React Native has become the predominant development framework for cross-platform mobile applications. By interacting with native APIs largely under the hood and requiring only a fractional proportion of platform-specific code, it allows you to build applications for iOS, Android, and the browser using the same declarative JavaScript. But this cross-platform adaptability has its downsides.
Datadog Real User Monitoring (RUM) provides end-to-end visibility into the user experience and performance of your browser and mobile applications. RUM allows you to capture and retain complete user sessions for 30 days. This means you can pinpoint bugs, prioritize issues, and determine fixes with data collected across an entire quarter.
Properly testing a service’s APIs to ensure that it can handle production traffic presents many challenges for engineers—SREs need to guarantee the resiliency of their application, while developers must ensure that their features perform well at any given scale. Speedscale is a testing framework built for Kubernetes applications that enables you to load test with real-world production scenarios by replaying actual API traffic that your application has experienced.
In 2021, we partnered with AWS to develop the Datadog Lambda extension which provides a simple, cost-effective way for teams to collect traces, logs, custom metrics, and enhanced metrics from Lambda functions and submit them to Datadog.
Monitoring HTTP sessions offers a potentially powerful way to gain visibility into your web servers, but in practice, doing so can be complex and resource-intensive. Extended Berkeley Packet Filter (eBPF) technology allows you to overcome these challenges, giving you a simple and efficient way to process application-layer traffic for your troubleshooting needs.
Google Kubernetes Engine (GKE) is a managed Kubernetes service that enables users to deploy and orchestrate containerized applications on Google’s infrastructure. Datadog’s GKE integration, when paired with our Kubernetes integration, has always provided deep visibility into the health and performance of your clusters at the node, pod, container, and application levels.
This post is part 1 of a 3-part series about monitoring MongoDB performance with the WiredTiger storage engine. Part 2 explains the different ways to collect MongoDB metrics, and Part 3 details how to monitor its performance with Datadog. If you are using the MMAPv1 storage engine, visit the companion article “Monitoring MongoDB performance metrics (MMAP)”.
For many organizations, making the most of the visibility Datadog offers into the health and performance of their infrastructure means displaying dashboards to stakeholders in various settings continuously and in real time. But the standard solutions for sharing dashboards to large-format displays can be onerous, involving sundry software and hardware and restrictive manual setups. These solutions can also pose significant security risks, since they tend to involve sharing passwords or devices.
Cloud-native infrastructure has become the standard for deploying applications that are performant and readily available to a globally distributed user base. While this has enabled organizations to quickly adapt to the demands of modern app users, the rapid nature of this migration has also made cloud resources a primary target for security threats.
Testing early and often in the development cycle is a must for ensuring that your application meets user expectations. Poor performance and errors can alienate users and prevent you from meeting crucial benchmarks and OKRs. Additionally, having to constantly implement fixes after new, under-tested features are added can fatigue developers and strain your resources, making your organization less nimble overall.
