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version: '2.2'
services:
es01:
image: docker.elastic.co/elasticsearch/elasticsearch:7.16.1 #3 # <--- get the latest version
container_name: es01
environment:
- node.name=es01
- cluster.name=es-docker-cluster
- discovery.type=single-node
- bootstrap.memory_lock=true
- "ES_JAVA_OPTS=-Xms512m -Xmx512m"
ulimits:
memlock:
soft: -1
hard: -1
volumes:
- data01:/usr/share/elasticsearch/data
ports:
- 9200:9200
networks:
- elastic
k01:
image: docker.elastic.co/kibana/kibana:7.16.1 3 # <--- get the latest version
container_name: k01
environment:
SERVER_NAME: kibana
ELASTICSEARCH_HOSTS: http://es01:9200
ports:
- 560180:5601
networks:
- elastic
volumes:
data01:
driver: local
networks:
elastic:
driver: bridge |
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$ docker ps | grep elastic CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 119eb7e8b36a kibana:7.16.13 "/bin/tini -- /usr/l…" 2 minutes ago Up 0.0.0.0:5601->5601/tcp, :::5601->5601/tcp k01 e902ffce0b93 elasticsearch:7.16.13 "/bin/tini -- /usr/l…" 2 minutes ago Up 9300/tcp, 0.0.0.0:9200->9200/tcp, :::9200->9200/tcp es01 |
Finally, we can connect to the address http://<FIP>:<port>. If we can see the Kibana dashboard, it means that the procedure carried out so far is correct (it may take a couple of minutes from startup to service activation). However, we are not yet able to view the logs generated by our cluster. In the next paragraph we will create a connection between the cluster and the newly instanced log collection service. Conversely, to temporarily interrupt the execution of containers or to permanently delete them use respectively (remember to run these commands inside the folder where the .yaml file is located)
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We have therefore created a DaemonSet according to the configuration present in the .yaml file. A DaemonSet generates a Pod for each VM that makes up the cluster (3 in our case). Each Pod has the task of investigating and collecting the logs of the node in which it is located and to send them to the destination set by us. Below is an example screen of the service in operation (Analytics/Discover). Of course, the mountain of data collected can be reduced through queries, by selecting fields or time frames.
