Marko Topolnik is a senior software engineer with over two decades of low-level systems experience and a recent focus on Rust-driven real-time and batch data processing. He designed and implemented core subsystems at Hazelcast—including a log-structured Hot Restart persistence layer and a coroutine-based Jet execution engine delivering billion-event-per-second throughput—and later built from scratch a Tokio-powered distributed operator DAG with queriable state. At QuestDB and in open-source contributions he’s driven bug fixes and SQL/engine optimizations, while at tvbeat he achieved a 6x batch performance improvement and cut memory complexity via streaming redesigns. Comfortable across Java, Rust, and Go, he blends deep performance tuning, systems design and practical product discovery, and has a track record of turning research-grade ideas into production-ready, highly parallel systems. An interesting throughline: he repeatedly rethinks storage and execution models to minimize IO and memory overhead, enabling millisecond-level latency at scale.
Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights.
Role in this project:
Back-end Developer
Contributions:7 reviews, 683 commits, 153 PRs in 5 years 9 months
Contributions summary:Marko primarily focused on refactoring and cleaning up code, specifically within the Hazelcast Jet core module. They were involved in optimizing the concurrent queue implementation, enhancing the performance and reliability of the job execution. Their work included code style improvements, Javadoc updates, and changes to the API.
1️⃣🐝🏎️ The One Billion Row Challenge -- A fun exploration of how quickly 1B rows from a text file can be aggregated with Java
Role in this project:
Back-end Developer
Contributions:9 reviews, 4 PRs, 34 comments in 1 month
Contributions summary:Marko primarily contributed to the development of a Java-based application within the context of the One Billion Row Challenge. Their work focused on generating measurement data with randomized station names, optimizing name generation, and integrating a SWAR-based temperature parsing mechanism. The commits reflect an iterative process of performance improvement, including the introduction of techniques like unsafe memory access and optimized hashing. The user's contributions demonstrate a strong focus on optimizing Java code for high-performance data processing.
1brc
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Marko Topolnik - Senior Software Engineer at QuestDB