The Auron project, a native vectorized execution engine donated by Kuaishou and now incubated by the Apache Software Foundation, leverages Rust and SIMD to cut resource overhead, achieve over‑two‑fold speedups on TPC‑DS benchmarks, and integrates seamlessly with Spark and other big‑data ecosystems.
The Auron project, originally the Blaze engine from Kuaishou, has entered the Apache Software Foundation incubator, offering a Rust‑based native vectorized execution engine that integrates with Spark, delivers over two‑fold performance gains on TPC‑DS benchmarks, and is supported by a growing open‑source community.
Tencent Cloud's TDSQL shattered world records in both TPC‑DS (OLAP) and TPC‑C (OLTP) benchmarks, achieving a 7260 M QphDS score at a cost of 37.52 CNY/kQphDS, and the article explains the three self‑developed technologies—MPP execution, parallel execution framework, and columnar‑vectorized engine—that made this performance possible.
This article presents a comprehensive overview of Meta's open‑source Velox query execution engine, detailing its architecture, vectorized execution model, memory‑pool hierarchy, arbitrator and allocator designs, spilling techniques, and future development plans for large‑scale data processing.
In this interview, graduate student Sun Yinzhen shares how he selected, designed, and implemented an ORC Chunk Writer for the StarRocks database during the Open Source Summer program, detailing the technical challenges, learning outcomes, and his perspective on open‑source contributions for computer science students.
This article introduces Apache Doris 1.0, detailing its simplified architecture, high‑concurrency support, MPP execution engine, vectorized engine, memory‑controlled stability, multi‑source integration, upcoming lake‑house unification, storage‑compute separation, real‑time ingestion, and community growth.
Apache Doris (incubating) announced its 1.0 release on April 18, 2022, featuring a new vectorized execution engine, Hive external tables, Lateral View syntax, Z‑Order indexing, SeaTunnel integration, numerous performance optimizations, new bitmap functions, security enhancements, and detailed upgrade instructions for users.
This article explains StarRocks' streamlined architecture, cost‑based optimizer, massively parallel processing and vectorized engine, and how they enable high‑performance queries over data stored in Hive, Iceberg, Hudi and other lake formats, backed by benchmark results and future roadmap details.
