GitLab Performance Monitoring with Grafana

Since GitLab v8.4 you can monitor your own instance with InfluxDB & Grafana stack by using the GitLab application performance measuring system called “Gitlab Performance Monitoring“.

GitLab writes metrics to InfluxDB via UDP. Therefore, this must be enabled in /etc/influxdb/influxdb.conf:

Restart your InfluxDB instance. Then, create a database to store GitLab metrics:

Next, go to Gitlab Setings Dashboard and enable InfluxDB Metrics as shown below:

Then, you need to restart GitLab:

Now your GitLab instance should send data to InfluxDB:

Finally, Point your browser to your Grafana URL, then login as the admin user. Choose ‘Data Sources‘ from the menu. Then, click ‘Add new‘ in the top bar.

Fill in the configuration details for the InfluxDB data source:

You can now import the dashboard.json file by opening the dashboard dropdown menu and click ‘Import‘ :

Note: Check my Github for more interactive & beautiful Grafana dashboards.

Exploring Swarm & Container Overview Dashboard in Grafana

In my previous post, your learnt how to monitor your Swarm Cluster with TICK Stack. In this part, I will show you how to use the same Stack but instead of using Chronograf as our visualization and exploration tool we will use Grafana.

Connect to your manager node via SSH, and clone the following Github repository:

Use the docker-compose.yml below to setup the monitoring stack:

Then, issue the following command to deploy the stack:

Once deployed, you should see the list of services running on the cluster:

Point your browser to http://IP:3000, you should be able to reach the Grafana Dashboard:

The default username & password are admin. Go ahead and log in.

Go to “Data Sources” and create 2 InfluxDB data sources:

  • Vms: pointing to your Cluster Nodes metrics database.
  • Docker: pointing to your Docker Services metrics database.

Finally, import the dashboard by hitting the “import” button:

From here, you can upload the dashboard.json, then pick the data sources you created earlier:

You will end up with an interactive and dynamic dashboard:

DialogFlow (API.AI) Golang SDK

DialogFlow (formerly API.AI) gives users new ways to interact with your bot by building engaging voice and text-based conversational interfaces powered by AI.

DialogFlow offers many SDKs in different programming languages:

But unfortunately, there’s no SDK for Golang

But dont be sad, I made an SDK to integrate DialogFlow with Golang:

Résultat de recherche d'images pour "thank you sir meme"

This library allows integrating agents from the DialogFlow natural language processing service with your Golang application.

Issue the following command to install the library:

The example below, display list of entities:

Note: for more details about the available methods, check the project Github repository.

For a real world example on how to use this library, check my previous tutorial, on how to create a Messenger Bot in Golang to show list of movies playing in cinema, and tv shows airing on TV:

Messenger Bot with DialogFlow & Golang

This post is part of “ChatOps” series. In this part, I will show you how to create a Messenger Bot in Golang with DialogFlow (formerly API.AI) to show list of movies playing today in cinema.

Note: all the code used in this demo can be found on my Github.

Start with an HTTP server exposing 2 endpoints:

1 – GET /webhook

Handles Facebook challenge verification. It simply looks for the Verify Token and responds with the challenge sent in the verification request.

2 – POST /webhook

Handles messages coming from Messenger:

It calls the ProcessMessage method which uses Facebook Graph API to send a GIF image to the user:

Note: for more in depth details check my tutorial Build a Facebook Messenger bot with Go and Messenger API

Create a Facebook page. It will be “identity ” of your bot:

Then create a Facebook application. It will be the middleware that connects the server and your public page.

Click Add Product from the left menu, then choose Messenger:

At the Token Generation, choose the page you just created from the dropdown menu, and it will generate a token:

Once you’ve gotten your PAGE_ACCESS_TOKEN and VERIFY_TOKEN, make sure you add those two as environment variables for the server:

In new terminal session, issue the following command to start the HTTP server:

In order to make our server publically accessible, I will use a tool called ngrok. It basically creates a secure tunnel on your local machine along with a public URL you can use for browsing your local server.

Note: Keep in mind, to use your bot in production, you need to use a :

  • IaaS like AWSGCP, Azure …
  • PaaS like Heroku, Clever Cloud …

Then, at the Webhooks section, click the Setup Webhooks button:

After you’ve configured your webhook, you will need to subscribe to the page you created earlier:

Go to the Facebook Page you created and and click on “Message” button, next to the “Like” button near the top of the page. Start sending your Page messages and the bot should reply with a GIF !

By default, the bot should respond to everything with a GIF image.

Now lets make it smarter, for that we will use an NLP (Natural Language Processing) backend like DialogFlow (formerly API.AI):

So after signing up to Dialogflow, create a new Agent:

Give it a name and fill out the required fields:

Once created, lets use Small Talk feature of DialogFlow to give our bot the ability to have simple conversations:

Enable the Small Talk checkbox. With this feature enabled we imported a lot of predefined answers for simple questions and phrases. You can easily change the responses to the questions if you don’t like them:

To test it out, you can use the Console at the right hand side:

Now lets use this feature in out bot. DialogFlow offers many SDKs in different programming languages:

But unfortunately, there’s no SDK for Golang

But dont be sad, I made an SDK to integrate DialogFlow with Golang:

Résultat de recherche d'images pour "thank you sir meme"

So, install DialogFlow Golang library:

Go back to DialogFlow dashboard and copy the Client Access Token:

Set it as environment variable:

Create a new function that takes the message sent from a user via Messenger as an argument, and pass it to DialogFlow Client to get the appropriate response:

Go to the Facebook Page and click on Message to start chatting :

But that’s not enough, lets take this further and make our bot tell us about the movies playing today in cinema, and series airing today on TV.

Create an entity, to store the type of the show (movie or series) the user is asking about:

Then, create a new intent, which represents a mapping between what a user says and what action should be taken:

Create some more questions:

Finally, update the ProcessMessage method to respond with a list of shows if the intent name is shows. The method is using the moviedb library to get the list of shows.

Let’s test the bot from Messenger:

Wow !! you have created your first chatbot in Golang with DialogFlow ! It was easy, wasn’t it ?

Image associée

In the upcoming tutorial, I will show you how to create a Serverless Messenger Bot with Lambda & API Gateway.

Monitor Swarm cluster with TICK stack & Slack

In this article, I will show you how to setup an Open Source time series platform to monitor your Docker Swarm cluster & send notification on Slack in case of anomaly detection.

Components of our monitoring Stack:

Plugin driven server agent for collecting and reporting metrics.

Scalable time series database for metrics, events and real-time analytics.

Real time visualization tool for building graphs on top of data.

Framework for processing, monitoring, and alerting on time series data.

Real-time team messaging application.

Note: all the code used in this post is available on my Github.

1 – Swarm Setup

If you already have an existing Swarm cluster, you can skip this part, if not use the following script to setup a Swarm with 3 nodes (1 manager & 2 workers):

Issue the following commands:

The output of the above command is as follows:

2 – Stack Setup

Once created, connect to your manager node via SSH, and clone the following repository:

To start all of these containers I’m using docker-compose:

Issue the following command to deploy the stack:

Wait for nodes to pull the images from DockerHub:

Once pulled you should see the services running:

Open your browser on http://IP:8888 (Chronograf Dashboard) and properly configure the data source:

3 – System usage Dashboard

Click on “create dashboard“, and assign a name to the dashboard:

Before adding graphs, we will use a concept called Dashboard Template Variable, to create dynamic & interactive graphs. Instead of hard-coding things like node name and container name in our metric queries we will use variables in their place. So click on “Templates Varibles” in top of the dashboard created earlier. And, create a variable called :host: as follows:

Note: currently, there’s no solution to set hostname for services created with swarm global mode (Github). Thats why we got list of IDs instead of names

You can now use the dropdown at the top of the dashboard to select the different options for the :host: template variable:

Now it’s time to create our first graph, so click on “Add Graph” button.

3.1 – Memory usage per Node

To create a query, you can either use the Query Builder or, if you’re already familar with InfluxQL, you can manually enter the query in the text input:

Our query calculates the average of the field keys freeused, and total in the measurement mem_vm, and it groups them by the time and node name.

You can change the graph type, X, and Y axes format by clicking on “Options” tab:

One visualization on a dashboard isn’t spectacularly interesting, so I added a couple more graphs to show you more possibilities:

3.2 – CPU usage per Node

3.3 – Disk usage per Node

We end up with a beautiful dashboard like this:

Let’s create another dashboard to monitor Docker Containers running on the Cluster.

4 – Swarm Services Dashboard

Create a second dashboard called “Services“, and create a template variable to store list of services running on cluster:

You can filter now metrics by service name:

4.1 – Memory usage per Service

4.2 – CPU usage per Service

4.3 – Network Transmit/Receive

4.4 – IO Read/Write per Service


Note: you can take this further, and filter metrics by the node on which the service is running on by creating another template variable:

Let’s see what happen if we create another service on Swarm:

If you go back to Chronograf, you should see the service has been added automatically to the container dropdown list:

And that’s it! You now have the foundation for building beautiful data visualizations and dashboards with Chronograf.

Kapacitor is the last piece of the puzzle. We now know how to store, get and read metrics, and now you need to elaborate on them to do something like alerting or proactive monitoring.

So on the “Configuration” tab, click on “Add config“:

Add new Kapacitor instance as below, and enable Slack:

Note: update the Slack channel & Webhook URL in case you didn’t update the kapacitor.conf file in the beginning of this tutorial. You can get a Webhook URL by going to this page:

5 – Alerts definition

5.1 – High Memory Utilization Alert

Navigate to the “Rule Configuration” page by visiting the “Alerting” page and click on the “Build Rule” button in the top right corner:

We will trigger an alert if the memory usage is over 60%:

Next, we select Slack as the event handler and configure the alert message:

Note: there’s no need to include a Slack channel in the Alert Message section if you specified a default channel in the initial Slack configuration.

5.2 – High CPU Utilization Alert

Create a second rule to trigger an alert if CPU usage is over 40%:

Alert endpoint:

Save the rule, and you’re all set !

Now our alert rules are defined, lets test them out by creating some load on our cluster.

6 – Stress Testing

I used stress, a tool for generating workload. It can produce CPU, memory, I/O, and disk stress.

6.1 – Stressing the CPU

Note: depending on the type of your CPU, make sure to replace ‘4‘ accordingly.

After few seconds, you should receive a Slack notification:

Kapacitor trigger an alert and also recovered them (Status OK) if the alert is resolved.

6.2 – Stressing the Memory

It will stress memory using three processes, with each about 256 Mb (override with the option –vm-bytes).

Let it run for a couple seconds :

That’s it! You’ve successfully setup a highly scalable, distributed monitoring platform for your Swarm cluster with only Open Source projects.

Résultat de recherche d'images pour "wow amazing meme"