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In this blog series we’ve examined Five Myths of Apache Spark Optimization. But one final, bonus myth remains unaddressed: I’ve done everything I can. The overprovisioned resources that lead to underutilized CPU and memory in my workloads is just the cost of doing business.

Running Kubernetes on AWS? You're probably using Karpenter, the open-source autoscaler that dynamically provisions new instances as your EKS workloads grow. Karpenter launches rightsized instances in real time in response to pending pods, based on available instance types and the resources applications need. It also terminates underutilized nodes to reduce costs.

InfluxDB 3.2 is now available for both Core and Enterprise, bringing the general availability of InfluxDB 3 Explorer, a new UI that simplifies how you query, explore, and visualize data. On top of that, 3.2 includes a wide range of performance improvements, feature updates, and bug fixes. InfluxDB 3 Core is free and open source, optimized for recent data, and licensed under MIT and Apache 2.

In this blog series we’re examining the Five Myths of Kubernetes Resource Optimization. The fifth and final myth in this series relates to another common assumption of many Kubernetes users: Dynamic Allocation for Apache Spark applications automatically prevents Spark from overprovisioning resources while improving workload utilization levels.

The following serves as a practical guide for those looking to simplify their architecture by migrating to a Rust monolith. Earlier this year, the platform team at InfluxData undertook a major rewrite of our core account and resource management APIs, moving from Go to Rust and from a microservices architecture to a single monolith. This change supported a new administrative UI for InfluxDB Cloud Dedicated and aligns with our broader effort to rewrite the InfluxDB database engine in Rust.