用于优化查询的诊断查询 - Amazon Redshift
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用于优化查询的诊断查询

使用以下查询发现与可能影响查询性能的查询或基础表有关的问题。我们建议将这些查询与 分析和改进查询 中讨论的查询优化过程结合使用。

确定最适合优化的查询

以下查询标识了过去 7 天内执行的前 50 个最耗时的语句。您可以利用这些结果发现特别耗时的查询以及经常运行的查询(在结果集中多次出现的查询)。通常可以优化此类查询以提高系统性能。

此外,此查询还提供与每个所发现的查询关联的提醒事件计数。这些提醒提供详细信息,供您用于提高查询的性能。有关更多信息,请参阅查看查询警报

select trim(database) as db, count(query) as n_qry, max(substring (qrytext,1,80)) as qrytext, min(run_minutes) as "min" , max(run_minutes) as "max", avg(run_minutes) as "avg", sum(run_minutes) as total, max(query) as max_query_id, max(starttime)::date as last_run, sum(alerts) as alerts, aborted from (select userid, label, stl_query.query, trim(database) as database, trim(querytxt) as qrytext, md5(trim(querytxt)) as qry_md5, starttime, endtime, (datediff(seconds, starttime,endtime)::numeric(12,2))/60 as run_minutes, alrt.num_events as alerts, aborted from stl_query left outer join (select query, 1 as num_events from stl_alert_event_log group by query ) as alrt on alrt.query = stl_query.query where userid <> 1 and starttime >= dateadd(day, -7, current_date)) group by database, label, qry_md5, aborted order by total desc limit 50;

确定具有数据偏斜或未排序行的表

以下查询标识数据分配不均匀(数据偏斜)或未排序行百分比较高的表。

skew 值较低表明表数据分配适当。如果表的 skew 值达到 4.00 或以上,可以考虑修改其数据分配方式。有关更多信息,请参阅非最优数据分配

如果表的 pct_unsorted 值大于 20%,可以考虑运行 VACUUM 命令。有关更多信息,请参阅未排序或排序错乱的行

还应检查每个表的 mbytespct_of_total 值。这些列标识表的大小及表占用的原始磁盘空间比例。原始磁盘空间包括 Amazon Redshift 保留供内部使用的空间,因此它大于名义磁盘容量(可供用户使用的磁盘空间量)。使用这些信息可确保可用磁盘空间等于最大表大小的 2.5 倍或以上。如果使用此空间,系统可以在处理复杂查询时将临时结果写入磁盘。

select trim(pgn.nspname) as schema, trim(a.name) as table, id as tableid, decode(pgc.reldiststyle,0, 'even',1,det.distkey ,8,'all') as distkey, dist_ratio.ratio::decimal(10,4) as skew, det.head_sort as "sortkey", det.n_sortkeys as "#sks", b.mbytes, decode(b.mbytes,0,0,((b.mbytes/part.total::decimal)*100)::decimal(5,2)) as pct_of_total, decode(det.max_enc,0,'n','y') as enc, a.rows, decode( det.n_sortkeys, 0, null, a.unsorted_rows ) as unsorted_rows , decode( det.n_sortkeys, 0, null, decode( a.rows,0,0, (a.unsorted_rows::decimal(32)/a.rows)*100) )::decimal(5,2) as pct_unsorted from (select db_id, id, name, sum(rows) as rows, sum(rows)-sum(sorted_rows) as unsorted_rows from stv_tbl_perm a group by db_id, id, name) as a join pg_class as pgc on pgc.oid = a.id join pg_namespace as pgn on pgn.oid = pgc.relnamespace left outer join (select tbl, count(*) as mbytes from stv_blocklist group by tbl) b on a.id=b.tbl inner join (select attrelid, min(case attisdistkey when 't' then attname else null end) as "distkey", min(case attsortkeyord when 1 then attname else null end ) as head_sort , max(attsortkeyord) as n_sortkeys, max(attencodingtype) as max_enc from pg_attribute group by 1) as det on det.attrelid = a.id inner join ( select tbl, max(mbytes)::decimal(32)/min(mbytes) as ratio from (select tbl, trim(name) as name, slice, count(*) as mbytes from svv_diskusage group by tbl, name, slice ) group by tbl, name ) as dist_ratio on a.id = dist_ratio.tbl join ( select sum(capacity) as total from stv_partitions where part_begin=0 ) as part on 1=1 where mbytes is not null order by mbytes desc;

确定具有嵌套循环的查询

以下查询标识已为嵌套循环记录提示事件的查询。有关如何修复嵌套循环条件的信息,请参阅嵌套循环

select query, trim(querytxt) as SQL, starttime from stl_query where query in ( select distinct query from stl_alert_event_log where event like 'Nested Loop Join in the query plan%') order by starttime desc;

查看查询的队列等待时间

以下查询显示了最近的查询在执行之前等待等待查询队列中打开的槽的时间。如果等待时间较长,可能需要修改查询队列配置,以获得更高的吞吐量。有关更多信息,请参阅实施手动 WLM

select trim(database) as DB , w.query, substring(q.querytxt, 1, 100) as querytxt, w.queue_start_time, w.service_class as class, w.slot_count as slots, w.total_queue_time/1000000 as queue_seconds, w.total_exec_time/1000000 exec_seconds, (w.total_queue_time+w.total_Exec_time)/1000000 as total_seconds from stl_wlm_query w left join stl_query q on q.query = w.query and q.userid = w.userid where w.queue_start_Time >= dateadd(day, -7, current_Date) and w.total_queue_Time > 0 and w.userid >1 and q.starttime >= dateadd(day, -7, current_Date) order by w.total_queue_time desc, w.queue_start_time desc limit 35;

按表查看查询警报

以下查询标识记录了提示事件的表,并标识了最频繁引发的提示类型。

如果所发现的表的行的 minutes 值较大,请检查该表以确认是否需要对其执行日常维护(如对其运行 ANALYZEVACUUM)。

如果某一行的 count 值高但 table 值为 null,则对 STL_ALERT_EVENT_LOG 运行查询以获得相关的 event 值,从而调查如此频繁引发提示的原因。

select trim(s.perm_table_name) as table, (sum(abs(datediff(seconds, s.starttime, s.endtime)))/60)::numeric(24,0) as minutes, trim(split_part(l.event,':',1)) as event, trim(l.solution) as solution, max(l.query) as sample_query, count(*) from stl_alert_event_log as l left join stl_scan as s on s.query = l.query and s.slice = l.slice and s.segment = l.segment and s.step = l.step where l.event_time >= dateadd(day, -7, current_Date) group by 1,3,4 order by 2 desc,6 desc;

确定统计数据缺失的表

以下查询提供了对缺少统计信息的表运行的查询的计数。如果此查询返回任何行,请查看 plannode 值来确定受影响的表,然后对其运行 ANALYZE

select substring(trim(plannode),1,100) as plannode, count(*) from stl_explain where plannode like '%missing statistics%' group by plannode order by 2 desc;