ApexBase is a high-performance embedded HTAP database with a Rust core and a Python-first API.
Install it, choose persistent .apex files or a true process-local in-memory database, run analytical SQL, import/export DataFrames, and optionally expose persistent 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, shared scan/selection operators, vectorized execution, SQL aggregations, joins, CTEs, windows, and indexes |
| Low-friction Python workflows | ApexClient, apexbase.execute, Pandas / Polars / PyArrow conversion, file table functions, and local or in-memory storage |
| 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.
For scratch work, tests, and short-lived analytics, use the same API without creating database, catalog, WAL, delta, or index files:
from apexbase import ApexClient
with ApexClient(":memory:") as client:
client.execute("CREATE TABLE metrics (name TEXT, value DOUBLE)")
client.execute("INSERT INTO metrics VALUES ('latency_ms', 2.4)")
print(client.execute("SELECT AVG(value) AS average FROM metrics").scalar())
For the smallest SQL-only scripts, apexbase.execute(...) uses one lazily
created process-local in-memory connection shared by later module-level calls;
create a table before running table-scoped queries.
Performance At A Glance
The latest retained complete public snapshot uses 1,000,000 tabular rows and 1,000,000 vectors x 128 dimensions on Apple arm64 with Python 3.12.
| Area | Snapshot |
|---|---|
| Public coverage | 103 tabular metrics, 6 exact-vector metrics, and 8 ApexBase quantized-vector precision rows |
| Comparable results | ApexBase wins 115 / 115 rows with a direct competitor in the retained complete snapshot |
| Exact vector search | All 6 single/batch Float32 TopK rows beat the compared engines and match brute-force exact top-k row sets |
| Reproducibility | Fixed data sizes, 2 warmups, 5 timed iterations, dependency metadata, and a retained JSON report |
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.
Starting with 1.33, filtered grouped queries can use a shared physical scan
protocol across persisted base data and delta/overlay state. The protocol keeps
predicate selection, grouping, HAVING, and ordered TopK as composable
operators with a generic fallback for unsupported or inexact predicate forms.
v1.34.0 adds a serial row-group pipeline, bounded parallel scan/fold execution,
streaming overlay reads, and a per-query aggregation memory budget on top of it.
See Scan & Physical Execution
and Resource Ownership.
v1.37.0 is the V5 architecture preview release. Files may declare format
version 5
and carry V5-only column encodings, _id ranges are reserved against a shared
on-disk high-water mark so several processes can write one table, and new read
lanes cover direct point reads, executor index lookups, full-text row-selection
pushdown, and distance TopK. V5 builds read V4 files; to keep a file readable by
a pre-V5 build, set APEX_WIDTH_FIDELITY=0 to write the V4 layout. The format
may still gain encodings before V5 is declared stable, so treat it as a preview
and see the release notes
and Storage Architecture
for the compatibility rules.
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 1.37.0
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-1.37.0.tar.gz | 2.4 MB | Details |
Built distributions (wheels)
Total release size: 169.2 MB
Release files / apexbase-1.37.0.tar.gz
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Release files / apexbase-1.37.0-cp313-cp313-win_amd64.whl
| Download URL | apexbase-1.37.0-cp313-cp313-win_amd64.whl |
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| Download URL | apexbase-1.37.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
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| Download URL | apexbase-1.37.0-cp313-cp313-macosx_11_0_arm64.whl |
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| Download URL | apexbase-1.37.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
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