Using Samza and Flink for streaming in Beego

With the advent of the big data era, the demand for processing massive data is getting higher and higher, so stream processing technology has become one of the important solutions. Apache Samza and Apache Flink are industry-leading streaming processing frameworks. In this article, we will explore how to use Samza and Flink for streaming in Beego.

1. Introduction to Beego framework

Beego is a web framework based on Go language, which provides many functions, such as RESTful API, template engine, ORM and streaming processing. It is a lightweight framework that is easy to use and develop. Beego also has strong extensibility and can be extended with custom middleware and modules. In addition, Beego's performance is also very good and can handle high-concurrency scenarios.

2. Introduction to Apache Samza

Apache Samza is an open source stream processing framework maintained and developed by the Apache Software Foundation. It uses Apache Kafka as the messaging system and handles data streams as stateless functions. Therefore, Samza can be easily integrated with Kafka and supports high-reliability, low-latency processing. Samza also supports streaming batch processing, which means that Kafka data can be integrated and processed, and supports window functions, aggregation and correlation operations, etc.

3. Introduction to Apache Flink

Apache Flink is a stream processing framework maintained and developed by the Apache Software Foundation. Unlike Samza, it can handle stateful data streams. The core design principle of Flink is to cope with low-latency and high-reliability scenarios and support advanced stream-batch hybrid computing functions. Flink also provides high-level APIs and tools, such as CEP, machine learning libraries, etc.

4. Using Samza and Flink in Beego for streaming

Beego, as a web framework, does not itself provide streaming processing functionality. However, since the Go language has excellent performance in high concurrency scenarios, using Samza and Flink for streaming processing in Beego is a solution.

First, import the Samza and Flink dependency packages in the application:

import (
    "github.com/apache/samza-go/api/runner"
    "github.com/apache/flink/.../api"
)

Next, use Beego’s router and controller to preprocess the data:

func (c *MainController) HandleStreamData() {
    data := c.Ctx.Input.RequestBody
    // 进行数据预处理
}

Then , pass the data to Samza or Flink in the form of messages for processing. Here we take Samza as an example:

First, define the processing function:

func handleStreamData(ctx runner.Context, msg *sarama.ConsumerMessage) {
    // 处理流数据
    ctx.Send("output-stream", ...)
}

Then, define the Samza task in the application:

task := runner.NewTask(func(ctx runner.Context) {
    // 定义输入和输出流
    input := sarama.ConsumerMessage{}
    output := sarama.ProducerMessage{}
    // 使用输入流订阅Kafka消息
    err := input.ReadKafka(...)
    if err != nil {...}
    defer input.Close()
    // 处理数据流
    for {
        select {
        case <-ctx.SignalChan():
            return
        case msg := <-input.Msg():
            handleStreamData(ctx, msg)
        }
    }
}, ...)

Finally, start Samza in the application Task:

task.Run()

5. Conclusion

This article introduces how to use Samza and Flink for streaming in Beego. By using Beego's routers and controllers to process data and passing it to Samza or Flink in the form of messages for processing, streaming data processing in high concurrency scenarios can be achieved. Since both Samza and Flink have high reliability, low latency, and provide rich stream-batch hybrid computing capabilities, they can become excellent solutions for stream processing.

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