步骤如下： 1、首先新建2个model（LogInfo，MonitorInfo）

public class LogInfo implements Serializable {private static final long serialVersionUID = 5289821541255302264L;private String sql;private String sqlParam;private int maxParallel;private long executeMillisTotal;private long executeCount;private long executeErrorCount;private Date maxOccurTime;private long histogram_0_1;private long histogram_1_10;private int histogram_100_1000;private int histogram_1000_10000;private int histogram_10000_100000;private int histogram_100000_1000000;private int histogram_1000000_more;private String lastError;private String lastErrorMessage;private String lastErrorStackTrace;private String lastErrorClass;private Date lastErrorTime;private int histogram_10_100;private long executeMillisMax;public String getSql() {return sql;}public void setSql(String sql) {this.sql = sql;}public String getSqlParam() {return sqlParam;}public void setSqlParam(String sqlParam) {this.sqlParam = sqlParam;}public int getMaxParallel() {return maxParallel;}public void setMaxParallel(int maxParallel) {this.maxParallel = maxParallel;}public long getExecuteMillisTotal() {return executeMillisTotal;}public void setExecuteMillisTotal(long executeMillisTotal) {this.executeMillisTotal = executeMillisTotal;}public long getExecuteCount() {return executeCount;}public void setExecuteCount(long executeCount) {this.executeCount = executeCount;}public long getExecuteErrorCount() {return executeErrorCount;}public void setExecuteErrorCount(long executeErrorCount) {this.executeErrorCount = executeErrorCount;}public Date getMaxOccurTime() {return maxOccurTime;}public void setMaxOccurTime(Date maxOccurTime) {this.maxOccurTime = maxOccurTime;}public long getHistogram_0_1() {return histogram_0_1;}public void setHistogram_0_1(long histogram_0_1) {this.histogram_0_1 = histogram_0_1;}public long getHistogram_1_10() {return histogram_1_10;}public void setHistogram_1_10(long histogram_1_10) {this.histogram_1_10 = histogram_1_10;}public int getHistogram_100_1000() {return histogram_100_1000;}public void setHistogram_100_1000(int histogram_100_1000) {this.histogram_100_1000 = histogram_100_1000;}public int getHistogram_1000_10000() {return histogram_1000_10000;}public void setHistogram_1000_10000(int histogram_1000_10000) {this.histogram_1000_10000 = histogram_1000_10000;}public int getHistogram_10000_100000() {return histogram_10000_100000;}public void setHistogram_10000_100000(int histogram_10000_100000) {this.histogram_10000_100000 = histogram_10000_100000;}public int getHistogram_100000_1000000() {return histogram_100000_1000000;}public void setHistogram_100000_1000000(int histogram_100000_1000000) {this.histogram_100000_1000000 = histogram_100000_1000000;}public int getHistogram_1000000_more() {return histogram_1000000_more;}public void setHistogram_1000000_more(int histogram_1000000_more) {this.histogram_1000000_more = histogram_1000000_more;}public String getLastError() {return lastError;}public void setLastError(String lastError) {this.lastError = lastError;}public String getLastErrorMessage() {return lastErrorMessage;}public void setLastErrorMessage(String lastErrorMessage) {this.lastErrorMessage = lastErrorMessage;}public String getLastErrorStackTrace() {return lastErrorStackTrace;}public void setLastErrorStackTrace(String lastErrorStackTrace) {this.lastErrorStackTrace = lastErrorStackTrace;}public String getLastErrorClass() {return lastErrorClass;}public void setLastErrorClass(String lastErrorClass) {this.lastErrorClass = lastErrorClass;}public Date getLastErrorTime() {return lastErrorTime;}public void setLastErrorTime(Date lastErrorTime) {this.lastErrorTime = lastErrorTime;}public int getHistogram_10_100() {return histogram_10_100;}public void setHistogram_10_100(int histogram_10_100) {this.histogram_10_100 = histogram_10_100;}public long getExecuteMillisMax() {return executeMillisMax;}public void setExecuteMillisMax(long executeMillisMax) {this.executeMillisMax = executeMillisMax;}
}public class MonitorInfo implements Serializable {private static final long serialVersionUID = -2127636421971490585L;private List<LogInfo> LogInfos;private String sysIdentify;public List<LogInfo> getLogInfos() {return LogInfos;}public void setLogInfos(List<LogInfo> logInfos) {LogInfos = logInfos;}public String getSysIdentify() {return sysIdentify;}public void setSysIdentify(String sysIdentify) {this.sysIdentify = sysIdentify;}}

2、model建好之后，自定义一个StatLogger，创建一个抽象类 MonitorDataSourceSql来实现获取监控数据，代码如下：

public abstract class MonitorDataSourceSql extends DruidDataSourceStatLoggerAdapterimplements DruidDataSourceStatLogger {private final static Log logger = LogFactory.getLog(DruidPooledConnection.class);public MonitorDataSourceSql() {this.configFromProperties(System.getProperties());}/* * DruidDataSourceStatValue包含这整个SQL监控数据，按需索取* **/@Overridepublic void log(DruidDataSourceStatValue statValue) {List<JdbcSqlStatValue> sqlList = statValue.getSqlList();if (sqlList != null && sqlList.size() > 0) {List<LogInfo> logInfos = new ArrayList<LogInfo>(sqlList.size());MonitorInfo monitorInfo = new MonitorInfo();JdbcSqlStatValue value = null;Throwable executeThrowable = null;LogInfo info = null;try {for (int i = 0; i < sqlList.size(); i++) {value = sqlList.get(i);info = new LogInfo();info.setHistogram_0_1(value.getHistogram_0_1());info.setHistogram_1_10(value.getHistogram_1_10());info.setHistogram_10_100(value.getHistogram_10_100());info.setHistogram_100_1000(value.getHistogram_100_1000());info.setHistogram_1000_10000(value.getHistogram_1000_10000());info.setHistogram_1000000_more(value.getHistogram_1000000_more());info.setHistogram_100000_1000000(value.getHistogram_100000_1000000());info.setHistogram_10000_100000(value.getHistogram_10000_100000());info.setMaxParallel(value.getConcurrentMax());info.setMaxOccurTime(value.getExecuteNanoSpanMaxOccurTime());info.setSql(value.getSql());info.setSqlParam(value.getLastSlowParameters());info.setExecuteMillisTotal(value.getExecuteMillisTotal());info.setExecuteErrorCount(value.getExecuteErrorCount());info.setExecuteMillisMax(value.getExecuteMillisMax());info.setExecuteCount(value.getExecuteCount());executeThrowable = value.getExecuteErrorLast();if (executeThrowable != null) {info.setLastErrorClass(executeThrowable.getClass().getName());info.setLastErrorMessage(executeThrowable.getMessage());info.setLastErrorStackTrace(Utils.toString(executeThrowable.getStackTrace()));info.setLastErrorTime(value.getExecuteErrorLastTime());}logInfos.add(info);}} catch (Exception e) {e.printStackTrace();logger.error("监控数据转换异常", e);}monitorInfo.setLogInfos(logInfos);monitorInfo.setSysIdentify(getIdentify());sendMonitorLog(monitorInfo);}}/*** 监控数据* **/public abstract void sendMonitorLog(MonitorInfo monitorInfo);public abstract String getIdentify();@Overridepublic void configFromProperties(Properties properties) {super.configFromProperties(properties);}@Overridepublic void setLogger(Log logger) {}@Overridepublic void setLoggerName(String loggerName) {super.setLoggerName(loggerName);}}

3、创建MonitorDataSourceSqlImpl 继承刚才创建的抽象类并引入redis，将监控数据放到redis中，这里的JSON使用的也是阿里巴巴的fastjson

public class MonitorDataSourceSqlImpl extends MonitorDataSourceSql {@Autowiredprivate IRedisService cacheService;@Overridepublic void sendMonitorLog(MonitorInfo monitorInfo) {try {String str = JSONObject.toJSONString(monitorInfo);this.cacheService.set(RedisKey.MONITORLOG, str);} catch (Exception e) {e.printStackTrace();}}@Overridepublic String getIdentify() {return null;}}

4、数据源配置，代码如下：

<bean id="webDataSource" class="com.alibaba.druid.pool.DruidDataSource"init-method="init" destroy-method="close"><property name="driverClassName" value="com.mysql.jdbc.Driver" /><property name="url" value="${web.jdbcUrl}" /><property name="username" value="${web.user}" /><property name="password" value="${web.password}" /><!-- 配置初始化大小、最小、最大 --><property name="initialSize" value="5" /><property name="minIdle" value="2" /><property name="maxActive" value="200" /><!-- 设置数据库编码 为utf8mb4 --><property name="connectionInitSqls" value="set names utf8mb4;" /><!-- 配置获取连接等待超时的时间 --><property name="maxWait" value="60000" /><!-- 配置间隔多久才进行一次检测，检测需要关闭的空闲连接，单位是毫秒 --><property name="timeBetweenEvictionRunsMillis" value="60000" /><!-- 配置一个连接在池中最小生存的时间，单位是毫秒 --><property name="minEvictableIdleTimeMillis" value="300000" /><property name="validationQuery" value="SELECT 'x'" /><property name="testWhileIdle" value="true" /><property name="testOnBorrow" value="false" /><property name="testOnReturn" value="false" /><!-- 记录sql --><property name="filters" value="config,stat,log4j,mergeStat" /><!-- 慢sql 时间定义 --><property name="connectionProperties"             value="druid.stat.slowSqlMillis=5000;config.decrypt=true;config.decrypt.key=${tgbweb.publicKey}" /> <!-- 每3秒保存日志 --><property name="timeBetweenLogStatsMillis" value="3000" /> <!-- 合并多个DruidDataSource的监控数据 --><!-- <property name="useGlobalDataSourceStat" value="true" /> --> <!-- 定制化日志输出 --><property name="statLogger" ref="myStatLogger" /></bean><bean id="myStatLogger" class="com.taoguba.monitor.sql.impl.MonitorDataSourceSqlImpl"></bean>

5、通过Controller获取数据并返回JSON给前端

@RestController
@RequestMapping(value = "/new/monitor")
@Scope(value = ConfigurableBeanFactory.SCOPE_PROTOTYPE)
public class MonitorController extends BaseController{@Autowiredprotected IRedisService cacheService;@GetMapping(value = "/getDataSourceSql")public Result getDataSourceSql() {String data = this.cacheService.get(RedisKey.MONITORLOG);MonitorInfo mon= JSONObject.parseObject(data,MonitorInfo.class);return new Result(mon);}
}

6、JSON数据，前端取到这些数据展示即可（此处暂不展示效果了。。）

{
status: true,
errorCode: 0,
errorMessage: "",
dto: {
sysIdentify: null,
logInfos: [
{
sql: "select count(*) from topi where beFg='Y' and usefulnum > 0 and catD between 2 and 3 and uid=?",
sqlParam: null,
maxParallel: 2,
executeMillisTotal: 3,
executeCount: 2,
executeErrorCount: 0,
maxOccurTime: 1505988865405,
histogram_0_1: 0,
histogram_1_10: 2,
histogram_100_1000: 0,
histogram_1000_10000: 0,
histogram_10000_100000: 0,
histogram_100000_1000000: 0,
histogram_1000000_more: 0,
lastError: null,
lastErrorMessage: null,
lastErrorStackTrace: null,
lastErrorClass: null,
lastErrorTime: null,
histogram_10_100: 0,
executeMillisMax: 1
}]
},
_t: 1505988871072

7、至此微服务项目集成Druid 监控数据 完成收集展示，如有问题 ，欢迎留言指正。