Micro-mordred via Docker-Compose
What is Mordred?
- Mordred is the tool used to orchestrate the execution of the GrimoireLab platform, via a configuration file. We can find more details about the sections in the configuration file here.
What is Micro-Mordred?
-
Micro-Mordred is a simplified version of Mordred which omits the use of its scheduler. Thus, Micro-Mordred allows to run single Mordred tasks (e.g. raw collection, enrichment) per execution. We can find the implementation of micro-mordred located in /utils directory and it can be executed via command line.
-
In this tutorial, we’ll try to execute micro-mordred with the help of docker-compose.
Docker-Compose
is a tool for defining and running multi-container Docker applications. As our application in this case (micro-mordred
), requires instances of ElasticSearch, Kibiter ( a soft-fork of Kibana ) and MariaDB. We’ll usedocker-compose
to handle the dependent instances.
Steps for execution
- We’ll use the following docker-compose configuration to instantiate the required components i.e ElasticSearch, Kibiter and MariaDB. Note that we can omit the
mariadb
section in case you have MySQL/MariaDB already installed in our system. We’ll name the following configuration asdocker-config.yml
.
elasticsearch:
restart: on-failure:5
image: bitergia/elasticsearch:6.1.0-secured
command: elasticsearch -Enetwork.bind_host=0.0.0.0 -Ehttp.max_content_length=2000mb
environment:
- ES_JAVA_OPTS=-Xms2g -Xmx2g
ports:
- 9200:9200
kibiter:
restart: on-failure:5
image: bitergia/kibiter:secured-v6.1.4-5
environment:
- PROJECT_NAME=Development
- NODE_OPTIONS=--max-old-space-size=1000
- ELASTICSEARCH_URL=https://elasticsearch:9200
- ELASTICSEARCH_USER=kibanaserver
- ELASTICSEARCH_PASSWORD=kibanaserver
links:
- elasticsearch
ports:
- 5601:5601
mariadb:
restart: on-failure:5
image: mariadb:10.0
expose:
- "3306"
ports:
- "3306:3306"
environment:
- MYSQL_ROOT_PASSWORD=
- MYSQL_ALLOW_EMPTY_PASSWORD=yes
- MYSQL_DATABASE=test_sh
command: --wait_timeout=2592000 --interactive_timeout=2592000 --max_connections=300
log_driver: "json-file"
log_opt:
max-size: "100m"
max-file: "3"
You can now run the following command in order to start the execution of individual instances.
docker-compose -f docker-config.yml up
Once you see something similar to the below log
on your console, it means that you’ve successfully instantiated the containers corresponding to the required components.
elasticsearch_1 | Search Guard Admin v6
elasticsearch_1 | Will connect to 0.0.0.0:9300 ... done
elasticsearch_1 | [2019-05-30T09:38:20,113][ERROR][c.f.s.a.BackendRegistry ] Not yet initialized (you may need to run sgadmin)
elasticsearch_1 | Elasticsearch Version: 6.1.0
elasticsearch_1 | Search Guard Version: 6.1.0-21.0
elasticsearch_1 | Connected as CN=kirk,OU=client,O=client,L=test,C=de
elasticsearch_1 | Contacting elasticsearch cluster 'elasticsearch' and wait for YELLOW clusterstate ...
elasticsearch_1 | Clustername: bitergia_elasticsearch
elasticsearch_1 | Clusterstate: GREEN
elasticsearch_1 | Number of nodes: 1
elasticsearch_1 | Number of data nodes: 1
...
elasticsearch_1 | Done with success
elasticsearch_1 | $@
...
kibiter_1 | {"type":"log","@timestamp":"2019-05-30T09:38:25Z","tags":["status","plugin:elasticsearch@6.1.4-1","info"],"pid":1,"state":"green","message":"Status changed from red to green - Ready","prevState":"red","prevMsg":"Service Unavailable"}
- Note: In case you face a memory related error, which might cause the elasticsearch instance not instantiating completely and lead the linked kibiter instance a
Request timeout
. In such a case, try adjusting theES_JAVA_OPTS
parameter in the environment attribute given in thedocker-config.yml
config file. for eg. ( -Xms1g -Xmx1g )
- At this point, you should be able to access the ElasticSearch instance via
http://admin:admin@localhost:9200
and Kibiter instance viahttp://admin:admin@localhost:5601
on the browser. (something like below)
Browser: Kibiter Instance
- As you can see on the
Kibiter Instance
above, it saysCouldn't find any Elasticsearch data. You'll need to index some data into Elasticsearch before you can create an index pattern
. Hence, in order to index some data, we’ll now execute micro-mordred using the following command, which will call theRaw
andEnrich
tasks for the Git config section from the providedsetup.cfg
file.
python3 micro.py --raw --enrich --cfg setup.cfg --backends git
The above command requires two files:
setup.cfg
: Contains section of configuration for different components and toolsprojects.json
: Contains a list of projects to analyze
Read more about the projects file here.
We’ll (for the purpose of this tutorial) use the files provided in the /utils
directory, but feel free to play around with the file and their configurations :)
- Note: In case the process fails to index the data to the ElasticSearch, check the
.perceval
folder in the home directory; which in this case may contain the same repositories as mentioned in theprojects.json
file. We can proceed after removing the repositories using the following command.
rm -rf .perceval/repositories/...
- Now, we can create the index pattern and after its successful creation we can analyze the data as per fields. Then, we execute the
panels
task to load the correspondingsigils panels
to Kibiter instance using the following command.
python3 micro.py --panels --cfg setup.cfg
On successful execution of the above command, we can manage to produce some dashboard similar to the one shown below.
Dashboard - Git: Areas of Code
- Hence, we have successfully executed micro-mordred with the help of docker-compose.