与语句相比，使用preparedStatement查询要慢得多 [英] query much slower with preparedStatement compared to statement

问题描述

我有两种包含相同SQL查询的不同方法。第一拳使用了非常慢的prepareStatement

I have two different methods that contain the same SQL query. The fist one uses preparedStatement which is very slow

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;
}

以下方法的解释分析

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

以下方法使用语句，我直接在查询中找到地址以测试性能

The method below uses statement and I have the address directly in the query to test performance

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

EXPLAIN ANALYZE for the method above

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

当我运行直接在数据库上查询的速度也非常快，就像使用语句而不是prepareStatement的方法一样。

When I run the query on the database directly it's also very fast, pretty much like the method that uses statement instead of preparedStatement.

为什么prepareStatement这么慢？在保持能够在查询中使用占位符的同时，我必须保持哪些选项？

Why is the preparedStatement so much slower? What options do I have to keep the performance of using statement while still being able to use a placeholder in the query?

推荐答案

您的准备好的语句将 full_address 转换为 text （Postgres的内置文本类型），而表似乎已创建 citext （不区分大小写）文本类型（或者，您在 full_address :: text 上缺少索引）。也许尝试在 full_address :: text 上创建一个索引，看看您准备好的语句是否可以使用它。

Your prepared statement casts full_address into text (Postgres' built-in text type), whereas it seems that your table is created with a citext (case-insensitive) text type (or, you lack an index on full_address::text). Maybe try creating an index on full_address::text and see if your prepared statement will pick it up.

另一个选项是对完整地址列使用文本类型，然后在 lower（full_address） －该选项的适用性取决于您的要求。

Another option is to use a text type for the full_address column, and then create a functional index on lower(full_address) -- the palatability of that option depends on what your requirements are.

我认为问题的一部分在于JDBC不知道 citext 类型，因此除非您可以获取JDBC以 citext 类型，查询计划人员会将其解释为文本，就像您的 setString（）方法一样

I think that part of the problem is that JDBC doesn't know about the citext type so unless you can get JDBC to send your address to the database as a citext type, it's going to be interpreted by the query planner as text, just as your setString() method probably does.

有趣的是，我最近遇到了类似的问题

披露：我为 EnterpriseDB（EDB）

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