April 2025 Summaries

Manufacturers face significant pressure to increase efficiency, reduce downtime, and future-proof their factories in order to stay competitive. This is challenging with legacy systems that limit visibility and adaptability, often operating in silos and making it hard to access real-time data or scale with modern technologies. However, Bosch Rexroth's ctrlX platform and InfluxDB work together to create scalable monitoring systems for modern manufacturing, offering a solution to these challenges by providing timely, accurate data that can be turned into actionable intelligence. This tech paper details the benefits of modernizing monitoring systems, including predictive maintenance, reduced unplanned downtime, and a data foundation for continuous improvement and innovation. The ctrlX platform brings modularity, openness, and edge computing to industrial environments, empowering manufacturers to create flexible control and data systems that evolve alongside production needs, when deployed with InfluxDB it forms a powerful monitoring stack that supports real-time dashboards, historical analysis, and enterprise-wide insights.

In this project, a time series forecasting pipeline is built in InfluxDB 3 Core without writing any code manually. The pipeline uses the Python Processing Engine and Facebook's Prophet library to forecast daily page views for a Wikipedia article over an entire year. A fully functioning interactive forecasting pipeline is created with three purpose-built plugins: one to load historical data, another to generate daily forecasts, and one to visualize results on demand. This project showcases how InfluxDB 3 Core can be used to build advanced time series workflows without requiring extensive coding knowledge, making it accessible to developers of all levels. The pipeline is built using a combination of natural language prompts to an AI model, which generates the necessary code for each component, and then iteratively validated until everything works end-to-end. This approach demonstrates the potential of AI-assisted development in building complex data pipelines.

InfluxDB 3 Core and Enterprise have been released, marking a significant milestone in the development of the time series platform. The new release brings several key features and improvements, including infinite cardinality support, object storage persistence, and a fully featured SQL query engine. InfluxDB 3 is designed to deliver high performance, scalability, and reliability for real-time monitoring, long-term analytics, and event-driven automation. The platform's architecture is built on the FDAP stack, which includes Apache Flight, DataFusion, Arrow, and Parquet. This foundation enables efficient storage, real-time analytics, and no limits on cardinality. InfluxDB 3 Core is an open source product, while Enterprise is a commercial offering with performance optimizations for long-term historical queries and high availability features. The platform provides a flexible architecture that delivers significant performance gains, including higher ingest rates, better compression, faster queries, and no limits on cardinality. InfluxDB 3 also introduces several new features, such as the embedded Python Processing Engine, which enables real-time data transformation and enrichment, custom monitoring and alerting logic, and integration with external systems. The platform is designed for operational simplicity, making it easy to integrate into existing infrastructure and deploy at scale. A free tier is available for home users, allowing them to explore the capabilities of InfluxDB 3 without incurring costs.

InfluxData has announced the general availability of InfluxDB 3 Core and InfluxDB 3 Enterprise, two new products that simplify how developers build with time series data. InfluxDB 3 Core is an open source, high-speed engine for real-time applications, while InfluxDB 3 Enterprise adds performance, high availability, security, and scalability for mission-critical workloads. Both products feature a built-in Python Processing Engine, which enables data transformation, enrichment, and alerting directly into the database. This allows developers to turn the database into an active intelligence engine that processes data in motion in real-time. The new products complement InfluxData's existing lineup and are designed for rapid development and large-scale production, making it easier for users to make time series data instantly actionable with real-time transformation and enrichment capabilities.

This tutorial demonstrates how to use the Python Processing Engine with InfluxDB 3 Core and Enterprise to build a deadman check and alert, which notifies users immediately when expected data stops arriving. The process involves creating an InfluxDB 3 instance, setting up a Slack webhook URL, writing data to the database, creating a trigger, and enabling the deadman check plugin. If no new data is received within a configurable time threshold, the plugin sends a notification to the designated Slack channel or HTTP endpoint. This tutorial provides a flexible way to monitor data pipeline durability in real-time and demonstrates how to build custom plugins using Python.

DataFusion, an industrial-strength query engine, optimizes SQL and DataFrames queries using various techniques such as Always Optimizations, Engine Specific Optimizations, and heuristics. The optimizer uses cost models to estimate the performance of different plans and chooses the one with the lowest estimated cost. It also considers factors such as join order, access paths, and materialized views in its optimization process. DataFusion's design goals include providing a reasonable default implementation along with extension points to customize behavior, allowing users to tailor the optimizer to their specific needs. The system's modular design and documentation make it an attractive platform for research and development in query engine optimization.

InfluxDB 3 Enterprise simplifies multi-node deployments by offering specialized server modes that allow for precise workload isolation and scaling. With these modes tailored to handle specific types of workloads, InfluxDB enables seamless scaling, higher availability, and consistent performance for time series data infrastructure. The five distinct server modes - ingest, query, compact, process, and all - let users tailor node functionality to their specific requirements, ensuring predictable performance across the cluster. By leveraging these modes, users can create a workload-isolated setup without complex overhead, optimizing resource utilization and delivering reliable insights in real-time.