The practice of go-zero and Kafka+Avro: building a high-performance interactive data processing system

In recent years, with the rise of big data and active open source communities, more and more enterprises have begun to look for high-performance interactive data processing systems to meet the growing data needs. In this wave of technology upgrades, go-zero and Kafka Avro are being paid attention to and adopted by more and more enterprises.

go-zero is a microservice framework developed based on the Golang language. It has the characteristics of high performance, ease of use, easy expansion, and easy maintenance. It is designed to help enterprises quickly build efficient microservice application systems. Its rapid growth is due to the excellent performance and high development efficiency of Golang itself, as well as the continuous iteration and optimization of the go-zero team.

Kafka is a distributed stream processing system developed by Apache. It has the characteristics of high availability and high throughput. It is one of the most popular message queues in the current big data ecosystem. Avro is a data serialization tool developed by Apache. It can convert data streams into binary formats, thereby improving data compression and transmission efficiency. It can also support data format upgrades and conversions.

In this article, we will introduce how to combine go-zero and Kafka Avro to build a high-performance interactive data processing system. The specific practical process is as follows:

1. Integrate Kafka client

First, we need to integrate the Kafka client in the go-zero service. go-zero provides a Kafka package that can easily interact with Kafka.

We only need to introduce the Kafka package into the project and configure the Kafka parameters in the configuration file to achieve connection and data interaction with Kafka. The following is a Kafka configuration example:

[kafka]
addrs = ["localhost:9092"]
version = "2.0.0"
maxMessageBytes = 10000000

In specific business logic, we can use the producer and consumer APIs provided by Kafka to send and receive data. The following is an example of a Kafka producer:

var (
    topic = "test"
)

func (s *Service) Produce(msg []byte) error {
    p, err := kafka.NewProducer(s.cfg.Kafka)
    if err != nil {
        return err
    }
    defer p.Close()

    return p.Send(context.TODO(), &kafka.Message{
        Key:   []byte(topic),
        Value: msg,
    })
}

In the above example, we created a Kafka topic named "test" and when the Produce method is called, data is sent to the topic.

2. Integrated Avro serialization

Next, we need to convert the data into Avro format for serialization and deserialization. go-zero provides an Avro package and supports code generation. By defining the Schema file, we can generate the corresponding Go code to encode and decode Avro data.

The following is an Avro Schema configuration example:

{
    "namespace": "com.example",
    "type": "record",
    "name": "User",
    "fields": [
        {
            "name": "name",
            "type": "string"
        },
        {
            "name": "age",
            "type": "int"
        }
    ]
}

By executing the following command, the corresponding Go file can be automatically generated:

$ go run github.com/gogo/protobuf/protoc-gen-gogofaster --proto_path=./ example.proto --gogofaster_out

In the generated Go file, we can see To the mapping relationship between Avro field types and corresponding Go data types, thereby realizing data serialization and deserialization.

3. Building an interactive data processing system

After integrating Kafka and Avro, we can start to build a high-performance interactive data processing system. We can use Kafka as a data storage center and establish multiple partitions in it to achieve distributed storage and processing of data.

For each partition, we can create a consumer group to achieve parallel processing and load balancing of data. At the same time, we can use the coroutine pool and synchronization channel provided by go-zero to optimize the concurrency performance of data processing.

The following is an example of an interactive data processing system:

// 创建消费组
group, err := kafka.NewGroup(s.cfg.Kafka, "test", kafka.WithGroupID("test-group"))
if err != nil {
    return nil, err
}
// 创建消费者
consumer, err := group.NewConsumer(context.Background(), []string{"test"})
if err != nil {
    return nil, err
}
// 启动并发协程
for i := 0; i < s.cfg.WorkerNum; i++ {
    go func() {
        for {
            select {
                // 从同步通道中获取新消息
                case msg := <-msgs:
                    if err := s.processMsg(msg); err != nil {
                        log.Errorf("failed to process message(%v): %v", msg.Value, err)
                    }
                }
        }
    }()
}
// 消费数据
for {
    m, err := consumer.FetchMessage(context.Background())
    if err != nil {
        log.Errorf("failed to fetch message: %v", err)
        continue
    }
    // 将新消息发送到同步通道中
    msgs <- m
}

In the above example, we created a consumer group "test-group" and created the corresponding consumer. During the processing, we first start multiple concurrent coroutines to achieve parallel processing of data. When a new message is received, we send it to a synchronous channel and utilize a coroutine pool for asynchronous processing.

Through the above construction, we successfully integrated go-zero, Kafka and Avro to implement a high-performance interactive data processing system. Using this kind of system can easily handle massive data and improve the efficiency of data processing and analysis.

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