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nginx+lua访问流量实时上报kafka

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

在nginx这一层，接收到访问请求的时候，就把请求的流量上报发送给kafka

storm才能去消费kafka中的实时的访问日志，然后去进行缓存热数据的统计

从lua脚本直接创建一个kafka producer，发送数据到kafka

wget https://github.com/doujiang24/lua-resty-kafka/archive/master.zip

yum install -y unzip

unzip lua-resty-kafka-master.zip

cp -rf /usr/local/lua-resty-kafka-master/lib/resty /usr/hello/lualib
nginx -s reload

　　lua脚本：

local cjson = require("cjson")  
local producer = require("resty.kafka.producer")  

local broker_list = {  
    { host = "192.168.31.187", port = 9092 },  
    { host = "192.168.31.19", port = 9092 },  
    { host = "192.168.31.227", port = 9092 }
}

local log_json = {}  
log_json["headers"] = ngx.req.get_headers()  
log_json["uri_args"] = ngx.req.get_uri_args()  
log_json["body"] = ngx.req.read_body()  
log_json["http_version"] = ngx.req.http_version()  
log_json["method"] =ngx.req.get_method() 
log_json["raw_reader"] = ngx.req.raw_header()  
log_json["body_data"] = ngx.req.get_body_data()  

local message = cjson.encode(log_json);  

local productId = ngx.req.get_uri_args()["productId"]

local async_producer = producer:new(broker_list, { producer_type = "async" })   
local ok, err = async_producer:send("access-log", productId, message)  

if not ok then  
    ngx.log(ngx.ERR, "kafka send err:", err)  
    return  
end

　　

两台机器上都这样做，才能统一上报流量到kafka

bin/kafka-topics.sh --zookeeper 192.168.31.187:2181,192.168.31.19:2181,192.168.31.227:2181 --topic access-log --replication-factor 1 --partitions 1 --create

bin/kafka-console-consumer.sh --zookeeper 192.168.31.187:2181,192.168.31.19:2181,192.168.31.227:2181 --topic access-log --from-beginning

（1）kafka在187上的节点死掉了，可能是虚拟机的问题，杀掉进程，重新启动一下

nohup bin/kafka-server-start.sh config/server.properties &

（2）需要在nginx.conf中，http部分，加入resolver 8.8.8.8;

（3）需要在kafka中加入advertised.host.name = 192.168.31.187，重启三个kafka进程

（4）需要启动eshop-cache缓存服务，因为nginx中的本地缓存可能不在了

 

基于storm+kafka完成商品访问次数实时统计拓扑的开发：
总结思路：

1、kafka consumer spout

单独的线程消费，写入队列

nextTuple，每次都是判断队列有没有数据，有的话再去获取并发射出去，不能阻塞

2、日志解析bolt

3、商品访问次数统计bolt

4、基于LRUMap完成统计

import org.apache.storm.Config;
import org.apache.storm.LocalCluster;
import org.apache.storm.StormSubmitter;
import org.apache.storm.topology.TopologyBuilder;
import org.apache.storm.tuple.Fields;
import org.apache.storm.utils.Utils;

import com.roncoo.eshop.storm.bolt.LogParseBolt;
import com.roncoo.eshop.storm.bolt.ProductCountBolt;
import com.roncoo.eshop.storm.spout.AccessLogKafkaSpout;

/**
 * 热数据统计拓扑
 * @author Administrator
 *
 */
public class HotProductTopology {

	public static void main(String[] args) {
		TopologyBuilder builder = new TopologyBuilder();
	
		builder.setSpout("AccessLogKafkaSpout", new AccessLogKafkaSpout(), 1);
		builder.setBolt("LogParseBolt", new LogParseBolt(), 5)
				.setNumTasks(5)
				.shuffleGrouping("AccessLogKafkaSpout");  
		builder.setBolt("ProductCountBolt", new ProductCountBolt(), 5)
				.setNumTasks(10)
				.fieldsGrouping("LogParseBolt", new Fields("productId"));  
		
		Config config = new Config();
		
		if(args != null && args.length > 1) {
			config.setNumWorkers(3);  
			try {
				StormSubmitter.submitTopology(args[0], config, builder.createTopology());
			} catch (Exception e) {
				e.printStackTrace();
			}
		} else {
			LocalCluster cluster = new LocalCluster();
			cluster.submitTopology("HotProductTopology", config, builder.createTopology());  
			Utils.sleep(30000); 
			cluster.shutdown();
		}
	}
	
}

　　

import java.util.Map;

import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseRichBolt;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Tuple;
import org.apache.storm.tuple.Values;

import com.alibaba.fastjson.JSONObject;

/**
 * 日志解析的bolt
 * @author Administrator
 *
 */
public class LogParseBolt extends BaseRichBolt {

	private static final long serialVersionUID = -8017609899644290359L;

	private OutputCollector collector;
	
	@SuppressWarnings("rawtypes")
	public void prepare(Map conf, TopologyContext context, OutputCollector collector) {
		this.collector = collector;
	}
	
	public void execute(Tuple tuple) {
		String message = tuple.getStringByField("message");  
		JSONObject messageJSON = JSONObject.parseObject(message);
		JSONObject uriArgsJSON = messageJSON.getJSONObject("uri_args"); 
		Long productId = uriArgsJSON.getLong("productId"); 
		
		if(productId != null) {
			collector.emit(new Values(productId));  
		}
	}
	
	public void declareOutputFields(OutputFieldsDeclarer declarer) {
		declarer.declare(new Fields("productId"));   
	}

}

　　

import java.util.ArrayList;
import java.util.List;
import java.util.Map;

import org.apache.storm.shade.org.json.simple.JSONArray;
import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseRichBolt;
import org.apache.storm.trident.util.LRUMap;
import org.apache.storm.tuple.Tuple;
import org.apache.storm.utils.Utils;

import com.roncoo.eshop.storm.zk.ZooKeeperSession;

/**
 * 商品访问次数统计bolt
 * @author Administrator
 *
 */
public class ProductCountBolt extends BaseRichBolt {

	private static final long serialVersionUID = -8761807561458126413L;

	private LRUMap<Long, Long> productCountMap = new LRUMap<Long, Long>(1000);
	private ZooKeeperSession zkSession;
	private int taskid;
	
	@SuppressWarnings("rawtypes")
	public void prepare(Map conf, TopologyContext context, OutputCollector collector) {
		this.zkSession = ZooKeeperSession.getInstance();
		this.taskid = context.getThisTaskId();
		
		new Thread(new ProductCountThread()).start();
		
		// 1、将自己的taskid写入一个zookeeper node中，形成taskid的列表
		// 2、然后每次都将自己的热门商品列表，写入自己的taskid对应的zookeeper节点
		// 3、然后这样的话，并行的预热程序才能从第一步中知道，有哪些taskid
		// 4、然后并行预热程序根据每个taskid去获取一个锁，然后再从对应的znode中拿到热门商品列表
		initTaskId(context.getThisTaskId());
	}
	
	private void initTaskId(int taskid) {
		// ProductCountBolt所有的task启动的时候， 都会将自己的taskid写到同一个node的值中
		// 格式就是逗号分隔，拼接成一个列表
		// 111,211,355
		
		zkSession.acquireDistributedLock();
		
		String taskidList = zkSession.getNodeData();
		if(!"".equals(taskidList)) {
			taskidList += "," + taskid;
		} else {
			taskidList += taskid;
		}
		
		zkSession.setNodeData("/taskid-list", taskidList);  
		
		zkSession.releaseDistributedLock();
	}
	
	private class ProductCountThread implements Runnable {
		
		public void run() {
			List<Map.Entry<Long, Long>> topnProductList = new ArrayList<Map.Entry<Long, Long>>();   
			
			while(true) {
				topnProductList.clear();
				
				int topn = 3;
				
				if(productCountMap.size() == 0) {
					Utils.sleep(100);
					continue;
				}
				
				for(Map.Entry<Long, Long> productCountEntry : productCountMap.entrySet()) {
					if(topnProductList.size() == 0) {
						topnProductList.add(productCountEntry);
					} else {
						// 比较大小，生成最热topn的算法有很多种
						// 但是我这里为了简化起见，不想引入过多的数据结构和算法的的东西
						// 很有可能还是会有漏洞，但是我已经反复推演了一下了，而且也画图分析过这个算法的运行流程了
						boolean bigger = false;
						
						for(int i = 0; i < topnProductList.size(); i++){
							Map.Entry<Long, Long> topnProductCountEntry = topnProductList.get(i);
							
							if(productCountEntry.getValue() > topnProductCountEntry.getValue()) {
								int lastIndex = topnProductList.size() < topn ? topnProductList.size() - 1 : topn - 2;
								for(int j = lastIndex; j >= i; j--) {
									topnProductList.set(j + 1, topnProductList.get(j));  
								}
								topnProductList.set(i, productCountEntry);
								bigger = true;
								break;
							}
						}
						
						if(!bigger) {
							if(topnProductList.size() < topn) {
								topnProductList.add(productCountEntry);
							}
						}
					}
				}
				
				// 获取到一个topn list
				String topnProductListJSON = JSONArray.toJSONString(topnProductList);
				zkSession.setNodeData("/task-hot-product-list-" + taskid, topnProductListJSON);
				
				Utils.sleep(5000); 
			}
		}
		
	}
	
	public void execute(Tuple tuple) {
		Long productId = tuple.getLongByField("productId"); 
		
		Long count = productCountMap.get(productId);
		if(count == null) {
			count = 0L;
		}
		count++;
		
		productCountMap.put(productId, count);
	}

	public void declareOutputFields(OutputFieldsDeclarer declarer) {
		
	}

}

　　

import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.ArrayBlockingQueue;

import kafka.consumer.Consumer;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;

import org.apache.storm.spout.SpoutOutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseRichSpout;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Values;
import org.apache.storm.utils.Utils;

/**
 * kafka消费数据的spout
 */
public class AccessLogKafkaSpout extends BaseRichSpout {

	private static final long serialVersionUID = 8698470299234327074L;

	private ArrayBlockingQueue<String> queue = new ArrayBlockingQueue<String>(1000);
	
	private SpoutOutputCollector collector;
	
	@SuppressWarnings("rawtypes")
	public void open(Map conf, TopologyContext context,
			SpoutOutputCollector collector) {
		this.collector = collector;
		startKafkaConsumer();
	}
	
	@SuppressWarnings("rawtypes")
	private void startKafkaConsumer() {
		Properties props = new Properties();
        props.put("zookeeper.connect", "192.168.31.187:2181,192.168.31.19:2181,192.168.31.227:2181");
        props.put("group.id", "eshop-cache-group");
        props.put("zookeeper.session.timeout.ms", "40000");
        props.put("zookeeper.sync.time.ms", "200");
        props.put("auto.commit.interval.ms", "1000");
        ConsumerConfig consumerConfig = new ConsumerConfig(props);
		
		ConsumerConnector consumerConnector = Consumer.
				createJavaConsumerConnector(consumerConfig);
		String topic = "access-log";
		
		Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
        topicCountMap.put(topic, 1);
        
        Map<String, List<KafkaStream<byte[], byte[]>>> consumerMap = 
        		consumerConnector.createMessageStreams(topicCountMap);
        List<KafkaStream<byte[], byte[]>> streams = consumerMap.get(topic);
        
        for (KafkaStream stream : streams) {
            new Thread(new KafkaMessageProcessor(stream)).start();
        }
	}
	
	private class KafkaMessageProcessor implements Runnable {

		@SuppressWarnings("rawtypes")
		private KafkaStream kafkaStream;
		
		@SuppressWarnings("rawtypes")
		public KafkaMessageProcessor(KafkaStream kafkaStream) {
			this.kafkaStream = kafkaStream;
		}
		
		@SuppressWarnings("unchecked")
		public void run() {
			ConsumerIterator<byte[], byte[]> it = kafkaStream.iterator();
	        while (it.hasNext()) {
	        	String message = new String(it.next().message());
	        	try {
					queue.put(message);
				} catch (InterruptedException e) {
					e.printStackTrace();
				} 
	        }
		}
		
	}
	
	public void nextTuple() {
		if(queue.size() > 0) {
			try {
				String message = queue.take();
				collector.emit(new Values(message));  
			} catch (Exception e) {
				e.printStackTrace();
			}
		} else {
			Utils.sleep(100);  
		}
	}
	 
	public void declareOutputFields(OutputFieldsDeclarer declarer) {
		declarer.declare(new Fields("message"));  
	}
	
}

　　

import java.util.concurrent.CountDownLatch;

import org.apache.zookeeper.CreateMode;
import org.apache.zookeeper.WatchedEvent;
import org.apache.zookeeper.Watcher;
import org.apache.zookeeper.Watcher.Event.KeeperState;
import org.apache.zookeeper.ZooDefs.Ids;
import org.apache.zookeeper.ZooKeeper;
import org.apache.zookeeper.data.Stat;

/**
 * ZooKeeperSession
 * @author Administrator
 *
 */
public class ZooKeeperSession {
	
	private static CountDownLatch connectedSemaphore = new CountDownLatch(1);
	
	private ZooKeeper zookeeper;

	public ZooKeeperSession() {
		// 去连接zookeeper server，创建会话的时候，是异步去进行的
		// 所以要给一个监听器，说告诉我们什么时候才是真正完成了跟zk server的连接
		try {
			this.zookeeper = new ZooKeeper(
					"192.168.31.187:2181,192.168.31.19:2181,192.168.31.227:2181", 
					50000, 
					new ZooKeeperWatcher());
			// 给一个状态CONNECTING，连接中
			System.out.println(zookeeper.getState());
			
			try {
				// CountDownLatch
				// java多线程并发同步的一个工具类
				// 会传递进去一些数字，比如说1,2 ，3 都可以
				// 然后await()，如果数字不是0，那么久卡住，等待
				
				// 其他的线程可以调用coutnDown()，减1
				// 如果数字减到0，那么之前所有在await的线程，都会逃出阻塞的状态
				// 继续向下运行
				
				connectedSemaphore.await();
			} catch(InterruptedException e) {
				e.printStackTrace();
			}

			System.out.println("ZooKeeper session established......");
		} catch (Exception e) {
			e.printStackTrace();
		}
	}
	
	/**
	 * 获取分布式锁
	 * @param productId
	 */
	public void acquireDistributedLock() {
		String path = "/taskid-list-lock";
	
		try {
			zookeeper.create(path, "".getBytes(), 
					Ids.OPEN_ACL_UNSAFE, CreateMode.EPHEMERAL);
			System.out.println("success to acquire lock for taskid-list-lock");  
		} catch (Exception e) {
			// 如果那个商品对应的锁的node，已经存在了，就是已经被别人加锁了，那么就这里就会报错
			// NodeExistsException
			int count = 0;
			while(true) {
				try {
					Thread.sleep(1000); 
					zookeeper.create(path, "".getBytes(), 
							Ids.OPEN_ACL_UNSAFE, CreateMode.EPHEMERAL);
				} catch (Exception e2) {
					count++;
					System.out.println("the " + count + " times try to acquire lock for taskid-list-lock......");
					continue;
				}
				System.out.println("success to acquire lock for taskid-list-lock after " + count + " times try......");
				break;
			}
		}
	}
	
	/**
	 * 释放掉一个分布式锁
	 * @param productId
	 */
	public void releaseDistributedLock() {
		String path = "/taskid-list-lock";
		try {
			zookeeper.delete(path, -1); 
			System.out.println("release the lock for taskid-list-lock......");  
		} catch (Exception e) {
			e.printStackTrace();
		}
	}
	
	public String getNodeData() {
		try {
			return new String(zookeeper.getData("/taskid-list", false, new Stat()));  
		} catch (Exception e) {
			e.printStackTrace();
		}
		return "";
	}
	
	public void setNodeData(String path, String data) {
		try {
			zookeeper.setData(path, data.getBytes(), -1);
		} catch (Exception e) {
			e.printStackTrace();
		}
	}
	
	/**
	 * 建立zk session的watcher
	 * @author Administrator
	 *
	 */
	private class ZooKeeperWatcher implements Watcher {

		public void process(WatchedEvent event) {
			System.out.println("Receive watched event: " + event.getState());
			if(KeeperState.SyncConnected == event.getState()) {
				connectedSemaphore.countDown();
			} 
		}
		
	}
	
	/**
	 * 封装单例的静态内部类
	 * @author Administrator
	 *
	 */
	private static class Singleton {
		
		private static ZooKeeperSession instance;
		
		static {
			instance = new ZooKeeperSession();
		}
		
		public static ZooKeeperSession getInstance() {
			return instance;
		}
		
	}
	
	/**
	 * 获取单例
	 * @return
	 */
	public static ZooKeeperSession getInstance() {
		return Singleton.getInstance();
	}
	
	/**
	 * 初始化单例的便捷方法
	 */
	public static void init() {
		getInstance();
	}
	
}

　　于双重zookeeper分布式锁完成分布式并行缓存预热：


1、服务启动的时候，进行缓存预热

2、从zk中读取taskid列表

3、依次遍历每个taskid，尝试获取分布式锁，如果获取不到，快速报错，不要等待，因为说明已经有其他服务实例在预热了

4、直接尝试获取下一个taskid的分布式锁

5、即使获取到了分布式锁，也要检查一下这个taskid的预热状态，如果已经被预热过了，就不再预热了

6、执行预热操作，遍历productid列表，查询数据，然后写ehcache和redis

7、预热完成后，设置taskid对应的预热状态

ZKsession重载两个方法：

/**
	 * 获取分布式锁
	 * @param productId
	 */
	public boolean acquireFastFailedDistributedLock(String path) {
		try {
			zookeeper.create(path, "".getBytes(), 
					Ids.OPEN_ACL_UNSAFE, CreateMode.EPHEMERAL);
			System.out.println("success to acquire lock for " + path);  
			return true;
		} catch (Exception e) {
			System.out.println("fail to acquire lock for " + path);  
		}
		return false;
	}

/**
	 * 释放掉一个分布式锁
	 * @param productId
	 */
	public void releaseDistributedLock(String path) {
		try {
			zookeeper.delete(path, -1); 
			System.out.println("release the lock for " + path + "......");  
		} catch (Exception e) {
			e.printStackTrace();
		}
	}
public String getNodeData(String path) {
		try {
			return new String(zookeeper.getData(path, false, new Stat())); 
		} catch (Exception e) {
			e.printStackTrace();
		}
		return "";
	}
	
	public void setNodeData(String path, String data) {
		try {
			zookeeper.setData(path, data.getBytes(), -1);
		} catch (Exception e) {
			e.printStackTrace();
		}
	}

　　

/**
	 * 获取分布式锁
	 */
	public void acquireDistributedLock(String path) {
		try {
			zookeeper.create(path, "".getBytes(), 
					Ids.OPEN_ACL_UNSAFE, CreateMode.EPHEMERAL);
			System.out.println("success to acquire lock for " + path);  
		} catch (Exception e) {
			// 如果那个商品对应的锁的node，已经存在了，就是已经被别人加锁了，那么就这里就会报错
			// NodeExistsException
			int count = 0;
			while(true) {
				try {
					Thread.sleep(1000); 
					zookeeper.create(path, "".getBytes(), 
							Ids.OPEN_ACL_UNSAFE, CreateMode.EPHEMERAL);
				} catch (Exception e2) {
					count++;
					System.out.println("the " + count + " times try to acquire lock for " + path + "......");
					continue;
				}
				System.out.println("success to acquire lock for " + path + " after " + count + " times try......");
				break;
			}
		}
	}

　　

import com.alibaba.fastjson.JSONArray;
import com.alibaba.fastjson.JSONObject;
import com.roncoo.eshop.cache.model.ProductInfo;
import com.roncoo.eshop.cache.service.CacheService;
import com.roncoo.eshop.cache.spring.SpringContext;
import com.roncoo.eshop.cache.zk.ZooKeeperSession;

/**
 * 缓存预热线程
 */
public class CachePrewarmThread extends Thread {
	
	@Override
	public void run() {
		CacheService cacheService = (CacheService) SpringContext.
				getApplicationContext().getBean("cacheService"); 
		ZooKeeperSession zkSession = ZooKeeperSession.getInstance();
		
		// 获取storm taskid列表
		String taskidList = zkSession.getNodeData("/taskid-list"); 
		
		if(taskidList != null && !"".equals(taskidList)) {
			String[] taskidListSplited = taskidList.split(",");  
			for(String taskid : taskidListSplited) {
				String taskidLockPath = "/taskid-lock-" + taskid;
				
				boolean result = zkSession.acquireFastFailedDistributedLock(taskidLockPath);
				if(!result) {
					continue;
				}
				
				String taskidStatusLockPath = "/taskid-status-lock-" + taskid;
				zkSession.acquireDistributedLock(taskidStatusLockPath);  
				//检查越热的状态
				String taskidStatus = zkSession.getNodeData("/taskid-status-" + taskid);
				
				if("".equals(taskidStatus)) {
					String productidList = zkSession.getNodeData("/task-hot-product-list-" + taskid);
					JSONArray productidJSONArray = JSONArray.parseArray(productidList);
					
					for(int i = 0; i < productidJSONArray.size(); i++) {
						Long productId = productidJSONArray.getLong(i);
						String productInfoJSON = "{\"id\": " + productId + ", \"name\": \"iphone7手机\", \"price\": 5599, \"pictureList\":\"a.jpg,b.jpg\", \"specification\": \"iphone7的规格\", \"service\": \"iphone7的售后服务\", \"color\": \"红色,白色,黑色\", \"size\": \"5.5\", \"shopId\": 1, \"modifiedTime\": \"2017-01-01 12:00:00\"}";
						ProductInfo productInfo = JSONObject.parseObject(productInfoJSON, ProductInfo.class);
						cacheService.saveProductInfo2LocalCache(productInfo);
						cacheService.saveProductInfo2ReidsCache(productInfo);  
					}
					
					zkSession.setNodeData(taskidStatusLockPath, "success");   
				}
				
				zkSession.releaseDistributedLock(taskidStatusLockPath);
				
				zkSession.releaseDistributedLock(taskidLockPath);
			}
		}
	}
	
}

　　

import com.alibaba.fastjson.JSONArray;
import com.alibaba.fastjson.JSONObject;
import com.roncoo.eshop.cache.model.ProductInfo;
import com.roncoo.eshop.cache.service.CacheService;
import com.roncoo.eshop.cache.spring.SpringContext;
import com.roncoo.eshop.cache.zk.ZooKeeperSession;

/**
 * 缓存预热线程
 */
public class CachePrewarmThread extends Thread {
	
	@Override
	public void run() {
		CacheService cacheService = (CacheService) SpringContext.
				getApplicationContext().getBean("cacheService"); 
		ZooKeeperSession zkSession = ZooKeeperSession.getInstance();
		
		// 获取storm taskid列表
		String taskidList = zkSession.getNodeData("/taskid-list"); 
		
		if(taskidList != null && !"".equals(taskidList)) {
			String[] taskidListSplited = taskidList.split(",");  
			for(String taskid : taskidListSplited) {
				String taskidLockPath = "/taskid-lock-" + taskid;
				
				boolean result = zkSession.acquireFastFailedDistributedLock(taskidLockPath);
				if(!result) {
					continue;
				}
				
				String taskidStatusLockPath = "/taskid-status-lock-" + taskid;
				zkSession.acquireDistributedLock(taskidStatusLockPath);  
				//检查越热的状态
				String taskidStatus = zkSession.getNodeData("/taskid-status-" + taskid);
				
				if("".equals(taskidStatus)) {
					String productidList = zkSession.getNodeData("/task-hot-product-list-" + taskid);
					JSONArray productidJSONArray = JSONArray.parseArray(productidList);
					
					for(int i = 0; i < productidJSONArray.size(); i++) {
						Long productId = productidJSONArray.getLong(i);
						String productInfoJSON = "{\"id\": " + productId + ", \"name\": \"iphone7手机\", \"price\": 5599, \"pictureList\":\"a.jpg,b.jpg\", \"specification\": \"iphone7的规格\", \"service\": \"iphone7的售后服务\", \"color\": \"红色,白色,黑色\", \"size\": \"5.5\", \"shopId\": 1, \"modifiedTime\": \"2017-01-01 12:00:00\"}";
						ProductInfo productInfo = JSONObject.parseObject(productInfoJSON, ProductInfo.class);
						cacheService.saveProductInfo2LocalCache(productInfo);
						cacheService.saveProductInfo2ReidsCache(productInfo);  
					}
					
					zkSession.setNodeData(taskidStatusLockPath, "success");   
				}
				
				zkSession.releaseDistributedLock(taskidStatusLockPath);
				
				zkSession.releaseDistributedLock(taskidLockPath);
			}
		}
	}
	
}

　　

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