Don't use the cloudsqlsuperuser user to import the file. Instead, in the destination database, specify a user who's a member of the ROLE_NAME role. To specify the user, run the following command:

   gcloud sql import sql INSTANCE URI [--async]
[--database=DATABASE, -d DATABASE] [--user=USER] [GCLOUD_WIDE_FLAG …]

The log_statement flag can be set to none and the logging_collector flag can be set to off. If logging is still occurring, there may be other log-related flags that can be tuned. You can edit the instance to modify these flags.

For example, a user was deleted but you can't find out who did it. The logs show the operation started but don't provide any more information. You must enable audit logging for detailed and personal identifying information (PII) like this to be logged.

To download the logs as JSON:

gcloud logging read \
"resource.type=cloudsql_database \
AND logName=projects/PROJECT_ID \
/logs/cloudsql.googleapis.com%2FLOG_NAME" \
--format json \
--project=PROJECT_ID \
--freshness="1d" \
> downloaded-log.json
    

To download the logs as TEXT:

gcloud logging read \
"resource.type=cloudsql_database \
AND logName=projects/PROJECT_ID \
/logs/cloudsql.googleapis.com%2FLOG_NAME" \
--format json \
--project=PROJECT_ID \
--freshness="1d"| jq -rnc --stream 'fromstream(1|truncate_stream(inputs)) \
| .textPayload' \
--order=asc
> downloaded-log.txt
   

1. From a terminal, connect to your database:

   gcloud sql connect INSTANCE_NAME
         

2. Run this command to create the extension:

   CREATE EXTENSION pgaudit;
         

3. Exit the database, and from a terminal run the following command:

   gcloud sql instances patch INSTANCE_NAME \
   --database-flags=cloudsql.enable_pgaudit=on,pgaudit.log=all
        

SELECT datname, usename, application_name as appname, client_addr, state, now() - backend_start as conn_age, now() - xact_start as xact_age, now() - query_start as query_age, now() - state_change as last_activity_age, wait_event_type, wait_event, query FROM pg_stat_activity WHERE state <> 'idle' ORDER BY 8 DESC LIMIT 20;

SELECT name, setting, unit FROM pg_settings WHERE name = 'FIELD_NAME'.

SHOW SETTING_NAME;

Run SHOW ALL; to see all settings.

Run the following commands:

1.       GRANT pg_signal_backend TO USERNAME;
         

2. Find the process ID of the blocked or stuck process:

         SELECT pid, usename, state, query FROM pg_stat_activity;
         

3. Stop a running or idle process using these commands:

         SELECT pg_cancel_backend(pid)
               FROM pg_stat_activity
               WHERE usename = 'USERNAME';
         
         

         SELECT pg_terminate_backend(pid)
               FROM pg_stat_activity
               WHERE usename = 'USERNAME';
         
         

The autovacuum job might be blocked, or might not be reclaiming the transaction IDs fast enough to keep up with the workload.

In order to avoid any outages due to transaction wraparound problem, you can review these self-servicing tips for dealing with TXID wraparound.

For general tuning advice, see Optimizing, monitoring, and troubleshooting vacuum operations in PostgreSQL.

Restart deletes the temporary files but not reduce the storage. Only customer support can reset the instance size.

Look in the logs around the time of the deletion and see if there's a rogue script running from a dashboard or another automated process.

Some possible explanations include:

• Another operation is in progress. Cloud SQL operations do not run concurrently. Wait for the other operation to complete.
• The INSTANCE_RISKY_FLAG_CONFIG warning is triggered whenever at least one beta flag is being used. Remove the risky flag settings and restart the instance

Unfortunately, you can't shrink the ibtmp1 file by any method other than restarting the service.

One mitigation option is to create the temporary table with ROW_FORMAT=COMPRESSED, so it is stored in file-per-table tablespaces in the temporary file directory. However, the downside is performance costs associated with creating and removing a file-per-table tablespace for each temporary table.

If your instance runs out of storage, and the automatic storage increase capability isn't enabled, your instance goes offline. To avoid this issue, you can edit the instance to enable automatic storage increase.

By having connections that last less than 60 seconds, most unclean shutdowns can be avoided, including connections from the database command prompt. If you keep these connections open for hours or days, shutdowns can be unclean.

Find out which objects are dependent on the user, then drop or reassign those objects to a different user.

Refer to general performance tips in particular.

For slow database inserts, updates, or deletes, consider the following actions:

• Check the locations of the writer and database; sending data a long distance introduces latency.
• Check the location of the reader and database; latency affects read performance even more than write performance

To reduce the latency the recommendation is to locate both the source and destination resources in the same region.

There are other factors beside your workload that can impact memory usage, such as the number of active connections and internal overhead processes. These aren't always reflected in the monitoring charts.

Ensure that the instance has enough overhead to account for your workload plus some additional overhead.

To preserve your data, export it to Cloud Storage before you delete an instance.

The Cloud SQL Admin role includes the permission to delete the instance. To prevent accidental deletion, grant this role only as needed.

There are other ways to accomplish the goal by creating a new instance.

• You can clone the instance you want to rename and set a new name for the cloned instance. This allows you to create the new instance without having to import data manually. Just as when creating a new instance, the cloned instance has a new IP address.
• You can export data from your instance into a Cloud Storage bucket, create a new instance with the new name you want, and then import the data into the new instance.

In both cases, you can delete your old instance after the operation is done. We recommend going with the cloning route since it has no impact on performance and doesn't require you to redo any instance configuration settings such as flags, machine type, storage size and memory.

1. Check the endpoint's status.

   gcloud

   To check the status, use the
   gcloud compute forwarding-rules describe command.

   gcloud compute forwarding-rules describe ENDPOINT_NAME \
   --project=PROJECT_ID \
   --region=REGION_NAME \
   | grep pscConnectionStatus

   Make the following replacements:

   • ENDPOINT_NAME: the name of the endpoint
   • PROJECT_ID: the ID or project number of the Google Cloud project that contains the endpoint
   • REGION_NAME: the region name for the endpoint

   REST

   Before using any of the request data, make the following replacements:

   • PROJECT_ID: the ID or project number of the Google Cloud project that contains the Private Service Connect endpoint
   • REGION_NAME: the name of the region
   • ENDPOINT_NAME: the name of the endpoint

   HTTP method and URL:

   GET https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/regions/REGION_NAME/forwardingRules/ENDPOINT_NAME

   To send your request, expand one of these options:

   curl (Linux, macOS, or Cloud Shell)

   Execute the following command:

   curl -X GET \
        -H "Authorization: Bearer $(gcloud auth print-access-token)" \
        "https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/regions/REGION_NAME/forwardingRules/ENDPOINT_NAME"

   PowerShell (Windows)

   Execute the following command:

   $cred = gcloud auth print-access-token
   $headers = @{ "Authorization" = "Bearer $cred" }
   
   Invoke-WebRequest `
       -Method GET `
       -Headers $headers `
       -Uri "https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/regions/REGION_NAME/forwardingRules/ENDPOINT_NAME" | Select-Object -Expand Content

   You should receive a JSON response similar to the following:

   {
     "kind": "compute#forwardingRule",
     "id": "ENDPOINT_ID",
     "creationTimestamp": "2024-05-09T12:03:21.383-07:00",
     "name": "ENDPOINT_NAME",
     "region": "https://www.googleapis.com/compute/v1/projects/PROJECT_ID/regions/REGION_NAME",
     "IPAddress": "IP_ADDRESS",
     "target": "https://www.googleapis.com/compute/v1/projects/PROJECT_ID/regions/REGION_NAME/serviceAttachments/SERVICE_ATTACHMENT_NAME",
     "selfLink": "https://www.googleapis.com/compute/v1/projects/PROJECT_ID/regions/REGION_NAME/forwardingRules/ENDPOINT_NAME",
     "network": "https://www.googleapis.com/compute/v1/projects/PROJECT_ID/global/networks/default",
     "serviceDirectoryRegistrations": [
       {
         "namespace": "goog-psc-default"
       }
     ],
     "networkTier": "PREMIUM",
     "labelFingerprint": "LABEL_FINGERPRINT_ID",
     "fingerprint": "FINGERPRINT_ID",
     "pscConnectionId": "CONNECTION_ID",
     "pscConnectionStatus": "ACCEPTED",
     "allowPscGlobalAccess": true
   }
   

2. Verify that the status of the endpoint is ACCEPTED. If the status is PENDING, then the instance isn't allowing the Google Cloud project that contains the endpoint. Make sure that the network project in which the endpoint is created is allowed. For more information, see Edit an instance with Private Service Connect enabled.

First, check that the value of the max_connections flag is greater than or equal to the value on the primary.

If the max_connections flag is set appropriately, inspect the logs in Cloud Logging to find the actual error.

If the error is: set Service Networking service account as servicenetworking.serviceAgent role on consumer project, then disable and re-enable the Service Networking API. This action creates the service account necessary to continue with the process.

Restart the replica instance to reclaim the temporary memory space.

Edit the instance to enable automatic storage increase.

• Slow queries on the replica. Find and fix them.
• All tables must have a unique/primary key. Every update on such a table without a unique/primary key causes full table scans on th replica.
• Queries like DELETE ... WHERE field < 50000000 cause replication lag with row-based replication since a huge number of updates are piled up on the replica.

Some possible solutions include:

• Edit the instance to increase the size of the replica.
• Reduce the load on the database.
• Send read traffic to the read replica.
• Index the tables.
• Identify and fix slow write queries.
• Recreate the replica.

If you must use hash indexes, upgrade to PostgreSQL 10+. Otherwise, if you also want to use replicas, don't use hash indexes in PostgreSQL 9.6.

Recreate the replica after stopping all running queries.

To resolve this issue, complete the following steps:

1. Turn on the log_duration flag and set the log_statement parameter to ddl. This provides you with both the queries and the run time on the database. However, depending on your workload, this might cause performance issues.
2. On both the primary instance and the read replica, run explain analyze for the queries.
3. Compare the query plan and check for differences.

If this is a specific query, then modify the query. For example, you can change the order of the joins to see if you get better performance.