我需要一些帮助来提高查询速度。

我有3个表：
1- pairTable2一个4列的表：
-genomic_accession：分组列（不在乎这个问题）
-汇编：分组列（不在乎这个问题）
-product_accession：用于在其他位置搜索的列表
-tmpcol：用于在其他表中搜索的列
2- SBPDB 1列表：-product_accession
：用于在其他表中搜索的列
3- cacheDB 1列表：-product_accession：用于在其他表中搜索的列

这个想法是在表1中创建一个称为SBP的布尔列，TRUE如果它是列中的值product_accession和/或tmpcol是否在中的唯一列之内SBPDB；
并且，在表1中创建一个称为SBP的布尔列，TRUE如果该列的值product_accession和/或tmpcol位于中的唯一列之内cacheDB。

我将R与DBI和dplyr用作后端，那么查询可能看起来很奇怪。但是，我想做的查询是：

SELECT "genomic_accession", 
       "assembly", 
       "product_accession", 
       "tmpcol", 
       "product_accession" IN (SELECT product_accession 
                               FROM   "cachedb") 
        OR "tmpcol" IN (SELECT product_accession 
                        FROM   "cachedb") AS "CACHE", 
       "product_accession" IN (SELECT product_accession 
                               FROM   "sbpdb") 
        OR "tmpcol" IN (SELECT product_accession 
                        FROM   "sbpdb")   AS "SBP" 
FROM   (SELECT * 
        FROM   "pairtable2" 
        LIMIT  500000) "dbplyr_031";

（查看说明）

                                       QUERY PLAN                                       
----------------------------------------------------------------------------------------
 Subquery Scan on dbplyr_031  (cost=3242.27..3846856408.45 rows=500000 width=59)
   ->  Limit  (cost=0.00..10666.17 rows=500000 width=57)
         ->  Seq Scan on "pairTable2"  (cost=0.00..781515.16 rows=36635216 width=57)
   SubPlan 1
     ->  Seq Scan on "cacheDB"  (cost=0.00..1394.91 rows=90491 width=14)
   SubPlan 2
     ->  Seq Scan on "cacheDB" "cacheDB_1"  (cost=0.00..1394.91 rows=90491 width=14)
   SubPlan 3
     ->  Materialize  (cost=0.00..7001.57 rows=276838 width=14)
           ->  Seq Scan on "SBPDB"  (cost=0.00..4265.38 rows=276838 width=14)
   SubPlan 4
     ->  Materialize  (cost=0.00..7001.57 rows=276838 width=14)
           ->  Seq Scan on "SBPDB" "SBPDB_1"  (cost=0.00..4265.38 rows=276838 width=14)
(13 rows)

因此，这只是50万行的样本，并且在运行1小时后仍然运行。总行数为：

genomes=> select count(*) from "pairTable2";
  count   
----------
 36633962
(1 row)

我至少需要一些建议，以找出更好的查询来加快我的需求。

表格示例：
（1）

 genomic_accession |    assembly     | product_accession |     tmpcol     
-------------------+-----------------+-------------------+----------------
 NC_007777.1       | GCF_000013345.1 | WP_011437108.1    | WP_011437109.1
 NC_007777.1       | GCF_000013345.1 | WP_011437109.1    | WP_011437110.1
 NC_007777.1       | GCF_000013345.1 | WP_011437110.1    | WP_011437113.1
 NC_007777.1       | GCF_000013345.1 | WP_011437113.1    | WP_011437114.1
 NC_007777.1       | GCF_000013345.1 | WP_011437114.1    | WP_011437116.1
 NC_007777.1       | GCF_000013345.1 | WP_011437116.1    | WP_011437117.1
 NC_007777.1       | GCF_000013345.1 | WP_011437117.1    | WP_011437118.1
 NC_007777.1       | GCF_000013345.1 | WP_011437118.1    | WP_011437120.1
 NC_007777.1       | GCF_000013345.1 | WP_011437120.1    | WP_011437121.1
 NC_007777.1       | GCF_000013345.1 | WP_011437121.1    | WP_011437123.1
(10 rows)

（2）

 product_accession 
-------------------
 WP_005887071.1
 WP_005913801.1
 WP_002804432.1
 WP_010366489.1
 WP_012444785.1
 NP_636898.1
 WP_046342269.1
 WP_074057745.1
 WP_039420813.1
 WP_005932253.1
(10 rows)

（3）

 product_accession 
-------------------
 ABG85315.1
 ABG85570.1
 ABG86033.1
 ABG86301.1
 ABG87594.1
 ACX82524.1
 ACX83274.1
 ACX83416.1
 ADX79866.1
 ADX79880.1
(10 rows)

输出示例：

 genomic_accession |    assembly     | product_accession |     tmpcol     | CACHE | SBP 
-------------------+-----------------+-------------------+----------------+-------+-----
 NC_007899.1       | GCF_000009945.1 | WP_011457581.1    | WP_011457582.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457582.1    | WP_011457583.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457583.1    | WP_011457584.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457584.1    | WP_011457585.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457585.1    | WP_011457586.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457586.1    | WP_011457587.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457587.1    | WP_011457588.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457588.1    | WP_011457589.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457589.1    | WP_011457590.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457590.1    | WP_011457592.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457592.1    | WP_011457593.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457593.1    | WP_011457594.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457594.1    | WP_011457596.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457596.1    | WP_011457597.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457597.1    | WP_011457598.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457598.1    | WP_011457600.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457600.1    | WP_011457601.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457601.1    | WP_011457602.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457602.1    | WP_011457603.1 | f     | f
 NC_007899.1       | GCF_000009945.1 | WP_011457603.1    | WP_011457604.1 | f     | f

提前致谢