Logs and log management have been around far longer than monitoring and it is easy to forget just how useful and essential they can be for modern observability. Most of you will know us for VictoriaMetrics, our open source time series database and monitoring solution. Metrics are our “thing”; but as engineers, we’ve had our fair share of frustrations in the past caused by modern logging systems that tend to create further complexity, rather than removing it.

Preventing data loss for data in motion is a challenge that Cribl Stream Persistent Queues (PQ) can help prevent when the downstream Destination is unreachable. In this blog post, we’ll talk about how to configure and calculate PQ sizing to avoid disruption while the Destination is unreachable for a few minutes or a few hours. The example follows a real-world architecture, in which we have.

Although microservices and cloud architectures are the new norm for modern applications, cloud cost optimization could run high in observability. High costs are largely due to the number of components involved in cloud architectures. According to Cloud Data Insights in a recent report, around 71% of IT companies say that cloud observability logs are growing at an alarming rate— a driving factor for rising observability costs.