RocketMQ-broker状态管理及数据统计

RocketMQ-broker状态管理及数据统计

在RocketMQ中，状态管理有BrokerStatsManager，ConsumerStatsManager，FilterServerStatsManager，其实现的方式都是一样的。

这边就拿BrokerStatsManager做介绍

一个StatsItemSet，代表着一项数据统计指标，这个指标定义了各个key的统计单项StatsItem，具体的值和次数使用原子类表示

BrokerStatsManager支持这些统一指标，同时每一个指标对应一个StateItemSet，里面包含了各个topic和group的信息

    public static final String TOPIC_PUT_NUMS = "TOPIC_PUT_NUMS";
    public static final String TOPIC_PUT_SIZE = "TOPIC_PUT_SIZE";
    public static final String GROUP_GET_NUMS = "GROUP_GET_NUMS";
    public static final String GROUP_GET_SIZE = "GROUP_GET_SIZE";
    public static final String SNDBCK_PUT_NUMS = "SNDBCK_PUT_NUMS";
    public static final String BROKER_PUT_NUMS = "BROKER_PUT_NUMS";
    public static final String BROKER_GET_NUMS = "BROKER_GET_NUMS";
    public static final String GROUP_GET_FROM_DISK_NUMS = "GROUP_GET_FROM_DISK_NUMS";
    public static final String GROUP_GET_FROM_DISK_SIZE = "GROUP_GET_FROM_DISK_SIZE";
    public static final String BROKER_GET_FROM_DISK_NUMS = "BROKER_GET_FROM_DISK_NUMS";
    public static final String BROKER_GET_FROM_DISK_SIZE = "BROKER_GET_FROM_DISK_SIZE";
    // For commercial
    public static final String COMMERCIAL_SEND_TIMES = "COMMERCIAL_SEND_TIMES";
    public static final String COMMERCIAL_SNDBCK_TIMES = "COMMERCIAL_SNDBCK_TIMES";
    public static final String COMMERCIAL_RCV_TIMES = "COMMERCIAL_RCV_TIMES";
    public static final String COMMERCIAL_RCV_EPOLLS = "COMMERCIAL_RCV_EPOLLS";
    public static final String COMMERCIAL_SEND_SIZE = "COMMERCIAL_SEND_SIZE";
    public static final String COMMERCIAL_RCV_SIZE = "COMMERCIAL_RCV_SIZE";
    public static final String COMMERCIAL_PERM_FAILURES = "COMMERCIAL_PERM_FAILURES";



this.statsTable.put(TOPIC_PUT_NUMS, new StatsItemSet(TOPIC_PUT_NUMS, this.scheduledExecutorService, log));
this.statsTable.put(TOPIC_PUT_SIZE, new StatsItemSet(TOPIC_PUT_SIZE, this.scheduledExecutorService, log));
this.statsTable.put(GROUP_GET_NUMS, new StatsItemSet(GROUP_GET_NUMS, this.scheduledExecutorService, log));
this.statsTable.put(GROUP_GET_SIZE, new StatsItemSet(GROUP_GET_SIZE, this.scheduledExecutorService, log));
this.statsTable.put(GROUP_GET_LATENCY, new StatsItemSet(GROUP_GET_LATENCY, this.scheduledExecutorService, log));
this.statsTable.put(SNDBCK_PUT_NUMS, new StatsItemSet(SNDBCK_PUT_NUMS, this.scheduledExecutorService, log));
this.statsTable.put(BROKER_PUT_NUMS, new StatsItemSet(BROKER_PUT_NUMS, this.scheduledExecutorService, log));
this.statsTable.put(BROKER_GET_NUMS, new StatsItemSet(BROKER_GET_NUMS, this.scheduledExecutorService, log));
this.statsTable.put(GROUP_GET_FROM_DISK_NUMS, new StatsItemSet(GROUP_GET_FROM_DISK_NUMS, this.scheduledExecutorService, log));
this.statsTable.put(GROUP_GET_FROM_DISK_SIZE, new StatsItemSet(GROUP_GET_FROM_DISK_SIZE, this.scheduledExecutorService, log));
this.statsTable.put(BROKER_GET_FROM_DISK_NUMS, new StatsItemSet(BROKER_GET_FROM_DISK_NUMS, this.scheduledExecutorService, log));
this.statsTable.put(BROKER_GET_FROM_DISK_SIZE, new StatsItemSet(BROKER_GET_FROM_DISK_SIZE, this.scheduledExecutorService, log));

this.statsTable.put(COMMERCIAL_SEND_TIMES, new StatsItemSet(COMMERCIAL_SEND_TIMES, this.commercialExecutor, COMMERCIAL_LOG));
this.statsTable.put(COMMERCIAL_RCV_TIMES, new StatsItemSet(COMMERCIAL_RCV_TIMES, this.commercialExecutor, COMMERCIAL_LOG));
this.statsTable.put(COMMERCIAL_SEND_SIZE, new StatsItemSet(COMMERCIAL_SEND_SIZE, this.commercialExecutor, COMMERCIAL_LOG));
this.statsTable.put(COMMERCIAL_RCV_SIZE, new StatsItemSet(COMMERCIAL_RCV_SIZE, this.commercialExecutor, COMMERCIAL_LOG));
this.statsTable.put(COMMERCIAL_RCV_EPOLLS, new StatsItemSet(COMMERCIAL_RCV_EPOLLS, this.commercialExecutor, COMMERCIAL_LOG));
this.statsTable.put(COMMERCIAL_SNDBCK_TIMES, new StatsItemSet(COMMERCIAL_SNDBCK_TIMES, this.commercialExecutor, COMMERCIAL_LOG));
this.statsTable.put(COMMERCIAL_PERM_FAILURES, new StatsItemSet(COMMERCIAL_PERM_FAILURES, this.commercialExecutor, COMMERCIAL_LOG));

最后这些指标都会保存在    private final HashMap<String, StatsItemSet> statsTable = new HashMap<String, StatsItemSet>();

一个指标StatsItemSet里面有如下属性

private final ConcurrentMap<String/* key */, StatsItem> statsItemTable =
        new ConcurrentHashMap<String, StatsItem>(128);

    private final String statsName;
    private final ScheduledExecutorService scheduledExecutorService;
    private final Logger log;

比如一个TOPIC_PUT_NUMS的指标，在其statsItemTable 包含了各个topic的message数量和存放次数。其中key就是topic。

然后会初始化一些定时任务，比如会在每隔10s把当前的状态封装之后放在csListMinute，最多放6个统计记录，然后在一分钟内打印结果，格式如下

"TOPIC_PUT_NUMS [topicname] Stats In One Minute, SUM: %d TPS: %.2f AVGPT: %.2

再次之前会进行简单计算代码如下

  private static StatsSnapshot computeStatsData(final LinkedList<CallSnapshot> csList) {
        StatsSnapshot statsSnapshot = new StatsSnapshot();
        synchronized (csList) {
            double tps = 0;
            double avgpt = 0;
            long sum = 0;
            if (!csList.isEmpty()) {
                CallSnapshot first = csList.getFirst();
                CallSnapshot last = csList.getLast();
                // 比如统计前后的值差距
                sum = last.getValue() - first.getValue();
                // last.getTimestamp() - first.getTimestamp()代表的是统计的时候时间差
                tps = (sum * 1000.0d) / (last.getTimestamp() - first.getTimestamp());

                // 次数差距
                long timesDiff = last.getTimes() - first.getTimes();
                if (timesDiff > 0) {
                    // 平均每次的达到的数量级   ，在TOPIC_PUT_NUMS中表示 每次在在该topic中平均写入avgpt个message
                    avgpt = (sum * 1.0d) / timesDiff;
                }
            }

            statsSnapshot.setSum(sum);
            statsSnapshot.setTps(tps);
            statsSnapshot.setAvgpt(avgpt);
        }

        return statsSnapshot;
    }

每隔一小时，一天的统计结果处理逻辑雷同。