我有两种包含相同SQL查询的不同方法。拳头用的是preparedStatement，它很慢
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;
}
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
下面的方法使用语句，我直接在查询中有地址来测试性能
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？
答案 0 :(得分：2)
您准备好的语句将full_address转换为text（Postgres的内置文本类型），而您的表似乎是用citext（不区分大小写）文本类型创建的（或者，您在full_address::text上没有索引）。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。
另一种选择是在text列中使用full_address类型，然后在lower(full_address)上创建功能索引-该选项的适用性取决于您的需求。
我认为问题的一部分是JDBC不了解citext类型，因此除非您能获得JDBC以citext类型将地址发送到数据库，否则它将就像您的text方法一样，被查询计划者解释为setString()。
有趣的是，我ran into a similar issue recently
披露：我为EnterpriseDB (EDB)工作