Debugging distributed database mysteries with Rust, packet capture and Polars
QuestDB encountered high outbound bandwidth usage during its primary-replica replication feature development. A network profiling tool was created to analyze packet data, revealing inefficient metadata uploads. Solutions improved bandwidth efficiency.
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QuestDB, a high-performance time-series database, faced a significant network bandwidth issue during the development of its primary-replica replication feature. The outbound bandwidth usage was unexpectedly high, despite a constant ingestion rate. To diagnose the problem, the author created a network profiling tool using Rust and the pcap crate to capture packet data for both inbound and outbound connections. The captured data included timestamps and packet sizes, which were then written to disk in a columnar format for analysis.
Using Python and the Polars library, the author analyzed the captured data to visualize bandwidth usage over time. The analysis revealed that the database was re-uploading the entire transaction metadata from the start, leading to increased network usage. The solution involved distributing the table metadata across multiple files for incremental uploads, which improved bandwidth efficiency. The author also developed a replication tuning guide for QuestDB based on insights gained from this analysis.
The entire process highlighted the importance of effective network traffic monitoring and analysis in optimizing database performance. The tools and methods developed not only resolved the immediate issue but also contributed to enhancing the overall efficiency of the replication algorithm, making it more bandwidth-efficient than the ingestion process itself. This case illustrates the value of combining programming skills with data analysis to troubleshoot and optimize complex systems.
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