Creating a competitive edge in today’s market requires more than just innovation. A company’s ability to innovate quickly could decide the success or failure of its business. Speed is determined by applications that can provide real-time data. A real-time view of company metrics empowers leaders to make informed strategic decisions. However, when these systems malfunction, the business may make poor decisions. This is where Prometheus High Availability comes in.
Prometheus is a metrics-based monitoring tool designed for containerized applications like Kubernetes or Docker Swarm. This article will explore what Prometheus is and its features. Furthermore, this article discusses Prometheus High Availability and Fault Tolerance strategies.
Table of Contents
- What is Prometheus?
- High Availability vs Fault Tolerance
- Prometheus High Availability and Fault Tolerance Strategies
- Long Data Retention using VictoriaMetrics
What is Prometheus?
Prometheus is a metrics-based monitoring tool designed for containerized applications like Kubernetes or Docker Swarm. Nevertheless, it is also suitable for non-containerized applications. In 2012, SoundCloud Developers created Prometheus to integrate metrics across its distributed applications. Most of the code is written in Go, and the license is Apache 2.0.
Prometheus was accepted as a second incubated project for Kubernetes and Envoy by Cloud Native Computing Foundation (CNCF) in 2016. After this, Prometheus 1.0 was released in July 2016, and finally, Cloud Native Foundation announced in August 2018 that the Prometheus project had graduated.
Key Features of Prometheus
- Prometheus is an independent and self-contained monitoring system without any reliance on remote services.
- With this tool, you can store the metrics as time-series data with key/value pairs.
- Metrics are gathered from different targets by pulling them over HTTP.
- Locating a target location can be done via service discovery or static configuration.
- An HTTP server provides PromQL with access to the metrics and aggregated data of the time-series Database.
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High Availability vs Fault Tolerance
High availability refers to the ability of a system to minimize downtime in order to avoid service interruptions. It measures the percentage of total running time that the system is up. An uptime of 99.99% is considered the “holy grail” of availability. Fault tolerance, on the other hand, refers to a system’s ability to remain functional even if some of its components fail.
As a rule, business continuity strategies provide both high availability and fault tolerance for ensuring that the organization remains operational during minor problems and a disaster. Despite their similarities in terms of a system’s functionality over time, fault tolerance and high availability have differences that highlight each’s unique importance in ensuring business continuity.
To better understand fault tolerance and availability, consider the following analogy. An airplane with twin engines is a fault-tolerant system – if one stops working, the other continues to function, enabling the plane to fly. On the other hand, cars with spare tires have high availability. Even though a flat tire will stop the vehicle, downtime is minimal due to the ease of replacement of the tire.
The following are some important considerations when designing fault-tolerant and highly available systems in an organization:
- Downtime: Service interruptions are kept to a minimum in highly available systems. A “five nines” system is down on average for approximately 5 minutes a year. The fault-tolerant system is expected to operate continuously and without any unacceptable downtime.
- Scope: The key to high availability is shared resources that are used to minimize downtime, manage failures, and manage resources jointly. A fault-tolerant system uses backup power supplies and hardware and software that can quickly switch between redundant components in the case of a failure.
- Cost: Fault-tolerant systems can be expensive, as they require additional, redundant components to operate and maintain continuously. Therefore, a high availability service is typically part of an overall package from a service provider (e.g., a load balancer provider).
Now that you’re familiar with the concept of high availability and fault-tolerance, let’s delve a bit into Prometheus High Availability and Fault-Tolerance.
Prometheus High Availability and Fault Tolerant Strategies
A Prometheus Server can collect metrics from another Prometheus Server using a federation. This is good if you need to make part of the metrics available to tools such as Grafana or if you need to collect multiple metrics in one place. Examples would be business metrics and service metrics from different servers.
While widely adopted, this approach does not adhere to Prometheus High Availability and Fault Tolerance principles. We only work with a small portion of the metrics, and if any of the Prometheus Servers go down, the data won’t be collected.
The problem does not have any built-in solution, but you do not have to set up complex clusters or develop complex strategies for interacting with servers to overcome it. Prometheus.yml must be duplicated on both servers to collect the same metrics in the same way. Using this method, Server A will monitor Server B and vice versa.
Redundancy is an old-fashioned principle that is easy to implement and reliable. Moreover, this redundancy can easily be managed and maintained if we add an IaC (Infrastructure as Code) tool like Terraform and a Configuration Management (CM) system such as Ansible. Nevertheless, it is easier to duplicate small servers and store only short-term metrics than a large and expensive server. Furthermore, it is easier to duplicate smaller servers.
Putting Prometheus High Availability and Fault Tolerance into perspective, let’s look at other services.
- Alertmanager: This can be done in a cluster configuration. Data can be deduplicated from different Prometheus Servers using Alertmanager, and Alertmanager can communicate with other copies of Alertmanager to avoid sending multiple identical alerts. As such, you can install one copy of Alertmanager on each server, which we have duplicated: Prometheus A and Prometheus B. Remember to manage Alertmanager’s configuration using the code management tools IaC and CM.
- Exporters: There is no need to duplicate metrics when Exporters are installed on specific systems sources of metrics. Exporters are necessary for Prometheus High Availability and you just need to allow the Prometheus A and B Servers to connect to exporters.
- Pushgateway: Duplicating servers alone is not enough since we end up with duplicated data. We will need a central point to receive metrics in this case. You can duplicate Pushgateway and configure DNS Failover or a load balancer, so all requests are routed to the other server in case of failure (active/passive configuration) in order to achieve high availability. As a result, all processes can be accessed from one point, regardless of the presence of multiple servers.
- Blackbox: We can also duplicate the Prometheus A and Prometheus B servers using Blackbox. A total of 4 Prometheus Servers are in operation, 2 copies of Alertmanager are linked, 2 Pushgateways operate in active and passive modes, and 2 Blackbox are active and passive. The system is highly available and fault-tolerant.
To collect all of the service metrics using only these copies makes no sense. Instead, it is possible to run the service on several VPCs (Virtual Private Cloud) located in several different regions and owned by several accounts and providers. Alternatively, you can run it on your own servers.
If this is the case, the copies will be very large and probably more difficult to repair. In this case, having a separate set of applications for each part of the infrastructure is a common practice to achieve Prometheus High Availability and Fault Tolerance. Based on your needs, network and security settings, trust among your teams, etc., you can divide the infrastructure into pieces.
Consequently, we have relatively small copies of Prometheus, including all the components mentioned above. Fortunately, we have a way to quickly recreate these components. In addition, we are not worried about a single component failure in a group. This is better than simply crossing your fingers and hoping that nothing falls. You can implement these strategies for Prometheus High Availability and Fault Tolerance.
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Long Data Retention using VictoriaMetrics
Our goal was to make Prometheus and its ecosystem highly available and resilient. Prometheus consists of several small teams. Each focused on a different component of the infrastructure. This is a great solution for storing data over the short term. For most tasks, only 10 days of metrics are needed. But what happens if your data needs to be kept longer? As an example, if you need to establish a relationship between 2 weeks or months. Data that is long-term can be used by Prometheus, but the costs are high since the software needs access to it quickly.
In this scenario, Cortex, Thanos, M3DB, VictoriaMetrics, and many other tools are of great assistance. Our Prometheus Servers exist in 2 copies, so we will undoubtedly have duplicate metrics – all of them are able to collect metrics from multiple Prometheus servers and provide a single repository for collected metrics.
Well, this brings us to the end of Prometheus High Availability and Fault Tolerance strategies.
Implementing Prometheus High Availability enables IT departments to detect issues as quickly as possible and get real-time information about the system’s performance. This will optimize the system’s performance so that it runs uninterrupted with minimal to no interruptions. In the end, productivity increases, applications become more stable, and development processes become more flexible.
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Share your experience of understanding Prometheus High Availability in the comments section below.