Skip to main content Link Search Menu Expand Document (external link)

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 use docker-compose to handle the dependent instances.

Steps for execution

  1. 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 as docker-config.yml.
  restart: on-failure:5
  image: bitergia/elasticsearch:6.1.0-secured
  command: elasticsearch -Enetwork.bind_host= -Ehttp.max_content_length=2000mb
    - ES_JAVA_OPTS=-Xms2g -Xmx2g
    - 9200:9200

  restart: on-failure:5
  image: bitergia/kibiter:secured-v6.1.4-5
    - PROJECT_NAME=Development
    - NODE_OPTIONS=--max-old-space-size=1000
    - ELASTICSEARCH_URL=https://elasticsearch:9200
    - ELASTICSEARCH_USER=kibanaserver
    - elasticsearch
    - 5601:5601
  restart: on-failure:5
  image: mariadb:10.0
    - "3306"
    - "3306:3306"
    - MYSQL_DATABASE=test_sh
  command: --wait_timeout=2592000 --interactive_timeout=2592000 --max_connections=300
  log_driver: "json-file"
      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 ... 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 the ES_JAVA_OPTS parameter in the environment attribute given in the docker-config.yml config file. for eg. ( -Xms1g -Xmx1g )
  1. At this point, you should be able to access the ElasticSearch instance via http://admin:admin@localhost:9200 and Kibiter instance via http://admin:admin@localhost:5601 on the browser. (something like below)

Browser: Kibiter Instance

  1. As you can see on the Kibiter Instance above, it says Couldn'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 the Raw and Enrich tasks for the Git config section from the provided setup.cfg file.
python3 --raw --enrich --cfg setup.cfg --backends git

The above command requires two files:

  • setup.cfg: Contains section of configuration for different components and tools
  • projects.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 the projects.json file. We can proceed after removing the repositories using the following command.
rm -rf .perceval/repositories/...
  1. 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 corresponding sigils panels to Kibiter instance using the following command.
python3 --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.