Log sampling is the process of selecting a subset of log data to reduce volume while maintaining the usefulness of the data for debugging, monitoring, and alerting. By sampling intelligently, you can get insights from your logs without ingesting or storing every single log entry, which can be costly and inefficient.
In data terms, cardinality refers to the number of unique values in a dataset. High cardinality means that a column or field has a very large number of unique values. If you think about some of the typical metric labels we handle as DevOps engineers or software developers, things like user IDs, request paths, or device IDs often come with a seemingly endless variety of values.
managing log data efficiently is crucial for understanding application performance and solving issues promptly. Two popular tools in the Elastic Stack—Filebeat and Logstash—provide powerful means for managing log data. Both have their strengths, but choosing the right tool can depend on the specific needs of your system. This post aims to provide a detailed comparison of Filebeat and Logstash to help you decide which tool is right for your use case.
In today’s digital environment, developers and IT professionals are inundated with logs generated by various systems, applications, and devices. Analyzing these logs is critical for monitoring system health, troubleshooting issues, and ensuring security. This is where log parsing comes in. Log parsing refers to the process of extracting useful data from raw log files, making them easier to analyze and interpret.
At its core, the OpenTelemetry Collector is a pipeline for processing and exporting telemetry data. It sits between your application and your backend observability tools (like Prometheus, , DataDog, Splunk, Jaeger, or Elasticsearch), collecting data from various sources before enriching or transforming it and finally sending it off to the destination of your choice.
In today's data-driven world, managing and processing large volumes of data efficiently is crucial. Logstash is an open-source data processing pipeline from Elastic that ingests data from multiple sources simultaneously, transforms it,
In the era of cloud-native applications and microservices, observability has become a cornerstone of reliable software systems. Prometheus, an open-source monitoring and alerting toolkit, is often the go-to choice for organizations looking to build their own observability platforms. While Prometheus offers a rich feature set and the allure of customization, building an in-house observability
When it comes to telemetry and observability, one of the most important questions is: metrics or logs? These two approaches offer very different ways of understanding the behavior of your systems. Knowing when to use each one is essential to maintaining a high-performing infrastructure and troubleshooting issues effectively. In this post, we’ll explore the key differences between metrics and logs, highlight the “gotchas” to watch out for, and provide guidance on when to use one over the other.
