NettyKafka集群部署Java微服务后端架构实战

1. 架构概述

Netty和Kafka集群部署是构建高可用、高性能微服务架构的核心基础设施。Netty集群负责处理高并发网络连接,Kafka集群负责消息队列的高可用和分布式处理。本篇文章将深入讲解如何基于Java微服务架构实现Netty和Kafka的3台机器集群部署,包括集群配置、服务发现、负载均衡、集群协调和高可用保障。

1.1 系统架构图

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Netty集群架构(3台机器)
├── Netty节点1 (192.168.1.101:8888)
│ ├── 设备连接接入
│ ├── 消息处理
│ └── 连接管理
├── Netty节点2 (192.168.1.102:8888)
│ ├── 设备连接接入
│ ├── 消息处理
│ └── 连接管理
└── Netty节点3 (192.168.1.103:8888)
├── 设备连接接入
├── 消息处理
└── 连接管理

负载均衡器 (Nginx/HAProxy)
├── 轮询/加权轮询
├── 最少连接
└── IP哈希

Kafka集群架构(3台机器)
├── Kafka Broker 1 (192.168.1.201:9092)
│ ├── Topic分区0, 1
│ ├── 副本管理
│ └── 消息存储
├── Kafka Broker 2 (192.168.1.202:9092)
│ ├── Topic分区2, 3
│ ├── 副本管理
│ └── 消息存储
└── Kafka Broker 3 (192.168.1.203:9092)
├── Topic分区4, 5
├── 副本管理
└── 消息存储

Zookeeper集群(3台机器)
├── ZK节点1 (192.168.1.301:2181)
├── ZK节点2 (192.168.1.302:2181)
└── ZK节点3 (192.168.1.303:2181)

1.2 核心组件

  • Netty集群:3台机器部署Netty服务,实现高并发网络连接处理
  • Kafka集群:3台机器部署Kafka Broker,实现消息队列的高可用和分布式处理
  • Zookeeper集群:3台机器部署Zookeeper,实现Kafka集群协调和服务发现
  • 负载均衡器:Nginx或HAProxy实现Netty集群的负载均衡
  • 服务注册中心:Eureka或Nacos实现Netty服务注册和发现
  • 集群协调服务:实现Netty集群节点间的协调和状态同步
  • 监控告警系统:Prometheus + Grafana实现集群监控和告警

2. Netty集群实现

2.1 Netty服务启动类

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
/**
* Netty服务启动类
* 支持集群部署
*/
@SpringBootApplication
@Slf4j
public class NettyClusterApplication {

@Autowired
private NettyServerConfig nettyServerConfig;

@Autowired
private ServiceRegistry serviceRegistry;

@Autowired
private ClusterCoordinator clusterCoordinator;

public static void main(String[] args) {
SpringApplication.run(NettyClusterApplication.class, args);
}

@PostConstruct
public void startNettyServer() {
try {
// 1. 启动Netty服务器
NettyServer nettyServer = new NettyServer(nettyServerConfig);
nettyServer.start();

// 2. 注册服务到注册中心
serviceRegistry.register();

// 3. 加入集群
clusterCoordinator.joinCluster();

log.info("Netty集群节点启动成功: nodeId={}, port={}",
nettyServerConfig.getNodeId(), nettyServerConfig.getPort());

} catch (Exception e) {
log.error("Netty集群节点启动失败: error={}", e.getMessage(), e);
System.exit(1);
}
}

@PreDestroy
public void stopNettyServer() {
try {
// 1. 离开集群
clusterCoordinator.leaveCluster();

// 2. 注销服务
serviceRegistry.deregister();

log.info("Netty集群节点停止成功");

} catch (Exception e) {
log.error("Netty集群节点停止失败: error={}", e.getMessage(), e);
}
}
}

2.2 Netty服务器实现

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
/**
* Netty服务器
* 支持集群部署
*/
@Component
@Slf4j
public class NettyServer {

private EventLoopGroup bossGroup;
private EventLoopGroup workerGroup;
private Channel serverChannel;

@Autowired
private NettyServerConfig config;

@Autowired
private NettyChannelHandler channelHandler;

@Autowired
private ClusterConnectionManager connectionManager;

/**
* 启动Netty服务器
*/
public void start() throws InterruptedException {
// 1. 创建EventLoopGroup
bossGroup = new NioEventLoopGroup(config.getBossThreads());
workerGroup = new NioEventLoopGroup(config.getWorkerThreads());

try {
// 2. 创建ServerBootstrap
ServerBootstrap bootstrap = new ServerBootstrap();
bootstrap.group(bossGroup, workerGroup)
.channel(NioServerSocketChannel.class)
.option(ChannelOption.SO_BACKLOG, config.getBacklog())
.option(ChannelOption.SO_REUSEADDR, true)
.childOption(ChannelOption.SO_KEEPALIVE, true)
.childOption(ChannelOption.TCP_NODELAY, true)
.childOption(ChannelOption.SO_RCVBUF, config.getReceiveBufferSize())
.childOption(ChannelOption.SO_SNDBUF, config.getSendBufferSize())
.childHandler(new ChannelInitializer<SocketChannel>() {
@Override
protected void initChannel(SocketChannel ch) throws Exception {
ChannelPipeline pipeline = ch.pipeline();

// 添加编解码器
pipeline.addLast(new LengthFieldBasedFrameDecoder(
config.getMaxFrameLength(),
0, 4, 0, 4));
pipeline.addLast(new LengthFieldPrepender(4));
pipeline.addLast(new StringDecoder(CharsetUtil.UTF_8));
pipeline.addLast(new StringEncoder(CharsetUtil.UTF_8));

// 添加业务处理器
pipeline.addLast(channelHandler);
}
});

// 3. 绑定端口
ChannelFuture future = bootstrap.bind(config.getPort()).sync();
serverChannel = future.channel();

log.info("Netty服务器启动成功: nodeId={}, port={}, bossThreads={}, workerThreads={}",
config.getNodeId(), config.getPort(),
config.getBossThreads(), config.getWorkerThreads());

// 4. 等待服务器关闭
serverChannel.closeFuture().sync();

} finally {
// 5. 关闭EventLoopGroup
shutdown();
}
}

/**
* 关闭Netty服务器
*/
public void shutdown() {
if (serverChannel != null) {
serverChannel.close();
}
if (workerGroup != null) {
workerGroup.shutdownGracefully();
}
if (bossGroup != null) {
bossGroup.shutdownGracefully();
}
log.info("Netty服务器关闭成功");
}
}

2.3 Netty服务器配置

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
/**
* Netty服务器配置
*/
@Data
@Configuration
@ConfigurationProperties(prefix = "netty.server")
public class NettyServerConfig {

/**
* 节点ID
*/
private String nodeId;

/**
* 服务器端口
*/
private int port = 8888;

/**
* Boss线程数
*/
private int bossThreads = 1;

/**
* Worker线程数
*/
private int workerThreads = Runtime.getRuntime().availableProcessors() * 2;

/**
* 连接队列大小
*/
private int backlog = 1024;

/**
* 最大帧长度
*/
private int maxFrameLength = 65535;

/**
* 接收缓冲区大小
*/
private int receiveBufferSize = 65535;

/**
* 发送缓冲区大小
*/
private int sendBufferSize = 65535;

/**
* 集群节点列表
*/
private List<String> clusterNodes = new ArrayList<>();
}

2.4 集群连接管理器

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
/**
* 集群连接管理器
* 管理Netty集群节点间的连接和状态同步
*/
@Component
@Slf4j
public class ClusterConnectionManager {

@Autowired
private NettyServerConfig config;

@Autowired
private RedisTemplate<String, Object> redisTemplate;

private final Map<String, Channel> clusterChannels = new ConcurrentHashMap<>();
private final ScheduledExecutorService scheduler = Executors.newScheduledThreadPool(2);

/**
* 初始化集群连接
*/
@PostConstruct
public void init() {
// 1. 连接到其他集群节点
connectToClusterNodes();

// 2. 启动心跳检测
startHeartbeat();

// 3. 启动状态同步
startStatusSync();
}

/**
* 连接到集群节点
*/
private void connectToClusterNodes() {
for (String nodeAddress : config.getClusterNodes()) {
if (isCurrentNode(nodeAddress)) {
continue; // 跳过当前节点
}

try {
connectToNode(nodeAddress);
} catch (Exception e) {
log.error("连接到集群节点失败: nodeAddress={}, error={}",
nodeAddress, e.getMessage(), e);
}
}
}

/**
* 连接到指定节点
*/
private void connectToNode(String nodeAddress) {
String[] parts = nodeAddress.split(":");
String host = parts[0];
int port = Integer.parseInt(parts[1]);

EventLoopGroup group = new NioEventLoopGroup();
Bootstrap bootstrap = new Bootstrap();
bootstrap.group(group)
.channel(NioSocketChannel.class)
.option(ChannelOption.TCP_NODELAY, true)
.handler(new ChannelInitializer<SocketChannel>() {
@Override
protected void initChannel(SocketChannel ch) throws Exception {
ChannelPipeline pipeline = ch.pipeline();
pipeline.addLast(new LengthFieldBasedFrameDecoder(65535, 0, 4, 0, 4));
pipeline.addLast(new LengthFieldPrepender(4));
pipeline.addLast(new StringDecoder(CharsetUtil.UTF_8));
pipeline.addLast(new StringEncoder(CharsetUtil.UTF_8));
pipeline.addLast(new ClusterChannelHandler());
}
});

ChannelFuture future = bootstrap.connect(host, port);
future.addListener((ChannelFutureListener) f -> {
if (f.isSuccess()) {
clusterChannels.put(nodeAddress, f.channel());
log.info("连接到集群节点成功: nodeAddress={}", nodeAddress);
} else {
log.error("连接到集群节点失败: nodeAddress={}", nodeAddress);
}
});
}

/**
* 判断是否为当前节点
*/
private boolean isCurrentNode(String nodeAddress) {
String currentAddress = getCurrentNodeAddress();
return currentAddress.equals(nodeAddress);
}

/**
* 获取当前节点地址
*/
private String getCurrentNodeAddress() {
try {
String host = InetAddress.getLocalHost().getHostAddress();
return host + ":" + config.getPort();
} catch (Exception e) {
return "localhost:" + config.getPort();
}
}

/**
* 启动心跳检测
*/
private void startHeartbeat() {
scheduler.scheduleAtFixedRate(() -> {
try {
sendHeartbeat();
} catch (Exception e) {
log.error("发送心跳失败: error={}", e.getMessage(), e);
}
}, 5, 10, TimeUnit.SECONDS);
}

/**
* 发送心跳
*/
private void sendHeartbeat() {
ClusterMessage message = new ClusterMessage();
message.setType("HEARTBEAT");
message.setNodeId(config.getNodeId());
message.setTimestamp(System.currentTimeMillis());

broadcastMessage(message);

// 更新Redis中的节点状态
String key = "netty:cluster:node:" + config.getNodeId();
redisTemplate.opsForValue().set(key, message, 30, TimeUnit.SECONDS);
}

/**
* 启动状态同步
*/
private void startStatusSync() {
scheduler.scheduleAtFixedRate(() -> {
try {
syncConnectionStatus();
} catch (Exception e) {
log.error("同步连接状态失败: error={}", e.getMessage(), e);
}
}, 10, 30, TimeUnit.SECONDS);
}

/**
* 同步连接状态
*/
private void syncConnectionStatus() {
// 获取当前节点的连接数
int connectionCount = getConnectionCount();

// 更新到Redis
String key = "netty:cluster:status:" + config.getNodeId();
Map<String, Object> status = new HashMap<>();
status.put("nodeId", config.getNodeId());
status.put("connectionCount", connectionCount);
status.put("updateTime", System.currentTimeMillis());
redisTemplate.opsForHash().putAll(key, status);
redisTemplate.expire(key, 60, TimeUnit.SECONDS);
}

/**
* 获取连接数
*/
private int getConnectionCount() {
// 从连接管理器获取连接数
return 0; // 实际实现中从连接管理器获取
}

/**
* 广播消息到所有集群节点
*/
public void broadcastMessage(ClusterMessage message) {
String messageStr = JSON.toJSONString(message);
for (Map.Entry<String, Channel> entry : clusterChannels.entrySet()) {
Channel channel = entry.getValue();
if (channel != null && channel.isActive()) {
channel.writeAndFlush(messageStr);
}
}
}

/**
* 获取集群节点列表
*/
public List<String> getClusterNodes() {
return new ArrayList<>(clusterChannels.keySet());
}
}

/**
* 集群通道处理器
*/
@Slf4j
public class ClusterChannelHandler extends ChannelInboundHandlerAdapter {

@Override
public void channelRead(ChannelHandlerContext ctx, Object msg) throws Exception {
String message = (String) msg;
ClusterMessage clusterMessage = JSON.parseObject(message, ClusterMessage.class);

// 处理集群消息
handleClusterMessage(clusterMessage);
}

private void handleClusterMessage(ClusterMessage message) {
switch (message.getType()) {
case "HEARTBEAT":
log.debug("收到心跳消息: nodeId={}", message.getNodeId());
break;
case "STATUS_SYNC":
log.debug("收到状态同步消息: nodeId={}", message.getNodeId());
break;
default:
log.warn("未知的集群消息类型: type={}", message.getType());
}
}
}

2.5 服务注册实现

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
/**
* 服务注册实现
* 注册Netty服务到注册中心
*/
@Component
@Slf4j
public class ServiceRegistry {

@Autowired
private NettyServerConfig config;

@Autowired
private EurekaClient eurekaClient;

@Autowired
private RedisTemplate<String, Object> redisTemplate;

/**
* 注册服务
*/
public void register() {
try {
// 1. 注册到Eureka
registerToEureka();

// 2. 注册到Redis
registerToRedis();

log.info("服务注册成功: nodeId={}, port={}",
config.getNodeId(), config.getPort());

} catch (Exception e) {
log.error("服务注册失败: error={}", e.getMessage(), e);
throw new RuntimeException("服务注册失败", e);
}
}

/**
* 注册到Eureka
*/
private void registerToEureka() {
// Eureka自动注册,通过Spring Cloud配置
log.info("注册到Eureka: serviceName=netty-cluster, nodeId={}", config.getNodeId());
}

/**
* 注册到Redis
*/
private void registerToRedis() {
String key = "netty:service:node:" + config.getNodeId();
Map<String, Object> serviceInfo = new HashMap<>();
serviceInfo.put("nodeId", config.getNodeId());
serviceInfo.put("host", getLocalHost());
serviceInfo.put("port", config.getPort());
serviceInfo.put("status", "UP");
serviceInfo.put("registerTime", System.currentTimeMillis());

redisTemplate.opsForHash().putAll(key, serviceInfo);
redisTemplate.expire(key, 60, TimeUnit.SECONDS);

// 添加到服务列表
String listKey = "netty:service:nodes";
redisTemplate.opsForSet().add(listKey, config.getNodeId());
}

/**
* 注销服务
*/
public void deregister() {
try {
// 1. 从Redis注销
deregisterFromRedis();

log.info("服务注销成功: nodeId={}", config.getNodeId());

} catch (Exception e) {
log.error("服务注销失败: error={}", e.getMessage(), e);
}
}

/**
* 从Redis注销
*/
private void deregisterFromRedis() {
String key = "netty:service:node:" + config.getNodeId();
redisTemplate.delete(key);

String listKey = "netty:service:nodes";
redisTemplate.opsForSet().remove(listKey, config.getNodeId());
}

/**
* 获取本地主机地址
*/
private String getLocalHost() {
try {
return InetAddress.getLocalHost().getHostAddress();
} catch (Exception e) {
return "localhost";
}
}
}

2.6 集群协调器

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
/**
* 集群协调器
* 负责集群节点的加入和离开
*/
@Component
@Slf4j
public class ClusterCoordinator {

@Autowired
private NettyServerConfig config;

@Autowired
private RedisTemplate<String, Object> redisTemplate;

@Autowired
private ClusterConnectionManager connectionManager;

/**
* 加入集群
*/
public void joinCluster() {
try {
// 1. 注册节点信息到Redis
registerNode();

// 2. 获取集群节点列表
List<String> clusterNodes = getClusterNodes();

// 3. 连接到其他节点
connectionManager.connectToClusterNodes();

log.info("加入集群成功: nodeId={}, clusterNodes={}",
config.getNodeId(), clusterNodes);

} catch (Exception e) {
log.error("加入集群失败: error={}", e.getMessage(), e);
throw new RuntimeException("加入集群失败", e);
}
}

/**
* 离开集群
*/
public void leaveCluster() {
try {
// 1. 从Redis注销节点
unregisterNode();

// 2. 通知其他节点
notifyClusterLeave();

log.info("离开集群成功: nodeId={}", config.getNodeId());

} catch (Exception e) {
log.error("离开集群失败: error={}", e.getMessage(), e);
}
}

/**
* 注册节点
*/
private void registerNode() {
String key = "netty:cluster:node:" + config.getNodeId();
Map<String, Object> nodeInfo = new HashMap<>();
nodeInfo.put("nodeId", config.getNodeId());
nodeInfo.put("host", getLocalHost());
nodeInfo.put("port", config.getPort());
nodeInfo.put("status", "ACTIVE");
nodeInfo.put("joinTime", System.currentTimeMillis());

redisTemplate.opsForHash().putAll(key, nodeInfo);

// 添加到集群节点列表
String listKey = "netty:cluster:nodes";
redisTemplate.opsForSet().add(listKey, config.getNodeId());
}

/**
* 注销节点
*/
private void unregisterNode() {
String key = "netty:cluster:node:" + config.getNodeId();
redisTemplate.delete(key);

String listKey = "netty:cluster:nodes";
redisTemplate.opsForSet().remove(listKey, config.getNodeId());
}

/**
* 获取集群节点列表
*/
private List<String> getClusterNodes() {
String listKey = "netty:cluster:nodes";
Set<Object> nodeIds = redisTemplate.opsForSet().members(listKey);

List<String> nodes = new ArrayList<>();
if (nodeIds != null) {
for (Object nodeId : nodeIds) {
String key = "netty:cluster:node:" + nodeId;
Map<Object, Object> nodeInfo = redisTemplate.opsForHash().entries(key);
if (nodeInfo != null && !nodeInfo.isEmpty()) {
String host = (String) nodeInfo.get("host");
String port = String.valueOf(nodeInfo.get("port"));
nodes.add(host + ":" + port);
}
}
}

return nodes;
}

/**
* 通知集群离开
*/
private void notifyClusterLeave() {
ClusterMessage message = new ClusterMessage();
message.setType("NODE_LEAVE");
message.setNodeId(config.getNodeId());
message.setTimestamp(System.currentTimeMillis());

connectionManager.broadcastMessage(message);
}

/**
* 获取本地主机地址
*/
private String getLocalHost() {
try {
return InetAddress.getLocalHost().getHostAddress();
} catch (Exception e) {
return "localhost";
}
}
}

3. Kafka集群实现

3.1 Kafka集群配置

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
# server.properties - Broker 1
broker.id=1
listeners=PLAINTEXT://192.168.1.201:9092
advertised.listeners=PLAINTEXT://192.168.1.201:9092
log.dirs=/data/kafka-logs
num.network.threads=8
num.io.threads=16
socket.send.buffer.bytes=102400
socket.receive.buffer.bytes=102400
socket.request.max.bytes=104857600
log.retention.hours=168
log.segment.bytes=1073741824
log.retention.check.interval.ms=300000
zookeeper.connect=192.168.1.301:2181,192.168.1.302:2181,192.168.1.303:2181
zookeeper.connection.timeout.ms=18000
group.initial.rebalance.delay.ms=0
num.partitions=6
default.replication.factor=3
min.insync.replicas=2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
# server.properties - Broker 2
broker.id=2
listeners=PLAINTEXT://192.168.1.202:9092
advertised.listeners=PLAINTEXT://192.168.1.202:9092
log.dirs=/data/kafka-logs
num.network.threads=8
num.io.threads=16
socket.send.buffer.bytes=102400
socket.receive.buffer.bytes=102400
socket.request.max.bytes=104857600
log.retention.hours=168
log.segment.bytes=1073741824
log.retention.check.interval.ms=300000
zookeeper.connect=192.168.1.301:2181,192.168.1.302:2181,192.168.1.303:2181
zookeeper.connection.timeout.ms=18000
group.initial.rebalance.delay.ms=0
num.partitions=6
default.replication.factor=3
min.insync.replicas=2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
# server.properties - Broker 3
broker.id=3
listeners=PLAINTEXT://192.168.1.203:9092
advertised.listeners=PLAINTEXT://192.168.1.203:9092
log.dirs=/data/kafka-logs
num.network.threads=8
num.io.threads=16
socket.send.buffer.bytes=102400
socket.receive.buffer.bytes=102400
socket.request.max.bytes=104857600
log.retention.hours=168
log.segment.bytes=1073741824
log.retention.check.interval.ms=300000
zookeeper.connect=192.168.1.301:2181,192.168.1.302:2181,192.168.1.303:2181
zookeeper.connection.timeout.ms=18000
group.initial.rebalance.delay.ms=0
num.partitions=6
default.replication.factor=3
min.insync.replicas=2

3.2 Kafka集群管理服务

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
/**
* Kafka集群管理服务
* 管理Kafka集群的Topic创建、分区分配等
*/
@Service
@Slf4j
public class KafkaClusterService {

@Autowired
private KafkaAdmin kafkaAdmin;

@Autowired
private AdminClient adminClient;

/**
* 创建Topic(3个分区,3个副本)
*/
public void createTopic(String topicName, int partitions, int replicationFactor) {
try {
// 检查Topic是否已存在
if (topicExists(topicName)) {
log.warn("Topic已存在: topicName={}", topicName);
return;
}

// 创建Topic
NewTopic newTopic = new NewTopic(topicName, partitions, (short) replicationFactor);
CreateTopicsResult result = adminClient.createTopics(Collections.singletonList(newTopic));

// 等待创建完成
result.all().get(30, TimeUnit.SECONDS);

log.info("创建Topic成功: topicName={}, partitions={}, replicationFactor={}",
topicName, partitions, replicationFactor);

} catch (Exception e) {
log.error("创建Topic失败: topicName={}, error={}",
topicName, e.getMessage(), e);
throw new BusinessException("创建Topic失败: " + e.getMessage());
}
}

/**
* 检查Topic是否存在
*/
public boolean topicExists(String topicName) {
try {
DescribeTopicsResult result = adminClient.describeTopics(
Collections.singletonList(topicName));
result.all().get(5, TimeUnit.SECONDS);
return true;
} catch (Exception e) {
return false;
}
}

/**
* 获取Topic信息
*/
public TopicDescription getTopicInfo(String topicName) {
try {
DescribeTopicsResult result = adminClient.describeTopics(
Collections.singletonList(topicName));
Map<String, TopicDescription> topics = result.all().get(5, TimeUnit.SECONDS);
return topics.get(topicName);

} catch (Exception e) {
log.error("获取Topic信息失败: topicName={}, error={}",
topicName, e.getMessage(), e);
throw new BusinessException("获取Topic信息失败: " + e.getMessage());
}
}

/**
* 获取集群Broker列表
*/
public List<BrokerInfo> getClusterBrokers() {
try {
DescribeClusterResult result = adminClient.describeCluster();
Collection<Node> nodes = result.nodes().get(5, TimeUnit.SECONDS);

List<BrokerInfo> brokers = new ArrayList<>();
for (Node node : nodes) {
BrokerInfo broker = new BrokerInfo();
broker.setId(node.id());
broker.setHost(node.host());
broker.setPort(node.port());
brokers.add(broker);
}

return brokers;

} catch (Exception e) {
log.error("获取集群Broker列表失败: error={}", e.getMessage(), e);
throw new BusinessException("获取集群Broker列表失败: " + e.getMessage());
}
}

/**
* 获取Topic分区分布
*/
public Map<Integer, List<Integer>> getTopicPartitionDistribution(String topicName) {
try {
TopicDescription topicDescription = getTopicInfo(topicName);
Map<Integer, List<Integer>> distribution = new HashMap<>();

for (TopicPartitionInfo partitionInfo : topicDescription.partitions()) {
int partition = partitionInfo.partition();
List<Integer> replicas = partitionInfo.replicas().stream()
.map(Node::id)
.collect(Collectors.toList());
distribution.put(partition, replicas);
}

return distribution;

} catch (Exception e) {
log.error("获取Topic分区分布失败: topicName={}, error={}",
topicName, e.getMessage(), e);
throw new BusinessException("获取Topic分区分布失败: " + e.getMessage());
}
}
}

3.3 Kafka生产者配置(集群)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
/**
* Kafka生产者配置(集群)
*/
@Configuration
@Slf4j
public class KafkaProducerConfig {

@Value("${spring.kafka.bootstrap-servers}")
private String bootstrapServers;

/**
* 生产者工厂
*/
@Bean
public ProducerFactory<String, String> producerFactory() {
Map<String, Object> configProps = new HashMap<>();

// 集群配置
configProps.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
configProps.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
StringSerializer.class);
configProps.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
StringSerializer.class);

// 高可用配置
configProps.put(ProducerConfig.ACKS_CONFIG, "all"); // 等待所有副本确认
configProps.put(ProducerConfig.RETRIES_CONFIG, 3); // 重试次数
configProps.put(ProducerConfig.MAX_IN_FLIGHT_REQUESTS_PER_CONNECTION, 1);

// 性能优化
configProps.put(ProducerConfig.BATCH_SIZE_CONFIG, 16384); // 批次大小
configProps.put(ProducerConfig.LINGER_MS_CONFIG, 10); // 等待时间
configProps.put(ProducerConfig.BUFFER_MEMORY_CONFIG, 33554432); // 缓冲区大小
configProps.put(ProducerConfig.COMPRESSION_TYPE_CONFIG, "snappy"); // 压缩类型

// 分区策略
configProps.put(ProducerConfig.PARTITIONER_CLASS_CONFIG,
CustomPartitioner.class.getName());

return new DefaultKafkaProducerFactory<>(configProps);
}

/**
* Kafka模板
*/
@Bean
public KafkaTemplate<String, String> kafkaTemplate() {
return new KafkaTemplate<>(producerFactory());
}
}

/**
* 自定义分区器
* 实现负载均衡
*/
public class CustomPartitioner implements Partitioner {

@Override
public int partition(String topic, Object key, byte[] keyBytes,
Object value, byte[] valueBytes, Cluster cluster) {
// 根据key进行分区
if (key != null) {
int partitionCount = cluster.partitionCountForTopic(topic);
return Math.abs(key.hashCode()) % partitionCount;
}

// 如果没有key,使用轮询
return 0;
}

@Override
public void close() {
// 清理资源
}

@Override
public void configure(Map<String, ?> configs) {
// 配置初始化
}
}

3.4 Kafka消费者配置(集群)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
/**
* Kafka消费者配置(集群)
*/
@Configuration
@Slf4j
public class KafkaConsumerConfig {

@Value("${spring.kafka.bootstrap-servers}")
private String bootstrapServers;

@Value("${spring.kafka.consumer.group-id}")
private String groupId;

/**
* 消费者工厂
*/
@Bean
public ConsumerFactory<String, String> consumerFactory() {
Map<String, Object> configProps = new HashMap<>();

// 集群配置
configProps.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
configProps.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
configProps.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,
StringDeserializer.class);
configProps.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,
StringDeserializer.class);

// 高可用配置
configProps.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, false); // 手动提交
configProps.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "latest");
configProps.put(ConsumerConfig.MAX_POLL_RECORDS_CONFIG, 500); // 每次拉取最大记录数
configProps.put(ConsumerConfig.MAX_POLL_INTERVAL_MS_CONFIG, 300000); // 最大拉取间隔

// 分区分配策略
configProps.put(ConsumerConfig.PARTITION_ASSIGNMENT_STRATEGY_CONFIG,
Collections.singletonList(RangeAssignor.class));

return new DefaultKafkaConsumerFactory<>(configProps);
}

/**
* 消费者监听器容器工厂
*/
@Bean
public ConcurrentKafkaListenerContainerFactory<String, String>
kafkaListenerContainerFactory() {
ConcurrentKafkaListenerContainerFactory<String, String> factory =
new ConcurrentKafkaListenerContainerFactory<>();
factory.setConsumerFactory(consumerFactory());

// 并发消费者数量
factory.setConcurrency(3);

// 错误处理
factory.setCommonErrorHandler(new DefaultErrorHandler());

// 手动确认模式
factory.getContainerProperties().setAckMode(
ContainerProperties.AckMode.MANUAL_IMMEDIATE);

return factory;
}
}

3.5 Kafka集群监控服务

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
/**
* Kafka集群监控服务
* 监控Kafka集群状态、Topic状态、消费者组状态
*/
@Service
@Slf4j
public class KafkaClusterMonitorService {

@Autowired
private AdminClient adminClient;

@Autowired
private KafkaConsumer<String, String> kafkaConsumer;

@Autowired
private MeterRegistry meterRegistry;

/**
* 监控集群状态
*/
@Scheduled(fixedRate = 60000) // 每分钟执行一次
public void monitorClusterStatus() {
try {
// 1. 获取集群Broker信息
List<BrokerInfo> brokers = getClusterBrokers();

// 2. 检查Broker健康状态
for (BrokerInfo broker : brokers) {
checkBrokerHealth(broker);
}

// 3. 记录监控指标
recordClusterMetrics(brokers);

} catch (Exception e) {
log.error("监控集群状态失败: error={}", e.getMessage(), e);
}
}

/**
* 检查Broker健康状态
*/
private void checkBrokerHealth(BrokerInfo broker) {
try {
// 尝试连接Broker
Properties props = new Properties();
props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,
broker.getHost() + ":" + broker.getPort());
props.put(ConsumerConfig.GROUP_ID_CONFIG, "health-check-group");
props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,
StringDeserializer.class);
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,
StringDeserializer.class);

try (KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props)) {
consumer.listTopics();
log.debug("Broker健康检查通过: brokerId={}", broker.getId());
}

} catch (Exception e) {
log.error("Broker健康检查失败: brokerId={}, error={}",
broker.getId(), e.getMessage(), e);
}
}

/**
* 记录监控指标
*/
private void recordClusterMetrics(List<BrokerInfo> brokers) {
Gauge.builder("kafka.cluster.broker.count", brokers, List::size)
.register(meterRegistry);

for (BrokerInfo broker : brokers) {
Gauge.builder("kafka.cluster.broker.status", broker,
b -> b.isHealthy() ? 1 : 0)
.tag("broker_id", String.valueOf(broker.getId()))
.register(meterRegistry);
}
}

/**
* 获取集群Broker列表
*/
private List<BrokerInfo> getClusterBrokers() {
try {
DescribeClusterResult result = adminClient.describeCluster();
Collection<Node> nodes = result.nodes().get(5, TimeUnit.SECONDS);

List<BrokerInfo> brokers = new ArrayList<>();
for (Node node : nodes) {
BrokerInfo broker = new BrokerInfo();
broker.setId(node.id());
broker.setHost(node.host());
broker.setPort(node.port());
broker.setHealthy(true);
brokers.add(broker);
}

return brokers;

} catch (Exception e) {
log.error("获取集群Broker列表失败: error={}", e.getMessage(), e);
return Collections.emptyList();
}
}

/**
* 监控Topic状态
*/
public TopicMetrics getTopicMetrics(String topicName) {
try {
TopicDescription topicDescription = getTopicDescription(topicName);

TopicMetrics metrics = new TopicMetrics();
metrics.setTopicName(topicName);
metrics.setPartitionCount(topicDescription.partitions().size());

// 计算总消息数(需要从日志中获取)
long totalMessages = calculateTotalMessages(topicName);
metrics.setTotalMessages(totalMessages);

return metrics;

} catch (Exception e) {
log.error("获取Topic指标失败: topicName={}, error={}",
topicName, e.getMessage(), e);
return null;
}
}

/**
* 获取Topic描述
*/
private TopicDescription getTopicDescription(String topicName) {
try {
DescribeTopicsResult result = adminClient.describeTopics(
Collections.singletonList(topicName));
Map<String, TopicDescription> topics = result.all().get(5, TimeUnit.SECONDS);
return topics.get(topicName);
} catch (Exception e) {
throw new BusinessException("获取Topic描述失败: " + e.getMessage());
}
}

/**
* 计算总消息数
*/
private long calculateTotalMessages(String topicName) {
// 实际实现中需要从Kafka日志中计算
return 0;
}
}

4. 负载均衡配置

4.1 Nginx负载均衡配置

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
# nginx.conf
upstream netty_cluster {
# 轮询策略
server 192.168.1.101:8888 weight=1;
server 192.168.1.102:8888 weight=1;
server 192.168.1.103:8888 weight=1;

# 健康检查
keepalive 32;
}

server {
listen 80;
server_name netty.example.com;

location / {
proxy_pass http://netty_cluster;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;

# 连接超时
proxy_connect_timeout 60s;
proxy_send_timeout 60s;
proxy_read_timeout 60s;

# 缓冲设置
proxy_buffering on;
proxy_buffer_size 4k;
proxy_buffers 8 4k;
}

# 健康检查
location /health {
access_log off;
return 200 "healthy\n";
add_header Content-Type text/plain;
}
}

4.2 HAProxy负载均衡配置

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
# haproxy.cfg
global
log /dev/log local0
maxconn 4096
daemon

defaults
log global
mode tcp
option tcplog
option dontlognull
retries 3
timeout connect 5000ms
timeout client 50000ms
timeout server 50000ms

frontend netty_frontend
bind *:80
default_backend netty_backend

backend netty_backend
balance roundrobin
option tcp-check
tcp-check connect
tcp-check send "PING\r\n"
tcp-check expect string "PONG"

server netty1 192.168.1.101:8888 check inter 2000 rise 2 fall 3
server netty2 192.168.1.102:8888 check inter 2000 rise 2 fall 3
server netty3 192.168.1.103:8888 check inter 2000 rise 2 fall 3

listen stats
bind *:8404
stats enable
stats uri /stats
stats refresh 30s

5. Zookeeper集群配置

5.1 Zookeeper配置文件

1
2
3
4
5
6
7
8
9
10
11
# zoo.cfg - ZK节点1
dataDir=/data/zookeeper
clientPort=2181
server.1=192.168.1.301:2888:3888
server.2=192.168.1.302:2888:3888
server.3=192.168.1.303:2888:3888
initLimit=10
syncLimit=5
maxClientCnxns=60
autopurge.snapRetainCount=3
autopurge.purgeInterval=1
1
2
3
4
5
6
7
8
9
10
11
# zoo.cfg - ZK节点2
dataDir=/data/zookeeper
clientPort=2181
server.1=192.168.1.301:2888:3888
server.2=192.168.1.302:2888:3888
server.3=192.168.1.303:2888:3888
initLimit=10
syncLimit=5
maxClientCnxns=60
autopurge.snapRetainCount=3
autopurge.purgeInterval=1
1
2
3
4
5
6
7
8
9
10
11
# zoo.cfg - ZK节点3
dataDir=/data/zookeeper
clientPort=2181
server.1=192.168.1.301:2888:3888
server.2=192.168.1.302:2888:3888
server.3=192.168.1.303:2888:3888
initLimit=10
syncLimit=5
maxClientCnxns=60
autopurge.snapRetainCount=3
autopurge.purgeInterval=1

5.2 Zookeeper集群管理服务

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
/**
* Zookeeper集群管理服务
*/
@Service
@Slf4j
public class ZookeeperClusterService {

@Value("${zookeeper.connect}")
private String zookeeperConnect;

private CuratorFramework client;

@PostConstruct
public void init() {
// 创建Zookeeper客户端
client = CuratorFrameworkFactory.builder()
.connectString(zookeeperConnect)
.sessionTimeoutMs(5000)
.connectionTimeoutMs(3000)
.retryPolicy(new ExponentialBackoffRetry(1000, 3))
.build();

client.start();

log.info("Zookeeper客户端连接成功: connectString={}", zookeeperConnect);
}

@PreDestroy
public void destroy() {
if (client != null) {
client.close();
}
}

/**
* 检查Zookeeper集群状态
*/
public boolean checkClusterHealth() {
try {
// 检查连接状态
if (client.getZookeeperClient().isConnected()) {
// 获取集群状态
String state = client.getState().name();
log.debug("Zookeeper集群状态: state={}", state);
return "CONNECTED".equals(state);
}
return false;

} catch (Exception e) {
log.error("检查Zookeeper集群状态失败: error={}", e.getMessage(), e);
return false;
}
}

/**
* 创建节点
*/
public void createNode(String path, String data) {
try {
client.create()
.creatingParentsIfNeeded()
.withMode(CreateMode.PERSISTENT)
.forPath(path, data.getBytes());

log.info("创建Zookeeper节点成功: path={}", path);

} catch (Exception e) {
log.error("创建Zookeeper节点失败: path={}, error={}",
path, e.getMessage(), e);
throw new BusinessException("创建节点失败: " + e.getMessage());
}
}

/**
* 获取节点数据
*/
public String getNodeData(String path) {
try {
byte[] data = client.getData().forPath(path);
return new String(data);

} catch (Exception e) {
log.error("获取Zookeeper节点数据失败: path={}, error={}",
path, e.getMessage(), e);
throw new BusinessException("获取节点数据失败: " + e.getMessage());
}
}
}

6. 集群监控和告警

6.1 集群监控指标

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
/**
* 集群监控指标收集
*/
@Component
@Slf4j
public class ClusterMetricsCollector {

@Autowired
private MeterRegistry meterRegistry;

@Autowired
private NettyServerConfig nettyConfig;

@Autowired
private KafkaClusterMonitorService kafkaMonitor;

/**
* 收集Netty集群指标
*/
@Scheduled(fixedRate = 30000) // 每30秒收集一次
public void collectNettyMetrics() {
try {
// 收集连接数
int connectionCount = getConnectionCount();
Gauge.builder("netty.cluster.connection.count",
() -> connectionCount)
.tag("node_id", nettyConfig.getNodeId())
.register(meterRegistry);

// 收集消息处理数
long messageCount = getMessageCount();
Counter.builder("netty.cluster.message.total")
.tag("node_id", nettyConfig.getNodeId())
.register(meterRegistry)
.increment(messageCount);

} catch (Exception e) {
log.error("收集Netty集群指标失败: error={}", e.getMessage(), e);
}
}

/**
* 收集Kafka集群指标
*/
@Scheduled(fixedRate = 60000) // 每分钟收集一次
public void collectKafkaMetrics() {
try {
List<BrokerInfo> brokers = kafkaMonitor.getClusterBrokers();

Gauge.builder("kafka.cluster.broker.count", brokers, List::size)
.register(meterRegistry);

} catch (Exception e) {
log.error("收集Kafka集群指标失败: error={}", e.getMessage(), e);
}
}

/**
* 获取连接数
*/
private int getConnectionCount() {
// 实际实现中从连接管理器获取
return 0;
}

/**
* 获取消息数
*/
private long getMessageCount() {
// 实际实现中从消息统计器获取
return 0;
}
}

6.2 告警规则配置

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
# prometheus-alert-rules.yml
groups:
- name: cluster_alerts
rules:
- alert: NettyClusterNodeDown
expr: up{job="netty-cluster"} == 0
for: 1m
labels:
severity: critical
annotations:
summary: "Netty集群节点下线"
description: "Netty集群节点 {{ $labels.instance }} 已下线超过1分钟"

- alert: KafkaBrokerDown
expr: kafka_cluster_broker_status == 0
for: 2m
labels:
severity: critical
annotations:
summary: "Kafka Broker下线"
description: "Kafka Broker {{ $labels.broker_id }} 已下线超过2分钟"

- alert: NettyClusterHighConnectionCount
expr: netty_cluster_connection_count > 10000
for: 5m
labels:
severity: warning
annotations:
summary: "Netty集群连接数过高"
description: "Netty集群节点 {{ $labels.node_id }} 连接数超过10000,当前值: {{ $value }}"

7. 配置文件

7.1 application.yml配置

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
# application.yml
spring:
application:
name: netty-kafka-cluster

kafka:
bootstrap-servers: 192.168.1.201:9092,192.168.1.202:9092,192.168.1.203:9092
producer:
key-serializer: org.apache.kafka.common.serialization.StringSerializer
value-serializer: org.apache.kafka.common.serialization.StringSerializer
acks: all
retries: 3
consumer:
group-id: netty-cluster-group
key-deserializer: org.apache.kafka.common.serialization.StringDeserializer
value-deserializer: org.apache.kafka.common.serialization.StringDeserializer
enable-auto-commit: false
auto-offset-reset: latest

netty:
server:
node-id: netty-node-1
port: 8888
boss-threads: 1
worker-threads: 8
backlog: 1024
max-frame-length: 65535
receive-buffer-size: 65535
send-buffer-size: 65535
cluster-nodes:
- 192.168.1.101:8888
- 192.168.1.102:8888
- 192.168.1.103:8888

zookeeper:
connect: 192.168.1.301:2181,192.168.1.302:2181,192.168.1.303:2181
session-timeout: 5000
connection-timeout: 3000

eureka:
client:
service-url:
defaultZone: http://localhost:8761/eureka/
instance:
prefer-ip-address: true
lease-renewal-interval-in-seconds: 10
lease-expiration-duration-in-seconds: 30

management:
endpoints:
web:
exposure:
include: health,info,metrics,prometheus
metrics:
export:
prometheus:
enabled: true

8. 总结

本文深入讲解了Netty和Kafka集群部署的Java微服务后端架构实战,涵盖了以下核心内容:

  1. Netty集群部署:3台机器部署Netty服务,实现高并发网络连接处理、集群节点协调、服务注册发现
  2. Kafka集群部署:3台机器部署Kafka Broker,实现消息队列的高可用、分布式处理、Topic分区和副本管理
  3. Zookeeper集群:3台机器部署Zookeeper,实现Kafka集群协调和服务发现
  4. 负载均衡:通过Nginx或HAProxy实现Netty集群的负载均衡和健康检查
  5. 集群协调:实现Netty集群节点间的连接管理、心跳检测、状态同步
  6. 服务注册发现:通过Eureka和Redis实现服务注册和发现
  7. 高可用保障:通过多副本、故障转移、健康检查保障系统高可用
  8. 监控告警:通过Prometheus和Grafana实现集群监控和告警
  9. 性能优化:通过连接池、批量处理、压缩等优化系统性能
  10. 配置管理:通过配置文件管理集群节点、Topic、分区等配置

通过本文的学习,读者可以掌握如何基于Java微服务架构实现Netty和Kafka的3台机器集群部署,为实际项目的高可用架构设计提供参考和指导。