Information Technology has become a critical component in various industries, leading to an increased focus on software maintenance and monitoring. With the complexities of modern software systems, traditional maintenance approaches have become insufficient. The concept of AIOps has emerged to enhance predictive maintenance using Big Data and Machine Learning capabilities. However, exploiting AIOps requires addressing several challenges related to the complexity of data and incident management. Commercial solutions exist, but they may not be suitable for certain companies due to high costs, data governance issues, and limitations in covering private software. This paper investigates the feasibility of implementing on-premise AIOps solutions by leveraging open-source tools. We introduce a comprehensive AIOps infrastructure that we have successfully deployed in our company, and we provide the rationale behind different choices that we made to build its various components. Particularly, we provide insights into our approach and criteria for selecting a data management system and we explain its integration. Our experience can be beneficial for companies seeking to internally manage their software maintenance processes with a modern AIOps approach.

QuestDB 6.2, our previous minor version release, introduced JIT (Just-in-Time) compiler for SQL filters. As we mentioned last time, the next step would be to parallelize the query execution when suitable to improve the execution time even further and that's what we're going to discuss and benchmark today. QuestDB 6.3 enables JIT compiled filters by default and, what's even more noticeable, includes parallel SQL filter execution optimization allowing us to reduce both cold and hot query execution times quite dramatically. Prior to diving into the implementation details and running some before/after benchmarks for QuestDB, we'll be having a friendly competition with two popular time series and analytical databases, TimescaleDB and ClickHouse. The purpose of the competition is nothing more but an attempt to understand whether our parallel filter execution is worth the hassle or not.