就像我解释的那样before ，我的 mysql 数据库服务器出现了一些问题。我会知道您的意见并有一些想法，因为我处于黑洞中，我不知道，因为正在发生服务器的行为。
我会尽量解释所有的环境。我有 1 个数据库，有很多表。我们用 java 制作了一个导出器工具，可以从数据库中导出所有数据。数据存储在5个不同的表中，我需要将数据连接到5个表中。这些是表格:
DB的结构是一个从一些传感器接收信息并存储的系统。
测量表:我们从传感器接收到的测量值。
+--------------------+------------+------+-----+---------+----------------+
| Field | Type | Null | Key | Default | Extra |
+--------------------+------------+------+-----+---------+----------------+
| id | bigint(20) | NO | PRI | NULL | auto_increment |
| version | bigint(20) | NO | | NULL | |
| counter | char(2) | YES | | NULL | |
| datemeasurement_id | datetime | NO | MUL | NULL | |
| datereal_id | datetime | NO | MUL | NULL | |
| delayed | bit(1) | NO | | NULL | |
| frequency | tinyint(4) | YES | | NULL | |
| measuringentity_id | bigint(20) | NO | MUL | NULL | |
| real | bit(1) | NO | | NULL | |
| tamper | bit(1) | NO | | NULL | |
| value | float | NO | | NULL | |
+--------------------+------------+------+-----+---------+----------------+
measuring_entity 表:一个传感器可以测量不止一件事(温度、湿度)。这些就是实体。
+--------------+------------+------+-----+---------+----------------+
| Field | Type | Null | Key | Default | Extra |
+--------------+------------+------+-----+---------+----------------+
| id | bigint(20) | NO | PRI | NULL | auto_increment |
| version | bigint(20) | NO | | NULL | |
| household_id | varchar(4) | NO | MUL | NULL | |
| operative | bit(1) | NO | | NULL | |
| type | char(20) | NO | | NULL | |
| unit | char(3) | NO | | NULL | |
| interval | float | YES | | NULL | |
+--------------+------------+------+-----+---------+----------------+
sensor_measuring_entity:一个传感器可以关联多个实体。
+--------------------+------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+--------------------+------------+------+-----+---------+-------+
| sensor_id | bigint(20) | NO | PRI | NULL | |
| measuringentity_id | bigint(20) | NO | PRI | NULL | |
| version | bigint(20) | NO | | NULL | |
+--------------------+------------+------+-----+---------+-------+
传感器表:传感器的信息，与上表中的测量实体相关。
+---------------------+-------------+------+-----+---------+----------------+
| Field | Type | Null | Key | Default | Extra |
+---------------------+-------------+------+-----+---------+----------------+
| id | bigint(20) | NO | PRI | NULL | auto_increment |
| version | bigint(20) | NO | | NULL | |
| battery | bit(1) | NO | | NULL | |
| identifier | char(6) | NO | | NULL | |
| installationdate_id | datetime | NO | MUL | NULL | |
| lastreceiveddate_id | datetime | YES | MUL | NULL | |
| location_id | bigint(20) | NO | MUL | NULL | |
| operative | bit(1) | NO | | NULL | |
| tampererror | smallint(6) | NO | | NULL | |
+---------------------+-------------+------+-----+---------+----------------+
位置表:传感器的放置位置。
+------------+------------+------+-----+---------+----------------+
| Field | Type | Null | Key | Default | Extra |
+------------+------------+------+-----+---------+----------------+
| id | bigint(20) | NO | PRI | NULL | auto_increment |
| version | bigint(20) | NO | | NULL | |
| height | tinyint(4) | YES | | NULL | |
| operative | bit(1) | NO | | NULL | |
| place | char(15) | NO | MUL | NULL | |
| room | char(15) | NO | | NULL | |
| typesensor | char(15) | NO | | NULL | |
| formaster | bit(1) | YES | | NULL | |
+------------+------------+------+-----+---------+----------------+
导出信息的算法很重要，就像这样，交叉数据，尝试为我们可以在单独的 csv 文件中拥有的所有类型的传感器导出单独的信息。:
for (int i = 0; i < households.length; i++) {
openConnection();
for (int j = 0; j < values.length; j++) {
for (int k = 0; k < rooms.length; k++) {
if (places.length > 0) {
for (int l = 0; l < places.length; l++) {
for (int m = 0; m < height.length; m++) {
export(startDate2, endDate,
households[i], values[j],
rooms[k], places[l],height[m]);
}
}
} else {
for (int m = 0; m < height.length; m++) {
export(startDate2, endDate,
households[i], values[j],
rooms[k], null, height[m]);
}
}
}
}
try {
connection.close();
} catch (SQLException e1) {
e1.printStackTrace();
}
}
public void export(String startTime, String endTime, String household,
String type, String room, String place, String height)
throws ExporterException {
String sql = buildSQLStatement(startTime, endTime, household, type,
room, place, height);
Statement query;
try {
query = connection.createStatement();
ResultSet result = query.executeQuery(sql);
…
(The exporting to csv code)
…
private String buildSQLStatement(String startTime, String endTime,
String household, String type, String room, String place,
String height) {
String sql = "select HIGH_PRIORITY m.datemeasurement_id, me.type, l.place, m.value, l.room, l.height, s.identifier "
+ "FROM measurement as m STRAIGHT_JOIN measuring_entity as me ON m.measuringentity_id = me.id "
+ "STRAIGHT_JOIN sensor_measuring_entity as sme ON me.id = sme.measuringentity_id "
+ "STRAIGHT_JOIN sensor as s ON sme.sensor_id = s.id "
+ "STRAIGHT_JOIN location as l ON l.id = s.location_id"
+ " WHERE m.datemeasurement_id "
+ " >"
+ "'"
+ startTime
+ "'"
+ " AND m.datemeasurement_id"
+ " <"
+ "'"
+ endTime
+ "'"
+ " AND m.measuringentity_id"
+ " IN (SELECT me.id FROM measuring_entity AS me WHERE me.household_id="
+ "'"
+ household
+ "'"
+ ")";
我的大问题是:有时这个带有来自数据库的代码的应用程序运行起来真的很慢。 MySQL 运行得非常慢，但有时 MYSQL 运行得非常快。我们无法理解为什么会发生这种行为差异。
例如，当它很慢时(CPU 的 0.3-0%)，从数据库中导出所有数据可能需要大约 3 天的时间(大约 200.000 个查询)，但正如我之前所说，有些时候服务器在 30-40 分钟内完成相同的工作(85% 的 CPU)。
我们看到的问题是，当行为缓慢时，mysql 花费大量时间处于“准备”状态(每个查询大约 140 秒)，试图优化查询，但正如我所说，这种情况发生了只有某些时候。不是每次。
1016 | root | localhost:53936 | OptimAAL | Query | 10 | preparing | select HIGH_PRIORITY m.datemeasurement_id, me.type, l.place, m.value, l.room, l.height, s.identifier
这些是可以执行的查询之一:
EXPLAIN select HIGH_PRIORITY m.datemeasurement_id, me.type,
l.place,m.value, l.room, l.height, s.identifier
FROM measurement as m
STRAIGHT_JOIN measuring_entity as me ON m.measuringentity_id=me.id
STRAIGHT_JOIN sensor_measuring_entity as sme ON me.id=sme.measuringentity_id
STRAIGHT_JOIN sensor as s ON sme.sensor_id=s.id
STRAIGHT_JOIN location as l ON l.id=s.location_id
WHERE m.datemeasurement_id >'2012-01-19 06:19:00'
AND m.datemeasurement_id <'2012-01-19 06:20:00'
AND m.measuringentity_id IN (SELECT me.id FROM measuring_entity AS me
WHERE me.household_id='0022')
AND (height = '0')
AND (type = 'Brightness')
AND (place = 'Corner')
AND (room = 'Living room')
ORDER BY datemeasurement_id
这是解释的结果:
+----+--------------------+-------+-----------------+-----------------------------------------------+--------------------+---------+-------------------------------+------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+--------------------+-------+-----------------+-----------------------------------------------+--------------------+---------+-------------------------------+------+-------------+
| 1 | PRIMARY | m | range | FK93F2DBBC6292BE2,FK93F2DBBCA61A7F92 | FK93F2DBBC6292BE2 | 8 | NULL | 4 | Using where |
| 1 | PRIMARY | me | eq_ref | PRIMARY | PRIMARY | 8 | OptimAAL.m.measuringentity_id | 1 | Using where |
| 1 | PRIMARY | sme | ref | PRIMARY,FK951FA3ECA61A7F92,FK951FA3ECF9AE4602 | FK951FA3ECA61A7F92 | 8 | OptimAAL.m.measuringentity_id | 1 | Using index |
| 1 | PRIMARY | s | eq_ref | PRIMARY,FKCA0053BA3328FE22 | PRIMARY | 8 | OptimAAL.sme.sensor_id | 1 | |
| 1 | PRIMARY | l | eq_ref | PRIMARY,place | PRIMARY | 8 | OptimAAL.s.location_id | 1 | Using where |
| 2 | DEPENDENT SUBQUERY | me | unique_subquery | PRIMARY,FK11C7EA07E6EB51F2 | PRIMARY | 8 | func | 1 | Using where |
+----+--------------------+-------+-----------------+-----------------------------------------------+--------------------+---------+-------------------------------+------+-------------+
显然，如果我们更改日期间隔的值，数据量会增加很多，因为我们的数据库中有大约 100 万个测量值。
我尝试了一切:
更改 mysQL 配置文件 (/etc/my.cnf):
[mysqld]
#bind-address = 141.21.8.197
max_allowed_packet = 128M
sort_buffer_size = 512M
max_connections=500
query_cache_size = 512M
query_cache_limit = 512M
query-cache-type = 2
table_cache = 80
thread_cache_size=8
key_buffer_size = 512M
read_buffer_size=64M
read_rnd_buffer_size=64M
myisam_sort_buffer_size=64M
innodb_flush_log_at_trx_commit=2
innodb_buffer_pool_size=700M
innodb_additional_mem_pool_size=20M
datadir=/data/mysql
socket=/var/lib/mysql/mysql.sock
user=mysql
# Disabling symbolic-links is recommended to prevent assorted security risks
symbolic-links=0
#Enable logs
log = /var/log/mysql/mysql-log.log
log-error = /var/log/mysql/mysql-error.log
long_query_time = 1
log-slow-queries = /var/log/mysql/mysql-slow.log
[mysqld_safe]
log-error=/var/log/mysql/mysqld.log
pid-file=/var/run/mysqld/mysqld.pid
- 使用不同类型的 JOIN 来强制它避免优化查询。
- 使用 nice -20 为进程提供最高优先级。
- 在代码中使用存储过程而不是查询。
- 关闭与数据库的任何其他连接，让数据库仅供我使用，没有任何其他连接。
- 更改并尝试优化查询。
- ...
如您所见，我尝试了所有方法，但我不知道该怎么做才能确保服务器始终保持快速。
这些是服务器的信息:
MySQL version: 5.1.61-log / x86_64
RAM: 8 GB
OS: CentOS release 6.2 (Final)
CPU: 4 Cores / Xeon E6510 @ 1.73GHz
非常感谢您的帮助，
编辑:
我想补充一点，现在对我来说最大的问题是为什么服务器会发生不同的行为。因为我知道可以优化查询，但有时使用这段代码工作得非常快。
我现在的噩梦是知道为什么有时工作速度很快，而不是总是。现在我正在与 IT 人员核实是否可能是硬件访问问题、硬盘问题或类似问题。
看起来也可能是 SQL 配置的问题，或者它在 MYSQL 中的查询优化器的问题，但我无法发现我的黑洞的解决方案是什么。
非常感谢您的帮助
最佳答案
我能想到您可以做的一些优化。
首先，使用绑定(bind)变量和准备好的语句。
PreparedStatment stmt = connection.prepareStatement(
"select HIGH_PRIORITY m.datemeasurement_id, me.type, l.place, m.value, l.room, l.height, s.identifier "
+ "FROM measurement as m STRAIGHT_JOIN measuring_entity as me ON m.measuringentity_id = me.id "
+ "STRAIGHT_JOIN sensor_measuring_entity as sme ON me.id = sme.measuringentity_id "
+ "STRAIGHT_JOIN sensor as s ON sme.sensor_id = s.id "
+ "STRAIGHT_JOIN location as l ON l.id = s.location_id"
+ " WHERE m.datemeasurement_id > ? "
+ " AND m.datemeasurement_id < ? "
+ " AND m.measuringentity_id IN (SELECT me.id FROM measuring_entity AS me WHERE me.household_id= ? )";
stmt.setDate(1, startDate);
stmt.setDate(2, endDate);
stmt.setString(3, household);
stmt.executeQuery();
其次，删除 IN - 您不能在此处针对 measuring_entity 使用单独的联接吗？ IN 通常表现不佳。
第三，你能使用批处理来执行你的插入吗？批量插入应该会显着提高速度。
这些是我能即兴想到的几件事。如果您的查询未在 Java 端进行优化，那么世界上所有的 SQL 调优都无济于事。
关于java - MySQL 在 Preparing Statement 中以非常奇怪的行为被阻止，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/10447009/