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GaussDB(DWS)
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      Central de ajuda/ GaussDB(DWS)/ Melhores práticas/ Práticas da otimização de tabela/ Passo 5: testar o desempenho do sistema na nova tabela

      Passo 5: testar o desempenho do sistema na nova tabela

      Atualizado em 2024-05-09 GMT+08:00
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      Depois de recriar o conjunto de dados de teste com os modos de armazenamento selecionados, níveis de compactação, modos de distribuição e chaves de distribuição, você testará novamente o desempenho do sistema.

      1. Registre o uso do espaço de armazenamento de cada tabela.

        Determine quanto espaço em disco é usado para cada tabela usando a função pg_size_pretty e registre os resultados em tabelas básicas.

        1
        		 
        SELECT T_NAME, PG_SIZE_PRETTY(PG_RELATION_SIZE(t_name)) FROM (VALUES('store_sales'),('date_dim'),('store'),('item'),('time_dim'),('promotion'),('customer_demographics'),('customer_address'),('household_demographics'),('customer'),('income_band')) AS names1(t_name);

                 t_name         | pg_size_pretty
------------------------+----------------
 store_sales            | 14 GB
 date_dim               | 27 MB
 store                  | 4352 kB
 item                   | 259 MB
 time_dim               | 14 MB
 promotion              | 3200 kB
 customer_demographics  | 11 MB
 customer_address       | 27 MB
 household_demographics | 1280 kB
 customer               | 111 MB
 income_band            | 896 kB
(11 rows)

      2. Teste o desempenho da consulta e registre os dados de desempenho na tabela de referência.

        Execute as consultas a seguir novamente e registre o tempo gasto em cada consulta.

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        \timing on
SELECT * FROM (SELECT  COUNT(*)  
FROM store_sales 
    ,household_demographics  
    ,time_dim, store 
WHERE ss_sold_time_sk = time_dim.t_time_sk    
    AND ss_hdemo_sk = household_demographics.hd_demo_sk  
    AND ss_store_sk = s_store_sk 
    AND time_dim.t_hour = 8 
    AND time_dim.t_minute >= 30 
    AND household_demographics.hd_dep_count = 5 
    AND store.s_store_name = 'ese' 
ORDER BY COUNT(*) 
 ) LIMIT 100;

SELECT * FROM (SELECT  i_brand_id brand_id, i_brand brand, i_manufact_id, i_manufact,
SUM(ss_ext_sales_price) ext_price
 FROM date_dim, store_sales, item,customer,customer_address,store
 WHERE d_date_sk = ss_sold_date_sk
   AND ss_item_sk = i_item_sk
   AND i_manager_id=8
   AND d_moy=11
   AND d_year=1999
   AND ss_customer_sk = c_customer_sk 
   AND c_current_addr_sk = ca_address_sk
   AND substr(ca_zip,1,5) <> substr(s_zip,1,5) 
   AND ss_store_sk = s_store_sk 
 GROUP BY i_brand
      ,i_brand_id
      ,i_manufact_id
      ,i_manufact
 ORDER BY ext_price desc
         ,i_brand
         ,i_brand_id
         ,i_manufact_id
         ,i_manufact
 ) LIMIT 100;

SELECT * FROM (SELECT  s_store_name, s_store_id,
        SUM(CASE WHEN (d_day_name='Sunday') THEN ss_sales_price ELSE null END) sun_sales,
        SUM(CASE WHEN (d_day_name='Monday') THEN ss_sales_price ELSE null END) mon_sales,
        SUM(CASE WHEN (d_day_name='Tuesday') THEN ss_sales_price ELSE  null END) tue_sales,
        SUM(CASE WHEN (d_day_name='Wednesday') THEN ss_sales_price ELSE null END) wed_sales,
        SUM(CASE WHEN (d_day_name='Thursday') THEN ss_sales_price ELSE null END) thu_sales,
        SUM(CASE WHEN (d_day_name='Friday') THEN ss_sales_price ELSE null END) fri_sales,
        SUM(CASE WHEN (d_day_name='Saturday') THEN ss_sales_price ELSE null END) sat_sales
 FROM date_dim, store_sales, store
 WHERE d_date_sk = ss_sold_date_sk AND
       s_store_sk = ss_store_sk AND
       s_gmt_offset = -5 AND
       d_year = 2000 
 GROUP BY s_store_name, s_store_id
 ORDER BY s_store_name, s_store_id,sun_sales,mon_sales,tue_sales,wed_sales,thu_sales,fri_sales,sat_sales
  ) LIMIT 100;

        A tabela de referência a seguir mostra os resultados de validação do cluster usado neste tutorial. Seus resultados podem variar com base em vários fatores, mas os resultados relativos devem ser semelhantes. As durações de execução de consultas com a mesma estrutura de tabela podem ser diferentes, dependendo do cache do sistema operacional durante a execução. É aconselhável realizar várias rodadas de testes e selecionar um grupo com valores médios.

        Referência

        Antes de

        Depois

        Tempo de carregamento (11 tabelas)

        341584 ms

        257241 ms

        Espaço de armazenamento ocupado

        Store_Sales

        42 GB

        14 GB

        Date_Dim

        11 MB

        27 MB

        Armazenamento

        232 KB

        4352 KB

        Item

        110 MB

        259 MB

        Time_Dim

        11 MB

        14 MB

        Promotion

        256 KB

        3200 KB

        Customer_Demographics

        171 MB

        11 MB

        Customer_Address

        170 MB

        27 MB

        Household_Demographics

        504 KB

        1280 KB

        Cliente

        441 MB

        111 MB

        Income_Band

        88 KB

        896 KB

        Espaço total de armazenamento

        42 GB

        15 GB

        Tempo de execução da consulta

        Consulta 1

        14552,05 ms

        1783,353 ms

        Consulta 2

        27952,36 ms

        14247,803 ms

        Consulta 3

        17721,15 ms

        11441,659 ms

        Tempo total de execução

        60225,56 ms

        27472,815 ms

      3. Se você tiver expectativas mais altas para o desempenho após o design da tabela, poderá executar o comando EXPLAIN PERFORMANCE para exibir o plano de execução para ajuste.

        Para obter mais detalhes sobre planos de execução e ajuste de consulta, consulte Plano de execução SQL e Visão geral do ajuste de desempenho de consultas.

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