Installing the metric tools

For collecting the metrics you should install the following tools:

Installing the metric tools

Installing the metric tools

Installing the metric tools

1. Prometheus

Prometheus is an open-source monitoring system with a dimensional data model, flexible query language, efficient time series database and modern alerting approach.

You can see the official documentation of Prometheus here:

Prometheus - Monitoring system & time series databaseprometheus.io

2. Grafana

Grafana is an open-source, highly customizable analytics and monitoring platform that allows users to visualize and analyze time-series data. It is widely used to create interactive and dynamic dashboards for various applications, making it easier to monitor and understand complex systems and data trends.

Key Features:

• Data Source Support: Grafana supports a wide range of data sources, including popular databases like Prometheus, InfluxDB, Elasticsearch, and more. This versatility allows users to integrate data from different sources into a single dashboard.

• Interactive Visualizations: With Grafana, users can build interactive charts, graphs, and tables that update in real-time. This enables easy exploration and understanding of complex data sets.

• Alerting: Grafana provides alerting capabilities, allowing users to set up and receive notifications based on predefined thresholds and conditions. This feature is crucial for monitoring critical systems and responding to potential issues promptly.

• Dashboard Templating: Users can create templates in Grafana, making it easier to reuse dashboard components and adapt them for various data sources and scenarios.

• Community-Driven: Being open-source, Grafana benefits from an active community that contributes to its development, continuously expanding its capabilities and integrations.

Popular FAQs about Grafana:

1. What is Grafana used for? Grafana is primarily used for data visualization and monitoring. It helps users create interactive dashboards to monitor various metrics, visualize trends, and gain insights into complex systems, applications, and infrastructure.

2. How does Grafana work with data sources? Grafana integrates with various data sources through plugins. Users can connect Grafana to databases, cloud services, monitoring tools, and other systems, enabling them to fetch and visualize data from these sources in their dashboards.

3. Can I create alerts in Grafana? Yes, Grafana offers alerting functionality. Users can set up alert rules based on specific conditions and thresholds, and when these conditions are met, Grafana can send notifications via email, Slack, or other communication channels.

4. Is Grafana suitable for real-time monitoring? Yes, Grafana supports real-time data visualization and monitoring. With the appropriate data source (e.g., Prometheus, Graphite), users can create real-time dashboards that update dynamically as new data arrives.

5. Is Grafana user-friendly for beginners? While Grafana offers a rich set of features, it might have a learning curve for beginners. However, its user-friendly interface and extensive documentation make it accessible to users with varying levels of expertise in data visualization and monitoring.

6. How is Grafana different from other monitoring tools? Grafana's key differentiator is its focus on data visualization and the flexibility it provides in creating custom dashboards. It is often used in conjunction with other monitoring tools like Prometheus, which collects metrics data, while Grafana is used to visualize and analyze that data.

7. Can I share my Grafana dashboards with others? Yes, Grafana allows users to share dashboards with others. You can export dashboards as JSON files or use Grafana's built-in sharing options to collaborate with team members or share insights with stakeholders.

Conclusion: Grafana is a powerful and versatile tool that plays a vital role in data visualization and monitoring in various industries. Its intuitive interface, extensive data source support, and vibrant community make it a top choice for professionals seeking comprehensive insights from their time-series data.

You can see the official doumentation for Grafana here:

Install Grafana | Grafana documentationGrafana Labs

For the installation of Grafana use the following:

Run Grafana Docker image | Grafana documentationGrafana Labs

For our purposes it is more convinient to set up Prometheus and Grafana together, for that you should use the following link:

awesome-compose/prometheus-grafana at master · docker/awesome-composeGitHub

When Grafana and Prometheus are set on your local maching you should:

• in Prometheus write URL of the source where to get metrics and ports of the docker conteiners of RWManager (it is mentioned in Rw Manager MT5);

• in Garfana you shoul write the name of the metric that you want to visualize (you can get them from URL and metrics port that is mentioned in Rw Manager MT5).

3. Metrics types

Prometheus collects and stores various types of data for monitoring and analysis. It is designed to gather and manage time-series data, enabling the tracking of metric changes over time.

You can read about all of the data types Prometheus collects and retains on official types.

Our system collects:

1. Counter

A counter is a cumulative metric that represents a single monotonically increasing counter whose value can only increase or be reset to zero on restart. For example, you can use a counter to represent the number of requests served, tasks completed, or errors.

Do not use a counter to expose a value that can decrease. For example, do not use a counter for the number of currently running processes; instead use a gauge

1. Gauge

A gauge is a metric that represents a single numerical value that can arbitrarily go up and down.

Gauges are typically used for measured values like temperatures or current memory usage, but also "counts" that can go up and down, like the number of concurrent requests.

1. Histogram

A histogram samples observations (usually things like request durations or response sizes) and counts them in configurable buckets. It also provides a sum of all observed values.

A histogram with a base metric name of <basename> exposes multiple time series during a scrape:

• cumulative counters for the observation buckets, exposed as <basename>_bucket{le="<upper inclusive bound>"}

• the total sum of all observed values, exposed as <basename>_sum

• the count of events that have been observed, exposed as <basename>_count (identical to <basename>_bucket{le="+Inf"} above)

Use the histogram_quantile() function to calculate quantiles from histograms or even aggregations of histograms. A histogram is also suitable to calculate an Apdex score. When operating on buckets, remember that the histogram is cumulative. See histograms and summaries for details of histogram usage and differences to summaries.