The USE Method: A Framework for Systematic Diagnosis#

The USE method, developed by Brendan Gregg, provides a structured approach to system performance analysis. For every resource on the system – CPU, memory, disk, network – you check three things:

• Utilization: How busy is the resource? (e.g., CPU at 90%)
• Saturation: Is work queuing because the resource is overloaded? (e.g., CPU run queue length)
• Errors: Are there error events? (e.g., disk I/O errors, network packet drops)

This method prevents the common trap of randomly checking things. Instead, you systematically walk through each resource and check all three dimensions. If you find high utilization, saturation, or errors on a resource, you have found your bottleneck.

What Cardinality Means#

In Prometheus, cardinality is the number of unique time series. Every unique combination of metric name and label key-value pairs constitutes one series. The metric http_requests_total{method="GET", path="/api/users", status="200"} is one series. Change any label value and you get a different series. http_requests_total{method="POST", path="/api/users", status="201"} is a second series.

A single metric name can produce thousands or millions of series depending on its labels. A metric with no labels is exactly one series. A metric with one label that has 10 possible values is 10 series. A metric with three labels, each having 100 possible values, is up to 1,000,000 series (100 x 100 x 100), though in practice not every combination occurs.

Redis Deep Dive: Data Structures, Persistence, Performance, and Operational Patterns#

Redis is an in-memory data store, but calling it a “cache” undersells what it can do. It is a data structure server that happens to be extraordinarily fast. Understanding its data structures, persistence model, and operational characteristics determines whether Redis becomes a reliable part of your architecture or a source of mysterious production incidents.

Data Structures Beyond Key-Value#

Redis supports far more than simple string key-value pairs. Each data structure has specific use cases where it outperforms alternatives.