Flume和Kafka都是即時資料傳輸的開源平台。它們都具有高吞吐量、低延遲和可靠性的特點。但是,它們在設計和實現上存在一些差異。
Flume是分散式、可靠且可擴展的日誌收集、聚合和傳輸系統。它支援多種資料來源,包括檔案、Syslog、Taildir、Exec和HTTP。 Flume也支援多種資料格式,包括文字、JSON和Avro。
Flume的體系結構如下圖:
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Flume的元件包括:
# Name the agent a1.sources = r1 # Describe the source r1.type = exec r1.command = tail -F /var/log/messages # Describe the sink s1.type = hdfs s1.hdfs.path = hdfs://namenode:8020/flume/logs # Use a channel which buffers events in memory c1.type = memory c1.capacity = 1000 c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.channels = c1 c1.sinks = s1
# Create a topic named "my-topic" with 3 partitions and a replication factor of 2 kafka-topics --create --topic my-topic --partitions 3 --replication-factor 2 # Start a Kafka producer kafka-console-producer --topic my-topic # Start a Kafka consumer kafka-console-consumer --topic my-topic --from-beginning
# Create a Flume agent agent = AgentBuilder.newInstance().build() # Create a source source = ExecSourceBuilder.newInstance().setCommand("tail -F /var/log/messages").build() # Create a channel channel = MemoryChannelBuilder.newInstance().setCapacity(1000).setTransactionCapacity(100).build() # Create a sink sink = HDFSSinkBuilder.newInstance().setBasePath("hdfs://namenode:8020/flume/logs").build() # Add the source, channel, and sink to the agent agent.addSource("r1", source) agent.addChannel("c1", channel) agent.addSink("s1", sink) # Start the agent agent.start()
# Create a Kafka producer producer = KafkaProducerBuilder.newInstance() .setBootstrapServers("localhost:9092") .setValueSerializer(StringSerializer.class) .build() # Create a Kafka consumer consumer = KafkaConsumerBuilder.newInstance() .setBootstrapServers("localhost:9092") .setValueDeserializer(StringDeserializer.class) .setGroupId("my-group") .build() # Subscribe the consumer to the topic consumer.subscribe(Arrays.asList("my-topic")) # Send a message to the topic producer.send(new ProducerRecord<>("my-topic", "Hello, world!")); # Receive messages from the topic while (true) { ConsumerRecords<String, String> records = consumer.poll(100); for (ConsumerRecord<String, String> record : records) { System.out.println(record.value()); } }
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