Stateful, commonly monolithic, and absolutely fundamental to system design, the quality of your database administration and operation is a key determinant of your overall success. Databases are the cornerstone of modern architecture, requiring constant effort, investigation, and iteration to get the most out of a database. This makes it all the more terrifying when an outage occurs.
The way organizations process logs have changed over the past decade. From random files, scattered amongst a handful of virtual machines, to JSON documents effortlessly streamed into platforms. Metrics, too, have seen great strides, as providers expose detailed measurements of every aspect of their system. Traces, too, have become increasingly sophisticated and can now highlight even the most precise details about interactions between our services. But alerts have remained stationary.
Alerting has been a fundamental part of operations strategy for the past decade. An entire industry is built around delivering valuable, actionable alerts to engineers and customers as quickly as possible. We will explore what’s missing from your alerts and how Coralogix Flow Alerts solve a fundamental problem in the observability industry.
Cloud services were born at the beginning of 2000 with companies such as Salesforce and Amazon paving the way. Simple Queuing Service (SQS) was the first service to be launched by Amazon Web Services in November 2004. It was offered as a distributed queuing service and it is still one of the most popular services in AWS. By 2006 more and more services were added to the offering list.
Observable and secure platforms use three connected data sets: logs, metrics, and traces. Platforms can link these data to alerting systems to notify system administrators when an event requires intervention. There are nuances to setting up these alerts so the system is kept healthy and the system administrators are not chasing false positive alerts.
Tracing is often the last thought in any observability strategy. While engineers prioritize logs and metrics, tracing is truly the hallmark of a mature observability platform, but it is also the most difficult to implement. Once tracing is in place, engineers typically discover something else – many tracing solutions aren’t particularly feature-rich.
Like cloud-native and DevOps, full-stack observability is one of those software development terms that can sound like an empty buzzword. Look past the jargon, and you’ll find considerable value to be unlocked from building observability into each layer of your software stack. Before we get into the details of observability, let’s take a moment to discuss the context.
Log tracking, trace log, or logging traces… Although these three terms are easy to interchange (the wordplay certainly doesn’t help!), compare tracing vs. logging, and you’ll find they are quite distinct. Logs, traces, and metrics are the three pillars of observability, and they all work together to measure application performance effectively. Let’s first understand what logging is.
Tracing, or more specifically distributed tracing or distributed request tracing, is the ability to follow a request through a system, joining the dots between all the individual system calls required to service a particular request. Although tracing logs have been around for some time, the trend toward distributed architectures, microservices, and containerization has elevated it from nice-to-have status to an essential piece of the observability puzzle.
If your organization is embracing cloud-native practices, then breaking systems into smaller components or services and moving those services to containers is an essential step in that journey. Containers allow you to take advantage of cloud-hosted distributed infrastructure, move and replicate services as required to ensure your application can meet demand, and take instances offline when they’re no longer needed to save costs.
