March 2018 Summaries

AWS Fargate allows users to run applications in Amazon Elastic Container Service (ECS) without managing the underlying infrastructure. Datadog has integrated with Fargate, enabling real-time monitoring of containerized tasks, including CPU and memory utilization, network performance, and code-level performance data. The integration also supports Autodiscovery, which automatically detects and configures checks for containers running on Fargate. Additionally, Datadog provides native support for FireLens, allowing seamless routing of container logs to the platform. With Fargate, users can focus on services and applications rather than underlying infrastructure, gaining full visibility into application health and performance alongside other infrastructure components.

The text introduces the new programmatic way to set up Datadog integrations using APIs. This approach allows users to configure AWS, Slack, PagerDuty, and webhooks integrations in a scalable and repeatable manner. It provides examples of how to use these APIs for each integration type and demonstrates how to tie them together in a Datadog alert. The text also mentions that more API-supported integrations will be added soon.

You can now configure your Datadog integrations programmatically using the Datadog integration API, providing efficiency and consistency to organizations with complex environments. APIs are available for AWS, Slack, PagerDuty, and webhooks integrations, allowing you to automate the configuration of your monitoring and alerting coverage as your infrastructure scales or evolves. You can create a script to create a new AWS account, install the Datadog AWS integration, and connect it to a role that allows it to collect metrics from specific AWS resources. Similarly, you can integrate Slack, PagerDuty, and custom webhooks into Datadog using API calls, allowing you to trigger notifications and actions based on Datadog alerts. With this new approach, you can programmatically create alerts that feed messages to these integrations, ensuring seamless integration and automation of your monitoring and alerting coverage.

Explanatory journalism can teach product designers how to be better explainers by favoring the important over the new, preventing context collapse, and recognizing that space is not scarce. By collecting all relevant information and organizing it effectively, designers can create a comprehensive papertrail that helps them understand problems, articulate requirements, and make informed design decisions. This approach also ensures that feedback from various sources is appropriately weighted and considered in the design process.

This article discusses how to monitor MySQL on Amazon RDS using Datadog. It explains that by integrating Datadog with both CloudWatch and MySQL, users can get a comprehensive view of their database's health and performance. The integration process involves connecting Datadog to CloudWatch for gathering RDS metrics and then integrating it with MySQL to gather native metrics. Additionally, the article mentions that enabling the new RDS enhanced monitoring feature provides more than 50 system-level metrics at a high frequency of once per second. These metrics can be ingested into Datadog for monitoring in just minutes. The integration allows users to view a comprehensive dashboard called "Amazon - RDS (MySQL)" that gathers key performance metrics and resource utilization data. It also provides instructions on how to customize the dashboard according to specific needs.

Steve Boak discusses how the principles of explanatory journalism can be applied to product design, focusing on providing context and explaining complex issues. He emphasizes the importance of prioritizing important information over new developments and preventing "context collapse" by gathering diverse feedback from various stakeholders. Additionally, he highlights that space is not scarce in digital publishing, allowing for more detailed explanations to be shared. By organizing research and findings into a comprehensive "papertrail," designers can create summaries and conversations that provide clarity and context, ultimately leading to better design decisions.

This guide provides detailed instructions on how to use Datadog to monitor the performance of Django applications and their associated technologies such as PostgreSQL, NGINX, and Gunicorn. It covers installation and configuration of the Datadog Agent, enabling APM tracing for Python applications, integrating with PostgreSQL, NGINX, and Gunicorn, gathering custom metrics and events, and creating Django-specific dashboards. The guide also demonstrates how to use Datadog's features to troubleshoot performance issues in a Django application.

OpsGenie has enhanced its bidirectional integration with Datadog to provide timely notifications of triggered alerts for infrastructure components and applications monitored by the latter. This integration enables users to create powerful alert notification and incident response orchestration solutions, leveraging OpsGenie's smart escalation rules, on-call schedules, and rotations. Datadog triggers an alert when it detects a problem, and with the OpsGenie integration, when Datadog creates an alert, OpsGenie automatically creates one too. The integration also allows users to create alerts in OpsGenie from the Datadog UI by using @mentions when they create, acknowledge, or close alerts from their Datadog event stream. By sending Datadog alerts to OpsGenie, users can take full advantage of OpsGenie's alerting and incident response capabilities, such as defining the right recipients for their alerts, as well as defining severity levels of incidents and prioritizing them based on their impact on their business.

The Django web framework is an open-source Python-based framework that provides rapid development and clean, reusable code. It follows the Model-View-Template (MVT) design pattern and can support various web content formats such as HTML and XML. To deploy a Django application, supporting technologies like databases, web servers, and WSGI are required. Datadog is used to monitor Django performance, including metrics, logs, and request traces from the application, database, web server, and Gunicorn. The guide covers how to install and configure the Datadog Agent, integrate it with PostgreSQL, NGINX, and Gunicorn, and use it to collect custom Python metrics and events. It also provides examples of how to create a custom dashboard for Django applications.

Amazon Web Services (AWS) is known for its reliability; however, even the most dependable services can experience occasional outages. Such disruptions can significantly impact websites and applications that rely on AWS, potentially leading to revenue loss and user dissatisfaction. Datadog has introduced AWS Outage Alerts to help businesses mitigate these effects by providing immediate notifications whenever there is a change in AWS status. The feature monitors the AWS Service Health Dashboard for any issues and allows users to customize alert conditions, message content, and recipients. Additionally, Datadog's integration dashboards provide real-time visibility into AWS infrastructure and enable users to monitor service status changes at a glance. By leveraging these features, businesses can better manage the impact of AWS outages on their applications and maintain user satisfaction during such events.

This article discusses how to collect metrics from HAProxy, a popular open-source load balancer. It explains two methods for accessing these metrics - via a status page and through Unix sockets. The status page provides a human-readable view of the performance of your load balancer but is static and ephemeral. On the other hand, communicating over the socket interface is more practical for scripting or automation purposes. Additionally, it introduces HATop, an unofficial third-party tool that can be used to manage and diagnose HAProxy services. The article concludes by recommending using a dedicated monitoring system like Datadog for production environments.

Datadog has announced the general availability of its log management feature, allowing users to enrich, monitor, and analyze logs from all systems for troubleshooting, auditing, visualization, and alerting. This addition unifies the three pillars of observability - metrics, tracing, and logs - in one integrated platform, enabling seamless movement between related monitoring data sources without switching tools. The feature automatically collects and tags data from various tools and services, allowing users to pivot smoothly between views for rapid troubleshooting. With this update, many existing Datadog integrations will start collecting and enriching log data without the need for external log processors or parsing rules, including popular technologies such as Apache, NGINX, Docker, and AWS CloudTrail. The feature also includes customizable processing and analytics pipelines to parse and enrich logs, enabling users to visualize log data alongside relevant metrics and apply filters to extract meaningful information.