多级缓存的架构主要用来解决的问题是：时效高低数据的维度存储
时效性不高的数据，比如一些商品的基本信息，如果发生了变更，假设在 5 分钟之后再更新到页面中， 供用户观察到，也是 ok 的，那么我们采取的是异步更新缓存的策略
时效性要求很高的数据，如库存，采取的是数据库 + 缓存双写的技术方案，也解决了双写的一致性的问题
上面这两条可能直接看觉得好像差不多的，这里忽略了一个解释，对于页面来说，需要静态的生成页面， 这个过程可能稍微耗时一些，而对于双写来说则快太多了，它不负责页面渲染等工作，只需要把缓存数据更新即可
缓存数据生产服务，监听一个消息队列，然后数据源服务（商品信息管理服务）发生了数据变更之后， 就将数据变更的消息推送到消息队列中
缓存数据生产服务可以去消费到这个数据变更的消息，然后根据消息的指示提取一些参数， 然后调用对应的数据源服务的接口拉取数据，这个时候一般是从 mysql 库中拉去的
消息队列这里采用的是 kafka，这里选择 kafka 另外一个原因： 后面我们还要用 zookeeper 来解决缓存的分布式并发更新的问题（如分布式锁解决）
而 kafka 集群是依赖 zookeeper 集群，所以先搭建 zookeeper 集群，再搭建 kafka 集群
zookeeper + kafka 的集群，都至少是三节点
配置文件介绍
[root@master config]# more server.properties
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# see kafka.server.KafkaConfig for additional details and defaults
############################# Server Basics #############################
# The id of the broker. This must be set to a unique integer for each broker.
##每一个broker在集群中的唯一标示，要求是正数。在改变IP地址，不改变broker.id的话不会影响consumers
broker.id=0
# Switch to enable topic deletion or not, default value is false
## 是否允许自动创建topic ，若是false，就需要通过命令创建topic
delete.topic.enable=true
############################# Socket Server Settings #############################
# The address the socket server listens on. It will get the value returned from
# .InetAddress.getCanonicalHostName() if not configured.
# FORMAT:
# listeners = listener_name://host_name:port
# EXAMPLE:
# listeners = PLAINTEXT://:9092
#listeners=PLAINTEXT://:9092
##提供给客户端响应的端口
port=9092
=192.168.1.128
# Hostname and port the broker will advertise to producers and consumers. If not set,
# it uses the value for "listeners" if configured. Otherwise, it will use the value
# returned from .InetAddress.getCanonicalHostName().
#advertised.listeners=PLAINTEXT://:9092
# Maps listener names to security protocols, the default is for them to be the same. See the config documentation for more details
#listener.security.protocol.map=PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL
# The number of threads handling network requests
## broker 处理消息的最大线程数，一般情况下不需要去修改
num.network.threads=3
# The number of threads doing disk I/O
## broker处理磁盘IO 的线程数 ，数值应该大于你的硬盘数
.threads=8
# The send buffer (SO_SNDBUF) used by the socket server
## socket的发送缓冲区，socket的调优参数SO_SNDBUFF
socket.send.buffer.bytes=102400
# The receive buffer (SO_RCVBUF) used by the socket server
## socket的接受缓冲区，socket的调优参数SO_RCVBUFF
socket.receive.buffer.bytes=102400
# The maximum size of a request that the socket server will accept (protection against OOM)
## socket请求的最大数值，防止serverOOM，message.max.bytes必然要小于socket.request.max.bytes，会被topic创建时的指定参数覆盖
socket.request.max.bytes=104857600
############################# Log Basics #############################
# A comma seperated list of directories under which to store log files
##kafka数据的存放地址，多个地址的话用逗号分割/data/kafka-logs-1，/data/kafka-logs-2
log.dirs=/tmp/kafka-logs
# The default number of log partitions per topic. More partitions allow greater
# parallelism for consumption, but this will also result in more files across
# the brokers.
##每个topic的分区个数，若是在topic创建时候没有指定的话会被topic创建时的指定参数覆盖
num.partitions=1
# The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.
# This value is recommended to be increased for installations with data dirs located in RAID array.
##我们知道segment文件默认会被保留7天的时间，超时的话就
##会被清理，那么清理这件事情就需要有一些线程来做。这里就是
##用来设置恢复和清理data下数据的线程数量
num.recovery.threads.per.data.dir=1
############################# Log Flush Policy #############################
# Messages are immediately written to the filesystem but by default we only fsync() to sync
# the OS cache lazily. The following configurations control the flush of data to disk.
# There are a few important trade-offs here:
# 1. Durability: Unflushed data may be lost if you are not using replication.
# 2. Latency: Very large flush intervals may lead to latency spikes when the flush does occur as there will be a lot of data to flush.
# 3. Throughput: The flush is generally the most expensive operation, and a small flush interval may lead to exceessive seeks.
# The settings below allow one to configure the flush policy to flush data after a period of time or
# every N messages (or both). This can be done globally and overridden on a per-topic basis.
# The number of messages to accept before forcing a flush of data to disk
#log.flush.interval.messages=10000
# The maximum amount of time a message can sit in a log before we force a flush
#=1000
############################# Log Retention Policy #############################
# The following configurations control the disposal of log segments. The policy can
# be set to delete segments after a period of time, or after a given size has accumulated.
# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens
# from the end of the log.
# The minimum age of a log file to be eligible for deletion due to age
##segment文件保留的最长时间，默认保留7天（168小时），
##超时将被删除，也就是说7天之前的数据将被清理掉。
log.retention.hours=168
# A size-based retention policy for logs. Segments are pruned from the log as long as the remaining
# segments don't drop below log.retention.bytes. Functions independently of log.retention.hours.
#log.retention.bytes=1073741824
# The maximum size of a log segment file. When this size is reached a new log segment will be created.
###日志文件中每个segment的大小，默认为1G
log.segment.bytes=1073741824
# The interval at which log segments are checked to see if they can be deleted according
# to the retention policies
##上面的参数设置了每一个segment文件的大小是1G，那么
##就需要有一个东西去定期检查segment文件有没有达到1G，
##多长时间去检查一次，就需要设置一个周期性检查文件大小
##的时间（单位是毫秒）。
=300000
############################# Zookeeper #############################
# Zookeeper connection string (see zookeeper docs for details).
# This is a comma separated host:port pairs, each corresponding to a zk
# server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002".
# You can also append an optional chroot string to the urls to specify the
# root directory for all kafka znodes.
#zookeeper.connect=localhost:2181
##消费者集群通过连接Zookeeper来找到broker。
##zookeeper连接服务器地址
zookeeper.connect=master:2181,worker1:2181,worker2:2181
# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=6000
启动kafka
[root@master kafka]# ./bin/ config/server.properties &
[2018-06-25 02:31:21,931] INFO KafkaConfig values:
advertised. = null
advertised.listeners = null
advertised.port = null
=
auto.create.topics.enable = true
auto.leader.rebalance.enable = true
background.threads = 10
broker.id = 0
broker.id.generation.enable = true
broker.rack = null
compression.type = producer
= 600000
controlled.shutdown.enable = true
controlled.shutdown.max.retries = 3
= 5000
controller.socket.timeout.ms = 30000
创建topic #创建topic topic名字为gilbert
[root@master kafka]# ./bin/kafka-topics.sh --create --zookeeper master:2181,worker1:2181,worker2:2181 --replication-factor 3 --partitions 3 --topic gilbert
Created topic "gilbert".
查看topic
[root@master kafka]# ./bin/kafka-topics.sh --describe --zookeeper master:2181,worker1:2181,worker2:2181 --topic gilbert
Topic:gilbert PartitionCount:3 ReplicationFactor:3 Configs:
Topic: gilbert Partition: 0 Leader: 2 Replicas: 2,0,1 Isr: 2,0,1
Topic: gilbert Partition: 1 Leader: 0 Replicas: 0,1,2 Isr: 0,1,2
Topic: gilbert Partition: 2 Leader: 1 Replicas: 1,2,0 Isr: 1,2,0
[root@master kafka]# ./bin/kafka-topics.sh --list --zookeeper master:2181,worker1:2181,worker2:2181
gilbert
test
创建producer
./bin/ --broker-list master:9092 -topic gilbert
创建consumer，分别在3台服务器上执行创建消费者 #192.168.1.128服务器
[root@master kafka]# ./bin/kafka-console-consumer.sh --zookeeper master:2181,worker1:2181,worker2:2181 -topic gilbert --from-beginning
Using the ConsoleConsumer with old consumer is deprecated and will be removed in a future major release. Consider using the new consumer by passing [bootstrap-server] instead of [zookeeper].
#192.168.1.129服务器
[root@worker1 kafka_2.10-0.10.2.0]# ./bin/kafka-console-consumer.sh --zookeeper master:2181,worker1:2181,worker2:2181 -topic gilbert --from-beginning
Using the ConsoleConsumer with old consumer is deprecated and will be removed in a future major release. Consider using the new consumer by passing [bootstrap-server] instead of [zookeeper].
#192.168.1.130服务器
[root@worker2 kafka_2.10-0.10.2.0]# ./bin/kafka-console-consumer.sh --zookeeper master:2181,worker1:2181,worker2:2181 -topic gilbert --from-beginning
Using the ConsoleConsumer with old consumer is deprecated and will be removed in a future major release. Consider using the new consumer by passing [bootstrap-server] instead of [zookeeper].
在#192.168.1.128服务器上生产者控制台输入：hello kafka进行测试在3台服务器上的消费者都正常接收到消息
删除topic
[root@master kafka]# ./bin/kafka-topics.sh --delete --zookeeper master:2181,worker1:2181,worker2:2181 --topic test
Topic test is marked for deletion.
Note: This will have no impact if delete.topic.enable is not set to truespringboot集成kafka
1.生产者kafka-producer
a) pom文件
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.kafka</groupId>
<artifactId>spring-kafka</artifactId>
</dependency>
<dependency>
<groupId>com.google.code.gson</groupId>
<artifactId>gson</artifactId>
<version>2.8.2</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
b) yml配置文件,本例为kafka3节点集群
spring:
kafka:
bootstrap-servers: http://master:9092,http://worker1:9092,http://worker2:9092
producer:
retries: 0
batch-size: 16384
buffer-memory: 33554432
key-serializer: org.apache.kafka.common.serialization.StringSerializer
value-serializer: org.apache.kafka.common.serialization.StringSerializer
c) message消息实体类
@Data
public class Message {
private Long id; //id
private String msg; //消息
private Date sendTime; //时间戳
}
d) 生产者 @Component
@Slf4j
public class KafkaProducer {
@Autowired
private KafkaTemplate<String, String> kafkaTemplate;
private Gson gson = new GsonBuilder().create();
//发送消息方法
public void send() {
Message message = new Message();
message.setId(System.currentTimeMillis());
message.setMsg(UUID.randomUUID().toString());
message.setSendTime(new Date());
log.info("+++++++++++++++++++++ message = {}", gson.toJson(message));
//topic-ideal为主题
kafkaTemplate.send("topic-ideal", gson.toJson(message));
}
}
e) 测试类,运行kafkaProducer方法即可 @RunWith(SpringRunner.class)
@SpringBootTest
public class KafkaProducerApplicationTests {
@Autowired
private KafkaProducer kafkaProducer;
@Test
public void kafkaProducer(){
this.kafkaProducer.send();
}
@Test
public void contextLoads() {
}
}2. 消费者kafka-consumer
a) pom文件
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.kafka</groupId>
<artifactId>spring-kafka</artifactId>
</dependency>
<dependency>
<groupId>com.google.code.gson</groupId>
<artifactId>gson</artifactId>
<version>2.8.2</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
b) yml配置文件
server:
port: 9999
spring:
kafka:
bootstrap-servers: http://master:9092,http://worker1:9092,http://worker2:9092
consumer:
group-id: ideal-consumer-group
auto-offset-reset: earliest
enable-auto-commit: true
auto-commit-interval: 20000
key-deserializer: org.apache.kafka.common.serialization.StringDeserializer
value-deserializer: org.apache.kafka.common.serialization.StringDeserializer
c) 消费者类
@Component
@Slf4j
public class KafkaConsumer {
@KafkaListener(topics = {"topic-ideal"})
public void consumer(ConsumerRecord<?, ?> record){
Optional<?> kafkaMessage = Optional.ofNullable(record.value());
if (kafkaMessage.isPresent()) {
Object message = kafkaMessage.get();
log.info("----------------- record =" + record);
log.info("------------------ message =" + message);
}
}
}
运行消费者kafka-consumer，再运行kafka-producer工程测试类KafkaProducerApplicationTests中kafkaProducer()方法,可以看到消费者后台正常接收消息
编写业务逻辑
- 两种服务会发送来数据变更消息：商品信息服务和商品店铺信息服务，每个消息都包含服务名以及商品 id
- 接收到消息之后，根据商品 id 到对应的服务拉取数据
这一步，我们采取简化的模拟方式，就是在代码里面写死，会获取到什么数据，不去实际再写其他的服务去调用了 - 商品信息：id、名称、价格、图片列表、商品规格、售后信息、颜色、尺寸
- 商品店铺信息：其他维度
用这个维度模拟出来缓存数据维度化拆分：id、店铺名称、店铺等级、店铺好评率 - 分别拉取到了数据之后，将数据组织成 json 串，然后分别存储到 ehcache 中和 redis 缓存中
这里的业务逻辑代码，因为是模拟这个场景，所以事先比较简单，重要的类如下
接收到事件之后，分别处理事件
public class KafkaMessageProcessor implements Runnable {
private KafkaStream kafkaStream;
private CacheService cacheService;
private Logger log = LoggerFactory.getLogger(getClass());
public KafkaMessageProcessor(KafkaStream kafkaStream, CacheService cacheService) {
this.kafkaStream = kafkaStream;
this.cacheService = cacheService;
}
public void run() {
ConsumerIterator<byte[], byte[]> it = kafkaStream.iterator();
while (it.hasNext()) {
String message = new String(it.next().message());
// 首先将message转换成json对象
JSONObject messageJSONObject = JSONObject.parseObject(message);
// 从这里提取出消息对应的服务的标识
String serviceId = messageJSONObject.getString("serviceId");
// 如果是商品信息服务
if ("productInfoService".equals(serviceId)) {
processProductInfoChangeMessage(messageJSONObject);
} else if ("shopInfoService".equals(serviceId)) {
processShopInfoChangeMessage(messageJSONObject);
}
}
}
/**
* 处理商品信息变更的消息
*/
private void processProductInfoChangeMessage(JSONObject messageJSONObject) {
// 提取出商品id
Long productId = messageJSONObject.getLong("productId");
// 调用商品信息服务的接口
// 直接用注释模拟：getProductInfo?productId=1，传递过去
// 商品信息服务，一般来说就会去查询数据库，去获取productId=1的商品信息，然后返回回来
String productInfoJSON = "{\"id\": 1, \"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}";
ProductInfo productInfo = JSONObject.parseObject(productInfoJSON, ProductInfo.class);
cacheService.saveProductInfo2LocalCache(productInfo);
log.info("获取刚保存到本地缓存的商品信息：" + cacheService.getProductInfoFromLocalCache(productId));
cacheService.saveProductInfo2ReidsCache(productInfo);
}
/**
* 处理店铺信息变更的消息
*/
private void processShopInfoChangeMessage(JSONObject messageJSONObject) {
// 提取出商品id
Long productId = messageJSONObject.getLong("productId");
Long shopId = messageJSONObject.getLong("shopId");
// 这里也是模拟去数据库获取到了信息
String shopInfoJSON = "{\"id\": 1, \"name\": \"小王的手机店\", \"level\": 5, \"goodCommentRate\":0.99}";
ShopInfo shopInfo = JSONObject.parseObject(shopInfoJSON, ShopInfo.class);
cacheService.saveShopInfo2LocalCache(shopInfo);
log.info("获取刚保存到本地缓存的店铺信息：" + cacheService.getShopInfoFromLocalCache(shopId));
cacheService.saveShopInfo2ReidsCache(shopInfo);
}
}
把缓存的读写封装了到 service 中。上面通过 service 去操作缓存
@Service
public class CacheServiceImpl implements CacheService {
public static final String CACHE_NAME = "local";
@Resource
private JedisCluster jedisCluster;
/**
* 将商品信息保存到本地缓存中
*/
@CachePut(value = CACHE_NAME, key = "'key_'+#productInfo.getId()")
public ProductInfo saveLocalCache(ProductInfo productInfo) {
return productInfo;
}
/**
* 从本地缓存中获取商品信息
*/
@Cacheable(value = CACHE_NAME, key = "'key_'+#id")
public ProductInfo getLocalCache(Long id) {
return null;
}
/**
* 将商品信息保存到本地的ehcache缓存中
*/
@CachePut(value = CACHE_NAME, key = "'product_info_'+#productInfo.getId()")
public ProductInfo saveProductInfo2LocalCache(ProductInfo productInfo) {
return productInfo;
}
/**
* 从本地ehcache缓存中获取商品信息
*/
@Cacheable(value = CACHE_NAME, key = "'product_info_'+#productId")
public ProductInfo getProductInfoFromLocalCache(Long productId) {
return null;
}
/**
* 将店铺信息保存到本地的ehcache缓存中
*/
@CachePut(value = CACHE_NAME, key = "'shop_info_'+#shopInfo.getId()")
public ShopInfo saveShopInfo2LocalCache(ShopInfo shopInfo) {
return shopInfo;
}
/**
* 从本地ehcache缓存中获取店铺信息
*/
@Cacheable(value = CACHE_NAME, key = "'shop_info_'+#shopId")
public ShopInfo getShopInfoFromLocalCache(Long shopId) {
return null;
}
/**
* 将商品信息保存到redis中
*/
public void saveProductInfo2ReidsCache(ProductInfo productInfo) {
String key = "product_info_" + productInfo.getId();
jedisCluster.set(key, JSONObject.toJSONString(productInfo));
}
/**
* 将店铺信息保存到redis中
*/
public void saveShopInfo2ReidsCache(ShopInfo shopInfo) {
String key = "shop_info_" + shopInfo.getId();
jedisCluster.set(key, JSONObject.toJSONString(shopInfo));
}
}
测试业务逻辑
1. 创建一个 kafka topic # 创建 topic ，需要和程序中的一致 ：eshop-message
bin/kafka-topics.sh --zookeeper 192.168.99.170:2181,192.168.99.171:2181,192.168.99.172:2181 --topic eshop-message --replication-factor 1 --partitions 1 --create
2. 在命令行启动一个 kafka producer # 创建一个生产者
bin/ --broker-list 192.168.99.170:9092,192.168.99.171:9092,192.168.99.172:9092 --topic eshop-message
3. 启动系统，消费者开始监听 kafka topic
注意：在 boot 2.1.x 中，连不上也没有日志打印。需要把几个虚拟机 hostname 映射到本地
C:\Windows\System32\drivers\etc\hosts 4. 在 producer 中，分别发送两条消息，一个是商品信息服务的消息，一个是商品店铺信息服务的消息
由于在本次模拟中，只使用 serviceId 作为了判定，其他数据是程序中写死的，所以这里推送两条携带 serviceId 的信息即可 {"serviceId":"productInfoService","productId":"1"}
{"serviceId":"shopInfoService","shopId":"1"}- 能否接收到两条消息，并模拟拉取到两条数据，同时将数据写入 ehcache 中，并写入 redis 缓存中
- ehcache 通过打印日志方式来观察，redis 通过手工连接上去来查询
本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
