October 2016 Summaries

A colleague named Christian is participating in an intense 6-day running challenge where he aims to cover around 850km (528 miles). To monitor his progress, a Datadog dashboard was created using data scraped from the event website. The Python libraries Requests and BeautifulSoup were used for web scraping, while StatsD and the Datadog agent were employed to emit metrics. A dashboard displaying live video, gifs, and key metrics was built to support Christian during his challenge.

Anomaly detection has been added to Datadog to provide deeper context for dynamic metrics like application throughput, web requests, and user logins. The feature analyzes a metric's historical behavior to distinguish between normal and abnormal trends. It accounts for seasonality and can separate the trend component from the seasonal component of a timeseries. Anomaly detection is available in Datadog and complements outlier detection, which identifies unexpected differences in behavior among multiple entities reporting the same metric.

Datadog has introduced anomaly detection to its platform, allowing users to analyze historical behavior and distinguish between normal and abnormal metric trends. This feature is particularly useful for dynamic metrics such as application throughput, web requests, and user logins that exhibit pronounced peaks and valleys throughout the day or week. Anomaly detection can identify unexpected drops in metric values, which may indicate serious issues such as code changes or system disruptions. The algorithm takes into account seasonality and trends, allowing users to set up automatic alerting for abnormal metric trends with customizable bounds and algorithms, including basic, agile, and robust options. Additionally, anomaly detection provides instant historical context, enabling responders to quickly understand why an alert was triggered and investigate the underlying issue.

The team at Datadog created a custom dashboard to track the progress of their colleague, Christian, during his 6-day-run challenge. They used a simple crawler in Python with the Requests and BeautifulSoup libraries to extract data from the event website's HTML code, which included Christian's current ranking, total distance run, and other metrics. The extracted data was then fed into Datadog using StatsD and the Datadog agent, resulting in a live dashboard that displays meaningful metrics, videos, GIFs, and more, allowing the team to cheer Christian on throughout the challenge.

The text discusses integrating Windows Server 2012 with Datadog, a platform for monitoring and analyzing infrastructure performance. The process involves installing the Datadog Agent on the server, configuring it to collect specific metrics, events, and services, and setting up alerts for unusual behavior. The article provides guidance on customizing configurations, using tools like wbemtest and Powershell to discover metric classes and properties, and creating a comprehensive dashboard to monitor Windows Server 2012 alongside other systems. With Datadog, users can increase the observability of their environment, create automated alerts tailored to their infrastructure, and gain insights into performance and usage patterns.

Evan Mouzakitis discusses the various native tools available in Windows Server 2012 for monitoring system health and performance, as part of a three-part series on the topic. This article concentrates on different methods for accessing Windows metrics and performance data, highlighting both non-GUI and GUI tools such as Performance Monitor, Reliability Monitor, Resource Monitor, Service snap-in, Server Manager, and Task Manager. Powershell is emphasized as a powerful tool for gathering real-time or historical performance data using cmdlets, while Windows Management Instrumentation (WMI) provides a standardized interface for querying system information. The article also briefly touches on the use of Windows Server Manager for aggregating data and the importance of visualizing performance data for identifying trends and issues. Finally, it introduces the comprehensive monitoring capabilities of Datadog, which integrates with numerous technologies to enhance observability across systems.

This post provides an overview of how to monitor the health and performance of the Windows operating system, specifically focusing on Windows Server 2012. It discusses various types of data that can be tracked from Windows Server 2012, including performance counters/metrics, memory metrics, disk metrics, network metrics, and events. The post also provides a list of important services to monitor in order to ensure the proper functioning of the system. In part 2 of this series, it explains how to use native Windows tools to monitor these key metrics and services.

Monitoring the health and performance of a Windows operating system is crucial for ensuring optimal system resource utilization. A key aspect of this monitoring process is tracking various performance counters, metrics, and events that provide insights into CPU, memory, disk, network, and other resources. The goal is to identify potential bottlenecks, issues, or trends in system behavior before they become critical. This can be achieved by leveraging the Windows Server 2012's built-in tools, such as Performance Monitor (Perfmon), Event Viewer, and PowerShell scripts. By tracking key performance counters like CPU utilization, memory usage, disk space, network throughput, and services status, administrators can quickly identify areas that require attention. Additionally, monitoring system events, including error logs, security-related events, and service startup/stop events, helps to detect issues early on and prevent potential problems from escalating. By implementing these monitoring strategies, organizations can ensure the optimal performance, reliability, and security of their Windows Server 2012 infrastructure.

Atlassian JIRA is a project management tool that organizes bug tracking and issue resolution using projects and tickets. It helps teams manage complex projects by allowing them to update tickets and add comments. Datadog's new JIRA integration streamlines issue management related to infrastructure or application performance by enabling users to create, tag, and update JIRA tickets directly from Datadog alerts. This integration allows for automatic ticket creation with custom ticket types, adding tags to group similar issues, and triggering inbound events when a new ticket is created.

Datadog has integrated its platform with Atlassian's JIRA project management tool, allowing users to create, tag, and update tickets directly from Datadog alerts. This integration streamlines the process of managing issues related to infrastructure or application performance by providing a seamless workflow between the two platforms. With this integration, users can configure custom ticket types and add project IDs and issue types to automatically create JIRA tickets when an alert triggers. The integration also allows users to group tickets by tag, providing additional insights into the rate at which different issue types trigger ticket creation. By leveraging this integration, users can take their issue tracking to the next level and improve their overall project management experience.