IN in PostgreSQL (and generally)

Question

I know this was probably asked earlier, but I cannot find it with a SO search.

Suppose I have TABLE 1 and TABLE2, how should I expect a query to complete, such as:

SELECT * FROM TABLE1 WHERE id IN SUBQUERY_ON_TABLE2;

to reduce the number of rows in tables TABLE1 and TABLE2, and id is the primary key in TABLE1.

Yes, I know that using IN is such a n00b error, but TABLE2 has a common relation (the general relation of django) to several other tables, so I cannot think of another way to filter the data. What (aproximate) the number of rows in TABLE1 and TABLE2 should I expect to notice performance problems due to this? Will performance deteriorate linearly, exponentially, etc. Depending on the number of lines?

Answer 1

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, PostgreSQL 9.0, Dell Store 2. 1000 , . 10 000 , . , . , , - " ":

dellstore2=# EXPLAIN ANALYZE SELECT * FROM customers WHERE customerid IN 
  (SELECT customerid FROM orders WHERE orderid<2);
Nested Loop  (cost=8.27..16.56 rows=1 width=268) (actual time=0.051..0.060 rows=1 loops=1)
  ->  HashAggregate  (cost=8.27..8.28 rows=1 width=4) (actual time=0.028..0.030 rows=1 loops=1)
        ->  Index Scan using orders_pkey on orders  (cost=0.00..8.27 rows=1 width=4) (actual time=0.011..0.015 rows=1 loops=1)
              Index Cond: (orderid < 2)
  ->  Index Scan using customers_pkey on customers  (cost=0.00..8.27 rows=1 width=268) (actual time=0.013..0.016 rows=1 loops=1)
        Index Cond: (customers.customerid = orders.customerid)
Total runtime: 0.191 ms

dellstore2=# EXPLAIN ANALYZE SELECT * FROM customers WHERE customerid IN 
  (SELECT customerid FROM orders WHERE orderid<100);
Nested Loop  (cost=10.25..443.14 rows=100 width=268) (actual time=0.488..2.591 rows=98 loops=1)
  ->  HashAggregate  (cost=10.25..11.00 rows=75 width=4) (actual time=0.464..0.661 rows=98 loops=1)
        ->  Index Scan using orders_pkey on orders  (cost=0.00..10.00 rows=100 width=4) (actual time=0.019..0.218 rows=99 loops=1)
              Index Cond: (orderid < 100)
  ->  Index Scan using customers_pkey on customers  (cost=0.00..5.75 rows=1 width=268) (actual time=0.009..0.011 rows=1 loops=98)
        Index Cond: (customers.customerid = orders.customerid)
Total runtime: 2.868 ms

dellstore2=# EXPLAIN ANALYZE SELECT * FROM customers WHERE customerid IN 
  (SELECT customerid FROM orders WHERE orderid<1000);
Hash Semi Join  (cost=54.25..800.13 rows=1000 width=268) (actual time=4.574..80.319 rows=978 loops=1)
  Hash Cond: (customers.customerid = orders.customerid)
  ->  Seq Scan on customers  (cost=0.00..676.00 rows=20000 width=268) (actual time=0.007..33.665 rows=20000 loops=1)
  ->  Hash  (cost=41.75..41.75 rows=1000 width=4) (actual time=4.502..4.502 rows=999 loops=1)
        Buckets: 1024  Batches: 1  Memory Usage: 24kB
        ->  Index Scan using orders_pkey on orders  (cost=0.00..41.75 rows=1000 width=4) (actual time=0.056..2.487 rows=999 loops=1)
              Index Cond: (orderid < 1000)
Total runtime: 82.024 ms

dellstore2=# EXPLAIN ANALYZE SELECT * FROM customers WHERE customerid IN 
  (SELECT customerid FROM orders WHERE orderid<10000);
Hash Join  (cost=443.68..1444.68 rows=8996 width=268) (actual time=79.576..157.159 rows=7895 loops=1)
  Hash Cond: (customers.customerid = orders.customerid)
  ->  Seq Scan on customers  (cost=0.00..676.00 rows=20000 width=268) (actual time=0.007..27.085 rows=20000 loops=1)
  ->  Hash  (cost=349.97..349.97 rows=7497 width=4) (actual time=79.532..79.532 rows=7895 loops=1)
        Buckets: 1024  Batches: 1  Memory Usage: 186kB
        ->  HashAggregate  (cost=275.00..349.97 rows=7497 width=4) (actual time=45.130..62.227 rows=7895 loops=1)
              ->  Seq Scan on orders  (cost=0.00..250.00 rows=10000 width=4) (actual time=0.008..20.979 rows=9999 loops=1)
                    Filter: (orderid < 10000)
Total runtime: 167.882 ms