• Understand what Observability is
• Understand the importance of pursuing Observability
• Learn what an Observability platform is
In recent years, the application development landscape has changed. Software teams are building fewer bulky, monolithic applications that run on a few on-prem servers; instead, they opt for a series of services that run across a highly distributed cloud infrastructure and interact with one another.
Various advantages come from this, but the added complexity has also made it more challenging to evaluate how an application is performing and determine where problems are occurring and why in instances where it's breaking down. Keep reading for an overview of observability, platforms related to it, and an explanation of how these tools can assist in providing the visibility needed to support modern applications and their environments efficiently.
If you do a simple online search, you'll find that observability's definition reads as "a measure of how well users can infer the internal state of a system from knowledge of its external outputs." In the context of software systems, this means taking the data produced by the various components of the system and exposing it in a format that allows DevOps teams to evaluate its state, empowering them to quickly identify the existence of any problems and determine their cause.
Making software systems observable requires the collection of several types of data. This data comes in logs, metrics, and traces, collectively known as the three pillars of observability. When collected and correlated appropriately, this data facilitates deep visibility into applications' state and infrastructure. For instance, metrics data such as response time can indicate severe slowness in an application. By providing the ability to correlate this measurement with the appropriate log and trace data, developers and IT operations personnel can more quickly discern the reason for the increase in latency.
Observability unlocks a variety of essential benefits for a software development organization. It helps simplify the maintenance of complex applications and their environments. A good example is measuring the improvement of two key incident response metrics: mean time to acknowledgment (MTTA) and mean time to resolution (MTTR).
The distributed nature of modern application environments can make it difficult for developers and IT folks to identify problems before they severely impact end-users. By centralizing the data produced by the various components making up the system, observability provides a deeper level of visibility that increases the likelihood that DevOps teams will realize that something is wrong at the earliest possible moment. Moreover, by enabling developers and IT folks to utilize various data types in conjunction with one another effectively, software teams gain the context necessary to address problems more efficiently. An example of this is correlating metrics data with logs or traces that indicate the cause of a problematic measurement.
Developers, as a result, spend less time resolving issues with existing functionality and more time developing new features, thereby delivering increased value to the business and their customers.
An observability platform is a tool that assists in establishing visibility for software applications and their environments. It does so by facilitating the centralization, enrichment, and analysis of data in a manner that allows DevOps teams to gain the most significant possible insight into the state of their applications and infrastructure.
Observability platforms come in a few different flavors. There are single-pane-of-glass tools as well as observability data pipelines. Single-pane-of-glass tools enable DevOps teams to use one tool for a unified view of observability data across their applications and infrastructure. The idea here is to simplify correlating and analyzing the various types of observability data by providing a single management console with dashboards and analysis capabilities that help teams gather valuable insights with ease.
Observability pipelines serve as a more flexible solution for centralizing and analyzing observability data. These pipeline tools can be precious for organizations that need to deliver observability data to multiple platforms for visualization and analysis. Observability data pipelines allow DevOps teams to aggregate and normalize large volumes of observability data. The teams can then ship this data to whichever platforms make the most sense for the organization. By enabling teams to deliver observability data to various tools, teams can more easily leverage the data for the specific use cases required by different arms of the organization.
As the complexity of applications and their environments increases, DevOps teams require the ability to evaluate their state more easily. Observability serves as part of the answer to this challenge. The use of observability platforms helps to facilitate this process.
LogDNA features its observability platform in LogDNA Streaming, an observability data pipeline solution. This product provides a mechanism for aggregating large volumes of log data from disparate sources, enabling its delivery to the platforms necessary for an organization to get the most value from the data.