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PgSQL · 应用案例 · 任意字段组合查询

1062

背景

《PostgreSQL 设计优化case - 大宽表任意字段组合查询索引如何选择(btree, gin, rum) - (含单个索引列数超过32列的方法)》
https://github.com/digoal/blog/blob/master/201808/20180803_01.md
《PostgreSQL 任意字段数组合 AND\OR 条件,指定返回结果条数,构造测试数据算法举例》
https://github.com/digoal/blog/blob/master/201809/20180905_03.md
《PostgreSQL ADHoc(任意字段组合)查询(rums索引加速) - 非字典化,普通、数组等组合字段生成新数组》
https://github.com/digoal/blog/blob/master/201805/20180518_02.md
《PostgreSQL 实践 - 实时广告位推荐 2 (任意字段组合、任意维度组合搜索、输出TOP-K)》
https://github.com/digoal/blog/blob/master/201804/20180424_04.md
《PostgreSQL 实践 - 实时广告位推荐 1 (任意字段组合、任意维度组合搜索、输出TOP-K)》
https://github.com/digoal/blog/blob/master/201804/20180420_03.md
《PostgreSQL ADHoc(任意字段组合)查询 与 字典化 (rum索引加速) - 实践与方案1》
https://github.com/digoal/blog/blob/master/201802/20180228_01.md
《PostgreSQL 如何高效解决 按任意字段分词检索的问题 - case 1》
https://github.com/digoal/blog/blob/master/201607/20160725_05.md
《HTAP数据库 PostgreSQL 场景与性能测试之 20 - (OLAP) 用户画像圈人场景 - 多个字段任意组合条件筛选与透视》
https://github.com/digoal/blog/blob/master/201711/20171107_21.md
《PostgreSQL 多字段任意组合搜索的性能》
https://github.com/digoal/blog/blob/master/201711/20171102_01.md
1亿记录,128个字段,任意字段组合查询。性能如何?
PG凭什么可以搞定大数据量的任意字段组合实时搜索?
《PostgreSQL 并行计算解说 汇总》
https://github.com/digoal/blog/blob/master/201903/20190319_01.md
《PostgreSQL 9种索引的原理和应用场景》
https://github.com/digoal/blog/blob/master/201706/20170627_01.md

例子

1、测试表

    do language plpgsql $$  
    declare
    sql text;
    begin
    sql := 'create unlogged table test(id serial primary key,';
    for i in 1..64 loop
    sql := sql||' c'||i||' int default random()*100,';
    end loop;
    for i in 65..128 loop
    sql := sql||' c'||i||' int default random()*1000000,';
    end loop;
    sql := rtrim(sql,',');
    sql := sql||')';
    execute sql;
    end;
    $$;
    复制

    2、写入1亿数据

      vi test.sql  
      insert into test (c1) select random()*100 from generate_series(1,100);


      nohup pgbench -M prepared -n -r -P 1 -f ./test.sql -c 50 -j 50 -t 20000 >/dev/null 2>&1 &
      复制

      3、写完后的大小

        postgres=# \dt+ test  
        List of relations
        Schema | Name | Type | Owner | Size | Description
        --------+------+-------+----------+-------+-------------
        public | test | table | postgres | 55 GB |
        (1 row)


        postgres=# select count(*) from test;
        count
        -----------
        100000000
        (1 row)
        复制

        4、高效率创建索引

          vi idx.sql  

          vacuum (analyze,verbose) test;
          set maintenance_work_mem='8GB';
          set max_parallel_workers=128;
          set max_parallel_workers_per_gather=32;
          set min_parallel_index_scan_size=0;
          set min_parallel_table_scan_size=0;
          set parallel_setup_cost=0;
          set parallel_tuple_cost=0;
          set max_parallel_maintenance_workers=16;
          alter table test set (parallel_workers=64);

          do language plpgsql $$
          declare
          sql text;
          begin
          for i in 1..128 loop
          execute format('create index idx_test_%s on test (c%s) %s', i, i, 'tablespace tbs_8001');
          end loop;
          end;
          $$;

          vacuum (analyze,verbose) test;




          nohup psql -f ./idx.sql >/dev/null 2>&1 &
          复制

          5、建完索引后

            postgres=# \d+ test  
            Unlogged table "public.test"
            Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
            --------+---------+-----------+----------+------------------------------------------+---------+--------------+-------------
            id | integer | | not null | nextval('test_id_seq'::regclass) | plain | |
            c1 | integer | | | (random() * (100)::double precision) | plain | |
            c2 | integer | | | (random() * (100)::double precision) | plain | |
            c3 | integer | | | (random() * (100)::double precision) | plain | |
            c4 | integer | | | (random() * (100)::double precision) | plain | |
            c5 | integer | | | (random() * (100)::double precision) | plain | |
            c6 | integer | | | (random() * (100)::double precision) | plain | |
            c7 | integer | | | (random() * (100)::double precision) | plain | |
            c8 | integer | | | (random() * (100)::double precision) | plain | |
            c9 | integer | | | (random() * (100)::double precision) | plain | |
            c10 | integer | | | (random() * (100)::double precision) | plain | |
            c11 | integer | | | (random() * (100)::double precision) | plain | |
            c12 | integer | | | (random() * (100)::double precision) | plain | |
            c13 | integer | | | (random() * (100)::double precision) | plain | |
            c14 | integer | | | (random() * (100)::double precision) | plain | |
            c15 | integer | | | (random() * (100)::double precision) | plain | |
            c16 | integer | | | (random() * (100)::double precision) | plain | |
            c17 | integer | | | (random() * (100)::double precision) | plain | |
            c18 | integer | | | (random() * (100)::double precision) | plain | |
            c19 | integer | | | (random() * (100)::double precision) | plain | |
            c20 | integer | | | (random() * (100)::double precision) | plain | |
            c21 | integer | | | (random() * (100)::double precision) | plain | |
            c22 | integer | | | (random() * (100)::double precision) | plain | |
            c23 | integer | | | (random() * (100)::double precision) | plain | |
            c24 | integer | | | (random() * (100)::double precision) | plain | |
            c25 | integer | | | (random() * (100)::double precision) | plain | |
            c26 | integer | | | (random() * (100)::double precision) | plain | |
            c27 | integer | | | (random() * (100)::double precision) | plain | |
            c28 | integer | | | (random() * (100)::double precision) | plain | |
            c29 | integer | | | (random() * (100)::double precision) | plain | |
            c30 | integer | | | (random() * (100)::double precision) | plain | |
            c31 | integer | | | (random() * (100)::double precision) | plain | |
            c32 | integer | | | (random() * (100)::double precision) | plain | |
            c33 | integer | | | (random() * (100)::double precision) | plain | |
            c34 | integer | | | (random() * (100)::double precision) | plain | |
            c35 | integer | | | (random() * (100)::double precision) | plain | |
            c36 | integer | | | (random() * (100)::double precision) | plain | |
            c37 | integer | | | (random() * (100)::double precision) | plain | |
            c38 | integer | | | (random() * (100)::double precision) | plain | |
            c39 | integer | | | (random() * (100)::double precision) | plain | |
            c40 | integer | | | (random() * (100)::double precision) | plain | |
            c41 | integer | | | (random() * (100)::double precision) | plain | |
            c42 | integer | | | (random() * (100)::double precision) | plain | |
            c43 | integer | | | (random() * (100)::double precision) | plain | |
            c44 | integer | | | (random() * (100)::double precision) | plain | |
            c45 | integer | | | (random() * (100)::double precision) | plain | |
            c46 | integer | | | (random() * (100)::double precision) | plain | |
            c47 | integer | | | (random() * (100)::double precision) | plain | |
            c48 | integer | | | (random() * (100)::double precision) | plain | |
            c49 | integer | | | (random() * (100)::double precision) | plain | |
            c50 | integer | | | (random() * (100)::double precision) | plain | |
            c51 | integer | | | (random() * (100)::double precision) | plain | |
            c52 | integer | | | (random() * (100)::double precision) | plain | |
            c53 | integer | | | (random() * (100)::double precision) | plain | |
            c54 | integer | | | (random() * (100)::double precision) | plain | |
            c55 | integer | | | (random() * (100)::double precision) | plain | |
            c56 | integer | | | (random() * (100)::double precision) | plain | |
            c57 | integer | | | (random() * (100)::double precision) | plain | |
            c58 | integer | | | (random() * (100)::double precision) | plain | |
            c59 | integer | | | (random() * (100)::double precision) | plain | |
            c60 | integer | | | (random() * (100)::double precision) | plain | |
            c61 | integer | | | (random() * (100)::double precision) | plain | |
            c62 | integer | | | (random() * (100)::double precision) | plain | |
            c63 | integer | | | (random() * (100)::double precision) | plain | |
            c64 | integer | | | (random() * (100)::double precision) | plain | |
            c65 | integer | | | (random() * (1000000)::double precision) | plain | |
            c66 | integer | | | (random() * (1000000)::double precision) | plain | |
            c67 | integer | | | (random() * (1000000)::double precision) | plain | |
            c68 | integer | | | (random() * (1000000)::double precision) | plain | |
            c69 | integer | | | (random() * (1000000)::double precision) | plain | |
            c70 | integer | | | (random() * (1000000)::double precision) | plain | |
            c71 | integer | | | (random() * (1000000)::double precision) | plain | |
            c72 | integer | | | (random() * (1000000)::double precision) | plain | |
            c73 | integer | | | (random() * (1000000)::double precision) | plain | |
            c74 | integer | | | (random() * (1000000)::double precision) | plain | |
            c75 | integer | | | (random() * (1000000)::double precision) | plain | |
            c76 | integer | | | (random() * (1000000)::double precision) | plain | |
            c77 | integer | | | (random() * (1000000)::double precision) | plain | |
            c78 | integer | | | (random() * (1000000)::double precision) | plain | |
            c79 | integer | | | (random() * (1000000)::double precision) | plain | |
            c80 | integer | | | (random() * (1000000)::double precision) | plain | |
            c81 | integer | | | (random() * (1000000)::double precision) | plain | |
            c82 | integer | | | (random() * (1000000)::double precision) | plain | |
            c83 | integer | | | (random() * (1000000)::double precision) | plain | |
            c84 | integer | | | (random() * (1000000)::double precision) | plain | |
            c85 | integer | | | (random() * (1000000)::double precision) | plain | |
            c86 | integer | | | (random() * (1000000)::double precision) | plain | |
            c87 | integer | | | (random() * (1000000)::double precision) | plain | |
            c88 | integer | | | (random() * (1000000)::double precision) | plain | |
            c89 | integer | | | (random() * (1000000)::double precision) | plain | |
            c90 | integer | | | (random() * (1000000)::double precision) | plain | |
            c91 | integer | | | (random() * (1000000)::double precision) | plain | |
            c92 | integer | | | (random() * (1000000)::double precision) | plain | |
            c93 | integer | | | (random() * (1000000)::double precision) | plain | |
            c94 | integer | | | (random() * (1000000)::double precision) | plain | |
            c95 | integer | | | (random() * (1000000)::double precision) | plain | |
            c96 | integer | | | (random() * (1000000)::double precision) | plain | |
            c97 | integer | | | (random() * (1000000)::double precision) | plain | |
            c98 | integer | | | (random() * (1000000)::double precision) | plain | |
            c99 | integer | | | (random() * (1000000)::double precision) | plain | |
            c100 | integer | | | (random() * (1000000)::double precision) | plain | |
            c101 | integer | | | (random() * (1000000)::double precision) | plain | |
            c102 | integer | | | (random() * (1000000)::double precision) | plain | |
            c103 | integer | | | (random() * (1000000)::double precision) | plain | |
            c104 | integer | | | (random() * (1000000)::double precision) | plain | |
            c105 | integer | | | (random() * (1000000)::double precision) | plain | |
            c106 | integer | | | (random() * (1000000)::double precision) | plain | |
            c107 | integer | | | (random() * (1000000)::double precision) | plain | |
            c108 | integer | | | (random() * (1000000)::double precision) | plain | |
            c109 | integer | | | (random() * (1000000)::double precision) | plain | |
            c110 | integer | | | (random() * (1000000)::double precision) | plain | |
            c111 | integer | | | (random() * (1000000)::double precision) | plain | |
            c112 | integer | | | (random() * (1000000)::double precision) | plain | |
            c113 | integer | | | (random() * (1000000)::double precision) | plain | |
            c114 | integer | | | (random() * (1000000)::double precision) | plain | |
            c115 | integer | | | (random() * (1000000)::double precision) | plain | |
            c116 | integer | | | (random() * (1000000)::double precision) | plain | |
            c117 | integer | | | (random() * (1000000)::double precision) | plain | |
            c118 | integer | | | (random() * (1000000)::double precision) | plain | |
            c119 | integer | | | (random() * (1000000)::double precision) | plain | |
            c120 | integer | | | (random() * (1000000)::double precision) | plain | |
            c121 | integer | | | (random() * (1000000)::double precision) | plain | |
            c122 | integer | | | (random() * (1000000)::double precision) | plain | |
            c123 | integer | | | (random() * (1000000)::double precision) | plain | |
            c124 | integer | | | (random() * (1000000)::double precision) | plain | |
            c125 | integer | | | (random() * (1000000)::double precision) | plain | |
            c126 | integer | | | (random() * (1000000)::double precision) | plain | |
            c127 | integer | | | (random() * (1000000)::double precision) | plain | |
            c128 | integer | | | (random() * (1000000)::double precision) | plain | |
            Indexes:
            "test_pkey" PRIMARY KEY, btree (id)
            "idx_test_1" btree (c1), tablespace "tbs_8001"
            "idx_test_10" btree (c10), tablespace "tbs_8001"
            "idx_test_100" btree (c100), tablespace "tbs_8001"
            "idx_test_101" btree (c101), tablespace "tbs_8001"
            "idx_test_102" btree (c102), tablespace "tbs_8001"
            "idx_test_103" btree (c103), tablespace "tbs_8001"
            "idx_test_104" btree (c104), tablespace "tbs_8001"
            "idx_test_105" btree (c105), tablespace "tbs_8001"
            "idx_test_106" btree (c106), tablespace "tbs_8001"
            "idx_test_107" btree (c107), tablespace "tbs_8001"
            "idx_test_108" btree (c108), tablespace "tbs_8001"
            "idx_test_109" btree (c109), tablespace "tbs_8001"
            "idx_test_11" btree (c11), tablespace "tbs_8001"
            "idx_test_110" btree (c110), tablespace "tbs_8001"
            "idx_test_111" btree (c111), tablespace "tbs_8001"
            "idx_test_112" btree (c112), tablespace "tbs_8001"
            "idx_test_113" btree (c113), tablespace "tbs_8001"
            "idx_test_114" btree (c114), tablespace "tbs_8001"
            "idx_test_115" btree (c115), tablespace "tbs_8001"
            "idx_test_116" btree (c116), tablespace "tbs_8001"
            "idx_test_117" btree (c117), tablespace "tbs_8001"
            "idx_test_118" btree (c118), tablespace "tbs_8001"
            "idx_test_119" btree (c119), tablespace "tbs_8001"
            "idx_test_12" btree (c12), tablespace "tbs_8001"
            "idx_test_120" btree (c120), tablespace "tbs_8001"
            "idx_test_121" btree (c121), tablespace "tbs_8001"
            "idx_test_122" btree (c122), tablespace "tbs_8001"
            "idx_test_123" btree (c123), tablespace "tbs_8001"
            "idx_test_124" btree (c124), tablespace "tbs_8001"
            "idx_test_125" btree (c125), tablespace "tbs_8001"
            "idx_test_126" btree (c126), tablespace "tbs_8001"
            "idx_test_127" btree (c127), tablespace "tbs_8001"
            "idx_test_128" btree (c128), tablespace "tbs_8001"
            "idx_test_13" btree (c13), tablespace "tbs_8001"
            "idx_test_14" btree (c14), tablespace "tbs_8001"
            "idx_test_15" btree (c15), tablespace "tbs_8001"
            "idx_test_16" btree (c16), tablespace "tbs_8001"
            "idx_test_17" btree (c17), tablespace "tbs_8001"
            "idx_test_18" btree (c18), tablespace "tbs_8001"
            "idx_test_19" btree (c19), tablespace "tbs_8001"
            "idx_test_2" btree (c2), tablespace "tbs_8001"
            "idx_test_20" btree (c20), tablespace "tbs_8001"
            "idx_test_21" btree (c21), tablespace "tbs_8001"
            "idx_test_22" btree (c22), tablespace "tbs_8001"
            "idx_test_23" btree (c23), tablespace "tbs_8001"
            "idx_test_24" btree (c24), tablespace "tbs_8001"
            "idx_test_25" btree (c25), tablespace "tbs_8001"
            "idx_test_26" btree (c26), tablespace "tbs_8001"
            "idx_test_27" btree (c27), tablespace "tbs_8001"
            "idx_test_28" btree (c28), tablespace "tbs_8001"
            "idx_test_29" btree (c29), tablespace "tbs_8001"
            "idx_test_3" btree (c3), tablespace "tbs_8001"
            "idx_test_30" btree (c30), tablespace "tbs_8001"
            "idx_test_31" btree (c31), tablespace "tbs_8001"
            "idx_test_32" btree (c32), tablespace "tbs_8001"
            "idx_test_33" btree (c33), tablespace "tbs_8001"
            "idx_test_34" btree (c34), tablespace "tbs_8001"
            "idx_test_35" btree (c35), tablespace "tbs_8001"
            "idx_test_36" btree (c36), tablespace "tbs_8001"
            "idx_test_37" btree (c37), tablespace "tbs_8001"
            "idx_test_38" btree (c38), tablespace "tbs_8001"
            "idx_test_39" btree (c39), tablespace "tbs_8001"
            "idx_test_4" btree (c4), tablespace "tbs_8001"
            "idx_test_40" btree (c40), tablespace "tbs_8001"
            "idx_test_41" btree (c41), tablespace "tbs_8001"
            "idx_test_42" btree (c42), tablespace "tbs_8001"
            "idx_test_43" btree (c43), tablespace "tbs_8001"
            "idx_test_44" btree (c44), tablespace "tbs_8001"
            "idx_test_45" btree (c45), tablespace "tbs_8001"
            "idx_test_46" btree (c46), tablespace "tbs_8001"
            "idx_test_47" btree (c47), tablespace "tbs_8001"
            "idx_test_48" btree (c48), tablespace "tbs_8001"
            "idx_test_49" btree (c49), tablespace "tbs_8001"
            "idx_test_5" btree (c5), tablespace "tbs_8001"
            "idx_test_50" btree (c50), tablespace "tbs_8001"
            "idx_test_51" btree (c51), tablespace "tbs_8001"
            "idx_test_52" btree (c52), tablespace "tbs_8001"
            "idx_test_53" btree (c53), tablespace "tbs_8001"
            "idx_test_54" btree (c54), tablespace "tbs_8001"
            "idx_test_55" btree (c55), tablespace "tbs_8001"
            "idx_test_56" btree (c56), tablespace "tbs_8001"
            "idx_test_57" btree (c57), tablespace "tbs_8001"
            "idx_test_58" btree (c58), tablespace "tbs_8001"
            "idx_test_59" btree (c59), tablespace "tbs_8001"
            "idx_test_6" btree (c6), tablespace "tbs_8001"
            "idx_test_60" btree (c60), tablespace "tbs_8001"
            "idx_test_61" btree (c61), tablespace "tbs_8001"
            "idx_test_62" btree (c62), tablespace "tbs_8001"
            "idx_test_63" btree (c63), tablespace "tbs_8001"
            "idx_test_64" btree (c64), tablespace "tbs_8001"
            "idx_test_65" btree (c65), tablespace "tbs_8001"
            "idx_test_66" btree (c66), tablespace "tbs_8001"
            "idx_test_67" btree (c67), tablespace "tbs_8001"
            "idx_test_68" btree (c68), tablespace "tbs_8001"
            "idx_test_69" btree (c69), tablespace "tbs_8001"
            "idx_test_7" btree (c7), tablespace "tbs_8001"
            "idx_test_70" btree (c70), tablespace "tbs_8001"
            "idx_test_71" btree (c71), tablespace "tbs_8001"
            "idx_test_72" btree (c72), tablespace "tbs_8001"
            "idx_test_73" btree (c73), tablespace "tbs_8001"
            "idx_test_74" btree (c74), tablespace "tbs_8001"
            "idx_test_75" btree (c75), tablespace "tbs_8001"
            "idx_test_76" btree (c76), tablespace "tbs_8001"
            "idx_test_77" btree (c77), tablespace "tbs_8001"
            "idx_test_78" btree (c78), tablespace "tbs_8001"
            "idx_test_79" btree (c79), tablespace "tbs_8001"
            "idx_test_8" btree (c8), tablespace "tbs_8001"
            "idx_test_80" btree (c80), tablespace "tbs_8001"
            "idx_test_81" btree (c81), tablespace "tbs_8001"
            "idx_test_82" btree (c82), tablespace "tbs_8001"
            "idx_test_83" btree (c83), tablespace "tbs_8001"
            "idx_test_84" btree (c84), tablespace "tbs_8001"
            "idx_test_85" btree (c85), tablespace "tbs_8001"
            "idx_test_86" btree (c86), tablespace "tbs_8001"
            "idx_test_87" btree (c87), tablespace "tbs_8001"
            "idx_test_88" btree (c88), tablespace "tbs_8001"
            "idx_test_89" btree (c89), tablespace "tbs_8001"
            "idx_test_9" btree (c9), tablespace "tbs_8001"
            "idx_test_90" btree (c90), tablespace "tbs_8001"
            "idx_test_91" btree (c91), tablespace "tbs_8001"
            "idx_test_92" btree (c92), tablespace "tbs_8001"
            "idx_test_93" btree (c93), tablespace "tbs_8001"
            "idx_test_94" btree (c94), tablespace "tbs_8001"
            "idx_test_95" btree (c95), tablespace "tbs_8001"
            "idx_test_96" btree (c96), tablespace "tbs_8001"
            "idx_test_97" btree (c97), tablespace "tbs_8001"
            "idx_test_98" btree (c98), tablespace "tbs_8001"
            "idx_test_99" btree (c99), tablespace "tbs_8001"
            Options: parallel_workers=64
            复制

            写入性能如何

            当前有129个索引,写入性能如何?
            9505行/s。
              transaction type: ./test.sql
              scaling factor: 1
              query mode: prepared
              number of clients: 24
              number of threads: 24
              duration: 120 s
              number of transactions actually processed: 11433
              latency average = 252.195 ms
              latency stddev = 70.089 ms
              tps = 95.054689 (including connections establishing)
              tps = 95.058210 (excluding connections establishing)
              statement latencies in milliseconds:
              252.179 insert into test (c1) select random()*100 from generate_series(1,100);
              复制
              瓶颈,磁盘读写5.5GB/s。
                Total DISK READ :     207.91 K/s | Total DISK WRITE :       3.54 G/s  
                Actual DISK READ: 207.91 K/s | Actual DISK WRITE: 2015.64 M/s
                TID PRIO USER DISK READ DISK WRITE SWAPIN IO> COMMAND
                55887 be/4 digoal 15.40 K/s 158.54 M/s 0.00 % 1.05 % postgres: postgres postgres [local] INSERT
                55872 be/4 digoal 7.70 K/s 157.62 M/s 0.00 % 0.84 % postgres: postgres postgres [local] INSERT
                55886 be/4 digoal 23.10 K/s 158.78 M/s 0.00 % 0.78 % postgres: postgres postgres [local] INSERT
                55897 be/4 digoal 7.70 K/s 158.79 M/s 0.00 % 0.75 % postgres: postgres postgres [local] INSERT
                55889 be/4 digoal 0.00 B/s 158.72 M/s 0.00 % 0.69 % postgres: postgres postgres [local] INSERT
                55894 be/4 digoal 0.00 B/s 157.25 M/s 0.00 % 0.69 % postgres: postgres postgres [local] INSERT
                55888 be/4 digoal 7.70 K/s 136.26 M/s 0.00 % 0.68 % postgres: postgres postgres [local] INSERT
                55885 be/4 digoal 7.70 K/s 143.24 M/s 0.00 % 0.67 % postgres: postgres postgres [local] INSERT
                55890 be/4 digoal 0.00 B/s 159.07 M/s 0.00 % 0.67 % postgres: postgres postgres [local] INSERT
                55865 be/4 digoal 15.40 K/s 158.27 M/s 0.00 % 0.65 % postgres: postgres postgres [local] INSERT
                55900 be/4 digoal 7.70 K/s 151.00 M/s 0.00 % 0.64 % postgres: postgres postgres [local] INSERT
                55891 be/4 digoal 0.00 B/s 160.40 M/s 0.00 % 0.63 % postgres: postgres postgres [local] INSERT
                55896 be/4 digoal 0.00 B/s 158.79 M/s 0.00 % 0.62 % postgres: postgres postgres [local] INSERT
                55902 be/4 digoal 15.40 K/s 157.65 M/s 0.00 % 0.62 % postgres: postgres postgres [local] INSERT
                55875 be/4 digoal 0.00 B/s 158.52 M/s 0.00 % 0.58 % postgres: postgres postgres [local] INSERT
                55892 be/4 digoal 7.70 K/s 136.20 M/s 0.00 % 0.58 % postgres: postgres postgres [local] INSERT
                55868 be/4 digoal 0.00 B/s 139.10 M/s 0.00 % 0.58 % postgres: postgres postgres [local] INSERT
                55895 be/4 digoal 0.00 B/s 159.75 M/s 0.00 % 0.57 % postgres: postgres postgres [local] INSERT
                55898 be/4 digoal 0.00 B/s 113.43 M/s 0.00 % 0.55 % postgres: postgres postgres [local] INSERT
                55880 be/4 digoal 46.20 K/s 121.68 M/s 0.00 % 0.50 % postgres: postgres postgres [local] INSERT
                55884 be/4 digoal 23.10 K/s 126.35 M/s 0.00 % 0.47 % postgres: postgres postgres [local] INSERT
                55901 be/4 digoal 15.40 K/s 117.46 M/s 0.00 % 0.46 % postgres: postgres postgres [local] INSERT
                55899 be/4 digoal      7.70 K/s  115.13 M/s  0.00 %  0.46 % postgres: postgres postgres [local] INSERT  
                复制

                瓶颈在读写数据文件

                  postgres=# select wait_event_type,wait_event,count(*) from pg_stat_activity where wait_event is not null group by 1,2 order by 3 desc;  
                  wait_event_type | wait_event | count
                  -----------------+---------------------+-------
                  IO | DataFileWrite | 15
                  IO | DataFileRead | 5
                  Activity | WalWriterMain | 1
                  Activity | LogicalLauncherMain | 1
                  Activity | CheckpointerMain | 1
                  Activity | AutoVacuumMain | 1
                  (6 rows)
                  复制

                  任意字段组合查询性能如何

                  1、

                    postgres=# explain select count(*) from test where c1=2 and c99 between 100 and 1000 and c98 between 100 and 200 and c1=1;  
                    QUERY PLAN
                    ---------------------------------------------------------------------------------------------------
                    Aggregate (cost=1201.23..1201.24 rows=1 width=8)
                    -> Result (cost=1192.25..1201.22 rows=1 width=0)
                    One-Time Filter: false
                    -> Bitmap Heap Scan on test (cost=1192.25..1201.22 rows=1 width=0)
                    Recheck Cond: ((c98 >= 100) AND (c98 <= 200) AND (c99 >= 100) AND (c99 <= 1000))
                    Filter: (c1 = 2)
                    -> BitmapAnd (cost=1192.25..1192.25 rows=8 width=0)
                    -> Bitmap Index Scan on idx_test_98 (cost=0.00..125.98 rows=9571 width=0)
                    Index Cond: ((c98 >= 100) AND (c98 <= 200))
                    -> Bitmap Index Scan on idx_test_99 (cost=0.00..1066.02 rows=81795 width=0)
                    Index Cond: ((c99 >= 100) AND (c99 <= 1000))
                    (11 rows)


                    postgres=# select count(*) from test where c1=2 and c99 between 100 and 1000 and c98 between 100 and 200 and c2=1;
                    count
                    -------
                    0
                    (1 row)

                    Time: 1.087 ms
                    复制

                    2、

                      set min_parallel_index_scan_size=0;  
                      set min_parallel_table_scan_size=0;
                      set parallel_setup_cost=0;
                      set parallel_tuple_cost=0;



                      set work_mem='1GB';
                      set max_parallel_workers=128;
                      set max_parallel_workers_per_gather=24;
                      set random_page_cost =1.1;
                      set effective_cache_size ='400GB';
                      alter table test set (parallel_workers=64);
                      set enable_bitmapscan=off;
                      复制

                        postgres=# select count(*) from test where c1=2 and c99 between 100 and 10000;  
                        count
                        -------
                        9764
                        (1 row)

                        Time: 50.160 ms


                        postgres=# select count(*) from test where c1=2 and c99 between 100 and 1000 and c98 between 100 and 200 and c2=1;
                        count
                        -------
                        0
                        (1 row)

                        Time: 20.969 ms

                        postgres=# select count(*) from test where c1=2 and c99 between 100 and 10000 and c108 between 100 and 10000;
                        count
                        -------
                        102
                        (1 row)

                        Time: 72.359 ms

                        postgres=# select count(*) from test where c1=2 and c99=1;
                        count
                        -------
                        2
                        (1 row)

                        Time: 1.118 ms
                        复制

                        3、OR

                          set enable_bitmapscan=on;  

                          postgres=# explain select count(*) from test where c1=2 and c99=1 or c100 between 10 and 100;
                          QUERY PLAN
                          --------------------------------------------------------------------------------------------
                          Aggregate (cost=10000010781.91..10000010781.92 rows=1 width=8)
                          -> Bitmap Heap Scan on test (cost=10000000130.57..10000010758.33 rows=9430 width=0)
                          Recheck Cond: ((c99 = 1) OR ((c100 >= 10) AND (c100 <= 100)))
                          Filter: (((c1 = 2) AND (c99 = 1)) OR ((c100 >= 10) AND (c100 <= 100)))
                          -> BitmapOr (cost=130.57..130.57 rows=9526 width=0)
                          -> Bitmap Index Scan on idx_test_99 (cost=0.00..2.39 rows=96 width=0)
                          Index Cond: (c99 = 1)
                          -> Bitmap Index Scan on idx_test_100 (cost=0.00..123.47 rows=9430 width=0)
                          Index Cond: ((c100 >= 10) AND (c100 <= 100))
                          (9 rows)

                          Time: 1.281 ms
                          postgres=# select count(*) from test where c1=2 and c99=1 or c100 between 10 and 100;
                          count
                          -------
                          9174
                          (1 row)

                          Time: 18.785 ms
                          复制

                          小结

                          性能差异:
                          1、执行计划
                          2、扫描量
                          3、运算量(与结果集大小无直接关系,关键看扫描方法和中间计算量)。
                          写入能力:129个索引,写入9505行/s。瓶颈在IO侧,通过提升IO能力,加分区可以提高。

                          参考

                          《PostgreSQL 设计优化case - 大宽表任意字段组合查询索引如何选择(btree, gin, rum) - (含单个索引列数超过32列的方法)》
                          https://github.com/digoal/blog/blob/master/201808/20180803_01.md
                          《PostgreSQL 任意字段数组合 AND\OR 条件,指定返回结果条数,构造测试数据算法举例》
                          https://github.com/digoal/blog/blob/master/201809/20180905_03.md
                          《PostgreSQL ADHoc(任意字段组合)查询(rums索引加速) - 非字典化,普通、数组等组合字段生成新数组》
                          https://github.com/digoal/blog/blob/master/201805/20180518_02.md
                          《PostgreSQL 实践 - 实时广告位推荐 2 (任意字段组合、任意维度组合搜索、输出TOP-K)》
                          https://github.com/digoal/blog/blob/master/201804/20180424_04.md
                          《PostgreSQL 实践 - 实时广告位推荐 1 (任意字段组合、任意维度组合搜索、输出TOP-K)》
                          https://github.com/digoal/blog/blob/master/201804/20180420_03.md
                          《PostgreSQL ADHoc(任意字段组合)查询 与 字典化 (rum索引加速) - 实践与方案1》
                          https://github.com/digoal/blog/blob/master/201802/20180228_01.md
                          《PostgreSQL 如何高效解决 按任意字段分词检索的问题 - case 1》
                          https://github.com/digoal/blog/blob/master/201607/20160725_05.md
                          《HTAP数据库 PostgreSQL 场景与性能测试之 20 - (OLAP) 用户画像圈人场景 - 多个字段任意组合条件筛选与透视》
                          https://github.com/digoal/blog/blob/master/201711/20171107_21.md
                          《PostgreSQL 多字段任意组合搜索的性能》
                          https://github.com/digoal/blog/blob/master/201711/20171102_01.md
                          《PostgreSQL 并行计算解说 汇总》
                          https://github.com/digoal/blog/blob/master/201903/20190319_01.md
                          《PostgreSQL 9种索引的原理和应用场景》
                          https://github.com/digoal/blog/blob/master/201706/20170627_01.md

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