Pythonはkafkaを呼び出して完全なサンプル分析とアプリケーションを構築します

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最近、現在のリソース集約型インターフェースを非同期通信メカニズムに開発する必要があるという要件に直面しました。簡単に言えば、メッセージを継続的に配信し、タスクを処理するためのメッセージキューが存在します。ここでアプリケーション側から提示された解決策は Kafka を使用することですが、私はこれまでこれに触れたことがないので、わかりません。操作プロセス全体を理解するために、ローカルマシン上に Kafka 環境を構築したいと考えています。

次に、Kafka 環境のインストールを開始します。 kafka のインストールは zooeleper モジュールに依存します。 kafka には zooeleper モジュールが付属していますが、インターネット上のすべてのチュートリアルでは、事前に zooeleper モジュールを手動でインストールする必要があると記載されているため、ここでの最初の手順は zooeleper モジュールをインストールすることです。ダウンロードアドレスはこちらです:

 http://mirror.bit.edu.cn/apache/zookeeper/zookeeper-3.4.14/

バージョン3.4.14をダウンロードしました:

インストールは非常に簡単です。ローカルで解凍し、以下の手順に従ってください。

 1. 解凍後、confディレクトリに入り、「zoo_sample.cfg」の名前を「zoo.cfg」に変更します。  
 (zookeeper のデフォルト ポートは 2181 です。変更する必要がある場合は、zoo.cfg を編集できます)  
 2. binディレクトリに入り、zkServer.cmdをクリックして実行します。  
 3. 次のようにコマンドラインが表示され、操作が成功したことが示されます。

zooleeper モジュールを正常にインストールして起動したら、kafka モジュールをダウンロードしてインストールできます。ここでは、適切なバージョンをダウンロードすることに注意する必要があります。多くのオンラインチュートリアルは比較的古いため、提供されているバージョン情報が見つからないか、適切でなくなる可能性があります。ここでは試行錯誤しながらインストールを試みています。最初のインストールが失敗した後、多くのチュートリアルを確認したところ、インストールした scala が間違っていたことがわかりました。ここで注意する必要があるのは、kafka は scala に基づいて記述されているため、kafka を正常にインストールするには、scala が正常にインストールされていることを確認する必要があるということです。

Kafka をダウンロードする際に注意すべきもう 1 つの重要なポイントは、バイナリファイルをダウンロードしてインストールする必要があることです。私のダウンロードリンクは次のとおりです。

 http://kafka.apache.org/downloads

次のバージョンをダウンロードしました:

ローカルコンピュータにダウンロードしたら、解凍するだけです。結果のスクリーンショットは次のようになります。

現在のディレクトリに入ったら、CMD ウィンドウを開いて次のコマンドを実行します。

 .\bin\windows\kafka-server-start.bat .\config\server.properties

予期せぬ事態が発生しなければ、これまでの環境条件が満たされていれば、ここで正常に起動でき、ターミナルに大量の密なログが出力されるはずです。

この時点で、Kafka は正常にインストールされており、自分で使用できるようになります。

ここで簡単にお話しさせてください。プログラムを書くときにいくつか問題が発生しました。エラーは次のとおりです:

スレッド kafka-python-producer-1-network-thread で例外が発生しました (インタープリタの sh 中に発生した可能性が高い)

上記のエラーを解決するには、次の 2 か所に対処する必要があります。

1. システムのホストファイルを変更する

C:\Windows\System32\drivers\etc に移動し、管理者としてホストファイルを開きます。
最後に新しい行を追加します: 127.0.0.1 localhost
保存して終了

2. kafkaに対応する設定ファイルを変更する

設定フォルダに移動する
server.propertiesを開く
ファイルの31行目の下に次の2行を追加します #listeners=PLAINTEXT://:9092

アドバタイズされたリスナー=プレーンテキスト://localhost:9092  
アドバタイズされたリスナー=プレーンテキスト://127.0.0.1:9092

保存して終了

効果図は以下のとおりです。

上記の 2 つの部分を処理した後、Python に基づいて対応する例を開発します。

例1:

これは比較的単純な公式の例です。主に、対応するプロデューサーとコンシューマーを記述します。具体的なコード実装は次のとおりです。

プロデューサーコード:

 #!usr/bin/env python  
 #エンコーディング:utf-8  
 __future__ インポート部門から 
 '''  
 __著者__:沂水冷城 
機能: Kafka 学習例 
 '''  
 kafka からインポート KafkaConsumer、KafkaProducer  
プロデューサー= KafkaProducer ( bootstrap_servers = 'localhost:9092' )  
メッセージ= "HelloWorld" .encode('utf-8')  
印刷(メッセージ)  
プロデューサー= KafkaProducer ( bootstrap_servers = 'localhost:9092' )  
プロデューサー.send('demo',msg,パーティション= 0 )  
プロデューサー.close()

消費者コード:

 #!usr/bin/env python  
 #エンコーディング:utf-8  
 __future__ インポート部門から 
 '''  
 __著者__:沂水冷城 
機能: kafka コンシューマー 
 '''  
 kafka から KafkaConsumer をインポートする 
コンシューマー= KafkaConsumer ('demo'、 bootstrap_servers =['localhost:9092'])  
消費者のメッセージの場合:  
 info = "%s:%d:%d: キー=%s 値=%s" % (msg.topic、msg.partition、msg.offset、msg.key、msg.value)  
    印刷情報

次に、ターミナルでコンシューマーを起動し、プロデューサーを実行して、コンシューマーターミナルの出力を確認します。

ご覧のとおり、プロデューサーが対応するデモトピックを送信すると、コンシューマーはこのトピックによって生成されたデータを消費します。これは比較的単純なサンプルアプリケーションです。

例2:

この部分は主に、独自のアプリケーションに適合する例を構築するためのものです。ここで送信する必要があるのは、json データオブジェクトです。具体的なコード実装は次のとおりです。

プロデューサーコード:

 #!usr/bin/env python  
 #エンコーディング:utf-8  
 __future__ インポート部門から 
 '''  
 __著者__:沂水冷城 
機能: Kafka 学習例 
 '''  
 jsonをインポート 
 base64をインポート 
日時をインポート 
 kafka からインポート KafkaProducer  
 TS = str (datetime.datetime.now().strftime('%Y%m%d%H%M%S'))  
ファイル= {  
        「名前」: TS,  
        「データ」: base64.b64encode(open('demo.png', "rb").read())  
        }  
プロデューサー= KafkaProducer (  
 value_serializer = lambda v: json.dumps(v).encode('utf-8'),  
 bootstrap_servers = 'localhost:9092'    
                         ）
 iが範囲(3)内にある場合:  
    'i: '、ファイルを印刷 
    プロデューサー.send('D', ファイル)  
プロデューサー.close()

消費者コード:

 #!usr/bin/env python  
 #エンコーディング:utf-8  
 __future__ インポート部門から 
 '''  
 __著者__:沂水冷城 
機能: kafka コンシューマー 
 '''  
インポートシステム 
 jsonをインポート 
 kafka から KafkaConsumer をインポートする 
 KAFKA_TOPIC = 'D'    
 KAFKA_BROKERS = 'localhost:9092'    
コンシューマー= KafkaConsumer ( bootstrap_servers = KAFKA_BROKERS 、 auto_offset_reset = 'earliest' )  
コンシューマー.サブスクライブ([KAFKA_TOPIC])  
試す：  
    コンシューマーのメッセージの場合:  
        print(メッセージ.値)  
 KeyboardInterruptを除く:  
    sys.exit()

次に、ターミナルでコンシューマーを起動し、プロデューサーを実行します。プロデューサーの出力は次のとおりです。

 i: {'データ': '/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hy c5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wA ARCAFNAfQDASIAAhEBAxEB/8QAHAAAAgMBAQEBAAAAAAAAAAAAECAwQFBgcI/8QARhAAAQQBAgMFBQYEAgkEAgMAAQACAxEEEiEFMUETI lFhcQYUMoGRI0KhscHRFVJicjPhBxZDRFSSovDxJGNzgjRTRbLC/8QAGgEBAQADAQEAAAAAAAAAAAAAECAwQFBv/EAC4RAAICAQQCAQMEA QQDAAAAAAABAhEDBBIhMUFRBRMVIhQyYXGBQlKxwZGh8P/aAAwDAQACEQMRAD8A++qAki1G0WgJIUbTtANFpWi0A0WlaLQDRaXNFoBoQkgH aajaaAaEr3SvZAStFqNotANO1FFoBoSJRaAaErSJQerS6JWlaAlaLVE+RFjROlmkDGNFkkrzWT7fcMgf3Y5ZGDZztgR8ilEtHq7XOy+PcLwp HxT5sQmZ8UTTqeOtUFjb7SNzWA8HwsjPJAPaBvZxN9Xu2+lr5X7dDinBvaeHMy8PGhgyIiT7o4lr7Pfsmu/yN+hWM20uOzZjipPno+uY/tJ wrKI0ZbW3/wDsBb+PJdJkjJW6o3te08i0ghfJuGmBuJEzHOthYHNc7ewevquvw3iM3CsxuQ0ufEe7LH/M3xA8QvOh8h+e2a4PRn8d+G6D5Po lotU4+TFlwMngkD43CwQrF6adq0eY01wxotJCAEIStANFpIQAi0JIBoSQgGklaLQDtIlJCEsLQkhUAhJCAsRaVotQo0WlaLQEkWki0BJCSL 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 9k =', 'name': ' 20200326203003 '}
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