ApexBase is a high-performance embedded HTAP database with a Rust core and a Python-first API.
Install it, write local .apex table files, run analytical SQL, import/export DataFrames, and optionally expose the same data through PostgreSQL Wire or Arrow Flight. No separate database service is required.
Why ApexBase
| What you need | What ApexBase gives you |
|---|---|
| Fast local analytics | Columnar storage, vectorized execution, SQL aggregations, joins, CTEs, windows, and indexes |
| Low-friction Python workflows | ApexClient, Pandas / Polars / PyArrow conversion, file table functions, and simple local persistence |
| One engine for mixed workloads | HTAP design: fast writes, point lookups, analytical scans, transactions, and MVCC |
| Search built in | Full-text search, vector TopK, Float16/BFloat16/Int8/UInt8/1Bit/TurboQuant storage, and exact reranking from a retained source vector |
| Tool compatibility | PostgreSQL Wire for database clients and Arrow Flight for fast columnar transfer |
Install
pip install apexbase
Build from source:
python -m pip install maturin
maturin develop --release
30-Second Example: FTS + SQL + Vector Search In One Local File
from apexbase import ApexClient
with ApexClient("./rag-data") as client:
client.execute("""
CREATE TABLE articles (
title TEXT,
body TEXT,
category TEXT,
views INT,
embedding FLOAT16_VECTOR
)
""")
client.use_table("articles")
client.store([
{
"title": "Rust-powered local analytics",
"body": "A columnar embedded database for fast SQL and search.",
"category": "database",
"views": 4200,
"embedding": [0.10, 0.82, 0.20],
},
{
"title": "Hybrid retrieval for RAG",
"body": "Combine full-text recall, SQL filters, and semantic vector ranking.",
"category": "ai",
"views": 6100,
"embedding": [0.16, 0.74, 0.58],
},
{
"title": "SQLite migration notes",
"body": "Move local applications to an analytical embedded store.",
"category": "database",
"views": 2600,
"embedding": [0.80, 0.12, 0.10],
},
])
client.execute("CREATE FTS INDEX ON articles(title, body)")
# FTS recall + structured SQL guardrails + pgvector-style semantic rerank.
df = client.execute("""
SELECT
title,
category,
views,
cosine_distance(embedding, [0.12, 0.78, 0.25]) AS semantic_dist
FROM articles
WHERE MATCH('database')
AND category = 'database'
AND views > 3000
ORDER BY semantic_dist
LIMIT 5
""").to_pandas()
print(df)
ApexBase gives you pgvector-style semantic search, SQL filters, and full-text search in the same embedded database file. It is the kind of stack you would otherwise assemble from SQLite/DuckDB + FTS + pgvector, but without a server process or a separate search/vector service; results still convert directly to Pandas, Polars, or Arrow.
Performance At A Glance
Latest local snapshot (2026-08-15): ApexBase 1.29.0, 1M-row tabular dataset, 1M-vector dataset, Apple arm, Python 3.12.
| Area | Snapshot |
|---|---|
| Fair OLAP + OLTP comparison | 102 public tabular metrics tracked; ApexBase wins 87 / 102 in the benchmark harness. The remaining losses are the DDL gaps vs SQLite (Table DROP, Table CREATE+DROP cycle, ALTER TABLE ADD COLUMN, 1.3x–2.0x) and a dozen advanced-SQL metrics vs DuckDB at 1.2x–2.0x, plus NOT filter (4.6x) and ORDER BY LENGTH(...) (3.9x) as the two remaining larger gaps — down from the 1.5x–75x these metrics previously showed. |
| GROUP BY city | 2.0x faster than DuckDB in the representative snapshot |
| FTS search | 5.6x faster than SQLite in the representative snapshot |
| Batch vector TopK cosine | 7.1x faster than DuckDB in the representative snapshot |
Benchmarks are workload-sensitive. The default benchmark command tracks this public scoreboard; extended diagnostics live in benchmarks/bench_vs_sqlite_duckdb_extended.py. See the full reproducible setup in the Performance documentation.
Documentation
Start here: https://birchkwok.github.io/apexbase/
| Goal | Page |
|---|---|
| Get running quickly | Installation and Quick Start |
| Understand the model | Core Concepts |
| Use the Python API | Python Client Guide and API Reference |
| Write SQL | SQL Guide |
| Import files and DataFrames | Data Import |
| Use database tools or Arrow clients | Server Protocols |
| Search text or vectors | Full-Text Search, Float16 Vectors, and Vector Quantization |
| Embed from Rust | Rust Embedded API |
Interfaces
# Embedded Python
python -c "from apexbase import ApexClient; print(ApexClient)"
# PostgreSQL Wire + Arrow Flight together
apexbase-serve --dir ./data
# Individual protocol servers
apexbase-server --dir ./data --port 5432
apexbase-flight --dir ./data --port 50051
Lance Interop
from apexbase import ApexClient
with ApexClient("./data") as client:
client.use_table("articles")
client.to_lance("./articles.lance")
with ApexClient("./imported") as client:
client.from_lance("./articles.lance", table_name="articles")
Lance conversion uses Arrow tables as the handoff path. This keeps the in-process conversion lean and Arrow-native, while each format still writes its own on-disk layout.
License
Apache-2.0
1 maintainer
Metadata
1 maintainer
Release files for apexbase 1.31.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| apexbase-1.31.1.tar.gz | 1.6 MB | Details |
Built distributions (wheels)
Total release size: 157.9 MB
Release files / apexbase-1.31.1.tar.gz
| Download URL | apexbase-1.31.1.tar.gz |
|---|---|
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| Tags | Source |
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Release files / apexbase-1.31.1-cp313-cp313-win_amd64.whl
| Download URL | apexbase-1.31.1-cp313-cp313-win_amd64.whl |
|---|---|
| Size | 11.1 MB |
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| Download URL | apexbase-1.31.1-cp313-cp313-macosx_11_0_arm64.whl |
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| Download URL | apexbase-1.31.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
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| Tags | CPython 3.9 Linux glibc 2.17+ x86-64 |
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| Download URL | apexbase-1.31.1-cp39-cp39-macosx_11_0_arm64.whl |
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| Tags | CPython 3.9 macOS 11.0+ ARM64 |
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