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ByteDance Data Platform’s recent breakthroughs in database research—spanning query‑level Bayesian tuning, adaptive stream‑processing parallelism, and learned cardinality estimation—were highlighted by two papers accepted at VLDB 2025 and ICDE 2025, showcasing significant performance gains and real‑world deployments.
The article explains cardinality counting in Presto, comparing exact set‑based methods with memory‑efficient bitmap, Linear Count, and HyperLogLog approximations, detailing their algorithms, implementation in Presto’s query engine, and offering best‑practice recommendations for choosing the appropriate technique in business workloads.
This article explores the HyperLogLog algorithm for cardinality estimation, tracing its development from Linear and LogLog counting, detailing its Redis implementation with sparse and dense encodings and command workflows, and presenting experiments that demonstrate its memory efficiency and analyze observed error rates versus the theoretical 0.81% standard deviation.