1. Introduction
   • Architectures
     • Microservice Architecture
     • The Clean Architecture
     • BEM
   • Technologies Stack
     • Elastic stack (ELK)
     • Pattern Lab
     • Bits.dev
     • Storybook
     • Customized Json Web Token (jwt)
2. Requirements
3. Core Structure
4. Integration
   • Git
   • Git Actions
   • Travis Ci
   • Okteto
   • Docker Hub
   • bits.dev
   • Npm
   • Packagist
5. REST API Guideline
6. Setup Konga Admin UI
   • Setup Konga LoopBack
   • Enable Key Auth Plugin
   • Add Konga as Consumer
   • Create API key for Konga
   • Setup Connection
7. Elastic stack (ELK) Services
   • Initial setup
     • Disable Default Quota
     • Setting up user authentication
     • Injecting data
     • Default Kibana index pattern creation
   • Configuration
     • How to configure Elasticsearch
     • How to configure Kibana
     • How to configure Logstash
     • How to disable paid features
   • Extensibility
     • How to add plugins
     • How to enable the provided extensions
   • JVM tuning
     • How to specify the amount of memory used by a service
     • How to enable a remote JMX connection to a service
   • Going further
     • Using a newer stack version
     • Plugins and integrations
     • Swarm mode

Elastic stack (ELK) gives you the ability to analyze any data set by using the searching/aggregation capabilities of Elasticsearch and the visualization power of kibana.

Based on the offical Docker images form Elastic:

• Elasticsearch
• Logstash
• Kibana

Core structure files and folders ended up with the following concepts:

• Everything related to docker is placed in a .docker directory on on the same level as the main application

• In this directory

  • each service gets its own subdirectory for configuration is a .shared folder containing scripts and configuration required by multiple services
  • is an .env.example file containing variables for the docker-compose.yml
  • is a docker-test.sh file containing high level tests to validate the docker containers
  • a Makefile with common instructions to control Docker is placed in the repository root

Konga provide GUI for interacting with Kong Admin API.

Konga would ask you to configure some credentials (Username + Password) that required to access Konga Web. After that they going to prompt how we want to communicate with Kong there's 3 available option:

• Default (Not Recommended): Konga would access the Kong Admin API directly
• Key Auth (Recommended): Konga would access Kong Admin API that run behind Kong (Loop-Back API) using configured Api Key.
• JWT (Recommended): Konga would access Kong Admin API that run behind Kong (Loop-Back API) using JWT by using shared key and secrets.

In this project we use the second method using Key Auth. Before we setup connection let's go back to access our Kong Admin API. Currently Kong Admin API is publicly accessible for those anyone who has access to the url which is dangerous for production environment. Now we need to protect Kong Admin API by running it behind Kong using LoopBack.

With Kong's routing design it's possible to serve Admin API itself behind Kong proxy.

To configure this we need to following steps:

• Add Kong Admin API as services

$ curl --location --request POST 'http://localhost:8001/services/' \
--header 'Content-Type: application/json' \
--data-raw '{
    "name": "admin-api",
    "host": "localhost",
    "port": 8001
}'

• Add Admin API route: To register route on Admin API Services we need either service name or service id, you can replace the following command below:

$ curl --location --request POST 'http://localhost:8001/services/{service_id|service_name}/routes' \
--header 'Content-Type: application/json' \
--data-raw '{
    "paths": ["/admin-api"]
}'

Now our Kong Admin API is running behind Kong Proxy, so in order to access it you need to :

$ curl localhost:8000/admin-api/

Our Admin API already run behind kong, but is not secured yet. In order to protect Kong Admin API we need to enable key auth plugin at service level by doing this commands.

$ curl -X POST http://localhost:8001/services/{service_id|service_name}/plugins \
    --data "name=key-auth"

Our Admin API already run behind kong, but is not secured yet. In order to protect Kong Admin API we need to enable key auth plugin at service level by doing this commands.

Replace {custom_id} with any key for example custom_id=cebd360d-3de6-4f8f-81b2-31575fe9846a

$ curl --location --request POST 'http://localhost:8001/consumers/' \
--form 'username=konga' \
--form 'custom_id={custom_id}'

Using Consumer ID that generated when we're adding consumer, we will use that Consumer ID and generate API Key.

From the above output get id and replace cid below for example cid looks like e7b420e2-f200-40d0-9d1a-a0df359da56e:

$ curl --location --request POST 'http://localhost:8001/consumers/{cid}/key-auth'

Now we're already have all required component to setup Konga connection

Remember in picture above, because Kong and Konga are in the same network they can simply reach each other by using container name.

By default, Elasticsearch installed with homebrew on Linux goes into read-only mode when you have less than 5% of free disk space. If you see errors similar to this:

$ curl -XPUT -u elastic:elastic_pass -H "Content-Type: application/json" http://localhost:9200/_cluster/settings -d '{ "transient": { "cluster.routing.allocation.disk.threshold_enabled": false } }'

$ curl -XPUT -u elastic:elastic_pass -H "Content-Type: application/json" http://localhost:9200/_all/_settings -d '{"index.blocks.read_only_allow_delete": null}'

ℹ️ Refer to How to disable paid features to disable authentication.

The stack is pre-configured with the following privileged bootstrap user:

• user: elastic
• password: elastic_pass

Although all stack components work out-of-the-box with this user, we strongly recommend using the unprivileged built-in users instead for increased security.

1. Initialize passwords for built-in users

$ docker-compose exec -T elasticsearch bin/elasticsearch-setup-passwords auto --batch

Passwords for all 6 built-in users will be randomly generated. Take note of them.

2. Unset the bootstrap password (optional)

Remove the ELASTIC_PASSWORD environment variable from the elasticsearch service inside the Compose file (docker-compose.yml). It is only used to initialize the keystore during the initial startup of Elasticsearch.

3. Replace usernames and passwords in configuration files

Use the kibana user inside the Kibana configuration file (kibana/config/kibana.yml) and the logstash_system user inside the Logstash configuration file (logstash/config/logstash.yml) in place of the existing elastic user.

Replace the password for the elastic user inside the Logstash pipeline file (logstash/pipeline/logstash.conf).

ℹ️ Do not use the logstash_system user inside the Logstash pipeline file, it does not have sufficient permissions to create indices. Follow the instructions at Configuring Security in Logstash to create a user with suitable roles.

See also the Configuration section below.

4. Restart Kibana and Logstash to apply changes

$ docker-compose restart kibana logstash

ℹ️ Learn more about the security of the Elastic stack at Tutorial: Getting started with security.

Give Kibana about a minute to initialize, then access the Kibana web UI by hitting http://localhost:5601 with a web browser and use the following default credentials to log in:

• user: elastic
• password: <your generated elastic password>

Now that the stack is running, you can go ahead and inject some log entries. The shipped Logstash configuration allows you to send content via TCP:

# Using BSD netcat (Debian, Ubuntu, MacOS system, ...)
$ cat /path/to/logfile.log | nc -q0 localhost 5000

# Using GNU netcat (CentOS, Fedora, MacOS Homebrew, ...)
$ cat /path/to/logfile.log | nc -c localhost 5000

You can also load the sample data provided by your Kibana installation.

When Kibana launches for the first time, it is not configured with any index pattern.

ℹ️ You need to inject data into Logstash before being able to configure a Logstash index pattern via the Kibana web UI.

Navigate to the Discover view of Kibana from the left sidebar. You will be prompted to create an index pattern. Enter logstash-* to match Logstash indices then, on the next page, select @timestamp as the time filter field. Finally, click Create index pattern and return to the Discover view to inspect your log entries.

Refer to Connect Kibana with Elasticsearch and Creating an index pattern for detailed instructions about the index pattern configuration.

Create an index pattern via the Kibana API:

$ curl -XPOST -D- 'http://localhost:5601/api/saved_objects/index-pattern' \
    -H 'Content-Type: application/json' \
    -H 'kbn-version: 7.6.0' \
    -u elastic:<your generated elastic password> \
    -d '{"attributes":{"title":"logstash-*","timeFieldName":"@timestamp"}}'

The created pattern will automatically be marked as the default index pattern as soon as the Kibana UI is opened for the first time.

ℹ️ Configuration is not dynamically reloaded, you will need to restart individual components after any configuration change.

The Elasticsearch configuration is stored in .docker/elasticsearch/config/elasticsearch.yml.

You can also specify the options you want to override by setting environment variables inside the Compose file:

elasticsearch:

  environment:
    network.host: _non_loopback_
    cluster.name: my-cluster

Please refer to the following documentation page for more details about how to configure Elasticsearch inside Docker containers: Install Elasticsearch with Docker.

The Kibana default configuration is stored in .docker/kibana/config/kibana.yml.

It is also possible to map the entire config directory instead of a single file.

Please refer to the following documentation page for more details about how to configure Kibana inside Docker containers: Running Kibana on Docker.

The Logstash configuration is stored in .docker/logstash/config/logstash.yml.

It is also possible to map the entire config directory instead of a single file, however you must be aware that Logstash will be expecting a log4j2.properties file for its own logging.

Please refer to the following documentation page for more details about how to configure Logstash inside Docker containers: Configuring Logstash for Docker.

Switch the value of Elasticsearch's xpack.license.self_generated.type option from trial to basic (see License settings).

To add plugins to any ELK component you have to:

1. Add a RUN statement to the corresponding Dockerfile (eg. RUN logstash-plugin install logstash-filter-json)
2. Add the associated plugin code configuration to the service configuration (eg. Logstash input/output)
3. Rebuild the images using the docker-compose build command

A few extensions are available inside the extensions directory. These extensions provide features which are not part of the standard Elastic stack, but can be used to enrich it with extra integrations.

The documentation for these extensions is provided inside each individual subdirectory, on a per-extension basis. Some of them require manual changes to the default ELK configuration.

By default, both Elasticsearch and Logstash start with 1/4 of the total host memory allocated to the JVM Heap Size.

The startup scripts for Elasticsearch and Logstash can append extra JVM options from the value of an environment variable, allowing the user to adjust the amount of memory that can be used by each component:

To accomodate environments where memory is scarce (Docker for Mac has only 2 GB available by default), the Heap Size allocation is capped by default to 256MB per service in the docker-compose.yml file. If you want to override the default JVM configuration, edit the matching environment variable(s) in the docker-compose.yml file.

For example, to increase the maximum JVM Heap Size for Logstash:

logstash:

  environment:
    LS_JAVA_OPTS: -Xmx1g -Xms1g

As for the Java Heap memory (see above), you can specify JVM options to enable JMX and map the JMX port on the Docker host.

Update the {ES,LS}_JAVA_OPTS environment variable with the following content (I've mapped the JMX service on the port 18080, you can change that). Do not forget to update the -Djava.rmi.server.hostname option with the IP address of your Docker host (replace DOCKER_HOST_IP):

logstash:

  environment:
    LS_JAVA_OPTS: -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.port=18080 -Dcom.sun.management.jmxremote.rmi.port=18080 -Djava.rmi.server.hostname=DOCKER_HOST_IP -Dcom.sun.management.jmxremote.local.>

:Insert

: Under Development

Experimental support for Docker Swarm mode is provided in the form of a docker-stack.yml file, which can be deployed in an existing Swarm cluster using the following command:

$ docker stack deploy -c docker-stack.yml elk

If all components get deployed without any error, the following command will show 3 running services:

$ docker stack services elk

ℹ️ To scale Elasticsearch in Swarm mode, configure zen to use the DNS name tasks.elasticsearch instead of elasticsearch.

chmod 700 get_helm.sh

kubectl create -f namespaces.yml

Since prometheus & grafana services are available within a cluster (ClusterIP is the default Kubernetes service), therefore they can not be accessed outside of cluster.

In order to access the web GUI from outside of cluster, on our local machine we need to change default ClusterIP services to NodePort (Kubernetes transparently routes incoming traffic on the NodePort to your service).

$ export EDITOR=vim

$ kubectl edit -n guya-ltd-monitoring service/prometheus-operator-{randNum}-prometheus

and change type: ClusterIp to type: NodePort

Example:

$ curl http://api.example.com/paths?page=4&limit=10

It is possible to order the results by 1 or more keys using order_by(). The order may be specified by prepending each of the keys by “+” or “-”. Ascending order is assumed if there’s no prefix.:

Example:

$ curl http://api.example.com/paths?order_by=name,+age,-job

Filtering follows {column name}={operator name}:{value}

Operator name list:

• ne – not equal to
• lt – less than
• lte – less than or equal to
• gt – greater than
• gte – greater than or equal to
• not – negate a standard check, may be used before
• in – value is in list (a list of values should be provided)
• nin – value is not in list (a list of values should be provided)
• mod – value % x == y, where x and y are two provided values
• all – every item in list of values provided is in array
• size – the size of the array is
• exists – value for field exists

Example:

$ curl http://api.example.com/paths?age=ge:18&name=abebe

• 200 Ok, usage GET, /api/v1/items, /api/v1/items/12
• 201 No Content
• 401 Unauthorized
• 403 Forbidden
• 400 Not Found
• 400 Bad Request, ALL

    HTTP/1.1 200
    Content-Type: application/json

{
    status_code: 200,
    status: "OK",
    message: "All items retrived",
    links: {
        slef: "https://api.guya.com/v2/{resource}/{id}"
    }
    error: {
        message: "Error Message",
        type: "ValueError",
        code: 2500,
        trace_id: "dsfsf"
        ...
    },
    error: {
        email: {
            message: "Error Message",
            type: "ValueError",
            code: 2500,
            trace_id: "dsfsf"
            ...
        }
    }
    pagination: {
        count: 100,
        limit: 10,
        page: 2
    },
    // For Multi
    data: [
        {},
    ],
    // Single item
    data: {
        id: "id"
        name: "User",
        created_at: ""
    }
}

{
    username: "User one",
    password: "password"
}