ApexBase v1.24.0
2026-07-17
- Add streaming, fixed-size RecordBatch imports for CSV and Parquet temporary tables, preserving rows and schemas across batch boundaries while keeping large file materialization memory-efficient
- Add direct Parquet
COUNT(*)execution and numeric range-filteredGROUP BYaggregate fast paths to avoid unnecessary row materialization on common analytical queries - Improve mmap range scans, numeric range
LIMITcaching, aggregate WAL handling, and on-demand read/write paths for lower allocation and faster repeated queries - Compact medium-cardinality string dictionaries using capacity derived from expected unique values, reducing memory overhead during ingestion and temporary-table workloads
- Correct schema-only V4 table writes and strengthen Arrow conversion, DML, blob, and storage handling across empty and incrementally populated tables
- Add an out-of-core CSV/Parquet benchmark against DuckDB covering direct file analysis, disk-backed materialization, repeated queries, peak RSS, and storage size
- Refresh the public ApexBase/SQLite/DuckDB benchmark scoreboard and performance documentation, including updated OLAP, OLTP, and vector metric accounting
- Expand Python regression coverage for cross-batch CSV/Parquet imports, file table functions, temporary tables, numeric range caching, SQL execution, and benchmark profile consistency
- Update the Rust crate and Python package version metadata to 1.24.0