Changelog

0.1.0 (first release)

The first public release. Everything below is new.

Engine

Columnar tables in engine memory; expressions compiled into fused SIMD WebAssembly loops and run on every CPU core (Node worker threads, Web Workers on cross-origin-isolated pages), one thread elsewhere, and a vectorized interpreter where code generation isn’t allowed (Cloudflare Workers).
Filters, computed columns, aggregates, group by any keys (millions of groups: partitioned by key range on every thread, sorted by key), exact and approximate distinct counts, joins (inner, left, right, full), sort and top-k, zone maps, compressed columns. Top-k over a file reads only the blocks that can still hold a winning row.
A query planner: table.lazy() chains are optimized as a whole.
CSV (parallel parser), JSON lines, Apache Arrow (read and write).
Datasets bigger than memory (.vectro files): chunked, bit-packed, read in parallel; group-by, joins and sorts spill to disk; in browsers too.
Streams (CSV, JSON lines, WebSocket feeds) with running windows.
Vector search (cosine / dot / L2, exact top-k).

SQL

sql(text, tables): joins, subqueries (IN, EXISTS, correlated), CTEs, UNION / INTERSECT / EXCEPT, window functions, percentiles, text functions, dates and intervals. 40 queries checked against DuckDB give the same answers.
Shortcuts for common shapes: only the rows that can rank in the first k for ROW_NUMBER() / RANK() ... <= k; distinct counts of IDs with a byte map; medians from bucket counts; CASE with quoted labels as label codes.

Serving (beta)

vectro serve: SQL over HTTP (JSON, CSV, Arrow) and an OData v4 feed; the PostgreSQL wire protocol (Power BI, Tableau, Metabase, Grafana, psql); Arrow Flight SQL (pandas, Polars, DuckDB through ADBC). Checked with pgJDBC, Npgsql, Microsoft’s OData libraries and the ADBC driver.
An MCP server for AI assistants (vectro-mcp).

Connectors (experimental)

connectors/dist/Vectro.mez for Power BI and vectro.taco for Tableau. Not signed, not yet tried in the applications.

Missing values

SQL uses three-valued logic for missing values: NOT (x > 5), x <> 5, NOT IN, NOT BETWEEN, NOT LIKE and LIKE drop rows where the value is missing, as other databases do. Table expressions keep JavaScript’s rules (x != 5 is true where x is missing).
Blank text ('', the missing value of a text column) is not counted by count(x), countDistinct(x), approxDistinct(x) or SQL’s COUNT, on tables, datasets and split tables.
null in arrays and rows loads as missing (it used to load as 0); a Date in tableFromRows makes a date column.
Clear errors for a text param, for date columns in a Float32Array table (times don’t fit in float32) and for SQL on a split table.
readCSV(path, { compress: true }) packs the columns, as parseCSV does.

Testing

VECTRO_POISON=1 overwrites memory as it is freed, so code that reads an array after freeing it gives wrong answers in tests (part of test:all).

Known gaps

Slower than DuckDB on one shape measured: a join over a file on disk with a GROUP BY on the joined table’s columns (1.2-1.3x at 100M and 1B rows). Faster on all 20 SQL queries measured, on every group-by, join and distinct count measured in memory, and on 7 of 8 queries over a billion rows on disk (see the README’s numbers).
Free-text columns (text that rarely repeats) can be shown but not grouped or filtered, except when served (vectro serve loads them as categories).
Window functions and percentiles keep the rows they read in memory.
An engine holds at most 4 GB in memory; tables are freed with dispose().
Integers are exact up to 2^53; times are UTC.