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kafkaKafka 监控告警

Agent Skill

kafka 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

599

周安装

24

GitHub Stars

12

下载量

194
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:kafka(Kafka 监控告警)
来源仓库:https://github.com/claude-dev-suite/claude-dev-suite
仓库路径:skills/kafka
安装命令:
npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill kafka
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill kafka

简介

kafka 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态进行整理。

  • 它提供 Apache Kafka 的核心知识,包括 Docker 部署配置和消息生产消费模式。
  • 使用时需明确目标环境和账号权限,区分本地测试与生产操作;涉及删除资源时应先确认影响范围。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。
  • kafka 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Apache Kafka Core Knowledge

Deep Knowledge: Use mcp__documentation__fetch_docs with technology: kafka for comprehensive documentation.

Quick Start (Docker)

# docker-compose.yml
services:
  kafka:
    image: bitnami/kafka:latest
    ports:
      - "9092:9092"
    environment:
      - KAFKA_CFG_NODE_ID=0
      - KAFKA_CFG_PROCESS_ROLES=controller,broker
      - KAFKA_CFG_LISTENERS=PLAINTEXT://:9092,CONTROLLER://:9093
      - KAFKA_CFG_LISTENER_SECURITY_PROTOCOL_MAP=CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT
      - KAFKA_CFG_CONTROLLER_QUORUM_VOTERS=0@kafka:9093
      - KAFKA_CFG_CONTROLLER_LISTENER_NAMES=CONTROLLER
# Start
docker-compose up -d

# Create topic
docker exec kafka kafka-topics.sh --create --topic my-topic \
  --bootstrap-server localhost:9092 --partitions 3 --replication-factor 1

Core Concepts

ConceptDescription
TopicNamed stream of records, append-only log
PartitionOrdered, immutable sequence within topic
OffsetUnique ID for record within partition
Consumer GroupSet of consumers sharing topic consumption
BrokerKafka server handling storage and requests
Replication FactorNumber of partition copies across brokers

Architecture

┌─────────────────────────────────────────────────────────┐
│                    Kafka Cluster                        │
│  ┌─────────┐    ┌─────────┐    ┌─────────┐            │
│  │Broker 0 │    │Broker 1 │    │Broker 2 │            │
│  │ P0(L)   │    │ P0(F)   │    │ P1(L)   │            │
│  │ P1(F)   │    │ P1(F)   │    │ P0(F)   │            │
│  └─────────┘    └─────────┘    └─────────┘            │
└─────────────────────────────────────────────────────────┘
         ▲                              │
         │                              ▼
   ┌──────────┐                  ┌──────────────┐
   │ Producer │                  │Consumer Group│
   └──────────┘                  │  C1  C2  C3  │
                                 └──────────────┘

Producer Patterns

Node.js (kafkajs)

import { Kafka, Partitioners } from 'kafkajs';

const kafka = new Kafka({
  clientId: 'my-app',
  brokers: ['localhost:9092'],
});

const producer = kafka.producer({
  createPartitioner: Partitioners.DefaultPartitioner,
  idempotent: true, // Enable exactly-once
});

await producer.connect();

// Send single message
await producer.send({
  topic: 'orders',
  messages: [
    {
      key: orderId,           // Partition key
      value: JSON.stringify(order),
      headers: {
        'correlation-id': correlationId,
        'source': 'order-service',
      },
    },
  ],
});

// Batch send
await producer.sendBatch({
  topicMessages: [
    {
      topic: 'orders',
      messages: orders.map(o => ({
        key: o.id,
        value: JSON.stringify(o),
      })),
    },
  ],
});

await producer.disconnect();

Java (Spring Kafka)

@Configuration
public class KafkaConfig {
    @Bean
    public ProducerFactory<String, String> producerFactory() {
        Map<String, Object> config = new HashMap<>();
        config.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        config.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        config.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        config.put(ProducerConfig.ENABLE_IDEMPOTENCE_CONFIG, true);
        config.put(ProducerConfig.ACKS_CONFIG, "all");
        return new DefaultKafkaProducerFactory<>(config);
    }

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

@Service
public class OrderProducer {
    @Autowired
    private KafkaTemplate<String, String> kafkaTemplate;

    public void sendOrder(Order order) {
        kafkaTemplate.send("orders", order.getId(), objectMapper.writeValueAsString(order))
            .whenComplete((result, ex) -> {
                if (ex != null) {
                    log.error("Failed to send order", ex);
                }
            });
    }
}

Python (confluent-kafka)

from confluent_kafka import Producer
import json

conf = {
    'bootstrap.servers': 'localhost:9092',
    'client.id': 'my-app',
    'acks': 'all',
    'enable.idempotence': True,
}

producer = Producer(conf)

def delivery_callback(err, msg):
    if err:
        print(f'Message delivery failed: {err}')
    else:
        print(f'Message delivered to {msg.topic()}[{msg.partition()}]')

# Send message
producer.produce(
    topic='orders',
    key=order_id.encode('utf-8'),
    value=json.dumps(order).encode('utf-8'),
    callback=delivery_callback,
    headers={'correlation-id': correlation_id}
)

producer.flush()  # Wait for delivery

Go (segmentio/kafka-go)

package main

import (
    "context"
    "encoding/json"
    "github.com/segmentio/kafka-go"
)

func main() {
    writer := &kafka.Writer{
        Addr:         kafka.TCP("localhost:9092"),
        Topic:        "orders",
        Balancer:     &kafka.LeastBytes{},
        RequiredAcks: kafka.RequireAll,
    }

    defer writer.Close()

    order := Order{ID: "123", Amount: 100}
    value, _ := json.Marshal(order)

    err := writer.WriteMessages(context.Background(),
        kafka.Message{
            Key:   []byte(order.ID),
            Value: value,
            Headers: []kafka.Header{
                {Key: "correlation-id", Value: []byte("abc123")},
            },
        },
    )
}

Consumer Patterns

Node.js (kafkajs)

const consumer = kafka.consumer({
  groupId: 'order-processor',
  sessionTimeout: 30000,
  heartbeatInterval: 3000,
});

await consumer.connect();
await consumer.subscribe({ topics: ['orders'], fromBeginning: false });

await consumer.run({
  eachMessage: async ({ topic, partition, message }) => {
    const order = JSON.parse(message.value.toString());
    const correlationId = message.headers['correlation-id']?.toString();

    try {
      await processOrder(order);
      // Auto-commit on success
    } catch (error) {
      // Handle error - message will be redelivered
      throw error;
    }
  },
});

// Manual commit
await consumer.run({
  autoCommit: false,
  eachBatch: async ({ batch, resolveOffset, commitOffsetsIfNecessary }) => {
    for (const message of batch.messages) {
      await processMessage(message);
      resolveOffset(message.offset);
    }
    await commitOffsetsIfNecessary();
  },
});

Java (Spring Kafka)

@Configuration
@EnableKafka
public class KafkaConsumerConfig {
    @Bean
    public ConsumerFactory<String, String> consumerFactory() {
        Map<String, Object> config = new HashMap<>();
        config.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        config.put(ConsumerConfig.GROUP_ID_CONFIG, "order-processor");
        config.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        config.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        config.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, false);
        return new DefaultKafkaConsumerFactory<>(config);
    }

    @Bean
    public ConcurrentKafkaListenerContainerFactory<String, String> kafkaListenerContainerFactory() {
        ConcurrentKafkaListenerContainerFactory<String, String> factory =
            new ConcurrentKafkaListenerContainerFactory<>();
        factory.setConsumerFactory(consumerFactory());
        factory.getContainerProperties().setAckMode(AckMode.MANUAL);
        return factory;
    }
}

@Service
public class OrderConsumer {
    @KafkaListener(topics = "orders", groupId = "order-processor")
    public void consume(
            @Payload String message,
            @Header(KafkaHeaders.RECEIVED_KEY) String key,
            @Header("correlation-id") String correlationId,
            Acknowledgment ack) {

        Order order = objectMapper.readValue(message, Order.class);
        processOrder(order);
        ack.acknowledge();  // Manual commit
    }
}

Python (confluent-kafka)

from confluent_kafka import Consumer

conf = {
    'bootstrap.servers': 'localhost:9092',
    'group.id': 'order-processor',
    'auto.offset.reset': 'earliest',
    'enable.auto.commit': False,
}

consumer = Consumer(conf)
consumer.subscribe(['orders'])

try:
    while True:
        msg = consumer.poll(timeout=1.0)
        if msg is None:
            continue
        if msg.error():
            print(f"Consumer error: {msg.error()}")
            continue

        order = json.loads(msg.value().decode('utf-8'))
        process_order(order)
        consumer.commit(msg)  # Manual commit
finally:
    consumer.close()

Topic Configuration

# Create topic with configuration
kafka-topics.sh --create --topic orders \
  --bootstrap-server localhost:9092 \
  --partitions 12 \
  --replication-factor 3 \
  --config retention.ms=604800000 \
  --config cleanup.policy=delete \
  --config min.insync.replicas=2

# Alter topic config
kafka-configs.sh --alter --topic orders \
  --bootstrap-server localhost:9092 \
  --add-config retention.ms=172800000
ConfigDescriptionProduction Value
partitionsParallelism level3x consumer instances
replication.factorDurability3 (minimum)
min.insync.replicasWrite guarantee2
retention.msMessage retention7 days (604800000)
cleanup.policydelete or compactDepends on use case

When NOT to Use This Skill

Use alternative messaging solutions when:

  • Simple request/reply patterns - RabbitMQ or ActiveMQ are better suited
  • Low message volume (< 1000 msg/s) - Simpler brokers have less operational overhead
  • Strict message ordering across all messages - Use single partition or different broker
  • Serverless/managed services preferred - Use cloud-native options (SQS, Pub/Sub, Service Bus)
  • JMS compliance required - Use ActiveMQ
  • Lightweight microservices - NATS provides simpler operations
  • Primarily caching with messaging - Redis Pub/Sub may be sufficient

Anti-Patterns

Anti-PatternWhy It's BadSolution
Single partition for scaleLimits throughput to one consumerUse multiple partitions (3x consumer count)
No replication factorData loss on broker failureSet replication.factor >= 3
acks=1 in productionMessages can be lostUse acks=all with min.insync.replicas=2
Large messages (>1MB)Broker memory pressureUse external storage, send reference
Auto-commit offsetsDuplicate/lost messages on crashManual commit after processing
No consumer groupCan't scale consumersAlways use consumer groups
Synchronous sendPoor throughputUse async with callbacks
No dead letter topicFailed messages lostConfigure DLT for poison messages
Topic per message typeTopic explosionUse fewer topics with message headers
No monitoringInvisible consumer lagMonitor lag, throughput, errors

Quick Troubleshooting

IssueLikely CauseFix
Consumer lag growingSlow processing or insufficient consumersAdd consumers, optimize processing
Messages not arrivingTopic doesn't exist or wrong nameVerify topic with kafka-topics --list
Duplicate messagesConsumer crash before commitImplement idempotent processing
Out of order messagesMultiple partitionsUse single partition or partition key
"Leader not available"Broker down or partition reassignmentCheck broker health, wait for leader election
High latencyNetwork issues or under-replicatedCheck UnderReplicatedPartitions metric
Producer timeoutBroker overload or networkIncrease request.timeout.ms, check broker load
Offset commit failedRebalance in progressIncrease session.timeout.ms
Serialization errorsSchema mismatchUse schema registry, validate messages
Disk fullRetention too long or high volumeAdjust retention, add disk, compact logs

Production Readiness

Security Configuration

# Server (server.properties)
listeners=SASL_SSL://0.0.0.0:9093
security.inter.broker.protocol=SASL_SSL
sasl.mechanism.inter.broker.protocol=PLAIN
sasl.enabled.mechanisms=PLAIN

ssl.keystore.location=/path/to/keystore.jks
ssl.keystore.password=password
ssl.key.password=password
ssl.truststore.location=/path/to/truststore.jks
ssl.truststore.password=password

# ACLs
authorizer.class.name=kafka.security.authorizer.AclAuthorizer
super.users=User:admin
// Client with SASL/SSL
const kafka = new Kafka({
  clientId: 'my-app',
  brokers: ['kafka:9093'],
  ssl: {
    rejectUnauthorized: true,
    ca: [fs.readFileSync('/path/to/ca.pem')],
  },
  sasl: {
    mechanism: 'plain',
    username: 'user',
    password: 'password',
  },
});

Monitoring Metrics

MetricAlert Threshold
Consumer lag> 10000 messages
Under-replicated partitions> 0
Request latency p99> 100ms
Broker disk usage> 80%
Active controller count!= 1

Producer Best Practices

const producer = kafka.producer({
  idempotent: true,                    // Exactly-once
  maxInFlightRequests: 5,              // Ordering with idempotence
  retry: {
    retries: 5,
    initialRetryTime: 100,
    maxRetryTime: 30000,
  },
});

Consumer Best Practices

const consumer = kafka.consumer({
  groupId: 'order-processor',
  sessionTimeout: 30000,               // Failure detection
  heartbeatInterval: 3000,             // Session keepalive
  maxBytesPerPartition: 1048576,       // 1MB per partition
  retry: {
    retries: 5,
  },
});

Checklist

  • TLS/SSL encryption enabled
  • SASL authentication configured
  • ACLs defined for topics
  • Replication factor >= 3
  • min.insync.replicas = 2
  • Consumer lag monitoring
  • Dead letter topic configured
  • Schema registry for evolution
  • Idempotent producers enabled
  • Proper partition key strategy

Reference Documentation

Deep Knowledge: Use mcp__documentation__fetch_docs with technology: kafka for comprehensive documentation.

Available topics: basics, producers, consumers, streams, connect, configuration, production

适合场景

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02

需要根据任务场景推荐可安装能力包时

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能力概览

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能力 2

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能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.69%
按下载量换算71

Claude

30.75%
按下载量换算60

Cursor

20.42%
按下载量换算40

Gemini CLI

8.6%
按下载量换算17

安全审计

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通过

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Snyk

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权限和风险

external-service

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安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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