2 links tagged with all of: performance + data-processing
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Lakesail rewrote Apache Spark in Rust, removing the JVM layer. The new implementation runs eight times faster and cuts infrastructure costs by 94%.
- Rewrote Apache Spark's core in Rust (~200,000 lines), eliminating the JVM entirely while keeping familiar Spark APIs
- Claims ~8x throughput improvement on standard analytics workloads by avoiding GC pauses and Java bytecode overhead
- Reports ~94% cost reduction on AWS C5 instances running identical TPC-DS queries, due to lower CPU/memory use
- Core engine is open source (Apache 2.0) and called production-ready, though advanced Spark SQL window functions and some MLlib algorithms are still missing
DuckDB has proven to be superior to Polars when handling large datasets, particularly 1TB of data. While DuckDB effectively manages memory and execution with a robust design, Polars struggles with large data processing, leading to out-of-memory errors.
- DuckDB handled the 1TB benchmark successfully while Polars crashed with out-of-memory errors
- DuckDB's memory management and execution engine scale more effectively to large datasets than Polars'
- Polars is optimized more for in-memory, smaller-scale workloads and breaks down at larger scales like 1TB