使用JDBC查询运行慢得多
当我使用Adminer或DBeaver计时时，我有两个查询花费的时间差不多。
查询一个
select * from state where state_name = 'Florida';
当我在Adminer中运行上面的查询时，它需要
0.032 s至0.058 s
解释分析
Seq Scan on state (cost=0.00..3981.50 rows=1 width=28) (actual time=1.787..15.047 rows=1 loops=1)
Filter: (state_name = 'Florida'::citext)
Rows Removed by Filter: 50
Planning Time: 0.486 ms
Execution Time: 15.779 ms
查询两个
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';
上面的查询来自
0.025 s至0.048 s
解释分析
Nested Loop (cost=29.82..65.96 rows=1 width=97) (actual time=0.668..0.671 rows=1 loops=1)
-> Nested Loop (cost=29.53..57.65 rows=1 width=107) (actual time=0.617..0.620 rows=1 loops=1)
-> Nested Loop (cost=29.25..49.30 rows=1 width=120) (actual time=0.582..0.585 rows=1 loops=1)
-> Nested Loop (cost=28.97..41.00 rows=1 width=127) (actual time=0.532..0.534 rows=1 loops=1)
-> Bitmap Heap Scan on property (cost=28.54..32.56 rows=1 width=131) (actual time=0.454..0.456 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=0.426..0.426 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=0.070..0.070 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=0.047..0.047 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=0.032..0.032 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=0.048..0.048 rows=1 loops=1)
Index Cond: (id = property.zipcode_id)
Planning Time: 5.473 ms
Execution Time: 1.601 ms
我有以下使用JDBCTemplate执行相同查询的方法。
查询一个
public void performanceTest(String str) {
template.queryForObject(
"select * from state where state_name = ?",
new Object[] { str }, (result, rowNum) -> {
return result.getObject("state_name");
});
}
时间：140ms，即0.14秒
查询两个
public void performanceTest(String str) {
template.queryForObject(
"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 = ?",
new Object[] { str }, (result, rowNum) -> {
return result.getObject("property_id");
});
}
执行上述方法所需的时间是
时间：828毫秒，即0.825秒
我正在使用下面的这段代码来计时方法的执行时间
long startTime1 = System.nanoTime();
propertyRepo.performanceTest(address); //or "Florida" depending which query I'm testing
long endTime1 = System.nanoTime();
long duration1 = TimeUnit.MILLISECONDS.convert((endTime1 - startTime1), TimeUnit.NANOSECONDS);
System.out.println("time: " + duration1);
为什么从JDBC运行查询时比从Adminer运行查询时慢两个查询？我可以做些什么来提高查询二的性能？
编辑：
我创建了两个分别包含查询的不同PHP脚本。他们使用PHP花费的时间相同，所以我认为这与JDBC有关？以下是PHP脚本的结果。 PHP花费的时间比Java使用Query One花费的时间更长，因为我没有使用任何连接池。但是，两个查询所花的时间几乎相同。某些原因导致JDBC上的查询2延迟。
编辑：
当我使用准备好的语句运行查询时，它的速度很慢。但是，当我使用statement运行它时，速度很快。我使用prepareStatement和statement做过EXPLAIN ANALYZE
preparedStatement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
声明解释分析
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
1 个答案:
答案 0 :(得分：-2)
这是因为不同的客户端使用了连接池。 您可以像这样为JDBC设置像HikariC这样的快速连接池：
public class HikariCPDataSource {
private static HikariConfig config = new HikariConfig();
private static HikariDataSource ds;
static {
config.setJdbcUrl("jdbc:h2:mem:test");
config.setUsername("user");
config.setPassword("password");
config.addDataSourceProperty("cachePrepStmts", "true");
config.addDataSourceProperty("prepStmtCacheSize", "250");
config.addDataSourceProperty("prepStmtCacheSqlLimit", "2048");
ds = new HikariDataSource(config);
}
public static Connection getConnection() throws SQLException {
return ds.getConnection();
}
private HikariCPDataSource(){}
}
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