数据仓库服务 GAUSSDB(DWS)-数据倾斜调优:存储层数据倾斜

时间：2024-12-02 17:18:12

存储层数据倾斜

GaussDB (DWS)数据库中，数据分布存储在各个DN上，通过分布式执行提高查询的效率。但是，如果数据分布存在倾斜，则会导致分布式执行某些DN成为瓶颈，影响查询性能。这种情况通常是由于分布列选择不合理，可以通过调整分布列的方式解决。

例如下例：

  
   
     
     
       explain performance select count(*) from inventory;
5 -- CS tore Scan on lmz.inventory
         dn_6001_6002 (actual time=0.444..83.127 rows=42000000 loops=1)
         dn_6003_6004 (actual time=0.512..63.554 rows=27000000 loops=1)
         dn_6005_6006 (actual time=0.722..99.033 rows=45000000 loops=1)
         dn_6007_6008 (actual time=0.529..100.379 rows=51000000 loops=1)
         dn_6009_6010 (actual time=0.382..71.341 rows=36000000 loops=1)
         dn_6011_6012 (actual time=0.547..100.274 rows=51000000 loops=1)
         dn_6013_6014 (actual time=0.596..118.289 rows=60000000 loops=1)
         dn_6015_6016 (actual time=1.057..132.346 rows=63000000 loops=1)
         dn_6017_6018 (actual time=0.940..110.310 rows=54000000 loops=1)
         dn_6019_6020 (actual time=0.231..41.198 rows=21000000 loops=1)
         dn_6021_6022 (actual time=0.927..114.538 rows=54000000 loops=1)
         dn_6023_6024 (actual time=0.637..118.385 rows=60000000 loops=1)
         dn_6025_6026 (actual time=0.288..32.240 rows=15000000 loops=1)
         dn_6027_6028 (actual time=0.566..118.096 rows=60000000 loops=1)
         dn_6029_6030 (actual time=0.423..82.913 rows=42000000 loops=1)
         dn_6031_6032 (actual time=0.395..78.103 rows=39000000 loops=1)
         dn_6033_6034 (actual time=0.376..51.052 rows=24000000 loops=1)
         dn_6035_6036 (actual time=0.569..79.463 rows=39000000 loops=1)

      

    

  
 

在performance信息中，可以看到inventory表各DN的scan行数，发现各DN的行数差距较大，最大的为63000000，最小的只有15000000，差了4倍。这个差距对于数据扫描的性能影响还可以接受，但如果上层有join算子，则影响较大。

通常，数据表在各DN上是hash分布的，因此分布列的选择很重要。通过table_skewness()来查看上述inventory表在各DN的数据分布倾斜，查询结果如下：

  
   
     
     
       select table_skewness('inventory');
              table_skewness              
------------------------------------------
 ("dn_6015_6016        ",63000000,8.046%)
 ("dn_6013_6014        ",60000000,7.663%)
 ("dn_6023_6024        ",60000000,7.663%)
 ("dn_6027_6028        ",60000000,7.663%)
 ("dn_6017_6018        ",54000000,6.897%)
 ("dn_6021_6022        ",54000000,6.897%)
 ("dn_6007_6008        ",51000000,6.513%)
 ("dn_6011_6012        ",51000000,6.513%)
 ("dn_6005_6006        ",45000000,5.747%)
 ("dn_6001_6002        ",42000000,5.364%)
 ("dn_6029_6030        ",42000000,5.364%)
 ("dn_6031_6032        ",39000000,4.981%)
 ("dn_6035_6036        ",39000000,4.981%)
 ("dn_6009_6010        ",36000000,4.598%)
 ("dn_6003_6004        ",27000000,3.448%)
 ("dn_6033_6034        ",24000000,3.065%)
 ("dn_6019_6020        ",21000000,2.682%)
 ("dn_6025_6026        ",15000000,1.916%)
(18 rows)

      

    

  
 

通过查询建表定义，可以发现，目前该表是以inv_date_sk作为分布列的，导致存在倾斜。通过查看各列的数据分布情况，改为inv_item_sk作为分布列，则倾斜情况分布如下：

  
   
     
     
       select table_skewness('inventory');
              table_skewness              
------------------------------------------
 ("dn_6001_6002        ",43934200,5.611%)
 ("dn_6007_6008        ",43829420,5.598%)
 ("dn_6003_6004        ",43781960,5.592%)
 ("dn_6031_6032        ",43773880,5.591%)
 ("dn_6033_6034        ",43763280,5.589%)
 ("dn_6011_6012        ",43683600,5.579%)
 ("dn_6013_6014        ",43551660,5.562%)
 ("dn_6027_6028        ",43546340,5.561%)
 ("dn_6009_6010        ",43508700,5.557%)
 ("dn_6023_6024        ",43484540,5.554%)
 ("dn_6019_6020        ",43466800,5.551%)
 ("dn_6021_6022        ",43458500,5.550%)
 ("dn_6017_6018        ",43448040,5.549%)
 ("dn_6015_6016        ",43247700,5.523%)
 ("dn_6005_6006        ",43200240,5.517%)
 ("dn_6029_6030        ",43181360,5.515%)
 ("dn_6025_6026        ",43179700,5.515%)
 ("dn_6035_6036        ",42960080,5.487%)
(18 rows)

      

    

  
 