{"id":5236,"date":"2023-06-08T00:44:07","date_gmt":"2023-06-07T21:44:07","guid":{"rendered":"https:\/\/bentego.com\/?p=5236"},"modified":"2025-10-20T16:09:18","modified_gmt":"2025-10-20T16:09:18","slug":"kafka-connect-ile-hdfse-veri-gonderme-islemi","status":"publish","type":"post","link":"https:\/\/bentego.com\/en\/kafka-connect-ile-hdfse-veri-gonderme-islemi\/","title":{"rendered":"Kafka Connect ile HDFS\u2019e Veri G\u00f6nderme \u0130\u015flemi"},"content":{"rendered":"

Kafka Connect ile HDFS\u2019e Veri G\u00f6nderme \u0130\u015flemi<\/h2>\n

<\/p>\n

Merhabalar bu yaz\u0131mda sizlere\u00a0Kafka<\/strong>\u2019da bulunan verilerimizi nas\u0131l\u00a0Kafka Connector<\/strong>\u00a0yard\u0131m\u0131 ile\u00a0HDFS<\/strong>\u2019e ta\u015f\u0131yabilece\u011fimizi g\u00f6sterece\u011fim. \u0130lk \u00f6nce gerekli\u00a0Connector<\/strong>\u2019umuzu indirelim ve servislerimizi ba\u015flatal\u0131m.<\/p>\n

# HDFS3 Sink Connector indirme i\u015flemi<\/span><\/strong>
\nconfluent-hub install confluentinc\/kafka-connect-hdfs3:1.1.25<\/p>\n

# Gerekli servisleri ba\u015flatma<\/span><\/strong>
\nconfluent local<\/span> services zookeeper start
\nconfluent local<\/span> services kafka start
\nconfluent local<\/span> services schema-registry start
\nconfluent local<\/span> services connect start<\/p>\n

# Servislerin durumunu takip etmek i\u00e7in <\/span><\/strong>
\nconfluent local<\/span> services status<\/p>\n

# Control Center yerine kullanaca\u011f\u0131m AKHQ servisini ba\u015flatmak i\u00e7in<\/span><\/strong>
\njava -Dmicronaut.config.files=akhq.yml -jar akhq.jar<\/p><\/blockquote>\n

# akhq.yml i\u00e7eri\u011fi <\/span><\/strong>
\nakhq:<\/span>
\n\u00a0 connections:<\/span>
\n\u00a0 \u00a0 local:<\/span>
\n\u00a0 \u00a0 \u00a0 properties:<\/span>
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0bootstrap.servers:<\/span> “localhost:9092”<\/span>
\n\u00a0 \u00a0 \u00a0schema-registry:<\/span>
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0url:<\/span> “http:\/\/localhost:8881”<\/span>
\n\u00a0 \u00a0 \u00a0connect:<\/span>
\n\u00a0 \u00a0 \u00a0 –<\/span> name:<\/span> “connect”<\/span>
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0url:<\/span> “http:\/\/localhost:8083”<\/span>
\nmicronaut:<\/span>
\n\u00a0 server:<\/span>
\n\u00a0 \u00a0 \u00a0 port:<\/span> 8880<\/span><\/p>\n

AKHQ Web UI<\/strong> eri\u015fimi i\u00e7in: http:\/\/localhost:8880\/ui\/<\/p><\/blockquote>\n

Gerekli servislerimizi ba\u015flatt\u0131k ve \u00e7al\u0131\u015fma durumlar\u0131n\u0131 kontrol ettikten sonra \u015fimdi ise\u00a0Kafka<\/strong>\u2019da berlitti\u011fimiz topic\u2019e veri aktaracak olan\u00a0Python<\/strong> kodumuza bakal\u0131m.<\/p>\n

\n
import pandas as pd\nfrom faker import Faker\nimport random as rd\nimport time\nfrom confluent_kafka import avro\nfrom confluent_kafka.admin import AdminClient, NewTopic\nfrom confluent_kafka.avro import AvroProducer,CachedSchemaRegistryClient\nimport re\nimport datetime\n\nSCHEMA_REGISTRY_URL = \"http:\/\/localhost:8881\"\nBROKER_URL = \"localhost:9092\"\n\nadmin_client = AdminClient({'bootstrap.servers': BROKER_URL})\norder_topic = [NewTopic('order-topic', num_partitions=1, replication_factor=1)]\n\nfs = admin_client.create_topics(order_topic)\n\nschema = avro.loads(\n        \"\"\"{\n    \"type\":\"record\",\n    \"name\":\"myrecord\",\n    \"fields\": [\n        {\n            \"name\": \"CustomerID\",\n            \"type\": \"int\"\n        },\n        {\n            \"name\": \"CustomerName\",\n            \"type\": \"string\"\n        },\n        {\n            \"name\": \"Email\",\n            \"type\": \"string\"\n        },\n        {\n            \"name\": \"Address\",\n            \"type\": \"string\"\n        },\n        {\n            \"name\": \"State\",\n            \"type\": \"string\"\n        },\n        {\n            \"name\": \"PhoneNumber\",\n            \"type\": \"string\"\n        },\n        {\n            \"name\": \"InvoiceDate\",\n            \"type\": \"string\"\n        },\n        {\n            \"name\": \"StockCode\",\n            \"type\": \"string\"\n        },\n        {\n            \"name\": \"Description\",\n            \"type\": \"string\"\n        },\n        {\n            \"name\": \"UnitPrice\",\n            \"type\": \"float\"\n        },\n        {\n            \"name\": \"Quantity\",\n            \"type\": \"int\"\n        }\n    ]\n}\"\"\"\n    )\n\nschema_registry = CachedSchemaRegistryClient({\"url\": SCHEMA_REGISTRY_URL})\n\ndata = pd.read_csv('OnlineRetail.csv', sep = ';')\n\ndata[\"InvoiceNo\"] = data[\"InvoiceNo\"].str.replace('A','')\ndata[\"InvoiceNo\"] = data[\"InvoiceNo\"].str.replace('C','')\ndata[\"InvoiceNo\"] = data[\"InvoiceNo\"].astype(\"int64\")\ndata[\"StockCode\"] = data[\"StockCode\"].astype(\"string\")\ndata[\"Description\"] = data[\"Description\"].astype(\"string\")\ndata[\"UnitPrice\"] = data[\"UnitPrice\"].str.replace(',','.').astype(\"float\")\n\ncols = ['StockCode','Description', 'UnitPrice']\nStock_Desc_Unit_lst = data[cols].values.tolist()\n\nfake = Faker()\n\ndef create_data(x):\n    project_data = {}\n    for i in range(x):\n        project_data[i] = {}\n        project_data[i]['CustomerID'] = i + 1\n        project_data[i]['Name'] = fake.name()\n\n    return project_data\n\ndf = pd.DataFrame(create_data(5000)).transpose()\n\ncols = ['CustomerID','Name']\nCustomerID_Name = df[cols].values.tolist()\nQ_df = data[data[\"Quantity\"]>0]\nQuantity = Q_df[\"Quantity\"].values.tolist()\n\nnow = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\ndef produce_kafka_dict(topic_name,json_as_dict):\n    producer=AvroProducer({\"bootstrap.servers\": BROKER_URL}, schema_registry=schema_registry)\n\n    producer.produce( topic=topic_name,\n                value=json_as_dict,\n                value_schema=schema)\n    producer.flush()\n\nwhile True:\n    order={}\n    n = rd.randint(1,10)\n    time.sleep(rd.randint(1,15))\n    CID_Name = rd.choice(CustomerID_Name)\n    order[\"CustomerID\"]= CID_Name[0]\n    order[\"CustomerName\"] = CID_Name[1]\n    order[\"Email\"] = CID_Name[1].lower().replace(' ','.') + \"@gmail.com\"\n    order[\"Address\"] = fake.address().replace('\\n',' ')\n    order[\"State\"] = re.findall(\"[A-Z]{2}\", order[\"Address\"])[0]\n    order[\"PhoneNumber\"] = fake.phone_number()[0:10]\n    order[\"InvoiceDate\"] = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n    for a in range(n):\n        Stock_Desc_Unit = rd.choice(Stock_Desc_Unit_lst)\n        order[\"StockCode\"] = Stock_Desc_Unit[0]\n        order[\"Description\"] = Stock_Desc_Unit[1]\n        order[\"UnitPrice\"] = Stock_Desc_Unit[2]\n        order[\"Quantity\"]  = rd.choice(Quantity)\n        produce_kafka_dict('order-topic',order)\n        print(order) \n<\/pre>\n<\/blockquote>\n
Yukar\u0131daki kod\u2019u \u00f6zetlemek gerekirse yapaca\u011f\u0131 i\u015f random bir \u015fekilde sipari\u015f verisi yaratarak bunu\u00a0avro\u00a0format\u0131nda\u00a0Kafka<\/strong>\u2019da istedi\u011fimiz\u00a0topic\u2019e iletmek. \u00d6rnek \u00e7\u0131kt\u0131 a\u015fa\u011f\u0131daki gibidir:<\/p>\n
\n
{‘CustomerID’: 824, ‘CustomerName’: ‘Robert Bullock’, ‘Email’: ‘robert.bullock@gmail.com’, ‘Address’: ‘9618 Chambers Centers Apt. 343 Kevinmouth, WA 39994’, ‘State’: ‘WA’, ‘PhoneNumber’: ‘399.352.69’, ‘InvoiceDate’: ‘2023-06-04 21:44:54’, ‘StockCode’: ‘23371’, ‘Description’: ‘SET 36 COLOUR PENCILS SPACEBOY’, ‘UnitPrice’: 2.46, ‘Quantity’: 1}<\/p>\n<\/blockquote>\n
Kafka<\/strong>\u2019ya iletilen verileri\u00a0Kafka CLI\u00a0<\/strong>yard\u0131m\u0131 ile de g\u00f6r\u00fcnt\u00fclemek istersek a\u015fa\u011f\u0131daki script\u2019i kullanabiliriz.<\/p>\n
\n
kafka-avro-console-consumer \\
\n–bootstrap-server localhost:9092 \\
\n–topic order-topic \\
\n–property schema.registry.url=http:\/\/localhost:8881<\/p>\n<\/blockquote>\n
\"\"<\/p>\n
Python<\/strong> kodunu durdurmad\u0131\u011f\u0131n\u0131z s\u00fcrece s\u00fcrekli sipari\u015f verisi \u00fcretilip istedi\u011fimiz topic\u2019e iletilecek. \u015eimdi gelin bu verileri parquet<\/strong> format\u0131nda HDFS<\/strong>\u2019e yazmam\u0131z\u0131 sa\u011fl\u0131yacak connector konfig\u00fcrasyonunu<\/strong> olu\u015fturup bunu \u00e7al\u0131\u015ft\u0131ral\u0131m.<\/p>\n
\n
{\n    \"name\": \"hdfs3-sink-connector\",\n    \"config\": {\n        \"connector.class\": \"io.confluent.connect.hdfs3.Hdfs3SinkConnector\",\n        \"tasks.max\": \"1\",\n        \"topics\": \"order-topic\",\n        \"hdfs.url\": \"hdfs:\/\/localhost:9000\",\n        \"flush.size\": \"3\",\n        \"key.converter\": \"org.apache.kafka.connect.storage.StringConverter\",\n        \"value.converter\": \"io.confluent.connect.avro.AvroConverter\",\n        \"value.converter.schema.registry.url\":\"http:\/\/localhost:8881\",\n        \"confluent.topic.bootstrap.servers\": \"localhost:9092\",\n        \"confluent.topic.replication.factor\": \"1\",\n        \"format.class\":\"io.confluent.connect.hdfs3.parquet.ParquetFormat\",\n        \"partitioner.class\":\"io.confluent.connect.storage.partitioner.FieldPartitioner\",\n        \"partition.field.name\":\"State\"\n    }\n}\n<\/pre>\n<\/blockquote>\n
Konfig\u00fcrasyonumuz yukar\u0131daki gibidir. \u0130lgili\u00a0connector\u2019u \u00e7al\u0131\u015ft\u0131rmak i\u00e7in ben\u00a0Postman<\/strong>\u00a0arac\u0131n\u0131 kulland\u0131m siz\u00a0CLI<\/strong>\u00a0yard\u0131m\u0131yla da bunu yapabilirsiniz.
\n\"\"<\/p>\n
AKHQ Web UI<\/strong>\u00a0ekran\u0131na giderek ba\u015far\u0131l\u0131 \u015fekilde \u00e7al\u0131\u015f\u0131p \u00e7al\u0131\u015fmama durumunu takip edebiliriz.<\/p>\n
\"\"<\/p>\n
Sa\u011fl\u0131kl\u0131 bir \u015fekilde \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 g\u00f6rd\u00fck. \u015eimdi son olarak\u00a0HDFS<\/strong>\u00a0taraf\u0131ndaki kontrol\u00fcm\u00fcz\u00fc yaparak senaryomuzu tamamlam\u0131\u015f olaca\u011f\u0131z.<\/p>\n
\n
hdfs dfs -ls \/topics\/order-topic\/\n<\/pre>\n<\/blockquote>\n
\"\"<\/p>\n
\"\"<\/p>\n
HDFS<\/strong>\u00a0taraf\u0131na bakt\u0131\u011f\u0131m\u0131zda istedi\u011fimiz gibi\u00a0State\u00a0kolonunu dikkate alarak verileri ay\u0131rm\u0131\u015f ve\u00a0parquet<\/strong>\u00a0format\u0131nda kaydetmi\u015f.<\/p>\n
<\/p>\n
Umar\u0131m faydal\u0131 bir yaz\u0131 olmu\u015ftur. Sorular\u0131n\u0131z ve \u00f6nerileriniz i\u00e7in benimle ileti\u015fime ge\u00e7ebilirsiniz. Te\u015fekk\u00fcrler.<\/p>\n","protected":false},"excerpt":{"rendered":"
Kafka Connect ile HDFS\u2019e Veri G\u00f6nderme \u0130\u015flemi Merhabalar bu yaz\u0131mda sizlere\u00a0Kafka\u2019da bulunan verilerimizi nas\u0131l\u00a0Kafka Connector\u00a0yard\u0131m\u0131 ile\u00a0HDFS\u2019e ta\u015f\u0131yabilece\u011fimizi g\u00f6sterece\u011fim. \u0130lk \u00f6nce gerekli\u00a0Connector\u2019umuzu indirelim […]<\/p>\n","protected":false},"author":2,"featured_media":1011,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-5236","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"yoast_head":"\nKafka Connect ile HDFS\u2019e Veri G\u00f6nderme \u0130\u015flemi - Bentego<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/bentego.com\/en\/kafka-connect-ile-hdfse-veri-gonderme-islemi\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Kafka Connect ile HDFS\u2019e Veri G\u00f6nderme \u0130\u015flemi - 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