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Hologres:Test plan overview

Última atualização: Sep 18, 2026

Use o TPC-H para avaliar o desempenho de consultas OLAP, consultas pontuais de chave/valor e atualizações de dados no Hologres.

TPC-H

A seguir, um trecho da especificação do TPC Benchmark™ H (TPC-H):

O TPC-H é um benchmark de suporte a decisões. Compõe-se de um conjunto de consultas ad-hoc orientadas a negócios e de modificações concorrentes de dados. As consultas e os dados que populam o banco de dados foram escolhidos por sua ampla relevância no setor. Este benchmark representa sistemas de suporte a decisões que examinam grandes volumes de dados, executam consultas com alto grau de complexidade e fornecem respostas a questões críticas de negócio.

Baixe a especificação completa do TPCH.

Nota

A implementação do TPC-H descrita aqui é baseada no benchmark TPC-H, mas não está totalmente em conformidade com ele. Portanto, estes resultados de teste não são comparáveis aos resultados publicados do benchmark TPC-H.

Dataset

O TPC-H é um benchmark de suporte a decisões desenvolvido pelo Transaction Processing Performance Council (TPC), amplamente utilizado para avaliar o desempenho de consultas analíticas.

O TPC-H modela um data warehouse para um sistema de vendas baseado em um ambiente de produção real. Inclui 8 tabelas, e seu volume de dados varia de 1 GB a 3 TB. O benchmark contém 22 consultas. A métrica principal é o tempo de resposta das consultas, conforme definido no benchmark TPC-H.

Casos de uso

Este plano de testes abrange os seguintes cenários:

A ferramenta de geração de dados do TPC-H usa o fator de escala (SF) para controlar o tamanho do dataset. Um SF igual a 1 produz 1 GB de dados.

Nota

O raw data volume indicado acima não inclui índices. Reserve espaço em disco adicional conforme necessário.

Observações

Para garantir resultados de teste consistentes, crie uma nova instância para cada teste. Não utilize uma instância redimensionada.

Testar cenários de consulta OLAP

  1. Prepare o ambiente.

    1. Create a Hologres instance. Este teste utiliza uma instância dedicada de pagamento conforme o uso com 96 núcleos e 384 GB de memória. Selecione os recursos de computação de acordo com suas necessidades.

    2. Create an ECS instance com as seguintes especificações:

      Parâmetro

      Especificação

      Specification

      ecs.g6.4xlarge

      Image

      Alibaba Cloud Linux 3.2104 LTS 64-bit

      Data disk

      Um ESSD. A capacidade depende do volume dos dados de teste.

  2. Baixe e configure o toolkit Hologres Benchmark.

    1. Faça login na instância ECS. Connect to an instance.

    2. Instale o client psql.

      yum update -y
      yum install postgresql-server -y
      yum install postgresql-contrib -y
    3. Baixe e extraia o toolkit Hologres Benchmark.

      wget https://oss-tpch.oss-cn-hangzhou.aliyuncs.com/hologres_benchmark.tar.gz
      tar xvf hologres_benchmark.tar.gz
    4. Acesse o diretório hologres_benchmark.

      cd hologres_benchmark
    5. Execute o comando vim group_vars/all para configurar os parâmetros do benchmark.

      # db config
      login_host: ""
      login_user: ""
      login_password: "" 
      login_port: ""
      # benchmark run cluster: hologres
      cluster: "hologres"
      RUN_MODE: "HOTRUN"
      # benchmark config
      scale_factor: 1
      work_dir_root: /your/working_dir/benchmark/workdirs
      dataset_generate_root_path: /your/working_dir/benchmark/datasets

      Descrição dos parâmetros:

      Tipo

      Parâmetro

      Descrição

      Parâmetros de conexão do service Hologres

      login_host

      O endpoint VPC da instância Hologres.

      Para encontrar esse valor, faça login no console do Hologres e acesse a página de detalhes da instância. O endpoint VPC está na coluna Domain Name da seção Network Information.

      Nota

      Este endpoint não inclui o número da porta. Exemplo: hgpostcn-cn-nwy364b5v009-cn-shanghai-vpc-st.hologres.aliyuncs.com

      login_port

      A porta do endpoint VPC da instância Hologres.

      Para encontrar esse valor, faça login no console do Hologres e acesse a página de detalhes da instância. A porta está na coluna Domain Name da seção Network Information.

      login_user

      Seu AccessKey ID.

      Acesse o gerenciamento de AccessKey para obter seu AccessKey ID.

      login_password

      Seu AccessKey secret.

      Parâmetros de configuração do benchmark

      scale_factor

      O fator de escala do dataset, que controla o tamanho dos dados gerados em GB. O valor padrão é 1.

      work_dir_root

      O diretório de trabalho raiz. Esse diretório armazena os dados do TPC-H, como instruções de criação de tabelas e instruções SQL executadas. O valor padrão é /your/working_dir/benchmark/workdirs.

      dataset_generate_root_path

      O caminho de armazenamento do dataset de teste gerado. O valor padrão é /your/working_dir/benchmark/datasets.

  3. Execute o seguinte comando para iniciar um teste TPC-H automatizado de ponta a ponta.

    Este teste gera os dados, cria um banco de dados de teste (por exemplo, tpc_h_sf1000), cria as tabelas e importa os dados.

    bin/run_tpch.sh

    Como alternativa, execute o comando a seguir para rodar apenas o teste de consultas TPC-H.

    bin/run_tpch.sh query
  4. Visualize os resultados do teste.

    • Visão geral dos resultados do teste

      O comando bin/run_tpch.sh exibe resultados semelhantes ao seguinte:

      TASK [tpc_h : debug] **************************************************************************************************
      skipping: [worker-1]
      ok: [master] => {
          "command_output.stdout_lines": [
              "[info] 2024-06-28 14:46:09.768 | Run sql queries started.",
              "[info] 2024-06-28 14:46:09.947 | Run q10.sql started.",
              "[info] 2024-06-28 14:46:10.088 | Run q10.sql finished. Time taken: 0:00:00, 138 ms",
              "[info] 2024-06-28 14:46:10.239 | Run q11.sql started.",
              "[info] 2024-06-28 14:46:10.396 | Run q11.sql finished. Time taken: 0:00:00, 154 ms",
              "[info] 2024-06-28 14:46:10.505 | Run q12.sql started.",
              "[info] 2024-06-28 14:46:10.592 | Run q12.sql finished. Time taken: 0:00:00, 85 ms",
              "[info] 2024-06-28 14:46:10.703 | Run q13.sql started.",
              "[info] 2024-06-28 14:46:10.793 | Run q13.sql finished. Time taken: 0:00:00, 88 ms",
              "[info] 2024-06-28 14:46:10.883 | Run q14.sql started.",
              "[info] 2024-06-28 14:46:10.981 | Run q14.sql finished. Time taken: 0:00:00, 95 ms",
              "[info] 2024-06-28 14:46:11.132 | Run q15.sql started.",
              "[info] 2024-06-28 14:46:11.266 | Run q15.sql finished. Time taken: 0:00:00, 131 ms",
              "[info] 2024-06-28 14:46:11.441 | Run q16.sql started.",
              "[info] 2024-06-28 14:46:11.609 | Run q16.sql finished. Time taken: 0:00:00, 165 ms",
              "[info] 2024-06-28 14:46:11.728 | Run q17.sql started.",
              "[info] 2024-06-28 14:46:11.818 | Run q17.sql finished. Time taken: 0:00:00, 88 ms",
              "[info] 2024-06-28 14:46:12.017 | Run q18.sql started.",
              "[info] 2024-06-28 14:46:12.184 | Run q18.sql finished. Time taken: 0:00:00, 164 ms",
              "[info] 2024-06-28 14:46:12.287 | Run q19.sql started.",
              "[info] 2024-06-28 14:46:12.388 | Run q19.sql finished. Time taken: 0:00:00, 98 ms",
              "[info] 2024-06-28 14:46:12.503 | Run q1.sql started.",
              "[info] 2024-06-28 14:46:12.597 | Run q1.sql finished. Time taken: 0:00:00, 93 ms",
              "[info] 2024-06-28 14:46:12.732 | Run q20.sql started.",
              "[info] 2024-06-28 14:46:12.888 | Run q20.sql finished. Time taken: 0:00:00, 154 ms",
              "[info] 2024-06-28 14:46:13.184 | Run q21.sql started.",
              "[info] 2024-06-28 14:46:13.456 | Run q21.sql finished. Time taken: 0:00:00, 269 ms",
              "[info] 2024-06-28 14:46:13.558 | Run q22.sql started.",
              "[info] 2024-06-28 14:46:13.657 | Run q22.sql finished. Time taken: 0:00:00, 97 ms",
              "[info] 2024-06-28 14:46:13.796 | Run q2.sql started.",
              "[info] 2024-06-28 14:46:13.935 | Run q2.sql finished. Time taken: 0:00:00, 136 ms",
              "[info] 2024-06-28 14:46:14.051 | Run q3.sql started.",
              "[info] 2024-06-28 14:46:14.155 | Run q3.sql finished. Time taken: 0:00:00, 101 ms",
              "[info] 2024-06-28 14:46:14.255 | Run q4.sql started.",
              "[info] 2024-06-28 14:46:14.341 | Run q4.sql finished. Time taken: 0:00:00, 83 ms",
              "[info] 2024-06-28 14:46:14.567 | Run q5.sql started.",
              "[info] 2024-06-28 14:46:14.799 | Run q5.sql finished. Time taken: 0:00:00, 230 ms",
              "[info] 2024-06-28 14:46:14.881 | Run q6.sql started.",
              "[info] 2024-06-28 14:46:14.950 | Run q6.sql finished. Time taken: 0:00:00, 67 ms",
              "[info] 2024-06-28 14:46:15.138 | Run q7.sql started.",
              "[info] 2024-06-28 14:46:15.320 | Run q7.sql finished. Time taken: 0:00:00, 180 ms",
              "[info] 2024-06-28 14:46:15.572 | Run q8.sql started.",
              "[info] 2024-06-28 14:46:15.831 | Run q8.sql finished. Time taken: 0:00:00, 256 ms",
              "[info] 2024-06-28 14:46:16.081 | Run q9.sql started.",
              "[info] 2024-06-28 14:46:16.322 | Run q9.sql finished. Time taken: 0:00:00, 238 ms",
              "[info] 2024-06-28 14:46:16.325 | ----------- HOT RUN finished. Time taken: 3255 mill_sec -----------------"
          ]
      }
      skipping: [worker-2]
      skipping: [worker-3]
      skipping: [worker-4]
      TASK [tpc_h : clear Env] **********************************************************************************************
      skipping: [worker-1]
      skipping: [worker-2]
      skipping: [worker-3]
      skipping: [worker-4]
      ok: [master]
      TASK [tpc_h : debug] **************************************************************************************************
      ok: [master] => {
          "work_dir": "/your/working_dir/benchmark/workdirs/tpc_h/sf1"
      }
      skipping: [worker-1]
      skipping: [worker-2]
      skipping: [worker-3]
      skipping: [worker-4]
    • Detalhes dos resultados do teste

      O comando bin/run_tpch.sh cria um diretório de trabalho e exibe seu caminho como <work_dir>. Acesse esse diretório para visualizar as instruções de consulta, as instruções de criação de tabelas e os logs de execução.

      TASK [tpc_h : debug] ************************************************************
      ok: [master] => {
          "work_dir": "/your/working_dir/benchmark/workdirs/tpc_h/sf1"
      }
      skipping: [worker-1]
      skipping: [worker-2]
      skipping: [worker-3]
      skipping: [worker-4]

      Execute cd <work_dir>/logs para acessar o diretório de logs e visualizar os resultados do teste e a saída detalhada do SQL.

      A estrutura de diretórios de <work_dir> é a seguinte:

      working_dir/
      `-- benchmark
          |-- datasets
          |   `-- tpc_h
          |       `-- sf1
          |           |-- worker-1
          |           |   |-- customer.tbl
          |           |   `-- lineitem.tbl
          |           |-- worker-2
          |           |   |-- orders.tbl
          |           |   `-- supplier.tbl
          |           |-- worker-3
          |           |   |-- nation.tbl
          |           |   `-- partsupp.tbl
          |           `-- worker-4
          |               |-- part.tbl
          |               `-- region.tbl
          `-- workdirs
              `-- tpc_h
                  `-- sf1
                      |-- config
                      |-- hologres
                      |   |-- logs
                      |   |   |-- q10.sql.err
                      |   |   |-- q10.sql.out
                      |   |   |-- q11.sql.err
                      |   |   |-- q11.sql.out
                      |   |   |-- q12.sql.err
                      |   |   |-- q12.sql.out
                      |   |   |-- q13.sql.err
                      |   |   |-- q13.sql.out
                      |   |   |-- q14.sql.err
                      |   |   |-- q14.sql.out
                      |   |   |-- q15.sql.err
                      |   |   |-- q15.sql.out
                      |   |   |-- q16.sql.err
                      |   |   |-- q16.sql.out
                      |   |   |-- q17.sql.err
                      |   |   |-- q17.sql.out
                      |   |   |-- q18.sql.err
                      |   |   |-- q18.sql.out
                      |   |   |-- q19.sql.err
                      |   |   |-- q19.sql.out
                      |   |   |-- q1.sql.err
                      |   |   |-- q1.sql.out
                      |   |   |-- q20.sql.err
                      |   |   |-- q20.sql.out
                      |   |   |-- q21.sql.err
                      |   |   |-- q21.sql.out
                      |   |   |-- q22.sql.err
                      |   |   |-- q22.sql.out
                      |   |   |-- q2.sql.err
                      |   |   |-- q2.sql.out
                      |   |   |-- q3.sql.err
                      |   |   |-- q3.sql.out
                      |   |   |-- q4.sql.err
                      |   |   |-- q4.sql.out
                      |   |   |-- q5.sql.err
                      |   |   |-- q5.sql.out
                      |   |   |-- q6.sql.err
                      |   |   |-- q6.sql.out
                      |   |   |-- q7.sql.err
                      |   |   |-- q7.sql.out
                      |   |   |-- q8.sql.err
                      |   |   |-- q8.sql.out
                      |   |   |-- q9.sql.err
                      |   |   |-- q9.sql.out
                      |   |   `-- run.log
                      |   `-- logs-20240628144609
                      |       |-- q10.sql.err
                      |       |-- q10.sql.out
                      |       |-- q11.sql.err
                      |       |-- q11.sql.out
                      |       |-- q12.sql.err
                      |       |-- q12.sql.out
                      |       |-- q13.sql.err
                      |       |-- q13.sql.out
                      |       |-- q14.sql.err
                      |       |-- q14.sql.out
                      |       |-- q15.sql.err
                      |       |-- q15.sql.out
                      |       |-- q16.sql.err
                      |       |-- q16.sql.out
                      |       |-- q17.sql.err
                      |       |-- q17.sql.out
                      |       |-- q18.sql.err
                      |       |-- q18.sql.out
                      |       |-- q19.sql.err
                      |       |-- q19.sql.out
                      |       |-- q1.sql.err
                      |       |-- q1.sql.out
                      |       |-- q20.sql.err
                      |       |-- q20.sql.out
                      |       |-- q21.sql.err
                      |       |-- q21.sql.out
                      |       |-- q22.sql.err
                      |       |-- q22.sql.out
                      |       |-- q2.sql.err
                      |       |-- q2.sql.out
                      |       |-- q3.sql.err
                      |       |-- q3.sql.out
                      |       |-- q4.sql.err
                      |       |-- q4.sql.out
                      |       |-- q5.sql.err
                      |       |-- q5.sql.out
                      |       |-- q6.sql.err
                      |       |-- q6.sql.out
                      |       |-- q7.sql.err
                      |       |-- q7.sql.out
                      |       |-- q8.sql.err
                      |       |-- q8.sql.out
                      |       |-- q9.sql.err
                      |       |-- q9.sql.out
                      |       `-- run.log
                      |-- queries
                      |   |-- ddl
                      |   |   |-- hologres_analyze_tables.sql
                      |   |   `-- hologres_create_tables.sql
                      |   |-- q10.sql
                      |   |-- q11.sql
                      |   |-- q12.sql
                      |   |-- q13.sql
                      |   |-- q14.sql
                      |   |-- q15.sql
                      |   |-- q16.sql
                      |   |-- q17.sql
                      |   |-- q18.sql
                      |   |-- q19.sql
                      |   |-- q1.sql
                      |   |-- q20.sql
                      |   |-- q21.sql
                      |   |-- q22.sql
                      |   |-- q2.sql
                      |   |-- q3.sql
                      |   |-- q4.sql
                      |   |-- q5.sql
                      |   |-- q6.sql
                      |   |-- q7.sql
                      |   |-- q8.sql
                      |   `-- q9.sql
                      |-- run_hologres.sh
                      |-- run_mysql.sh
                      |-- run.sh
                      `-- tpch_tools
                          |-- dbgen
                          |-- qgen
                          `-- resouces
                              |-- dists.dss
                              `-- queries
                                  |-- 10.sql
                                  |-- 11.sql
                                  |-- 12.sql
                                  |-- 13.sql
                                  |-- 14.sql
                                  |-- 15.sql
                                  |-- 16.sql
                                  |-- 17.sql
                                  |-- 18.sql
                                  |-- 19.sql
                                  |-- 1.sql
                                  |-- 20.sql
                                  |-- 21.sql
                                  |-- 22.sql
                                  |-- 2.sql
                                  |-- 3.sql
                                  |-- 4.sql
                                  |-- 5.sql
                                  |-- 6.sql
                                  |-- 7.sql
                                  |-- 8.sql
                                  `-- 9.sql

Teste de consulta pontual por chave/valor

Este teste utiliza o banco de dados hologres_tpch e a tabela orders do OLAP query test.

  1. Create a table

    Consultas pontuais por chave/valor exigem uma tabela orientada a linhas. Conecte-se ao Hologres com um cliente psql e execute as seguintes instruções para criar a tabela orders_row.

    Nota

    Para se conectar com um cliente psql, siga as etapas em Connect to Hologres for Development.

    DROP TABLE IF EXISTS public.orders_row;
    BEGIN;
    CREATE TABLE public.orders_row(
        O_ORDERKEY       BIGINT         NOT NULL PRIMARY KEY
        ,O_CUSTKEY       INT            NOT NULL
        ,O_ORDERSTATUS   TEXT           NOT NULL
        ,O_TOTALPRICE    DECIMAL(15,2)  NOT NULL
        ,O_ORDERDATE     TIMESTAMPTZ    NOT NULL
        ,O_ORDERPRIORITY TEXT           NOT NULL
        ,O_CLERK         TEXT           NOT NULL
        ,O_SHIPPRIORITY  INT            NOT NULL
        ,O_COMMENT       TEXT           NOT NULL
    );
    CALL SET_TABLE_PROPERTY('public.orders_row', 'orientation', 'row');
    CALL SET_TABLE_PROPERTY('public.orders_row', 'clustering_key', 'o_orderkey');
    CALL SET_TABLE_PROPERTY('public.orders_row', 'distribution_key', 'o_orderkey');
    COMMIT;
  2. Import data

    Use a instrução INSERT INTO a seguir para importar dados da tabela orders do dataset TPC-H para a tabela orders_row.

    Nota

    O Hologres V2.1.17 e versões posteriores oferecem suporte ao Serverless Computing. Para cenários como importação offline em larga escala, grandes jobs de ETL e consultas de alto volume em tabelas externas, utilize o Serverless Computing para executar essas tarefas. Esse recurso emprega recursos serverless adicionais em vez dos recursos da sua instância, o que aumenta a estabilidade da instância e reduz a probabilidade de erros de out-of-memory (OOM). Não é necessário reservar recursos de computação extras para sua instância, e você paga apenas pelas tarefas executadas. Para mais informações sobre o Serverless Computing, consulte Serverless Computing. Para instruções sobre como usar o Serverless Computing, consulte Use Serverless Computing.

    -- (Optional) It's recommended to use Serverless Computing for bulk offline imports and ETL jobs.
    SET hg_computing_resource = 'serverless';
    INSERT INTO public.orders_row SELECT * FROM public.orders;
    -- Reset the configuration to ensure that subsequent SQL statements do not use serverless resources.
    RESET hg_computing_resource;
  3. Execute queries

    1. Gere as instruções de consulta.

      As consultas pontuais por chave/valor se dividem em dois tipos:

      Tipo

      Instrução de consulta

      Descrição

      consulta pontual de valor único

      SELECT  column_a
              ,column_b
              ,...
              ,column_x
      FROM    table_x
      WHERE   pk = value_x
      ;

      Essa consulta filtra por um único valor na cláusula WHERE.

      consulta pontual de múltiplos valores

      SELECT  column_a
              ,column_b
              ,...
              ,column_x
      FROM    table_x
      WHERE   pk IN ( value_a, value_b,..., value_x )
      ;

      Essa consulta filtra por múltiplos valores na cláusula WHERE.

      Execute o seguinte script para gerar as instruções SQL necessárias.

      rm -rf kv_query
      mkdir kv_query
      cd kv_query
      echo "
      \set column_values random(1,99999999)
      select O_ORDERKEY,O_CUSTKEY,O_ORDERSTATUS,O_TOTALPRICE,O_ORDERDATE,O_ORDERPRIORITY,O_CLERK,O_SHIPPRIORITY,O_COMMENT from public.orders_row WHERE o_orderkey =:column_values;
      " >> kv_query_single.sql
      echo "
      \set column_values1 random(1,99999999)
      \set column_values2 random(1,99999999)
      \set column_values3 random(1,99999999)
      \set column_values4 random(1,99999999)
      \set column_values5 random(1,99999999)
      \set column_values6 random(1,99999999)
      \set column_values7 random(1,99999999)
      \set column_values8 random(1,99999999)
      \set column_values9 random(1,99999999)
      select O_ORDERKEY,O_CUSTKEY,O_ORDERSTATUS,O_TOTALPRICE,O_ORDERDATE,O_ORDERPRIORITY,O_CLERK,O_SHIPPRIORITY,O_COMMENT from public.orders_row WHERE o_orderkey in(:column_values1,:column_values2,:column_values3,:column_values4,:column_values5,:column_values6,:column_values7,:column_values8,:column_values9);
      " >> kv_query_in.sql

      O script gera dois arquivos SQL:

      • kv_query_single.sql: Contém o SQL para a consulta pontual de valor único.

      • kv_query_in.sql: Contém o SQL para a consulta pontual de múltiplos valores. Esse script gera uma instrução SQL que filtra por nove valores aleatórios.

    2. Para coletar estatísticas de consulta, utilize a ferramenta pgbench. Instale-a executando o seguinte comando.

      yum install postgresql-contrib -y

      Instale o pgbench 13 ou versão posterior. Se o pgbench já estiver instalado, verifique se a versão é 9.6 ou posterior. Para verificar a versão:

      pgbench --version
    3. Execute as instruções de teste.

      Nota

      Execute os comandos a seguir no diretório onde as instruções de consulta foram geradas.

      • Para o cenário de consulta pontual de valor único, use o pgbench para executar um teste de estresse.

        PGUSER=<AccessKey ID> PGPASSWORD=<AccessKey Secret> PGDATABASE=<database> pgbench -h <endpoint> -p <port> -c <Client_Num> -T <Query_Seconds> -M prepared -n -f kv_query_single.sql
      • Para o cenário de consulta pontual de múltiplos valores, use o pgbench para executar um teste de estresse.

        PGUSER=<AccessKey ID> PGPASSWORD=<AccessKey Secret> PGDATABASE=<database> pgbench -h <endpoint> -p <port> -c <Client_Num> -T <Query_Seconds> -M prepared -n -f kv_query_in.sql

      Descrição dos parâmetros:

      Parâmetro

      Descrição

      AccessKey ID

      O AccessKey ID da sua conta da Alibaba Cloud.

      Acesse a página AccessKey Management para obter o AccessKey ID.

      AccessKey Secret

      O AccessKey Secret da sua conta da Alibaba Cloud.

      Acesse a página AccessKey Management para obter o AccessKey Secret.

      database

      • O nome do banco de dados do Hologres.

      • Ao criar uma instância do Hologres, o sistema cria automaticamente um banco de dados chamado postgres.

      • É possível usar o banco de dados padrão postgres, mas ele possui recursos limitados. Para cargas de trabalho em produção, Create a database.

      endpoint

      O endpoint da instância do Hologres.

      Acesse a página de detalhes da instância no Hologres console e obtenha o endpoint na seção Network Information.

      port

      A porta da instância do Hologres.

      Acesse a página Instance Details no Hologres console para obter a porta.

      Client_Num

      O número de clientes simultâneos.

      Como este teste mede o desempenho de consulta e não a concorrência, defina este parâmetro como 1.

      Query_Seconds

      A duração total do teste de estresse, em segundos. Por exemplo, defina este parâmetro como 300.

Atualização de dados

Este cenário testa o desempenho do mecanismo OLAP em atualizações completas de linha disparadas por conflito de chave primária.

  • Gere o arquivo de consulta.

    echo "
    \set O_ORDERKEY random(1,99999999)
    INSERT INTO public.orders_row(o_orderkey,o_custkey,o_orderstatus,o_totalprice,o_orderdate,o_orderpriority,o_clerk,o_shippriority,o_comment) VALUES (:O_ORDERKEY,1,'demo',1.1,'2021-01-01','demo','demo',1,'demo') on conflict(o_orderkey) do update set (o_orderkey,o_custkey,o_orderstatus,o_totalprice,o_orderdate,o_orderpriority,o_clerk,o_shippriority,o_comment)= ROW(excluded.*);
    " > /root/insert_on_conflict.sql
  • Insira e atualize os dados. Parameters.

    PGUSER=<AccessKey_ID> PGPASSWORD=<AccessKey_Secret> PGDATABASE=<Database> pgbench -h <Endpoint> -p <Port> -c <Client_Num> -T <Query_Seconds> -M prepared -n -f /root/insert_on_conflict.sql
  • Exemplo de resultado:

    transaction type: Custom query
    scaling factor: 1
    query mode: prepared
    number of clients: 249
    number of threads: 1
    duration: 60 s
    number of transactions actually processed: 1923038
    tps = 32005.850214 (including connections establishing)
    tps = 36403.145722 (excluding connections establishing)

Gravações em tempo real com o Flink

Este caso de uso avalia o desempenho de gravação de dados em tempo real.

  • DDL do Hologres

    A tabela do Hologres utilizada neste caso possui 10 colunas, e a coluna key é a chave primária.

    DROP TABLE IF EXISTS flink_insert;
    BEGIN ;
    CREATE TABLE IF NOT EXISTS flink_insert(
      key INT PRIMARY KEY
      ,value1 TEXT
      ,value2 TEXT
      ,value3 TEXT
      ,value4 TEXT
      ,value5 TEXT
      ,value6 TEXT
      ,value7 TEXT
      ,value8 TEXT
      ,value9 TEXT
    );
    CALL SET_TABLE_PROPERTY('flink_insert', 'orientation', 'row');
    CALL SET_TABLE_PROPERTY('flink_insert', 'clustering_key', 'key');
    CALL SET_TABLE_PROPERTY('flink_insert', 'distribution_key', 'key');
    COMMIT;
  • Script do job Flink

    Este script usa o gerador de dados aleatórios integrado do Realtime Compute for Apache Flink para gravar dados no Hologres. Ele atualiza a linha inteira em caso de conflito de chave primária, e cada linha ultrapassa 512 B de tamanho.

    CREATE TEMPORARY TABLE flink_case_1_source (
        key INT,
        value1 VARCHAR,
        value2 VARCHAR,
        value3 VARCHAR,
        value4 VARCHAR,
        value5 VARCHAR,
        value6 VARCHAR,
        value7 VARCHAR,
        value8 VARCHAR,
        value9 VARCHAR
      )
    WITH (
        'connector' = 'datagen',
         -- optional options --
        'rows-per-second' = '1000000000',
        'fields.key.min'='1',
        'fields.key.max'='2147483647',
        'fields.value1.length' = '57',
        'fields.value2.length' = '57',
        'fields.value3.length' = '57',
        'fields.value4.length' = '57',
        'fields.value5.length' = '57',
        'fields.value6.length' = '57',
        'fields.value7.length' = '57',
        'fields.value8.length' = '57',
        'fields.value9.length' = '57'
      );
    -- Create a Hologres sink table.
    CREATE TEMPORARY TABLE flink_case_1_sink (
        key INT,
        value1 VARCHAR,
        value2 VARCHAR,
        value3 VARCHAR,
        value4 VARCHAR,
        value5 VARCHAR,
        value6 VARCHAR,
        value7 VARCHAR,
        value8 VARCHAR,
        value9 VARCHAR
      )
    WITH (
        'connector' = 'hologres',
        'dbname'='<yourDbname>',  -- The name of the Hologres database.
        'tablename'='<yourTablename>',  -- The name of the Hologres table that receives the data.
        'username'='<yourUsername>',  -- Your AccessKey ID.
        'password'='<yourPassword>',  -- Your AccessKey Secret.
        'endpoint'='<yourEndpoint>',  -- The VPC endpoint of your Hologres instance.
        'connectionSize' = '10',  -- The default value is 3.
        'jdbcWriteBatchSize' = '1024',  -- The default value is 256.
        'jdbcWriteBatchByteSize' = '2147483647',  -- The default value is 20971520.
        'mutatetype'='insertorreplace'  -- Inserts a new row or replaces an existing row on conflict.
      );
    -- Write data from the source to the sink.
    insert into flink_case_1_sink
    select key,
      value1,
      value2,
      value3,
      value4,
      value5,
      value6,
      value7,
      value8,
      value9
    from
      flink_case_1_source
    ;

    Hologres sink table.

  • Exemplo de resultado

    No Hologres console, acesse a página Monitoring Information. No gráfico Query Latency (ms), a métrica select_avg atinge um pico de aproximadamente 400 ms. No gráfico Real-time Import RPS (records/second), a métrica sdk oscila entre 200.000 e 400.000 registros/segundo.

22 instruções de consulta TPC-H

Clique em uma consulta na tabela para visualize a instrução SQL.

Nome

Consulta

As 22 instruções de consulta TPC-H

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q10

Q11

Q12

Q13

Q14

Q15

Q16

Q17

Q18

Q19

Q20

Q21

Q22

-

-

  • Q1

    select
            l_returnflag,
            l_linestatus,
            sum(l_quantity) as sum_qty,
            sum(l_extendedprice) as sum_base_price,
            sum(l_extendedprice * (1 - l_discount)) as sum_disc_price,
            sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge,
            avg(l_quantity) as avg_qty,
            avg(l_extendedprice) as avg_price,
            avg(l_discount) as avg_disc,
            count(*) as count_order
    from
            lineitem
    where
            l_shipdate <= date '1998-12-01' - interval '120' day
    group by
            l_returnflag,
            l_linestatus
    order by
            l_returnflag,
            l_linestatus;
  • Q2

    select
            s_acctbal,
            s_name,
            n_name,
            p_partkey,
            p_mfgr,
            s_address,
            s_phone,
            s_comment
    from
            part,
            supplier,
            partsupp,
            nation,
            region
    where
            p_partkey = ps_partkey
            and s_suppkey = ps_suppkey
            and p_size = 48
            and p_type like '%STEEL'
            and s_nationkey = n_nationkey
            and n_regionkey = r_regionkey
            and r_name = 'EUROPE'
            and ps_supplycost = (
                    select
                            min(ps_supplycost)
                    from
                            partsupp,
                            supplier,
                            nation,
                            region
                    where
                            p_partkey = ps_partkey
                            and s_suppkey = ps_suppkey
                            and s_nationkey = n_nationkey
                            and n_regionkey = r_regionkey
                            and r_name = 'EUROPE'
            )
    order by
            s_acctbal desc,
            n_name,
            s_name,
            p_partkey
    limit 100;
  • Q3

    select
            l_orderkey,
            sum(l_extendedprice * (1 - l_discount)) as revenue,
            o_orderdate,
            o_shippriority
    from
            customer,
            orders,
            lineitem
    where
            c_mktsegment = 'MACHINERY'
            and c_custkey = o_custkey
            and l_orderkey = o_orderkey
            and o_orderdate < date '1995-03-23'
            and l_shipdate > date '1995-03-23'
    group by
            l_orderkey,
            o_orderdate,
            o_shippriority
    order by
            revenue desc,
            o_orderdate
    limit 10;
  • Q4

    select
            o_orderpriority,
            count(*) as order_count
    from
            orders
    where
            o_orderdate >= date '1996-07-01'
            and o_orderdate < date '1996-07-01' + interval '3' month
            and exists (
                    select
                            *
                    from
                            lineitem
                    where
                            l_orderkey = o_orderkey
                            and l_commitdate < l_receiptdate
            )
    group by
            o_orderpriority
    order by
            o_orderpriority;
  • Q5

    select
            n_name,
            sum(l_extendedprice * (1 - l_discount)) as revenue
    from
            customer,
            orders,
            lineitem,
            supplier,
            nation,
            region
    where
            c_custkey = o_custkey
            and l_orderkey = o_orderkey
            and l_suppkey = s_suppkey
            and c_nationkey = s_nationkey
            and s_nationkey = n_nationkey
            and n_regionkey = r_regionkey
            and r_name = 'EUROPE'
            and o_orderdate >= date '1996-01-01'
            and o_orderdate < date '1996-01-01' + interval '1' year
    group by
            n_name
    order by
            revenue desc;
  • Q6

    select
            sum(l_extendedprice * l_discount) as revenue
    from
            lineitem
    where
            l_shipdate >= date '1996-01-01'
            and l_shipdate < date '1996-01-01' + interval '1' year
            and l_discount between 0.02 - 0.01 and 0.02 + 0.01
            and l_quantity < 24;
  • Q7

    select
            supp_nation,
            cust_nation,
            l_year,
            sum(volume) as revenue
    from
            (
                    select
                            n1.n_name as supp_nation,
                            n2.n_name as cust_nation,
                            extract(year from l_shipdate) as l_year,
                            l_extendedprice * (1 - l_discount) as volume
                    from
                            supplier,
                            lineitem,
                            orders,
                            customer,
                            nation n1,
                            nation n2
                    where
                            s_suppkey = l_suppkey
                            and o_orderkey = l_orderkey
                            and c_custkey = o_custkey
                            and s_nationkey = n1.n_nationkey
                            and c_nationkey = n2.n_nationkey
                            and (
                                    (n1.n_name = 'CANADA' and n2.n_name = 'BRAZIL')
                                    or (n1.n_name = 'BRAZIL' and n2.n_name = 'CANADA')
                            )
                            and l_shipdate between date '1995-01-01' and date '1996-12-31'
            ) as shipping
    group by
            supp_nation,
            cust_nation,
            l_year
    order by
            supp_nation,
            cust_nation,
            l_year;
  • Q8

    select
            o_year,
            sum(case
                    when nation = 'BRAZIL' then volume
                    else 0
            end) / sum(volume) as mkt_share
    from
            (
                    select
                            extract(year from o_orderdate) as o_year,
                            l_extendedprice * (1 - l_discount) as volume,
                            n2.n_name as nation
                    from
                            part,
                            supplier,
                            lineitem,
                            orders,
                            customer,
                            nation n1,
                            nation n2,
                            region
                    where
                            p_partkey = l_partkey
                            and s_suppkey = l_suppkey
                            and l_orderkey = o_orderkey
                            and o_custkey = c_custkey
                            and c_nationkey = n1.n_nationkey
                            and n1.n_regionkey = r_regionkey
                            and r_name = 'AMERICA'
                            and s_nationkey = n2.n_nationkey
                            and o_orderdate between date '1995-01-01' and date '1996-12-31'
                            and p_type = 'LARGE ANODIZED COPPER'
            ) as all_nations
    group by
            o_year
    order by
            o_year;
  • Q9

    select
            nation,
            o_year,
            sum(amount) as sum_profit
    from
            (
                    select
                            n_name as nation,
                            extract(year from o_orderdate) as o_year,
                            l_extendedprice * (1 - l_discount) - ps_supplycost * l_quantity as amount
                    from
                            part,
                            supplier,
                            lineitem,
                            partsupp,
                            orders,
                            nation
                    where
                            s_suppkey = l_suppkey
                            and ps_suppkey = l_suppkey
                            and ps_partkey = l_partkey
                            and p_partkey = l_partkey
                            and o_orderkey = l_orderkey
                            and s_nationkey = n_nationkey
                            and p_name like '%maroon%'
            ) as profit
    group by
            nation,
            o_year
    order by
            nation,
            o_year desc;
  • Q10

    select
            c_custkey,
            c_name,
            sum(l_extendedprice * (1 - l_discount)) as revenue,
            c_acctbal,
            n_name,
            c_address,
            c_phone,
            c_comment
    from
            customer,
            orders,
            lineitem,
            nation
    where
            c_custkey = o_custkey
            and l_orderkey = o_orderkey
            and o_orderdate >= date '1993-02-01'
            and o_orderdate < date '1993-02-01' + interval '3' month
            and l_returnflag = 'R'
            and c_nationkey = n_nationkey
    group by
            c_custkey,
            c_name,
            c_acctbal,
            c_phone,
            n_name,
            c_address,
            c_comment
    order by
            revenue desc
    limit 20;
  • Q11

    select
            ps_partkey,
            sum(ps_supplycost * ps_availqty) as value
    from
            partsupp,
            supplier,
            nation
    where
            ps_suppkey = s_suppkey
            and s_nationkey = n_nationkey
            and n_name = 'EGYPT'
    group by
            ps_partkey having
                    sum(ps_supplycost * ps_availqty) > (
                            select
                                    sum(ps_supplycost * ps_availqty) * 0.0001000000
                            from
                                    partsupp,
                                    supplier,
                                    nation
                            where
                                    ps_suppkey = s_suppkey
                                    and s_nationkey = n_nationkey
                                    and n_name = 'EGYPT'
                    )
    order by
            value desc;
  • Q12

    select
            l_shipmode,
            sum(case
                    when o_orderpriority = '1-URGENT'
                            or o_orderpriority = '2-HIGH'
                            then 1
                    else 0
            end) as high_line_count,
            sum(case
                    when o_orderpriority <> '1-URGENT'
                            and o_orderpriority <> '2-HIGH'
                            then 1
                    else 0
            end) as low_line_count
    from
            orders,
            lineitem
    where
            o_orderkey = l_orderkey
            and l_shipmode in ('FOB', 'AIR')
            and l_commitdate < l_receiptdate
            and l_shipdate < l_commitdate
            and l_receiptdate >= date '1997-01-01'
            and l_receiptdate < date '1997-01-01' + interval '1' year
    group by
            l_shipmode
    order by
            l_shipmode;
  • Q13

    select
            c_count,
            count(*) as custdist
    from
            (
                    select
                            c_custkey,
                            count(o_orderkey) as c_count
                    from
                            customer left outer join orders on
                                    c_custkey = o_custkey
                                    and o_comment not like '%special%deposits%'
                    group by
                            c_custkey
            ) c_orders
    group by
            c_count
    order by
            custdist desc,
            c_count desc;
  • Q14

    select
            100.00 * sum(case
                    when p_type like 'PROMO%'
                            then l_extendedprice * (1 - l_discount)
                    else 0
            end) / sum(l_extendedprice * (1 - l_discount)) as promo_revenue
    from
            lineitem,
            part
    where
            l_partkey = p_partkey
            and l_shipdate >= date '1997-06-01'
            and l_shipdate < date '1997-06-01' + interval '1' month;
  • Q15

    with revenue0(SUPPLIER_NO, TOTAL_REVENUE)  as
        (
        select
            l_suppkey,
            sum(l_extendedprice * (1 - l_discount))
        from
            lineitem
        where
            l_shipdate >= date '1995-02-01'
            and l_shipdate < date '1995-02-01' + interval '3' month
        group by
            l_suppkey
        )
    select
        s_suppkey,
        s_name,
        s_address,
        s_phone,
        total_revenue
    from
        supplier,
        revenue0
    where
        s_suppkey = supplier_no
        and total_revenue = (
            select
                max(total_revenue)
            from
                revenue0
        )
    order by
        s_suppkey;
  • Q16

    select
            p_brand,
            p_type,
            p_size,
            count(distinct ps_suppkey) as supplier_cnt
    from
            partsupp,
            part
    where
            p_partkey = ps_partkey
            and p_brand <> 'Brand#45'
            and p_type not like 'SMALL ANODIZED%'
            and p_size in (47, 15, 37, 30, 46, 16, 18, 6)
            and ps_suppkey not in (
                    select
                            s_suppkey
                    from
                            supplier
                    where
                            s_comment like '%Customer%Complaints%'
            )
    group by
            p_brand,
            p_type,
            p_size
    order by
            supplier_cnt desc,
            p_brand,
            p_type,
            p_size;
  • Q17

    select
            sum(l_extendedprice) / 7.0 as avg_yearly
    from
            lineitem,
            part
    where
            p_partkey = l_partkey
            and p_brand = 'Brand#51'
            and p_container = 'WRAP PACK'
            and l_quantity < (
                    select
                            0.2 * avg(l_quantity)
                    from
                            lineitem
                    where
                            l_partkey = p_partkey
            );
  • Q18

    select
            c_name,
            c_custkey,
            o_orderkey,
            o_orderdate,
            o_totalprice,
            sum(l_quantity)
    from
            customer,
            orders,
            lineitem
    where
            o_orderkey in (
                    select
                            l_orderkey
                    from
                            lineitem
                    group by
                            l_orderkey having
                                    sum(l_quantity) > 312
            )
            and c_custkey = o_custkey
            and o_orderkey = l_orderkey
    group by
            c_name,
            c_custkey,
            o_orderkey,
            o_orderdate,
            o_totalprice
    order by
            o_totalprice desc,
            o_orderdate
    limit 100;
  • Q19

    select
            sum(l_extendedprice* (1 - l_discount)) as revenue
    from
            lineitem,
            part
    where
            (
                    p_partkey = l_partkey
                    and p_brand = 'Brand#52'
                    and p_container in ('SM CASE', 'SM BOX', 'SM PACK', 'SM PKG')
                    and l_quantity >= 3 and l_quantity <= 3 + 10
                    and p_size between 1 and 5
                    and l_shipmode in ('AIR', 'AIR REG')
                    and l_shipinstruct = 'DELIVER IN PERSON'
            )
            or
            (
                    p_partkey = l_partkey
                    and p_brand = 'Brand#43'
                    and p_container in ('MED BAG', 'MED BOX', 'MED PKG', 'MED PACK')
                    and l_quantity >= 12 and l_quantity <= 12 + 10
                    and p_size between 1 and 10
                    and l_shipmode in ('AIR', 'AIR REG')
                    and l_shipinstruct = 'DELIVER IN PERSON'
            )
            or
            (
                    p_partkey = l_partkey
                    and p_brand = 'Brand#52'
                    and p_container in ('LG CASE', 'LG BOX', 'LG PACK', 'LG PKG')
                    and l_quantity >= 21 and l_quantity <= 21 + 10
                    and p_size between 1 and 15
                    and l_shipmode in ('AIR', 'AIR REG')
                    and l_shipinstruct = 'DELIVER IN PERSON'
            );
  • Q20

    select
            s_name,
            s_address
    from
            supplier,
            nation
    where
            s_suppkey in (
                    select
                            ps_suppkey
                    from
                            partsupp
                    where
                            ps_partkey in (
                                    select
                                            p_partkey
                                    from
                                            part
                                    where
                                            p_name like 'drab%'
                            )
                            and ps_availqty > (
                                    select
                                            0.5 * sum(l_quantity)
                                    from
                                            lineitem
                                    where
                                            l_partkey = ps_partkey
                                            and l_suppkey = ps_suppkey
                                            and l_shipdate >= date '1996-01-01'
                                            and l_shipdate < date '1996-01-01' + interval '1' year
                            )
            )
            and s_nationkey = n_nationkey
            and n_name = 'KENYA'
    order by
            s_name;
  • Q21

    select
            s_name,
            count(*) as numwait
    from
            supplier,
            lineitem l1,
            orders,
            nation
    where
            s_suppkey = l1.l_suppkey
            and o_orderkey = l1.l_orderkey
            and o_orderstatus = 'F'
            and l1.l_receiptdate > l1.l_commitdate
            and exists (
                    select
                            *
                    from
                            lineitem l2
                    where
                            l2.l_orderkey = l1.l_orderkey
                            and l2.l_suppkey <> l1.l_suppkey
            )
            and not exists (
                    select
                            *
                    from
                            lineitem l3
                    where
                            l3.l_orderkey = l1.l_orderkey
                            and l3.l_suppkey <> l1.l_suppkey
                            and l3.l_receiptdate > l3.l_commitdate
            )
            and s_nationkey = n_nationkey
            and n_name = 'PERU'
    group by
            s_name
    order by
            numwait desc,
            s_name
    limit 100;
  • Q22

    select
            cntrycode,
            count(*) as numcust,
            sum(c_acctbal) as totacctbal
    from
            (
                    select
                            substring(c_phone from 1 for 2) as cntrycode,
                            c_acctbal
                    from
                            customer
                    where
                            substring(c_phone from 1 for 2) in
                                    ('24', '32', '17', '18', '12', '14', '22')
                            and c_acctbal > (
                                    select
                                            avg(c_acctbal)
                                    from
                                            customer
                                    where
                                            c_acctbal > 0.00
                                            and substring(c_phone from 1 for 2) in
                                                    ('24', '32', '17', '18', '12', '14', '22')
                            )
                            and not exists (
                                    select
                                            *
                                    from
                                            orders
                                    where
                                            o_custkey = c_custkey
                            )
            ) as custsale
    group by
            cntrycode
    order by
            cntrycode;