一、存储文件
RocketMQ使用文件系统作为消息存储
存储效率:文件系统 > K-V存储 > 关系型数据库
可靠性:关系型数据库 > K-V存储 > 文件系统
CommitLog
所有Topic的消息按照顺序写的方式存储在CommitLog中
所在目录:{ROCKET_HOME}/store/commitlog
每一个文件默认大小为1G,一个文件写满后再创建另外一个,以该文件中第一条消息的物理偏移量为文件名,偏移量小于20位用0补齐,根据物理偏移量就能快速定位到消息
上图中有两个文件,第二个文件名的1073741824表示在第二个文件存储的第一条消息的物理偏移量为1073741824,而1073741824字节 / 1024 / 1024 / 1024 = 1G,也就是上个文件(每个文件)的大小
一条消息由以下20个部分组成:
-
TotalSize:此消息的总长度(4字节)
-
MagicCode:魔数,固定值Oxdaa320a7(4字节)
-
BodyCRC:消息体CRC校验码(4字节)
-
QueueID:消息消费队列ID(4字节)
-
Flag:消息Flag,RocketMQ不做处理,供应用程序使用(4字节)
-
QueueOffset:消息在消息消费队列的下标,QueueOffset值*20后表示偏移量(8字节)
-
PhysicalOffset:消息在CommitLog文件中的物理偏移量(8字节)
-
SysFlag:消息系统Flag,例如是否压缩、是否是事务消息等(4字节)
-
BornTimestamp:消息生产者调用消息发送API的时间戳(8字节)
-
BornHost:消息发送者IP和端口号(8字节)
-
StoreTimeStamp:消息存储时的毫秒时间戳(8字节)
-
StoreHostAddress:Broker服务器IP和端口号(8字节)
-
ReConsumeTimes:消息重试次数(4字节)
-
PreparedTransactionOffset:事务消息物理偏移量(8字节)
-
BodyLength:消息体的长度(4字节)
-
Body:消息体内容(长度为BodyLenth中存储的值)
-
TopicLength:Topic名称的长度(1字节)
-
Topic:Topic名称(长度为TopicLength中存储的值)
-
PropertiesLength:消息属性长度(2字节)
-
Properties:消息属性(长度为PropertiesLength中存储的值)
ConsumeQueue
RocketMQ基于Topic的订阅模式实现消息消费,消费者关心的是一个Topic下的所有消息,但由于同一个Topic的消息不连续地存储在CommitLog文件中,如果直接从CommitLog文件遍历查找消息,效率将非常低下。而ConsumeQueue正是为了解决这个问题,可以把ConsumeQueue当作CommitLog中消息的"索引"文件
所在目录:{ROCKET_HOME}/store/consumequeue
第一级目录为Topic,第二级目录为队列。就是说每个Topic都会建立一个文件夹,Topic中的每个队列又会建立一个文件夹,默认配置下会有4个队列,对应的文件夹名称为0、1、2、3
单个文件中默认包含30万个条目,大小为6M(30w×20字节),一个文件写满后再创建另外一个,以该文件中第一个条目的物理偏移量为文件名,偏移量小于20位用0补齐。当消息到达Commitlog文件后,会由专门的线程进行转发任务,从而构建ConsumeQueue
一个条目由以下3个部分组成:
-
CommitLog Offset:对应CommitLog中的PhysicalOffset值(8字节)
-
Size:对应CommitLog中的TotalSize值(4字节)
-
Tag HashCode:消息标签的哈希值,用来进一步区分某个Topic下的消息分类(8字节)
Index
提供了一种可以通过key或时间区间来查询消息的能力,文件名以创建时的时间戳命名,固定的单个Index文件大小约为400M,一个IndexFile可以保存2000W个索引,Index底层存储设计为在文件系统中实现HashMap结构
所在目录:{ROCKET_HOME}/store/index
举个栗子吧!
(1)启动NamesrvStartup、BrokerStartup、MyProducer和MyConsumer
public class MyProducer {
public static void main(String[] args) throws MQClientException, InterruptedException {
DefaultMQProducer producer = new DefaultMQProducer("ProducerA");
producer.setNamesrvAddr("127.0.0.1:9876");
producer.start();
for (int i = 0; i < 10; i++) {
try {
Message msg = new Message(
"Topic-01",
("Store Msg " + i).getBytes(RemotingHelper.DEFAULT_CHARSET));
producer.send(msg);
} catch (Exception e) {
e.printStackTrace();
}
}
}
}
public class MyConsumer {
public static void main(String[] args) throws InterruptedException, MQClientException {
DefaultMQPushConsumer consumer = new DefaultMQPushConsumer("ConsumerA");
consumer.setNamesrvAddr("localhost:9876");
consumer.setConsumeFromWhere(ConsumeFromWhere.CONSUME_FROM_FIRST_OFFSET);
consumer.subscribe("Topic-01", "*");
consumer.registerMessageListener(
(MessageListenerConcurrently) (msgs, context) -> {
System.out.printf("%s Receive New Messages: %n", msgs);
return ConsumeConcurrentlyStatus.CONSUME_SUCCESS;
});
consumer.start();
}
}
(2)再进入{ROCKET_HOME}/store/commitlog目录
(3)使用xxd 00000000000000000000 | less命令查看第一个文件的内容
重点需要关注的部分已经用红点标记出来了,红点上的序号与上述文件格式中的序号一致
① TotalSize = 000000b9:该消息的总长度为185字节
② MagicCode = daa320a7:魔数,固定值Oxdaa320a7
④ QueueID = 00000000:消息在ConsumeQueue的0号队列
⑥ QueueOffset = 000000000000000:消息在ConsumeQueue的0号队列的偏移量为0
⑦ PhysicalOffset = 0000000000000000:消息在CommitLog文件中的偏移量为0
⑨ BornTimestamp = 0000017e093e326c:消息生产者调用消息发送API的时间戳为1640832578156
⑮ BodyLength = 0000000b:消息体长度为11字节,对应的消息内容为
53746f7265204d73672031 = Store Msg 1
⑰ TopicLength = 08:Topic名称长度为8字节,对应的名称为
546f7069632d3031 = Topic-01
CommitLog中的第一条消息到红色竖线的位置为止,紧接着便是第二条消息的内容了
(4)进入{ROCKET_HOME}/store/consumequeue/Topic-01/0目录
(5)使用xxd 00000000000000000000 | less命令查看第一个文件的内容
① CommitLog Offset = 00000000:此条目对应的消息在CommitLog中的PhysicalOffset值为0
② Size = 000000b9:此条目对应的消息在CommitLog中的TotalSize值为185字节
③ Tag HashCode = 00000000:此条目对应的消息标签的哈希值为0
另外,在{ROCKET_HOME}/store/config/consumerOffset.json文件中还记录了ConsumeQueue中每条队列的消费进度
{
"offsetTable":{
"%RETRY%ConsumerA@ConsumerA":{0:0},
"Topic-01@ConsumerA":{0:3,1:2,2:2,3:3}
}
}
小结一下
- RocketMQ采用的是混合型的存储结构,即Broker单个实例下所有的Topic共用一个数据文件(CommitLog)来存储
- 针对Producer和Consumer采用了数据和索引相分离的存储结构,Producer发送消息至Broker端,然后Broker端使用同步或者异步的方式对消息刷盘持久化,保存至CommitLog中
- 在Broker中有专门的后台线程
ReputMessageService不停地分发请求并异步构建ConsumeQueue和Index的数据 - 消费时Consumer通过ConsumeQueue中记录的CommitLog Offset和Size在CommitLog找到相应的消息来进行消费
二、存储流程
核心对象
先来认识一下存储流程中主要的几个对象
DefaultMessageStore:消息存储的顶层对象,在Broker启动时会对其进行初始化,是对{ROCKET_HOME}/store目录的封装
public class DefaultMessageStore implements MessageStore {
// 消息存储配置,包含70多个属性
private final MessageStoreConfig messageStoreConfig;
// CommitLog
private final CommitLog commitLog;
// Topic、QueueId、ConsumeQueue三者关联Map
private final ConcurrentMap<String, ConcurrentMap<Integer, ConsumeQueue>> consumeQueueTable;
}
CommitLog:对{ROCKET_HOME}/store/commitlog目录的封装
public class CommitLog {
// MappedFileQueue
protected final MappedFileQueue mappedFileQueue;
// 通过DefaultMessageStore来获取相关的配置和操作
protected final DefaultMessageStore defaultMessageStore;
// Topic-QueueId和Offset的关联Map
// 例:key = MyTopic-0, value = 1 表示在名称为MyTopic的Topic里,Id为0的队列中已经存储了一条消息
protected HashMap<String, Long> topicQueueTable = new HashMap<String, Long>(1024);
// 存储消息时用的锁,默认实现为for循环+CAS实现的自旋锁
protected final PutMessageLock putMessageLock;
// 刷盘服务,默认采用异步刷盘
private final FlushCommitLogService flushCommitLogService;
}
MappedFileQueue:对{ROCKET_HOME}/store/commitlog目录下所有文件的封装
public class MappedFileQueue {
// 维护了MappedFile集合
private final CopyOnWriteArrayList<MappedFile> mappedFiles = new CopyOnWriteArrayList<MappedFile>();
}
MappedFile:对{ROCKET_HOME}/store/commitlog目录下单个文件的封装
public class MappedFile extends ReferenceResource {
// 已写入消息的偏移量,不能大于fileSize
protected final AtomicInteger wrotePosition = new AtomicInteger(0);
// 已提交消息的偏移量
protected final AtomicInteger committedPosition = new AtomicInteger(0);
// 已刷盘消息的偏移量
private final AtomicInteger flushedPosition = new AtomicInteger(0);
// 文件大小,默认1G
protected int fileSize;
// FileChannel
protected FileChannel fileChannel;
// 文件名称
private String fileName;
// 文件名称对应的物理偏移量
private long fileFromOffset;
// 文件对象
private File file;
// 从FileChannel中获取,是实际读写的操作对象,与下述的ByteBuffer二选一
private MappedByteBuffer mappedByteBuffer;
// 从TransientStorePool中获取,与上述的MappedByteBuffer二选一
protected ByteBuffer writeBuffer = null;
核心代码
以下仅展示关键步骤的代码
public class DefaultMessageStore implements MessageStore {
// 消息存储的入口方法
public CompletableFuture<PutMessageResult> asyncPutMessage(MessageExtBrokerInner msg) {
// 由上文可知消息是存储在CommitLog中的
// 所以在这里调用MessageStore维护的CommitLog的同名方法
CompletableFuture<PutMessageResult> putResultFuture =
this.commitLog.asyncPutMessage(msg);
}
}
public class CommitLog {
public CompletableFuture<PutMessageResult> asyncPutMessage(final MessageExtBrokerInner msg) {
// 从MappedFileQueue中获取最新的MappedFile,即文件名的数字最大的文件
MappedFile mappedFile = this.mappedFileQueue.getLastMappedFile();
// 获取锁
putMessageLock.lock();
try {
// 将消息添加到内存中
result = mappedFile.appendMessage(msg, this.appendMessageCallback);
// 文件刷盘
CompletableFuture<PutMessageStatus> flushResultFuture =
submitFlushRequest(result, msg);
// 主从复制
CompletableFuture<PutMessageStatus> replicaResultFuture =
submitReplicaRequest(result, msg);
// 合并上述两步的结果并返回
return flushResultFuture.thenCombine(...)
} finally {
// 释放锁
putMessageLock.unlock();
}
}
}
第一步:将消息添加到内存中
最终会调用CommitLog.DefaultAppendMessageCallback#doAppend()方法
四个方法参数简介:
-
fileFromOffset:当前最新MappedFile的物理偏移量,即此MappedFile的文件名
-
byteBuffer:当前最新MappedFile对应的ByteBuffer对象
-
maxBlank:根据当前最新MappedFile的文件大小
fileSize- 已写完的消息位置wrotePosition计算得到,表示此文件的剩余大小 -
msgInner:在内部重新封装后的消息对象
public AppendMessageResult doAppend(final long fileFromOffset, final ByteBuffer byteBuffer, final int maxBlank, final MessageExtBrokerInner msgInner) {
// CommitLog物理偏移量 = 当前最新MappedFile的物理偏移量 + 当前最新MappedFile对应的ByteBuffer内部的位置
long wroteOffset = fileFromOffset + byteBuffer.position();
int sysflag = msgInner.getSysFlag();
// 生成唯一的MsgId:UtilAll.bytes2string(Broker的IP地址 + CommitLog的物理偏移量)
int bornHostLength = (sysflag & MessageSysFlag.BORNHOST_V6_FLAG) == 0 ? 4 + 4 : 16 + 4;
int storeHostLength = (sysflag & MessageSysFlag.STOREHOSTADDRESS_V6_FLAG) == 0 ? 4 + 4 : 16 + 4;
ByteBuffer bornHostHolder = ByteBuffer.allocate(bornHostLength);
ByteBuffer storeHostHolder = ByteBuffer.allocate(storeHostLength);
this.resetByteBuffer(storeHostHolder, storeHostLength);
String msgId;
if ((sysflag & MessageSysFlag.STOREHOSTADDRESS_V6_FLAG) == 0) {
msgId = MessageDecoder.createMessageId(this.msgIdMemory,
msgInner.getStoreHostBytes(storeHostHolder), wroteOffset);
} else {
msgId = MessageDecoder.createMessageId(this.msgIdV6Memory,
msgInner.getStoreHostBytes(storeHostHolder), wroteOffset);
}
// 获取TopicQueueTable中对应队列的偏移量queueOffset
keyBuilder.setLength(0);
keyBuilder.append(msgInner.getTopic());
keyBuilder.append('-');
keyBuilder.append(msgInner.getQueueId());
String key = keyBuilder.toString();
Long queueOffset = CommitLog.this.topicQueueTable.get(key);
if (null == queueOffset) {
queueOffset = 0L;
CommitLog.this.topicQueueTable.put(key, queueOffset);
}
// 事务消息要对queueOffset进行特殊处理
final int tranType = MessageSysFlag.getTransactionValue(msgInner.getSysFlag());
switch (tranType) {
case MessageSysFlag.TRANSACTION_PREPARED_TYPE:
case MessageSysFlag.TRANSACTION_ROLLBACK_TYPE:
queueOffset = 0L;
break;
case MessageSysFlag.TRANSACTION_NOT_TYPE:
case MessageSysFlag.TRANSACTION_COMMIT_TYPE:
default:
break;
}
// 获取消息属性Properties
final byte[] propertiesData =
msgInner.getPropertiesString() == null ? null : msgInner.getPropertiesString().getBytes(MessageDecoder.CHARSET_UTF8);
final int propertiesLength = propertiesData == null ? 0 : propertiesData.length;
if (propertiesLength > Short.MAX_VALUE) {
log.warn("putMessage message properties length too long. length={}", propertiesData.length);
return new AppendMessageResult(AppendMessageStatus.PROPERTIES_SIZE_EXCEEDED);
}
// 获取Topic
final byte[] topicData = msgInner.getTopic().getBytes(MessageDecoder.CHARSET_UTF8);
final int topicLength = topicData.length;
final int bodyLength = msgInner.getBody() == null ? 0 : msgInner.getBody().length;
// 获取消息长度
final int msgLen = calMsgLength(msgInner.getSysFlag(), bodyLength, topicLength, propertiesLength);
if (msgLen > this.maxMessageSize) {
CommitLog.log.warn("message size exceeded, msg total size: " + msgLen + ", msg body size: " + bodyLength + ", maxMessageSize: " + this.maxMessageSize);
return new AppendMessageResult(AppendMessageStatus.MESSAGE_SIZE_EXCEEDED);
}
// 判断是否有足够的空间
if ((msgLen + END_FILE_MIN_BLANK_LENGTH) > maxBlank) {
this.resetByteBuffer(this.msgStoreItemMemory, maxBlank);
this.msgStoreItemMemory.putInt(maxBlank);
this.msgStoreItemMemory.putInt(CommitLog.BLANK_MAGIC_CODE);
final long beginTimeMills = CommitLog.this.defaultMessageStore.now();
byteBuffer.put(this.msgStoreItemMemory.array(), 0, maxBlank);
return new AppendMessageResult(AppendMessageStatus.END_OF_FILE, wroteOffset, maxBlank, msgId, msgInner.getStoreTimestamp(), queueOffset, CommitLog.this.defaultMessageStore.now() - beginTimeMills);
}
// 初始化msgStoreItemMemory
this.resetByteBuffer(msgStoreItemMemory, msgLen);
// 1 TOTALSIZE
this.msgStoreItemMemory.putInt(msgLen);
// 2 MAGICCODE
this.msgStoreItemMemory.putInt(CommitLog.MESSAGE_MAGIC_CODE);
// 3 BODYCRC
this.msgStoreItemMemory.putInt(msgInner.getBodyCRC());
// 4 QUEUEID
this.msgStoreItemMemory.putInt(msgInner.getQueueId());
// 5 FLAG
this.msgStoreItemMemory.putInt(msgInner.getFlag());
// 6 QUEUEOFFSET
this.msgStoreItemMemory.putLong(queueOffset);
// 7 PHYSICALOFFSET
this.msgStoreItemMemory.putLong(fileFromOffset + byteBuffer.position());
// 8 SYSFLAG
this.msgStoreItemMemory.putInt(msgInner.getSysFlag());
// 9 BORNTIMESTAMP
this.msgStoreItemMemory.putLong(msgInner.getBornTimestamp());
// 10 BORNHOST
this.resetByteBuffer(bornHostHolder, bornHostLength);
this.msgStoreItemMemory.put(msgInner.getBornHostBytes(bornHostHolder));
// 11 STORETIMESTAMP
this.msgStoreItemMemory.putLong(msgInner.getStoreTimestamp());
// 12 STOREHOSTADDRESS
this.resetByteBuffer(storeHostHolder, storeHostLength);
this.msgStoreItemMemory.put(msgInner.getStoreHostBytes(storeHostHolder));
// 13 RECONSUMETIMES
this.msgStoreItemMemory.putInt(msgInner.getReconsumeTimes());
// 14 Prepared Transaction Offset
this.msgStoreItemMemory.putLong(msgInner.getPreparedTransactionOffset());
// 15 BODY
this.msgStoreItemMemory.putInt(bodyLength);
if (bodyLength > 0)
this.msgStoreItemMemory.put(msgInner.getBody());
// 16 TOPIC
this.msgStoreItemMemory.put((byte) topicLength);
this.msgStoreItemMemory.put(topicData);
// 17 PROPERTIES
this.msgStoreItemMemory.putShort((short) propertiesLength);
if (propertiesLength > 0)
this.msgStoreItemMemory.put(propertiesData);
final long beginTimeMills = CommitLog.this.defaultMessageStore.now();
// 最终将消息写入byteBuffer!
byteBuffer.put(this.msgStoreItemMemory.array(), 0, msgLen);
AppendMessageResult result = new AppendMessageResult(AppendMessageStatus.PUT_OK,
wroteOffset, msgLen, msgId, msgInner.getStoreTimestamp(), queueOffset,
CommitLog.this.defaultMessageStore.now() - beginTimeMills);
switch (tranType) {
case MessageSysFlag.TRANSACTION_PREPARED_TYPE:
case MessageSysFlag.TRANSACTION_ROLLBACK_TYPE:
break;
case MessageSysFlag.TRANSACTION_NOT_TYPE:
case MessageSysFlag.TRANSACTION_COMMIT_TYPE:
// The next update ConsumeQueue information
CommitLog.this.topicQueueTable.put(key, ++queueOffset);
break;
default:
break;
}
return result;
}
小结:计算CommitLog的物理偏移量,通过约定的格式将消息写入到MappedFile的ByteBuffer对象中
第二步:文件刷盘
CommitLog在初始化时会根据配置选择不同的刷盘方式
if (FlushDiskType.SYNC_FLUSH == defaultMessageStore.getMessageStoreConfig().getFlushDiskType()) {
// 同步刷盘
this.flushCommitLogService = new GroupCommitService();
} else {
// 异步刷盘
this.flushCommitLogService = new FlushRealTimeService();
}
在Broker启动时就会开启刷盘线程
public void start() {
this.flushCommitLogService.start(); // 开启刷盘线程
if (defaultMessageStore.getMessageStoreConfig().isTransientStorePoolEnable()) {
this.commitLogService.start();
}
}
在执行到CommitLog#submitFlushRequest()方法时,也会调用wakeup()方法来主动唤醒刷盘线程
两种刷盘线程里run()方法的内容都是死循环+固定时间休眠+刷盘
if (FlushDiskType.SYNC_FLUSH == this.defaultMessageStore.getMessageStoreConfig()
.getFlushDiskType()) {
final GroupCommitService service = (GroupCommitService) this.flushCommitLogService;
// waitStoreMsgOK为true表示需要等待消息同步刷新到磁盘后再返回结果
if (messageExt.isWaitStoreMsgOK()) {
GroupCommitRequest request = new GroupCommitRequest(
result.getWroteOffset() + result.getWroteBytes(),
this.defaultMessageStore.getMessageStoreConfig().getSyncFlushTimeout());
// 将刷盘请求放到GroupCommitService里的写队列中
service.putRequest(request);
return request.future();
} else {
service.wakeup();
return CompletableFuture.completedFuture(PutMessageStatus.PUT_OK);
}
}
同步刷盘
同步刷盘的实现对象为GroupCommitService
实现原理比较巧妙,在内部维护了两个GroupCommitRequest队列,代表写队列和读队列,先将请求都放到写队列中,然后交换两个队列的对象,再从读队列中获取刚才的请求。这样做的好处是在对其进行加锁处理时,两个队列不会相互影响,达到同时读写的效果
GroupCommitRequest对象里面包含了nextOffset属性,在run()方法的死循环中默认每隔10ms执行一次doCommit()方法和swapRequests()方法
class GroupCommitService extends FlushCommitLogService {
// 写队列
private volatile List<GroupCommitRequest> requestsWrite = new ArrayList<>();
// 读队列
private volatile List<GroupCommitRequest> requestsRead = new ArrayList<>();
public synchronized void putRequest(final GroupCommitRequest request) {
synchronized (this.requestsWrite) { // 加锁后不会对下面的swapRequests()方法产生阻塞
this.requestsWrite.add(request);
}
this.wakeup();
}
// 通过交换两条队列, 来达到同时读写的效果!
private void swapRequests() {
List<GroupCommitRequest> tmp = this.requestsWrite;
this.requestsWrite = this.requestsRead;
this.requestsRead = tmp;
}
private void doCommit() {
synchronized (this.requestsRead) {
if (!this.requestsRead.isEmpty()) {
// 当写队列不为空时,遍历写入消息请求
for (GroupCommitRequest req : this.requestsRead) {
boolean flushOK = CommitLog.this.mappedFileQueue.getFlushedWhere() >= req.getNextOffset();
for (int i = 0; i < 2 && !flushOK; i++) {
CommitLog.this.mappedFileQueue.flush(0); // 进行刷盘
flushOK = CommitLog.this.mappedFileQueue.getFlushedWhere() >= req.getNextOffset();
}
req.wakeupCustomer(flushOK ? PutMessageStatus.PUT_OK : PutMessageStatus.FLUSH_DISK_TIMEOUT);
}
long storeTimestamp = CommitLog.this.mappedFileQueue.getStoreTimestamp();
if (storeTimestamp > 0) {
CommitLog.this.defaultMessageStore.getStoreCheckpoint().setPhysicMsgTimestamp(storeTimestamp);
}
this.requestsRead.clear();
} else {
CommitLog.this.mappedFileQueue.flush(0);
}
}
}
public void run() {
while (!this.isStopped()) {
try {
this.waitForRunning(10); // 内部会调用swapRequests()
this.doCommit();
} catch (Exception e) {
CommitLog.log.warn(this.getServiceName() + " service has exception. ", e);
}
}
}
}
异步刷盘
异步刷盘的实现对象为FlushRealTimeService
定期进行刷盘即可
class FlushRealTimeService extends FlushCommitLogService {
public void run() {
while (!this.isStopped()) {
// 刷盘间隔时间,默认500毫秒
int interval = CommitLog.this.defaultMessageStore.getMessageStoreConfig().getFlushIntervalCommitLog();
// 每次至少要刷盘的页大小,每页为4k,默认4页
int flushPhysicQueueLeastPages = CommitLog.this.defaultMessageStore.getMessageStoreConfig().getFlushCommitLogLeastPages();
try {
if (flushCommitLogTimed) {
Thread.sleep(interval);
} else {
this.waitForRunning(interval);
}
CommitLog.this.mappedFileQueue
.flush(flushPhysicQueueLeastPages); // 进行刷盘
} catch (Throwable e) {
CommitLog.log.warn(this.getServiceName() + " service has exception. ", e);
}
}
}
不管是同步刷盘还是异步刷盘,最终都会执行MappedFileQueue#flush()方法
public boolean flush(final int flushLeastPages) {
boolean result = true;
// 找到需要进行刷盘的MappedFile
MappedFile mappedFile = this.findMappedFileByOffset(this.flushedWhere,
this.flushedWhere == 0);
if (mappedFile != null) {
long tmpTimeStamp = mappedFile.getStoreTimestamp();
// 刷盘!
// 内部会调用FileChannel#force()或MappedByteBuffer#force()
int offset = mappedFile.flush(flushLeastPages);
// 更新CommitLog的物理偏移量
long where = mappedFile.getFileFromOffset() + offset;
result = where == this.flushedWhere;
this.flushedWhere = where;
if (0 == flushLeastPages) {
this.storeTimestamp = tmpTimeStamp;
}
}
return result;
}
三、恢复
由于RocketMQ存储首先将消息全量存储在CommitLog文件中,然后异步生成转发任务更新 ConsumeQueue、Index文件。如果消息成功存储到CommitLog文件中,转发任务未成功执行,此时消息服务器Broker由于某个原因宕机,导致CommitLog、ConsumeQueue、IndexFile文件数据不一致。如果不加以人工修复的话,会有一部分消息即便在CommitLog文件中存在,但由于并没有转发到ConsumeQueue,这部分消息将永远不会被消费者消费
正常退出:从倒数第三个CommitLog文件开始,遍历判断每条消息的正确性,以最后一条消息的全局物理偏移量作为标准,更新ConsumeQueue和Index文件
异常退出:从最后一个CommitLog文件开始,遍历判断每条消息的正确性,以最后一条消息的全局物理偏移量作为标准,更新ConsumeQueue和Index文件
四、过期删除
为了避免内存与磁盘的浪费,不可能将消息永久存储在消息服务器上。如果非当前写文件在一定时间间隔内没有再次被更新,则认为是过期文件,可以被删除,RocketMQ不会关注这个文件上的消息是否全部被消费。默认每个文件的过期时间为72小时