Last Tested: 04 MAR 2021 using Docker Engine v20.10.2, Compose v1.27.4, Machine v0.16.1
Configurations in this repository can be used in complimentary version of appropriately scaled Elastic clusters. Other projects or service providers (e.g., AWS) provide excellent guidance for deploying production clusters.
docker-machine can be found here. Note that
docker-machine was removed from Docker Desktop in ver 2.1.4. If you are running later versions you will need to install Docker/Machine manually. Alternatively, you can use
virtualbox or other methods of provisioning vms to run docker containers in. The instructions assume you are running
The single-node deployment steps below will build a single-node logging server on a single machine. This is suitable for demonstrations and very limited data collections.
Create docker-machine instance. Note: If using Docker Desktop bundle for OSX, there is a known bug in the bundled version of virtualbox that will prevent a successful docker-machine creation. Before installation, check that virtualbox version is at least 5.2.
Reinstall virtualbox, if needed. Note: If using Docker Desktop bundle for Windows 10, you will need to use
Hyper-V instead of virtual box. Refer to Docker's guide on creating vms with Hyper-V.
$ docker-machine create --virtualbox-memory 3072 --virtualbox-cpu-count 2 flagon # Windows 10 users will need to use Hyper-V: > docker-machine create -d hyperv --hyperv-virtual-switch "Primary Virtual Switch" --hyperv-memory 3072 --hyperv-cpu-count 2 flagon
Before launching the Docker containers, ensure your
vm_max_map_count kernel setting is set to at least 262144. Visit
Running Elasticsearch in Production mode for OS specific instructions.
# Example for Linux systems $ docker-machine ssh flagon sudo sysctl -w vm.max_map_count=262144
Create externel docker network to enable system monitoring. Only enable if running Elasticsearch >6.5.4 configuration (single and cluster mode)
$ docker network create esnet
Start Elasticsearch 6.6.2 or 6.8.2 (Recommended) Give Elasticsearch about 1-2 minutes to start before confirming its state.
#start Elasticsearch v6.6.2 (Deprecated) $ docker-compose -f docker-compose-6.6.2.yml up -d elasticsearch or #start Elasticsearch v6.8.2 (Recommended) $ docker-compose up -d elasticsearch
# if Flagon vm is remote $ docker-machine ssh flagon curl -XGET http://localhost:9200/_cluster/health?pretty # if Flagon virtual machine is running on your local machine, no need for ssh, instead: $ curl -XGET http://localhost:9200/_cluster/health?pretty #output should look like this: "cluster_name" : "Flagon", "status" : "green", "timed_out" : false, "number_of_nodes" : 1, "number_of_data_nodes" : 1, "active_primary_shards" : 0, "active_shards" : 0, "relocating_shards" : 0, "initializing_shards" : 0, "unassigned_shards" : 0, "delayed_unassigned_shards" : 0, "number_of_pending_tasks" : 0, "number_of_in_flight_fetch" : 0, "task_max_waiting_in_queue_millis" : 0, "active_shards_percent_as_number" : 100.0
Launch logging server. Give Logstash about 2 minutes to start before confirming its state.
$ docker-compose up -d logstash # if Flagon vm is remote $ docker-machine ssh flagon curl -XGET http://localhost:8100 # if Flagon virtual machine is running on your local machine, no need for ssh, instead: $ curl -XGET http://localhost:8100 #ouput should look like this: ok
Before Kibana can be used, we will need to generate some data. We provide an example instrumented website to assist.
$ docker-compose up -d site # for remote users, forwards port to localhost $ ssh docker@$(docker-machine ip flagon) -L 8080:localhost:8080
http://localhost:8080 and you will see Apache Flagon's home page.
Note that the
userale index uses dynamic mapping configurations for many fields. This means that if no valid data exists for certain fields in the logs you collect at this step, Kibana won't know to map these fields to data types (e.g., string, text, boolean, etc.). This can prevent certain dashboards and visualizations from appropriately displaying log aggregations. It is worth 1-2 mins collecting some UserALE.js data in whichever way best emulates your use-case: from the same website, the
UserALE.js example utilty, or the
UserALE.js Web Extension. If you run into issues with data fields or visualizations, see the
Having Issues? section below.
Launch Kibana. Give Kibana about 2-5 minutes to start before accessing
$ docker-compose up -d kibana # for remote users, forwards port to localhost $ ssh docker@$(docker-machine ip flagon) -L 5601:localhost:5601
Register an index in Kibana to see the logs:
Goto: Management -> Index Patterns and enter
userale in the Index pattern box. Choose
clientTime in the drop down
Time Filter field name field. Alternatively, to explore our “interval” logs, select
Load example Dashboard and Visualizations under docker/kibana/.
Goto: Management -> Saved Objects and select the
Import button. Import the
Apache Flagon Visualizations.json,
Apache Flagon Page Usage Dashboard.json, and
Apache Flagon User Access Dashboard.json files from the “Saved Objects” folder in the kibana directory.
userale index if Kibana detects conflicts when you load visualizations and searches.
Once that is complete, navigate to the
Dashboard view in Kibana and click the
Apache Flagon Page Usage Dashboard object.
To see container performance metrics, launch Metricbeat:
$ docker-compose up -d metricbeat
Once the container is running, metricbeat dashboards will automatically load in Kibana. Navigate to the
Note that metricbeat and logging with UserALE.js will strain the single-node container. We recommend that metricbeat be started only for demonstration purposes.
To stop all containers.
$ docker-compose stop
To kill the Flagon machine.
$ docker-machine stop flagon
To remove the Flagon machine.
$ docker-machine rm flagon
If running on a single machine, on reboot or restart your Flagon machine is available, but in a “stopped” state. You‘ll need to restart the machine, then you’ll need to use docker- compose up commands above to restart containers.
$ docker-machine start flagon $ docker-machine ls #confirm state #output should look like this: NAME ACTIVE DRIVER STATE URL SWARM DOCKER ERRORS flagon - virtualbox Running tcp://192. ... v19.03.1
Check out the docker-compose logs for the service(s) that are having issues.
$ docker-compose ps
If you find containers failing, you may have duplicate or dangling images!
This can happen if you've played around with multiple machines and builds of the containers on the same machine. Visit
this excellent how to guide for removing images, containers, and volumes. Remove any duplicate images and rebuild the containers.
If you find that the
site containers don't respond immediately, give them a few minutes. In the case of the
site container, you might try giving it a kick if its taking more than three minutes to load in browser"
#after loading container, confirming status is "up", and localhost:8080 still isn't loading, bring the container down $ docker-compose kill site # then bring it back up, and see if it loads $ docker-compose up -d site
If you find that Apache Flagon Kibana Dashboards aren‘t loading, or Apache UserALE.js log fields in Kibana’s
Discover view appear with a warning icon, it could be that you didn‘t collect logs with valid data for those fields prior to loading the userale index in Kibana. Don’t worry, your data is fine--just navigate to the Management -> Index Patterns page, and click the “refresh” button in the upper right hand of the page (Disregard the “popularity metrics” warning). This will refresh the index, making those fields aggregatable, and Dashboards should render properly.
Make sure to send us the docker-compose logs to help diagnose your issues please!
$ docker-compose logs > err.dump
You can attach logs directly to tickets on our
Apache Jira board
Still having issues? Reach out to us at at our dev list.
Apache Flagon is provided under Apache License version 2.0. See LICENSE and NOTICE (at Master) file for more details.