In this article, we explain what is worth considering when selecting the observability tool and also present the pros and cons of the most popular platforms available in the market.
Table of contents:
Each system has its own requirements and needs for it to be fully observable. When selecting an observability tool, suitable for a given system, it is worth to pay attention to certain factors, including:
- The degree of transparency and intuitiveness of the user interface, which is reflected in the ability of efficient utilization of the tool. When an issue with the usage of the application occurs, we usually search for a solution in the official documentation or on online forums. Although it may seem trivial it is of great importance, because the popularity of a tool is reflected in it having a large community, and this fact, with the addition of well-written documentation, significantly facilitates the usage of the tool.
- Another important aspect is the scope of the tool’s functionality. Basic functionalities such as searching or filtering data, collecting data (on an ongoing from logs, from the XML or JSON files), indexing, modeling and visualizing, should be an indispensable component of any utility.
- Additional functionalities such as APM (system performance management), insight into the system’s infrastructure, ML solutions (artificial intelligence), UX monitoring (user experience with the application), sending alerts, notifications, etc., offer a clearer view of the entire system. This, in turn, enhances the efficiency of observability.
- Ability to integrate the tool with the external software.
- Ability to automate the selected processes.
- An ease of installation and initial configuration of the tool.
- Cost of the purchasing and using the tool.
Splunk Observability
Splunk is one of the most popular observability tools. A huge number of functionalities provides the user with virtually unlimited possibilities in the work with the system.
The data can be transferred to Splunk via the Universal Forwarder. Its main advantage is the low consumption of hardware resources, due to the fact that it does not possess its own user interface. The Forwarder allows for instance to tag the metadata, configure the catching and data compression, plus it is secured with the SSL.
Splunk allows users to search the data that it receives by using SPL (Search Processing Language) to sort, filter and extract fields from the data, making it easier to understand and analyze it. Next the data can be subsequently visualized in the form of tables, summaries, charts, or maps and compiled on dashboards (panels), which are helpful in understanding them.
Splunk also provides more specialized tools designed for working with specific types of data.
Splunk Infrastructure Monitoring
Splunk Infrastructure Monitoring allows gaining an insight into the infrastructure and into the assets of the multi-cloud environments and conduct their efficient analysis. The utility also provides extensive support for collecting all kinds of data – from system metrics to custom application data.
Splunk Application Performance Monitoring gives us the ability to troubleshoot microservices and applications with the distributed tracking. The tool collects and analyzes the traces from each service connected to the Splunk Observability Cloud and gives full access to all application data.
Splunk Log Observer
Splunk Log Observer allows us to study and explore the logs without the need to learn the query language. It also allows us to extract fields from logs to configure their processing rules and transform them on the fly as they come in.
Splunk Real User Monitoring
Splunk Real User Monitoring provides user experience’s monitoring throughout the whole process. The tool has two solutions:
- one for web applications created for browsers;
- one for mobile applications for iOS and Android.
Splunk Synthetic Monitoring
Splunk Synthetic Monitoring allows us to improve the user experience with the API and web browsers. The utility gives the technical teams the ability to create detailed tests to proactively monitor the speed and reliability of websites and web applications.
Splunk provides a consistent and intuitive user interface. The entire application uses specialized vocabulary, and the application menu allows users to quickly access all functionalities.
It is also worth mentioning the extended Splunk community and carefully and intelligibly written documentation, which results in the large amount of publicly available knowledge.
An additional, substantial advantage of Splunk is the number of over 2,400 supported applications located in the Splunkbase – they provide solutions to most of the issues that may occur when working with data.
Splunk Observability – summary
- Splunk offers a very wide range of useful tools and observability functionalities, which are extremely helpful on many levels of data processing and analysis.
- Splunk is an ideal solution for monitoring complex environments and working with large amounts of different types of data coming from various sources.
Elastic Observability
Elastic is another popular solution in the terms of observability. Elastic observability gives the ability to collect all kinds of data. The data is then transferred to Elasticsearch, thanks to which the data can be: transformed, enriched, extract the selected fields from them and subject them to analysis.
Properly prepared data can also be visualized in many ways, e.g., in the form of diagrams, maps and charts, by using Kibana.
Elastic Logs
Thanks to the built-in Elastic Logs application the users have the ability to analyze the logs from hosts, services, Kubernetes, Apache, and many others. The application allows sorting, filtering, pinning and marking the data that interest us in real time.
Elastic Infrastructure
The Elastic Infrastructure application handles the system metrics collected from multiple sources, e.g., servers, Docker, Kubernetes, as well as services and applications. Thanks to this utility, the user has the ability to continuously monitor the state of the system. Metrics can be sorted and filtered by hosts, containers, or instances.
Elastic also provides its own application for monitoring the data on the system’s performance and errors. Elastic Application Performance Monitoring (APM) provides an insight into this data while the system’s runtime. The application provides the support for the most popular programming languages and OpenTelemetry. Thanks to the use of machine learning, the user has the ability to quickly correlate the infrastructure and the application’s metadata in order to determine if any abnormal values are present and to capture incorrect applications’ behaviors.
Elastic Heartbeat
Installation and configuration of the Heartbeat application gives the ability to monitor the application’s uptime data (host availability, service uptime, website, endpoints, and monitoring API).
Elastic Uptime
Uptime application provides the detailed data on the state of the services and applications. Thanks to the integration with Elastic Synthetics, it is possible to monitor the network endpoints using HTTP, ICMP, and TCP monitors.
Elastic Real User Monitoring
Elastic also provides insight into the data containing the experiences of the users with the system through the Real User Monitoring (RUM) application. RUM allows to collect, measure and analyze performance of the data which reflects the real experiences of the users. The data can be analyzed by users’ operating systems, their browsers, locations, and URL addresses’.
Thanks to that data, the administrator of the application is able to determine how the application works on the systems of the end-users. This allows them to identify the issues both on the client side (e.g., performance problems) and on the server side (e.g., application delays).
Kibana Management
Elastic provides an extensive alerting mechanism, thanks to which the user can learn about the issues occurring in the Elastic’s environment and applications on an ongoing basis. This mechanism provides a large set of built-in actions and rules that can be utilized if the irregularities are detected. Management of this mechanism is possible from the level of Kibana Management.
Elastic observability – summary
- Elastic is a very convenient and enjoyable tool for the users. A large amount of scientific materials and guides – both in the form of extensive documentation and videos – allows one to quickly learn the rules of operation of this environment.
- Elastic, unlike Splunk, is an ideal solution to monitor small to medium amounts of data.
Datadog Observability
Datadog is a fully cloud-based, relatively recent observability platform that allows to collect, visualize and analyze the data from over 500 technologies. The utility gives a full insight on the entire system’s infrastructure, applications, and services. To collect the data, Datadog uses agents that are installed on hosts and their source code is available on GitHub.
Datadog – data visualization
Data visualization in Datadog is done using:
- dashboards (grid layout, can include images, charts, and logs);
- timeboards (automatic system, represents the state of a single point in time);
- screen boards (dashboards with free layout).
Datadog – alert management
Datadog features an extensive alert management system. This system consists of monitors that constantly check i.a. the metrics, accessibility of integration or the network endpoints. It informs the user when critical changes are taking place in the system. Thanks to the use of machine learning, the platform eliminates the display of false positive alerts.
Datadog Application Performance Management (APM) is an application that gives insight and information on the performance and occurring errors, thanks to the ready-made, built-in dashboards.
Datadog Real User Monitoring (RUM)
Datadog Real User Monitoring (RUM) gives a fully fledged real-time view of the user’s experience. Thanks to this functionality, the administrator of the system gets the information about the performance of websites, mobile application screens, user activities and network requests. The application also allows for:
- efficient management of errors occurring in the system;
- analysis of application’s users, thanks to the information on the: country, device, operating system, as well as the manner in which the user interacts with the system.
Datadog Watchdog
Datadog Watchdog is an algorithmic function for APM and metrics that automatically detects anomalies and the potential threats for the system and applications.
Datadog Continuous Integration (CI) Visibility
Datadog Continuous Integration (CI) Visibility combines the collected information on the CI tests and the pipelines’ results, enriching them with the data on performance, trends, and reliability of CI.
Datadog Database Monitoring
Datadog allows for a deep insight into the state of the databases thanks to the Database Monitoring function. It provides the ability of monitoring the query efficiency on the basis of data and its execution plan (cost data access algorithm). Thanks to this, the database administrator gains full visibility into its state and can more efficiently solve the occurring issues.
Datadog observability – summary
- Datadog is easy to use and has a clear interface.
- The platform ensures smooth transitions between functionalities, which positively affects the speed of work and user experience.
- A wide range of Datadog solutions intended for working with the data is enriched with modern functions, e.g., drag-and-drop support, which facilitates the designing of dashboards.
- The disadvantage of Datadog is the visible decrease in performance when integrating the platform with many applications.
AppDynamics Observability
AppDynamics is a cloud application that facilitates the introduction of full-scale observability into the system. The tool gives an insight into the operation of the entire system, enabling troubleshooting and identifying the causes of issues, and also allows increasing the performance of the system.
The platform collects the data using the agents alongside their controllers. Agents are the plug-ins or extensions that work across the entire application ecosystem, collecting the data on the performance, runtime, and behavior of the system. Controllers collect the data from agents in the real-time, visualize the behavior of applications, and also serve in managing the activities of agents.
AppDynamics, by collecting the data on the flow of requests in the environment, creates a dynamically transforming map that represents the performance of the system. The tool also gives the ability of tracking the users’ experience with websites and mobile applications in the real time.
AppDynamics – alerting mechanism
AppDynamics provides an alert mechanism that can be configured to notify the user of the issues occurring in the selected spaces of the system. A special “anomaly detection” function gives the ability to detect all the occuring in the system.
AppDynamics Dash Studio
The built-in Dash Studio application provides the data visualization through the use of modern, highly configurable and relatively easy to use panels. Together with the ThousandEyes technology used, it significantly facilitates data analysis.
AppDynamics Observability – summary
- AppDynamics platform provides a high level of observability services and offers many tools to the users to work with data on many levels – from data collection, through searching and processing of the collected data, to their visualization.
- The program, although it has a complicated installation process, is intuitive and easy to use.
- The disadvantage of the AppDynamics is a quite poor documentation and a slightly excessive price compared to other tools available in the market.
KubeSphere Observability
KubeSphere provides the integration of multiple convenient tools devised for:
- multidimensional monitoring of system metrics,
- collecting and processing logs,
- visualization of collected data,
- handling alerts and notifications.
KubeSphere Monitoring
KubeSphere Monitoring allows for constant monitoring of the hardware resource metrics (CPU, RAM, network, storage) divided into all the nodes, as well as monitoring the service components towards the fast locating of their failures.
The usage of a multi-tenant structure in the collection and management of logs reduces the resource consumption and, at the same time, ensures that each service has access only to its own logs. This is a fairly common policy employed in various utilities.
KubeSphere – log collecting and searching
In addition to its own log collecting system, the KubeSphere can also retrieve them from other tools such as Elastisearch, Kafka and Fluentd. The platform provides multi-level queries for log searching.
KubeSphere – alert mechanism
The extensive rules of alerts and notifications are based on the state of the metrics. A flexible alert policy allows us to customize the time of anomaly detection, alert duration and their priority.
The platform gives the ability to visualize the connections between the microservices and the entire topology of the system.
KubeSphere Observability – summary
- KubeSphere is an open source utility that is easy to deploy and use.
- The platform is being extensively expanded. In each version of the utility, additional, useful functionalities appear, such as the support for non-standard metrics or AlertManager with the additional notification channels.
- The utility has quite an extensive documentation, but its navigation needs some improvement (finding the answer to the issues that bothers us may turn out to be time-consuming).
There are a multiple number of commercially available platforms and programs dedicated to observability. The majority does not differ when it comes to the access to the primary functionalities used when working with the data, such as searching, filtering, sorting and visualizing.
However, observability tools do differ regarding the access to the more advanced functionalities handling specific types of data, which often affects the price – the tools utilized to process large amounts of data by using specialized applications are more expensive).
It is also worth paying attention to the accessibility and quality of the documentation of a given tool. The popular platforms have the advantage of having numerous teaching aids and manuals that enhance the quality of work.
Every system has its own requirements and needs, which have to be fulfilled for it to be fully observable. This means that each one of the systems has a different, optimal observability tool. Therefore, the best possible tool or tools should always be chosen according to the needs of the particular system. For this purpose, it is essential to conduct an analysis of the system’s needs and requirements in terms of the criteria mentioned in the article.
The choice is not always obvious – in that case it is worth consulting with the industry specialists.
There are many benefits of using observability. From the most obvious ones, such as quick and effective detection and elimination of errors occurring in the system to the less obvious benefits, such as improving the system’s performance, which is reflected in the increased profits. However, in order to fully utilize the advantages of observability, it is crucial to select the suitable tools.
Looking for help in choosing the right observability tools for your business? Explore our observability consulting services and connect with our experts today to gain insights into every aspect of your IT ecosystem and enhance your system’s performance!