![]() ![]() Then, run the following SQL query to identify queries consuming high CPU: select stq.userid, stq.query, trim(stq.label) as label, stq.xid, stq.pid, svq.service_class, For example, make sure that all transactions starting with a BEGIN statement are accompanied by an END or COMMIT statement. To prevent these sessions from remaining open, be sure that all transactions are closed. Then, run PG_TERMINATE_BACKEND to stop any long-running transactions. To identify long-running sessions, use the following SQL query: select *,datediff(s,txn_start,getdate())/86400||' days '||datediff(s,txn_start,getdate())%86400/3600||' hrs '||datediff(s,txn_start,getdate())%3600/60||' mins '||datediff(s,txn_start,getdate())%60||' secs' as "duration"įrom svv_transactions where lockable_object_type='transactionid' and pidpg_backend_pid() order by 3 While the queries are running, retrieve locking information. Idle sessions can cause additional lock contention issues. This can be a result of idle sessions present in the cluster. The higher number of concurrent queries impacts resource contention, lock wait time, and workload management (WLM) queue wait time.The increase in workload increases the number of database connections, causing higher query concurrency. An increased workload (due to more queries running).The following factors can impact the CPU utilization on your Redshift cluster: Use Amazon CloudWatch to monitor spikes in CPU utilization.Check for spikes in your leader node CPU usage.However, if your CPU usage impacts your query time, then consider the following approaches: An increase in CPU utilization can depend on factors such as cluster workload, skewed and unsorted data, or leader node tasks. That means that you can expect to see spikes in CPU usage in your Redshift cluster. ![]() Amazon Redshift is designed to utilize all available resources while running queries.
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