背景
之前有提到过使用Prometheus做Springboot的监控，这次以一个实例来说明，通过一种统一的方式，监控数据库连接池的运行情况。
原理
其实在Springboot内部监控都是结合了micrometer来做的，基于他的MeterRegistry，实现多种方式的监控，如PrometheusMeterRegistry。他也提供了对于很多的监控实现，如缓存，线程池，tomcat，JVM，OKhttp等。对应在io.micrometer.core.instrument.binder包中。
数据库相关datasource的监控默认在spring-boot-actuator（使用版本为2.1.4.RELEASE），具体在org.springframework.boot.actuate.metrics.jdbc.DataSourcePoolMetrics。这里会参考已有数据库连接池监控的实现，完成对项目中使用的druid的简单监控。
关于mecrometer的相关的依赖，可以引入如下：
io.micrometer
micrometer-core
1.5.1
io.micrometer
micrometer-registry-prometheus
1.5.1
步骤
分析已有实现
我们看org.springframework.boot.actuate.metrics.jdbc.DataSourcePoolMetrics中的实现，是使用的org.springframework.boot.jdbc.metadata.DataSourcePoolMetadata来获取数据库连接池运行数据的。再看这个接口，在spring-boot:2.1.4.RELEASE中包括tomcat,dbcp2,hikari是有默认实现，比如下面是hikari的实现，获取到了当前运行连接数，最高连接数，探活SQL等。
/**
* {@link DataSourcePoolMetadata} for a Hikari {@link DataSource}.
*
* @author Stephane Nicoll
* @since 2.0.0
*/
public class HikariDataSourcePoolMetadata
extends AbstractDataSourcePoolMetadata {
public HikariDataSourcePoolMetadata(HikariDataSource dataSource) {
super(dataSource);
}
@Override
public Integer getActive() {
try {
return getHikariPool().getActiveConnections();
}
catch (Exception ex) {
return null;
}
}
private HikariPool getHikariPool() {
return (HikariPool) new DirectFieldAccessor(getDataSource())
.getPropertyValue("pool");
}
@Override
public Integer getMax() {
return getDataSource().getMaximumPoolSize();
}
@Override
public Integer getMin() {
return getDataSource().getMinimumIdle();
}
@Override
public String getValidationQuery() {
return getDataSource().getConnectionTestQuery();
}
@Override
public Boolean getDefaultAutoCommit() {
return getDataSource().isAutoCommit();
}
}
DataSourcePoolMetadata的定义，可以参考注释理解各个方法的意义。
public interface DataSourcePoolMetadata {
/**
* Return the usage of the pool as value between 0 and 1 (or -1 if the pool is not
* limited).
*
* - 1 means that the maximum number of connections have been allocated
* - 0 means that no connection is currently active
* - -1 means there is not limit to the number of connections that can be allocated
*
*
* This may also return {@code null} if the data source does not provide the necessary
* information to compute the poll usage.
* @return the usage value or {@code null}
*/
Float getUsage();
/**
* Return the current number of active connections that have been allocated from the
* data source or {@code null} if that information is not available.
* @return the number of active connections or {@code null}
*/
Integer getActive();
/**
* Return the maximum number of active connections that can be allocated at the same
* time or {@code -1} if there is no limit. Can also return {@code null} if that
* information is not available.
* @return the maximum number of active connections or {@code null}
*/
Integer getMax();
/**
* Return the minimum number of idle connections in the pool or {@code null} if that
* information is not available.
* @return the minimum number of active connections or {@code null}
*/
Integer getMin();
/**
* Return the query to use to validate that a connection is valid or {@code null} if
* that information is not available.
* @return the validation query or {@code null}
*/
String getValidationQuery();
/**
* The default auto-commit state of connections created by this pool. If not set
* ({@code null}), default is JDBC driver default (If set to null then the
* java.sql.Connection.setAutoCommit(boolean) method will not be called.)
* @return the default auto-commit state or {@code null}
*/
Boolean getDefaultAutoCommit();
}
具体看下org.springframework.boot.actuate.metrics.jdbc.DataSourcePoolMetrics的实现，这里将单个datasource导入，然后根据实现的DataSourcePoolMetadata，获取到了具体的运行数据，然后，本身实现了MeterBinder接口，所以最终通过在bindTo(MetricRegistry registry)调用bindPoolMetadata() -> bindDataSource()完成监控的记录。
public class DataSourcePoolMetrics implements MeterBinder {
private final DataSource dataSource;
private final CachingDataSourcePoolMetadataProvider metadataProvider;
private final Iterable tags;
public DataSourcePoolMetrics(DataSource dataSource,
Collection metadataProviders,
String dataSourceName, Iterable tags) {
this(dataSource, new CompositeDataSourcePoolMetadataProvider(metadataProviders),
dataSourceName, tags);
}
public DataSourcePoolMetrics(DataSource dataSource,
DataSourcePoolMetadataProvider metadataProvider, String name,
Iterable tags) {
Assert.notNull(dataSource, "DataSource must not be null");
Assert.notNull(metadataProvider, "MetadataProvider must not be null");
this.dataSource = dataSource;
this.metadataProvider = new CachingDataSourcePoolMetadataProvider(
metadataProvider);
this.tags = Tags.concat(tags, "name", name);
}
@Override
public void bindTo(MeterRegistry registry) {
if (this.metadataProvider.getDataSourcePoolMetadata(this.dataSource) != null) {
bindPoolMetadata(registry, "active", DataSourcePoolMetadata::getActive);
bindPoolMetadata(registry, "max", DataSourcePoolMetadata::getMax);
bindPoolMetadata(registry, "min", DataSourcePoolMetadata::getMin);
}
}
private void bindPoolMetadata(MeterRegistry registry,
String metricName, Function function) {
bindDataSource(registry, metricName,
this.metadataProvider.getValueFunction(function));
}
private void bindDataSource(MeterRegistry registry,
String metricName, Function function) {
if (function.apply(this.dataSource) != null) {
registry.gauge("jdbc.connections." + metricName, this.tags, this.dataSource,
(m) -> function.apply(m).doubleValue());
}
}
private static class CachingDataSourcePoolMetadataProvider
implements DataSourcePoolMetadataProvider {
private static final Map cache = new ConcurrentReferenceHashMap<>();
private final DataSourcePoolMetadataProvider metadataProvider;
CachingDataSourcePoolMetadataProvider(
DataSourcePoolMetadataProvider metadataProvider) {
this.metadataProvider = metadataProvider;
}
public Function getValueFunction(
Function function) {
return (dataSource) -> function.apply(getDataSourcePoolMetadata(dataSource));
}
@Override
public DataSourcePoolMetadata getDataSourcePoolMetadata(DataSource dataSource) {
DataSourcePoolMetadata metadata = cache.get(dataSource);
if (metadata == null) {
metadata = this.metadataProvider.getDataSourcePoolMetadata(dataSource);
cache.put(dataSource, metadata);
}
return metadata;
}
}
}
而关于什么时候使用了DataSourceMetric，可以看它的构造方法引用，找到org.springframework.boot.actuate.autoconfigure.metrics.jdbc.DataSourcePoolMetricsAutoConfiguration，如下，可以看到，是通过spring的依赖注入，完成对所有datasource构造DataSourceMetric并注册到MeterRegistry的。
//注入所有Datasource ，结构为Map，名称和对应实例
@Autowired
public void bindDataSourcesToRegistry(Map dataSources) {
dataSources.forEach(this::bindDataSourceToRegistry);
}
private void bindDataSourceToRegistry(String beanName, DataSource dataSource) {
String dataSourceName = getDataSourceName(beanName);
new DataSourcePoolMetrics(dataSource, this.metadataProviders, dataSourceName,
Collections.emptyList()).bindTo(this.registry);
}
/**
* Get the name of a DataSource based on its {@code beanName}.
* @param beanName the name of the data source bean
* @return a name for the given data source
*/
private String getDataSourceName(String beanName) {
if (beanName.length() > DATASOURCE_SUFFIX.length()
&& StringUtils.endsWithIgnoreCase(beanName, DATASOURCE_SUFFIX)) {
return beanName.substring(0,
beanName.length() - DATASOURCE_SUFFIX.length());
}
return beanName;
}
实现druid连接池监控
完成了原理的分析，现在我们再来了解怎么实现对druid的监控。参考上面，定义一个自定义的DruidDataSourcePoolMetadata实现自DataSourcePoolMetadata或者AbstractDataSourcePoolMetadata。但目前问题是，上面使用的是
CachingDataSourcePoolMetadataProvider一个内部类的provider，没法直接使用将DruidDataSourcePoolMetadata添加到里边。但回到DataSourcePoolMetrics和DataSourcePoolMetricsAutoConfiguration，可以看到DataSourcePoolMetadataProvider可以注入多个。因此，再实现个针对DruidDataSourcePoolMetadata的DataSourcePoolMetadataProvider，然后执行注入，就可以得到我们想要的功能。
@Bean
public DataSourcePoolMetadataProvider druidPoolDataSourceMetadataProvider() {
return (dataSource) -> {
DruidDataSource ds = DataSourceUnwrapper.unwrap(dataSource,
DruidDataSource.class);
if (ds != null) {
return new DruidDataSourcePoolMetadata(ds);
}
return null;
};
}
/**
* 参考 org.springframework.boot.jdbc.metadata.HikariDataSourcePoolMetadata
*/
private class DruidDataSourcePoolMetadata extends AbstractDataSourcePoolMetadata {
/**
* Create an instance with the data source to use.
*
* @param dataSource the data source
*/
DruidDataSourcePoolMetadata(DruidDataSource dataSource) {
super(dataSource);
}
@Override
public Integer getActive() {
return getDataSource().getActiveCount();
}
@Override
public Integer getMax() {
return getDataSource().getMaxActive();
}
@Override
public Integer getMin() {
return getDataSource().getMinIdle();
}
@Override
public String getValidationQuery() {
return getDataSource().getValidationQuery();
}
@Override
public Boolean getDefaultAutoCommit() {
return getDataSource().isDefaultAutoCommit();
}
}
结果
总结
以上就是本期的所有内容，感谢阅读。可以看到spring-boot在模块化方面特别好，各种自定义实现可以在多个层次很好的组合起来，对于开发人员来说是十分方便的。这个例子中很多的思路可以作为以后开发的参考。同时，druid本身自带很多运行参数的监控，参考com.alibaba.druid.stat，如果能添加更多指标的数据并和DataSourceMetric结合，也是不错的选择，看起来实现也并不是很复杂。
参考资料
- micrometer
- custom_metrics_micrometer_prometheus_spring_boot_actuator