示例

下面这个例子，你可以看到每个阶段（Stage）的CPU时间消耗，每个计划节点相应的代价。

这个代价是基于现实时间（wall time），而非CPU 的相关时间。

对每一个计划节点，都可以看到额外的统计信息，例如每个节点实例的输入平均值，哈希碰撞（hash collisions）的平均次数。这些统计信息对于分析一条SQL语句中的数据异常情况（skewness数据倾斜，abnormal hash collisions）非常有用。

EXPLAIN  ANALYZE  SELECT count(*),sum(totalprice) FROM new_orders GROUP BY orderstatus; 
                                                      Query Plan                                                           
-------------------------------------------------------------------------------------------------------------------------------
 Fragment 1 [HASH]                                                                                                             
     CPU: 29.19ms, Scheduled: 134.78ms, Input: 2 rows (77B); per task: avg.: 1.00 std.dev.: 1.00, Output: 2 rows (36B)         
     Output layout: [count, sum]                                                                                               
     Output partitioning: SINGLE []                                                                                            
     Stage Execution Strategy: UNGROUPED_EXECUTION                                                                             
     Project[]                                                                                                                 
     │   Layout: [count:bigint, sum:double]                                                                                    
     │   Estimates: {rows: ? (?), cpu: ?, memory: ?, network: ?}                                                               
     │   CPU: 4.00ms (2.34%), Scheduled: 10.00ms (33.33%), Output: 2 rows (36B)                                                
     │   Input avg.: 0.06 rows, Input std.dev.: 387.30%                                                                        
     └─ Aggregate(FINAL)[orderstatus][$hashvalue]                                                                              
        │   Layout: [orderstatus:varchar, $hashvalue:bigint, count:bigint, sum:double]                                         
        │   Estimates: {rows: ? (?), cpu: ?, memory: ?, network: ?}                                                            
        │   CPU: 6.00ms (3.51%), Scheduled: 17.00ms (56.67%), Output: 2 rows (77B)                                             
        │   Input avg.: 0.06 rows, Input std.dev.: 387.30%                                                                     
        │   count := count("count_9")                                                                                          
        │   sum := sum("sum_10")                                                                                               
        └─ LocalExchange[HASH][$hashvalue] ("orderstatus")                                                                     
           │   Layout: [orderstatus:varchar, sum_10:double, count_9:bigint, $hashvalue:bigint]                                 
           │   Estimates: {rows: ? (?), cpu: ?, memory: ?, network: ?}                                                         
           │   CPU: 2.00ms (1.17%), Scheduled: 3.00ms (10.00%), Output: 2 rows (77B)                                           
           │   Input avg.: 0.06 rows, Input std.dev.: 556.78%                                                                  
           └─ RemoteSource[2]                                                                                                  
                  Layout: [orderstatus:varchar, sum_10:double, count_9:bigint, $hashvalue_11:bigint]                           
                  CPU: 1.00ms (0.58%), Scheduled: 3.00ms (10.00%), Output: 2 rows (77B)                                        
                  Input avg.: 0.06 rows, Input std.dev.: 556.78%                                                               

 Fragment 2 [SOURCE]                                                                                                           
     CPU: 17.35ms, Scheduled: 80.04ms, Input: 4 rows (81B); per task: avg.: 4.00 std.dev.: 0.00, Output: 2 rows (77B)          
     Output layout: [orderstatus, sum_10, count_9, $hashvalue_12]                                                              
     Output partitioning: HASH [orderstatus][$hashvalue_12]                                                                    
     Stage Execution Strategy: UNGROUPED_EXECUTION                                                                             
     Aggregate(PARTIAL)[orderstatus][$hashvalue_12]                                                                            
     │   Layout: [orderstatus:varchar, $hashvalue_12:bigint, sum_10:double, count_9:bigint]                                    
     │   CPU: 1.00ms (0.58%), Scheduled: 6.00ms (20.00%), Output: 2 rows (77B)                                                 
     │   Input avg.: 4.00 rows, Input std.dev.: 0.00%                                                                          
     │   sum_10 := sum("totalprice")                                                                                           
     │   count_9 := count(*)                                                                                                   
     └─ ScanProject[table = hive:default:new_orders, grouped = false]                                                          
            Layout: [orderstatus:varchar, totalprice:double, $hashvalue_12:bigint]                                             
            Estimates: {rows: 4 (292B), cpu: 256, memory: 0B, network: 0B}/{rows: 4 (292B), cpu: 548, memory: 0B, network: 0B} 
            CPU: 16.00ms (9.36%), Scheduled: 132.00ms (440.00%), Output: 4 rows (117B)                                         
            Input avg.: 4.00 rows, Input std.dev.: 0.00%                                                                       
            $hashvalue_12 := "combine_hash"(bigint '0', COALESCE("$operator$hash_code"("orderstatus"), 0))                     
            orderstatus := orderstatus:string:1:REGULAR                                                                        
            totalprice := totalprice:double:2:REGULAR                                                                          
            Input: 4 rows (81B), Filtered: 0.00%                                                                               
(1 row)