How to implement real-time stock analysis using PHP and Kafka

With the development of the Internet and technology, digital investment has become a topic of increasing concern. Many investors continue to explore and study investment strategies, hoping to obtain a higher return on investment. In stock trading, real-time stock analysis is very important for decision-making, and the use of Kafka real-time message queue and PHP technology is an efficient and practical means.

1. Introduction to Kafka

Kafka is a high-throughput distributed publish and subscribe message system developed by LinkedIn. The main features of Kafka are high real-time data, fast processing speed, and support for message subscriber groups to realize multicast of messages. The main components of Kafka are Broker, Producer and Consumer.

2. Introduction to PHP

PHP is a scripting language widely used in server-side web application development. PHP has the characteristics of simple syntax, fast running speed, easy to learn and use, etc. It is one of the commonly used programming languages ​​​​in web application development.

3. How to use Kafka and PHP to implement real-time stock analysis

1. Select PHP’s Kafka library

PHP developers can use Kafka’s PHP library to Use Kafka. There are some great PHP Kafka libraries on github and developers can choose the appropriate library according to their needs.

2. Create a Kafka producer

A Kafka producer is a client application that sends messages to Kafka Broker. You can use the Kafka producer API to write messages to Kafka. In Topic.

In PHP, you can use the following code to create a Kafka producer:

setLogLevel(LOG_DEBUG);
  $rk->addBrokers("kafka-broker1:9092,kafka-broker2:9092");
  $topic = $rk->newTopic("stock-market");
  
  // 生产一条数据
  $messagePayload = '{"time": "2021-01-01 10:00:00", "symbol": "AAPL", "price": 125.67}';
  $topic->produce(RD_KAFKA_PARTITION_UA, 0, $messagePayload);
  $rk->flush(1000);
  
?>

In the above code, we first create a Kafka producer instance and specify it with the addBrokers() method The address of Kafka Broker. Next, we created a Kafka topic object and used the produce() method to write a piece of JSON format data to this topic. Finally, the persistence of the message is ensured by calling the flush() method.

3. Create a consumer

A Kafka consumer is a client application that consumes messages from a Kafka Broker. Kafka consumers receive messages and perform appropriate actions on them, for example, storing in a database or rendering on the UI.

In PHP, you can use the following code to create a Kafka consumer:

addBrokers("kafka-broker1:9092,kafka-broker2:9092");
  $topicConf = new RdKafkaTopicConf();
  $topicConf->set("auto.commit.interval.ms", 100);
  $topicConf->set("offset.store.method", "broker");
  $topicConf->set("auto.offset.reset", "smallest");
  $topic = $rk->newTopic("stock-market", $topicConf);
  
  // 消费数据
  $topic->consumeStart(0, RD_KAFKA_OFFSET_STORED);
      while (true) {
      $msg = $topic->consume(0, 1000);
      switch ($msg->err) {
         case RD_KAFKA_RESP_ERR_NO_ERROR:
          echo "Received message: " . $msg->payload . " (" . $msg->len . " bytes)
";
          break;
         case RD_KAFKA_RESP_ERR__PARTITION_EOF:
          echo "No more messages; will wait for more
";
          break;
         case RD_KAFKA_RESP_ERR__TIMED_OUT:
          echo "Timed out
";
          break;
         default:
          echo "Error: " . $msg->errstr . "
";
          break;
      }
    }
  
?>

In the above code, we first create a consumer instance and specify Kafka with the addBrokers() method Broker's address. Next, we create a Kafka topic object and use the consumeStart() method to start consumption. Finally, consume the JSON data in this topic by calling the consume() method.

4. Implementing real-time stock analysis

In real-time stock analysis, it is necessary to consume data from the stock market from Kafka Broker, process it in real time, and visualize these Data to better understand market trends and changes. Developers can use charting libraries like Chart.js to visualize data in the stock market. The following is the sample code:

 "tcp",
    "host" => $redisConfig["host"],
    "port" => $redisConfig["port"]
]);

//设置消费者
$conf = new RdKafkaConf();
$rkConsumer = new RdKafkaConsumer($conf);
$rkConsumer->addBrokers($kafkaBrokerAddress);
$topicConsumerConf = new RdKafkaTopicConf();
$topicConsumerConf->set("auto.commit.interval.ms", 100);
$topicConsumerConf->set("offset.store.method", "broker");
$topicConsumerConf->set("auto.offset.reset", "earliest");
$topic = $rkConsumer->newTopic($kafkaTopic, $topicConsumerConf);
$topic->consumeStart(0, RD_KAFKA_OFFSET_STORED);

//标记数据是否重复
$lastProcessedMessage = array();

while (true) {
    $msg = $topic->consume(0, 1000);
    if (empty($msg)) {
        // 无消息
        continue;
    }

    if ($msg->err == RD_KAFKA_RESP_ERR_NO_ERROR) {
        $msgJson = json_decode($msg->payload, true);
        if (in_array($msgJson, $lastProcessedMessage)) {
            // 重复消息
            continue;
        }

        //写入redis中库存信息
        $redisKey = sprintf("%s:%s", "stock-market", $msgJson["symbol"]);
        $client->zadd($redisKey, time(), $msg->payload);
        $lastProcessedMessage[] = $msgJson;
    }
}

In the above sample code, we use Kafka's Consumer API to consume JSON format data in the topic, and then use Redis for data storage and sorting. The storage method is to use the sorted set data type, use the stock code as the key, use the timestamp as the value, and use the zadd() method to write the stock information into Redis.

After collecting and storing stock data, you can use chart libraries such as Chart.js to display the data on the UI to facilitate users to conduct real-time stock analysis.

4. Summary

This article introduces how to use Kafka and PHP to implement real-time stock analysis, and shows the creation of producers and consumers through code examples, and how to use Redis for processing and storage Real-time stock data. Building on this, we also explored how to use charting libraries to visualize stock market data. This is a very practical technology that can be used to quickly obtain and analyze stock data for better and favorable investment decisions.

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