与语句相比，用preparedStatement查询要慢得多

我有两种包含相同SQL查询的不同方法。拳头用的是preparedStatement，这很慢

public String getPropertyPreparedStatement(String address) throws Exception {
    Connection conn = null;
    PreparedStatement pst = null;
    ResultSet rs = null;

    String content = null;

    try {
        Class.forName("org.postgresql.Driver");
        conn = DataSourceUtils.getConnection(template.getDataSource());

        pst = conn.prepareStatement(
                "EXPLAIN ANALYZE SELECT property.id AS property_id , full_address, street_address, street.street, city.city as city, state.state_code as state_code, zipcode.zipcode as zipcode FROM property INNER JOIN street ON street.id = property.street_id INNER JOIN city ON city.id = property.city_id INNER JOIN state ON state.id = property.state_id INNER JOIN zipcode ON zipcode.id = property.zipcode_id WHERE full_address = ?");
        pst.setString(1, address);

        rs = pst.executeQuery();

        while (rs.next()) {
            // content = rs.getString("street_address");
            System.out.println(rs.getString(1));
        }

    } catch (Exception e) {
        e.printStackTrace();
    } finally {
        if (pst != null) {
            pst.close();
        }
        if (rs != null) {
            rs.close();
        }
        if (conn != null) {
            conn.close();
        }
    }

    return content;
}

上面方法的解释分析如下

Nested Loop  (cost=1.27..315241.91 rows=1 width=97) (actual time=0.091..688.583 rows=1 loops=1)
  ->  Nested Loop  (cost=0.98..315233.61 rows=1 width=107) (actual time=0.079..688.571 rows=1 loops=1)
        ->  Nested Loop  (cost=0.71..315225.26 rows=1 width=120) (actual time=0.069..688.561 rows=1 loops=1)
              ->  Nested Loop  (cost=0.42..315216.95 rows=1 width=127) (actual time=0.057..688.548 rows=1 loops=1)
                    ->  Seq Scan on property  (cost=0.00..315208.51 rows=1 width=131) (actual time=0.032..688.522 rows=1 loops=1)
                          Filter: ((full_address)::text = '139-Skillman-Ave-Apt-5C-Brooklyn-NY-11211'::text)
                          Rows Removed by Filter: 8790
                    ->  Index Scan using street_pkey on street  (cost=0.42..8.44 rows=1 width=28) (actual time=0.019..0.019 rows=1 loops=1)
                          Index Cond: (id = property.street_id)
              ->  Index Scan using city_id_pk on city  (cost=0.29..8.30 rows=1 width=25) (actual time=0.010..0.010 rows=1 loops=1)
                    Index Cond: (id = property.city_id)
        ->  Index Scan using state_id_pk on state  (cost=0.28..8.32 rows=1 width=19) (actual time=0.008..0.008 rows=1 loops=1)
              Index Cond: (id = property.state_id)
  ->  Index Scan using zipcode_id_pk on zipcode  (cost=0.29..8.30 rows=1 width=22) (actual time=0.010..0.010 rows=1 loops=1)
        Index Cond: (id = property.zipcode_id)
Planning Time: 2.400 ms
Execution Time: 688.674 ms

下面的方法使用语句，我直接在查询中拥有地址以测试性能

public String getPropertyStatement() throws Exception {
    Connection conn = null;
    Statement stmt = null;
    ResultSet rs = null;

    String content = null;

    try {
        Class.forName("org.postgresql.Driver");
        conn = DataSourceUtils.getConnection(template.getDataSource());
        stmt = conn.createStatement();

        rs = stmt.executeQuery(
                "EXPLAIN ANALYZE SELECT property.id AS property_id , full_address, street_address, street.street, city.city as city, state.state_code as state_code, zipcode.zipcode as zipcode FROM property INNER JOIN street ON street.id = property.street_id INNER JOIN city ON city.id = property.city_id INNER JOIN state ON state.id = property.state_id INNER JOIN zipcode ON zipcode.id = property.zipcode_id WHERE full_address = '139-Skillman-Ave-Apt-5C-Brooklyn-NY-11211'");

        while (rs.next()) {
            // content = rs.getString("street_address");
            System.out.println(rs.getString(1));
        }

    } catch (Exception e) {
        e.printStackTrace();
    } finally {
        if (stmt != null) {
            stmt.close();
        }
        if (rs != null) {
            rs.close();
        }
        if (conn != null) {
            conn.close();
        }
    }

    return content;
}

对上述方法进行解释分析

Nested Loop  (cost=29.82..65.96 rows=1 width=97) (actual time=0.232..0.235 rows=1 loops=1)
  ->  Nested Loop  (cost=29.53..57.65 rows=1 width=107) (actual time=0.220..0.223 rows=1 loops=1)
        ->  Nested Loop  (cost=29.25..49.30 rows=1 width=120) (actual time=0.211..0.213 rows=1 loops=1)
              ->  Nested Loop  (cost=28.97..41.00 rows=1 width=127) (actual time=0.198..0.200 rows=1 loops=1)
                    ->  Bitmap Heap Scan on property  (cost=28.54..32.56 rows=1 width=131) (actual time=0.175..0.177 rows=1 loops=1)
                          Recheck Cond: (full_address = '139-Skillman-Ave-Apt-5C-Brooklyn-NY-11211'::citext)
                          Heap Blocks: exact=1
                          ->  Bitmap Index Scan on property_full_address  (cost=0.00..28.54 rows=1 width=0) (actual time=0.162..0.162 rows=1 loops=1)
                                Index Cond: (full_address = '139-Skillman-Ave-Apt-5C-Brooklyn-NY-11211'::citext)
                    ->  Index Scan using street_pkey on street  (cost=0.42..8.44 rows=1 width=28) (actual time=0.017..0.017 rows=1 loops=1)
                          Index Cond: (id = property.street_id)
              ->  Index Scan using city_id_pk on city  (cost=0.29..8.30 rows=1 width=25) (actual time=0.010..0.010 rows=1 loops=1)
                    Index Cond: (id = property.city_id)
        ->  Index Scan using state_id_pk on state  (cost=0.28..8.32 rows=1 width=19) (actual time=0.007..0.007 rows=1 loops=1)
              Index Cond: (id = property.state_id)
  ->  Index Scan using zipcode_id_pk on zipcode  (cost=0.29..8.30 rows=1 width=22) (actual time=0.010..0.010 rows=1 loops=1)
        Index Cond: (id = property.zipcode_id)
Planning Time: 2.442 ms
Execution Time: 0.345 ms

当我直接在数据库上运行查询时，它也非常快，非常类似于使用语句而不是prepareStatement的方法。

为什么prepareStatement这么慢？在保持仍然可以在查询中使用占位符的同时，我必须具有哪些选择来保持using语句的性能？