Effectively Monitor Your Time Series Data-Using TICK Stack with Docker

TICK Stack is a widely used combination of four open-source software tools designed for collecting, storing, visualizing, and analyzing time series data. This article will present a guide on how to set up and utilize TICK Stack using Docker.

Telegraf: Telegraf is an agent responsible for collecting and gathering data from various sources, such as system metrics, application performance metrics, and logs. It supports a wide range of inputs and can collect data from databases, APIs, and other sources, making it a versatile data collection tool.

InfluxDB: InfluxDB is a time-series database that stores and manages collected data efficiently, handling heavy write and query loads, making it suitable for time-series data processing. It provides a flexible query language to easily retrieve and analyze time-series data. Additionally, it comes with a user-friendly interface and includes features like its Alarm system, Dashboard system, and Telegraf interface, functioning almost like a standalone TICK stack.

At this point, you may wonder why bother setting up the entire TICK stack when InfluxDB’s interface seems to provide everything you need. However, we prefer to add Chronograf and Kapacitor to our system for several reasons, such as better management of alarms, a wider range of visualization options, and an improved user interface.

Adding Chronograf to the stack provides us with a powerful tool for managing alarms more efficiently. It enhances the monitoring and alerting capabilities, allowing us to set up sophisticated alerting rules based on time-series data.

Kapacitor, on the other hand, brings real-time processing and analytics to the table. It enables the execution of custom algorithms and anomaly detection on streaming data. By incorporating Kapacitor into the TICK stack, we gain the ability to perform advanced data processing tasks, which may not be readily available within the InfluxDB interface alone.

Furthermore, Chronograf and Kapacitor enhance the overall user experience by offering a more intuitive and user-friendly interface for data visualization, analysis, and alert management. These additional components complement InfluxDB and provide a more comprehensive and versatile solution for handling time-series data.

In summary, while InfluxDB is a robust time-series database with a user-friendly interface, adding Chronograf and Kapacitor to the stack offers distinct advantages in terms of alarm management, advanced analytics, and a more intuitive user experience. The combination of these components creates a more powerful and comprehensive TICK stack tailored to the specific needs of time-series data analysis and management.

Chronograf: Chronograf is the UI and visualization tool in the TICK Stack, facilitating easy exploration of time-series data in InfluxDB through interactive dashboards and graphs. Its user-friendly interface ensures accessible data analysis for all users. Additionally, Chronograf’s integration with Kapacitor enables real-time data processing and alerting, empowering timely responses to critical events. Together, these features make Chronograf an essential tool for efficient data-driven decision-making and monitoring.

Kapacitor: Kapacitor is a component responsible for real-time data processing and alerting. It allows the creation of data-driven tasks and alert rules based on data stored in InfluxDB. Kapacitor can process and analyze data in real-time, making it a crucial component for monitoring time-sensitive events and responding accordingly. In Kapacitor, there are two options for analysis: batch and streaming. In the batch approach, data is fetched from InfluxDB by executing queries over a specific time interval for examination. On the other hand, in the streaming approach, data is mirrored from Telegraf, simultaneously writing it to both InfluxDB and Kapacitor for analysis. These approaches have their own advantages and disadvantages in terms of RAM usage, processing latency, and other factors.

We will now make the installation using a YAML file to set up everything in Docker at once. This YAML file will include information such as ports, file access points, software versions, the associated network, and many other details.

version: 
services:
  influxdb:
    image: "influxdb:latest"
    hostname: "monitoring_influxdb"
    container_name: monitoring_influxdb
    ports: 
        - "8086:8086"
    volumes:
        - "./influx-data:/var/lib/influxdb"
    networks:
        - TICKnetwork
  chronograf:
    image: "chronograf:latest"
    hostname: "monitoring_chronograf"
    container_name: monitoring_chronograf
    ports: 
        - "8888:8888"
    volumes: 
        - "./chronograf-data:/var/lib/chronograf"
    networks:
        - TICKnetwork
  telegraf:
    image: "telegraf:latest"
    hostname: "monitoring_telegraf"
    container_name: monitoring_telegraf
    volumes:
        - ./telegraf/etc/telegraf.conf:/etc/telegraf/telegraf.conf
    networks:
        - TICKnetwork
  kapacitor:
    image: "kapacitor:latest"
    hostname: "monitoring_kapacitor"
    container_name: "monitoring_kapacitor"
    volumes: 
        # - "./kapacitor-data:/var/lib/kapacitor"
        - ./kapacitor-data/etc/kapacitor.conf:/etc/kapacitor/kapacitor.conf

    environment: 
        - KAPACITOR_INFLUXDB_0_URLS_0=http://XXXXXXXXXXX:8086
        - KAPACITOR_INFLUXDB_ENABLED=true
        - KAPACITOR_INFLUXDB_NAME=monitoring
        - KAPACITOR_INFLUXDB_0_USERNAME=XXXXXXXXXXXX
        - KAPACITOR_INFLUXDB_0_PASSWORD=XXXXXXXXXXXX
    ports:
        - "9092:9092"
    networks:
        - TICKnetwork

volumes:
    telegraf-data: 
    chronograf-data:
    influx-data:
    kapacitor-data:

networks:
    monitornet:

When executing the ‘docker-compose’ command in the directory containing the file, the TICK stack will be set up in Docker. However, it is likely that the TICK stack might not function fully at this point. To ensure proper functionality, you must configure the ‘telegraf.conf’ and ‘kapacitor.conf’ files. These configurations involve establishing connections with InfluxDB and providing necessary register and token information, among other details.

Please navigate to the specified locations in the Volumes section and check the configuration files. If the files do not exist, please create them accordingly.

Once you properly configure the Telegraf and Kapacitor configuration files, you will see that all four applications are running smoothly. If you encounter any issues, you should check the Docker logs and troubleshoot to resolve the problem.

After successfully running the applications, you can access the InfluxDB web interface by visiting “localhost:8086” in your web browser. In the interface, you need to fill in the necessary details to connect to InfluxDB.

You can use the InfluxDB interface to test Telegraf. In the Telegraf interface, there are many pre-built plugins available. Here, you can select the bucket where you want to write the data and choose the plugin that will fetch the desired data. This allows you to easily perform data ingestion operations by configuring the appropriate settings.

To access Chronograf, go to “localhost:8888” and follow the provided steps.

If you want to examine a piece of data in detail, you can use the Explore tab, where you can run queries with tick script to perform more detailed analyses and thoroughly examine your data.

If you want to easily see different data on a single screen without setting up new queries each time, you can use the Dashboard tab. For example, you can create a dashboard to examine data related to your computer and keep it constantly displayed on your second monitor. With just a single click, you can view various data such as RAM, CPU, disk, and more without the need to set up the queries repeatedly.

If you want to monitor Kapacitor’s operation, you can do so through the Alerting tab in the Chronograf interface. Before that, you need to configure the necessary settings from the Configuration tab at the bottom.

You can create the alarm you want either by using the interface or by writing code with the tick script.

So let us create an alarm with interface. After giving a name to our alarm we need to choose an alert type.

Threshold Alarm: It is triggered when a specific metric exceeds a predefined threshold value, indicating a condition that is beyond the desired limit or condition. For example, you can set an alarm for “temperature data exceeding 30 degrees” or “temperature data going out of the range between 27 to 30 degrees.”

Relative Alarm: It detects relative changes by comparing the current value of a metric with its past values, and provides alerts when significant differences or deviations occur. For example, you can set an alarm for “a 10% increase compared to the previous state.”

Deadman Alarm: This alarm indicates potential issues or system problems by sending alerts when no expected data or activity is received within a specified time interval. In short, it serves as a connectivity or data-receiving status alarm.

Afterwards, we select the headers of the data we want to test, such as measurement, tags, and fields, and then feed the data into the system.

After introducing the data, we define the condition. Here, I have chosen the condition “trigger an alert if my computer’s CPU usage exceeds 15%.”

Finally, we need an output destination to send the alarm if the conditions are met. At this point, you need to set up an output destination through Kapacitor.

After making these configurations, we can actively monitor our alarm on telegram, teams, mail etc.

Now, you are all set to start fetching data, monitoring, and analyzing your data. You can monitor your RAM usage and receive a message when it exceeds 70%, you can explore various IoT projects with TICK stack. I hope you haven’t encountered any issues throughout the process. Thank you for reading my article. See you in my future posts.