option ( "forward_spark_s3_credentials", True ). option ( "query", "select x, count(*) group by x" ). load () // Read data from a query val df = spark. Read data from a table val df = spark. save () ) # Write back to a table using IAM Role based authentication ( df. load () ) # After you have applied transformations to the data, you can use # the data source API to write the data back to another table # Write back to a table ( df. load () ) # Read data from a query df = ( spark. Azure Synapse with Structured Streaming.Interact with external data on Databricks.The reboot can be performed by a cluster reboot through the Amazon Redshift console or through a database reboot using REBOOT_CLUSTER. If PG_TERMINATE_BACKEND(PID) doesn't successfully terminate the session, then reboot the cluster to terminate the process. Therefore, it's a best practice to also check STV_SESSIONS (and other relevant system tables) to confirm whether the PID was actually terminated. In some cases, the PID can't be determined depending on its internal status. However, this doesn't guarantee that the PID actually was terminated. Note: The value "1" returned by PG_TERMINATE_BACKEND(PID) typically indicates a successful termination request to the PID. The locks are then claimed by the other transactions that are waiting to acquire the lock. Terminating a PID rolls back all running transactions and releases all locks in the session. You can also manually terminate the session by running the following command: select pg_terminate_backend(PID) To release a lock, wait for the transaction that's holding the lock to finish. In this example, PID 19813 is holding the lock. The blocking_pid column shows the process ID of the session that's holding the lock. If the result in the granted column is f (false), then a transaction in another session is holding the lock. The output looks similar to: txn_owner | txn_db | xid | pid | txn_start | lock_mode | table_id | tablename | granted | blocking_pid | txn_duration | Left join (select * from stv_tbl_perm where slice=0) c On a.relation=b.relation and a.granted='f' and b.granted='t' Left join (select pid,relation,granted from pg_locks group by 1,2,3) b Run a query to identify sessions that are holding locks: select a.txn_owner, a.txn_db, a.xid, a.pid, a.txn_start, a.lock_mode, a.relation as table_id,nvl(trim(c."name"),d.relname) as tablename, a.granted,b.pid as blocking_pid ,datediff(s,a.txn_start,getdate())/86400||' days '||datediff(s,a.txn_start,getdate())%86400/3600||' hrs '||datediff(s,a.txn_start,getdate())%3600/60||' mins '||datediff(s,a.txn_start,getdate())%60||' secs' as txn_duration If the session doesn't terminate, reboot your cluster. To solve a locking problem, identify the session (PID) that is holding the lock and then terminate the session. For more information about why a query might hang, see Query hangs. Other queries or transactions that are waiting to acquire the same lock are blocked except for AccessShareLock. When a query or transaction acquires a lock on a table, the lock remains for the duration of the query or transaction. ShareRowExclusiveLock blocks AccessExclusiveLock and other ShareRowExclusiveLock attempts, but doesn't block AccessShareLock attempts. ShareRowExclusiveLock: Acquired during COPY, INSERT, UPDATE, or DELETE operations.AccessShareLock doesn't block other sessions that are trying to read or write on the table. AccessShareLock blocks only AccessExclusiveLock attempts. AccessShareLock: Acquired during UNLOAD, SELECT, UPDATE, or DELETE operations.AccessExclusiveLock blocks all other locking attempts. AccessExclusiveLock: Acquired primarily during DDL operations, such as ALTER TABLE, DROP, or TRUNCATE.You might experience locking conflicts if you perform frequent DDL statements on user tables or DML queries. However, Amazon Redshift uses table-level locks. Most relational databases use row-level locks. Locking also determines which operations can be performed in those sessions. Locking is a protection mechanism that controls how many sessions can access a table at the same time.
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