¡Hola, Mundo!

Now, make the pipeline. consumer application local CPU : my computer ram practice server : AWS server In my case, local CPU----->practice server----->telegraf----->csv input collect load ./kafka-topics.sh --create --bootstrap-server 3.35.49.20:9092 --replication-factor 1 --partitions 5 --topic joohyun-computer-metric Let's create topic which has 5 partitions at factor 1 names joohyun-computer-me..

import compile group plugins { id 'java' } group 'com.tacademy' version '1.0' repositories { mavenCentral() } dependencies { testCompile group: 'junit', name: 'junit', version: '4.12' compile group: 'org.apache.kafka', name: 'kafka-clients', version: '2.5.0' } compile group : creatable class which creates client consumer, producer instance is designated through dependencies, so firmly it needs t..

Then, let's put kafka producer and consumer in practice consecutively previous posts(2020/12/18 - [KAFKA] - install and execute kafka from AWS, 2020/12/18 - [KAFKA] - install and execute kafka on my MAC). set up the producer In my case, I used IntelliJ IDEA. package com.tacademy; import org.apache.kafka.clients.producer.KafkaProducer; import org.apache.kafka.clients.producer.ProducerConfig; impo..

Contiuedly from last post( 2020/12/18 - [KAFKA] - install and execute kafka from AWS ), let's install and execute it in local, my case is in macOS. curl http://mirror.navercorp.com/apache/kafka/2.5.0/kafka_2.13-2.5.0.tgz --output kafka.tgz Joses-MacBook-Pro:~ joohyunyoon$ curl http://mirror.navercorp.com/apache/kafka/2.5.0/kafka_2.13-2.5.0.tgz --output kafka.tgz % Total % Received % Xferd Averag..

In order to practice in kafka, I created AWS instance and issued pem file. chmod 400 kafka.pem Joses-MacBook-Pro:Downloads joohyunyoon$ chmod 400 kafka.pem chmod 400 : Change Mode, Gives the user read permission, and removes all other permission. These permissions are specified in octal, the first char is for the user, second for the group and the third is for other. The high bit (4) is for read..