JOIN pg_catalog.pg_stat_activity blocked_activity ON blocked_activity.pid = blocked_locks.pid To find locks on the associated tables, run this command in the source or target (depending where the error is appearing): SELECT blocked_locks.pid AS blocked_pid,īlocked_ename AS blocked_user,īlocking_ename AS blocking_user,īlocked_activity.query AS blocked_statement,īlocking_activity.query AS current_statement_in_blocking_process See this example error message from the PostgreSQL error log: ERROR: canceling statement due to statement timeout After identifying the command that failed, you can find the failed table names. You can also find this information in the PostgreSQL error log file if the parameter log_min_error_statement is set to ERROR or a lower severity. To find the command that failed to run during the timeout period, review the AWS DMS task log and the table statistics section of the task. Resolution Identify the cause of long run times for commands Increase the timeout value and check the slot creation timeout value.Identify the cause of long run times for commands.To troubleshoot and resolve these errors, follow these steps: "]E: RetCode: SQL_ERROR SqlState: 57014 NativeError: 1 Message: ERROR: canceling statement due to statement timeout Error while executing the query (ar_odbc_stmt.c:2738)" So, the task fails with an error that says "canceling statement due to statement timeout," and you see one of these entries in the log: If the source or target is heavily loaded or there are locks in the tables, then AWS DMS can't finish running those commands within 60 seconds. When AWS DMS tries to either get data from source or put data in the target, it uses the default timeout setting of 60 seconds. Then, it runs a COPY command to insert the net changes to the target. For batch apply mode, AWS DMS also creates CSV files during the CDC phase. Then, AWS DMS runs a COPY command to insert those records into the target during the full load phase.īut, during the CDC phase, AWS DMS runs the exact DML statements from the source WAL logs in transactional apply mode. If the PostgreSQL database is the target, then AWS DMS gets the data from the source and creates CSV files in the replication instance. Then, AWS DMS reads from the write-ahead logs (WALs) that are kept by the replication slot during the change data capture (CDC) phase. If the PostgreSQL database is the source of your migration task, then AWS DMS gets data from the table during the full load phase.
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