Many developers and product teams are iterating faster and deploying more frequently to meet user expectations for responsive and optimized apps. These constant deployments—which can number in the dozens or even hundreds per day for larger organizations—are essential for keeping your customer base engaged and delighted. However, they also make it harder to pinpoint the exact deployment that led to a rise in errors, a new error, or a performance regression in your app.

Blocked queries are one of the key issues faced by database analysts, engineers, and anyone managing database performance at scale. Blocking can be caused by inefficient query or database design as well as resource saturation, and can lead to increased latency, errors, and user frustration. Pinpointing root blockers—the underlying problematic queries that set off cascading locks on database resources—is key to troubleshooting and remediating database performance issues.

As the world’s leading local delivery platform, Delivery Hero brings groceries and household goods to customers in more than 70 countries. Their technology stack comprises over 200 services across 20 Kubernetes clusters running on Amazon EKS. This cloud-based, containerized infrastructure enabled them to scale their operation to support increasing demand as the volume of orders placed on their platform doubled during the pandemic.

StormForge Optimize Live is a machine learning-powered performance and resource optimization solution for Kubernetes workloads. Optimize Live ingests and analyzes production observability data and recommends specific actions to optimize CPU and memory utilization. You can take these actions manually or set them to occur automatically, making it easier to maintain a high level of application performance while minimizing cloud costs.

In dynamic production environments, unpredictable traffic loads and frequent code changes can make it difficult for organizations to consistently optimize their cloud infrastructure, resulting in application performance issues, latency, and wasted cloud spend. Teams that manage large-scale cloud infrastructure deployments are often forced to tune their workloads’ configurations using a complicated mesh of script jobs—or worse, manual remediation by on-call engineers prompted by alerts.

Microsoft SQL Server is a popular relational database management system that provides a wide range of performance and reliability features (e.g., AlwaysOn availability groups) to support business-critical applications. As your SQL Server workloads scale and increase in complexity, it can be difficult to monitor all of their components and pinpoint the exact issues that are degrading your databases’ performance.

Serverless developers are undoubtedly familiar with the challenge of cold starts, which describe spikes in latency caused by new function containers being initialized in response to increasing traffic. Though cold starts are usually rare in production deployments, it’s still important to understand their causes and how to mitigate their impact on your workload.

As your engineering teams grow and commit code more frequently, it becomes increasingly difficult to release high-quality, secure code while achieving your desired development velocity. To create smoother developer workflows that ensure high standards for code quality and security, it’s critical for developers to detect and remediate issues earlier in the software development lifecycle— without switching tools or contexts.