看了一下kafka，然后寫了消費Kafka數據的代碼。感覺自己功力還是不夠。
- 不能隨心所欲地操作數據，數據結構沒學好，spark的RDD操作沒學好。
- 不能很好地組織代碼結構，設計模式沒學好，面向對象思想理解不夠成熟。
消費程序特點
- 用隊列來存儲要消費的數據。
- 用隊列來存儲要提交的offest，然后處理線程將其給回消費者提交。
- 每個分區開一個處理線程來處理數據，分區與處理器的映射放在map中。
- 當處理到一定的數量或者距離上一次處理一定的時間間隔后, 由poll線程進行提交offset。
不好的地方：
- 每次處理的數據太少，而且每個數據都進行判斷其分區是否已經有處理線程在處理了。
- 獲取topic不太優雅。
流程圖
下面是多線程消費者實現：
1. 管理程序
/**
* 負責啟動消費者線程MsgReceiver, 保存消費者線程MsgReceiver, 保存處理任務和線程RecordProcessor, 以及銷毀這些線程
* Created by stillcoolme on 2018/10/12.
*/
public class KafkaMultiProcessorMain {
private static final Logger logger = LoggerFactory.getLogger(KafkaMultiProcessorMain.class);
// 消費者參數
private Properties consumerProps = new Properties();
// kafka消費者參數
Map<String, Object> consumerConfig;
//存放topic的配置
Map<String, Object> topicConfig;
//訂閱的topic
private String alarmTopic;
//消費者線程數組
private Thread[] threads;
//保存處理任務和線程的map
ConcurrentHashMap<TopicPartition, RecordProcessor> recordProcessorTasks = new ConcurrentHashMap<>();
ConcurrentHashMap<TopicPartition, Thread> recordProcessorThreads = new ConcurrentHashMap<>();
public void setAlarmTopic(String alarmTopic) {
this.alarmTopic = alarmTopic;
}
public static void main(String[] args) {
KafkaMultiProcessorMain kafkaMultiProcessor = new KafkaMultiProcessorMain();
//這樣設置topic不夠優雅啊！！！
kafkaMultiProcessor.setAlarmTopic("picrecord");
kafkaMultiProcessor.init(null);
}
private void init(String consumerPropPath) {
getConsumerProps(consumerPropPath);
consumerConfig = getConsumerConfig();
int threadsNum = 3;
logger.info("create " + threadsNum + " threads to consume kafka warn msg");
threads = new Thread[threadsNum];
for (int i = 0; i < threadsNum; i++) {
MsgReceiver msgReceiver = new MsgReceiver(consumerConfig, alarmTopic, recordProcessorTasks, recordProcessorThreads);
Thread thread = new Thread(msgReceiver);
threads[i] = thread;
}
for (int i = 0; i < threadsNum; i++) {
threads[i].start();
}
logger.info("finish creating" + threadsNum + " threads to consume kafka warn msg");
}
//銷毀啟動的線程
public void destroy() {
closeRecordProcessThreads();
closeKafkaConsumer();
}
private void closeRecordProcessThreads() {
logger.debug("start to interrupt record process threads");
for (Map.Entry<TopicPartition, Thread> entry : recordProcessorThreads.entrySet()) {
Thread thread = entry.getValue();
thread.interrupt();
}
logger.debug("finish interrupting record process threads");
}
private void closeKafkaConsumer() {
logger.debug("start to interrupt kafka consumer threads");
//使用interrupt中斷線程, 在線程的執行方法中已經設置了響應中斷信號
for (int i = 0; i < threads.length; i++) {
threads[i].interrupt();
}
logger.debug("finish interrupting consumer threads");
}
private Map<String,Object> getConsumerConfig() {
return ImmutableMap.<String, Object>builder()
.put("bootstrap.servers", consumerProps.getProperty("bootstrap.servers"))
.put("group.id", "group.id")
.put("enable.auto.commit", "false")
.put("session.timeout.ms", "30000")
.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
.put("max.poll.records", 1000)
.build();
}
/**
* 獲取消費者參數
*
* @param proPath
*/
private void getConsumerProps(String proPath) {
InputStream inStream = null;
try {
if (StringUtils.isNotEmpty(proPath)) {
inStream = new FileInputStream(proPath);
} else {
inStream = this.getClass().getClassLoader().getResourceAsStream("consumer.properties");
}
consumerProps.load(inStream);
} catch (IOException e) {
logger.error("讀取consumer配置文件失敗:" + e.getMessage(), e);
} finally {
if (null != inStream) {
try {
inStream.close();
} catch (IOException e) {
logger.error("讀取consumer配置文件失敗:" + e.getMessage(), e);
}
}
}
}
}
2. 消費者任務 MsgReceiver
/**
* 負責調用 RecordProcessor進行數據處理
* Created by zhangjianhua on 2018/10/12.
*/
public class MsgReceiver implements Runnable {
private static final Logger logger = LoggerFactory.getLogger(MsgReceiver.class);
private BlockingQueue<Map<TopicPartition, OffsetAndMetadata>> commitQueue = new LinkedBlockingQueue<>();
private ConcurrentHashMap<TopicPartition, Thread> recordProcessorThreads;
private ConcurrentHashMap<TopicPartition, RecordProcessor> recordProcessorTasks;
private String alarmTopic;
private Map<String, Object> consumerConfig;
public MsgReceiver(Map<String, Object> consumerConfig, String alarmTopic,
ConcurrentHashMap<TopicPartition, RecordProcessor> recordProcessorTasks,
ConcurrentHashMap<TopicPartition, Thread> recordProcessorThreads) {
this.consumerConfig = consumerConfig;
this.alarmTopic = alarmTopic;
this.recordProcessorTasks = recordProcessorTasks;
this.recordProcessorThreads = recordProcessorThreads;
}
@Override
public void run() {
//kafka Consumer是非線程安全的,所以需要每個線程建立一個consumer
KafkaConsumer kafkaConsumer = new KafkaConsumer(consumerConfig);
kafkaConsumer.subscribe(Arrays.asList(alarmTopic));
try{
while (!Thread.currentThread().isInterrupted()) {
try {
//看commitQueue里面是非有需要提交的offest， 這樣查看好頻繁啊！！！
//查看該消費者是否有需要提交的偏移信息, 使用非阻塞讀取
Map<TopicPartition, OffsetAndMetadata> offestToCommit = commitQueue.poll();
if (offestToCommit != null) {
logger.info(Thread.currentThread().getName() + "commit offset: " + offestToCommit);
kafkaConsumer.commitAsync();
}
//最多輪詢1000ms
ConsumerRecords<String, String> records = kafkaConsumer.poll(1000);
if (records.count() > 0) {
logger.info("poll records size: " + records.count());
}
for (ConsumerRecord record : records) {
String topic = record.topic();
int partition = record.partition();
TopicPartition topicPartition = new TopicPartition(topic, partition);
RecordProcessor processTask = recordProcessorTasks.get(topicPartition);
//每條消息都去檢查
//如果當前分區還沒有開始消費, 則就沒有消費任務在map中
if (processTask == null) {
//生成新的處理任務和線程, 然后將其放入對應的map中進行保存
processTask = new RecordProcessor(commitQueue);
recordProcessorTasks.put(topicPartition, processTask);
Thread processTaskThread = new Thread(processTask);
processTaskThread.setName("Thread-for " + topicPartition.toString());
logger.info("start processor Thread: " + processTaskThread.getName());
processTaskThread.start();
recordProcessorThreads.put(topicPartition, processTaskThread);
}
//有 processor 可以處理該分區的 record了
processTask.addRecordToQueue(record);
}
} catch (Exception e) {
e.printStackTrace();
logger.warn("MsgReceiver exception " + e + " ignore it");
}
}
} finally {
kafkaConsumer.close();
}
}
}
3. 消息處理任務 RecordProcessor
public class RecordProcessor implements Runnable{
private static Logger logger = LoggerFactory.getLogger(RecordProcessor.class);
//保存MsgReceiver線程發送過來的消息
private BlockingQueue<ConsumerRecord<String, String>> queue = new LinkedBlockingQueue<>();
//用於向consumer線程提交消費偏移的隊列
private BlockingQueue<Map<TopicPartition, OffsetAndMetadata>> commitQueue;
//上一次提交時間
private LocalDateTime lastTime = LocalDateTime.now();
//消費了20條數據, 就進行一次提交
private long commitLength = 20L;
//距離上一次提交多久, 就提交一次
private Duration commitTime = Duration.standardSeconds(2);
//當前該線程消費的數據條數
private int completeTask = 0;
//保存上一條消費的數據
private ConsumerRecord<String, String> lastUncommittedRecord;
public RecordProcessor(BlockingQueue<Map<TopicPartition, OffsetAndMetadata>> commitQueue) {
this.commitQueue = commitQueue;
}
@Override
public void run() {
while(!Thread.interrupted()){
ConsumerRecord<String, String> record = null;
try {
record = queue.poll(100, TimeUnit.MICROSECONDS);
if (record != null) {
process(record);
//完成任務數加1
this.completeTask++;
//保存上一條處理記錄
lastUncommittedRecord = record;
}
//提交偏移給queue中
commitTOQueue();
} catch (InterruptedException e) {
//線程被interrupt,直接退出
logger.info(Thread.currentThread() + "is interrupted");
}
}
}
//將當前的消費偏移量放到queue中, 由MsgReceiver進行提交
private void commitTOQueue() {
if(lastUncommittedRecord == null){
return;
}
//如果消費了設定的條數, 比如又消費了commitLength消息
boolean arrivedCommitLength = this.completeTask % commitLength == 0;
//獲取當前時間, 看是否已經到了需要提交的時間
LocalDateTime currentTime = LocalDateTime.now();
boolean arrivedTime = currentTime.isAfter(lastTime.plus(commitTime));
if(arrivedCommitLength || arrivedTime){
lastTime = currentTime;
long offset = lastUncommittedRecord.offset();
int partition = lastUncommittedRecord.partition();
String topic = lastUncommittedRecord.topic();
TopicPartition topicPartition = new TopicPartition(topic, partition);
logger.info("partition: " + topicPartition + " submit offset: " + (offset + 1L) + " to consumer task");
Map<TopicPartition, OffsetAndMetadata> map = Collections.singletonMap(topicPartition, new OffsetAndMetadata(offset + 1L));
commitQueue.add(map);
//置空
lastUncommittedRecord = null;
}
}
//consumer線程向處理線程的隊列中添加record
public void addRecordToQueue(ConsumerRecord<String, String> record) {
try {
queue.put(record);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
private void process(ConsumerRecord<String, String> record) {
//具體業務邏輯
//System.out.println(record);
}
}
改進
- 對處理程序RecordProcessor進行抽象，抽象出BasePropessor父類。以后業務需求需要不同的處理程序RecordProcessor就可以靈活改變了。
- 反射來構建RecordProcessor？？在配置文件配置具體要new的RecordProcessor類路徑，然后在創建MsgReceiver的時候傳遞進去。
參考
- Kafka Consumer多線程實例 : 如這篇文章所說的維護了多個worker來做具體業務處理，這篇文章用的是ThreadPoolExecutor線程池。