![]() ![]() If the percentages are high, run the Analyze & Vacuum schema utility from the AWS Labs GitHub repository to update your tables. These percentages should remain close to 0. Use the SVV_TABLE_INFO system view to retrieve stats_off and unsorted percentage data for a table. A poorly performing query negatively affects your Redshift cluster's CPU usage. Unsorted data can cause queries to scan unnecessary data blocks, which require additional I/O operations. A high percentage of both can cause the query optimizer to generate an execution plan where queries run inefficiently when referencing tables. For more information, see How do I resize a Redshift cluster?ĭata hygiene is gauged by the percentage of stale statistics and unsorted rows present in a table. Scaling a cluster provides more memory and computing power, which can help queries to run more quickly. Scale the Redshift cluster to accommodate the increased workload.Enable short query acceleration (SQA) to prioritize short-running queries over long-running queries.This reduction helps queries that require more memory to run more efficiently. Reduce query concurrency per queue to provide more memory to each query slot.Then, determine which of the following approaches can help you reduce queue wait time: You can also use the wlm_query_trend_hourly view to review the Redshift cluster workload pattern. ![]() To analyze segment and slice-level execution steps for each query, run the following query: select query, segment, step, label ,is_rrscan as rrS, is_diskbased as disk, is_delayed_scan as DelayS, min(start_time) as starttime, max(end_time) as endtime, datediff(ms, min(start_time), max(end_time)) as "elapsed_msecs", sum(rows) as row_s, sum(rows_pre_filter) as rows_pf, CASE WHEN sum(rows_pre_filter) = 0 THEN 100 ELSE sum(rows)::float/sum(rows_pre_filter)::float*100 END as pct_filter, SUM(workmem)/1024/1024 as "Memory(MB)", SUM(bytes)/1024/1024 as "MB_produced" from svl_query_report where query in () group by query, segment, step, label, is_rrscan, is_diskbased, is_delayed_scan order by query, segment, step, label įor more information about tuning these queries, see Top 10 performance tuning techniques for Amazon Redshift. Where query_cpu_usage_percent is not null and starttime > sysdate - 1 Join svl_query_metrics svq on stq.query=svq.query Substring(stq.querytxt,1,100) as querytext from stl_query stq Query_cpu_usage_percent as "cpu_%",starttime, endtime, datediff(s,starttime, endtime) as duration_s, 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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