---
layout: default
title: High-performance embedded SQL database in Rust
---
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<h1 class="hero-heading">The SQL Database That<br><span class="hero-accent">Ships With Your App</span></h1>
<p class="hero-tagline">Pure Rust. Zero C dependencies. ACID transactions, cost-based optimizer, parallel execution, vector search with HNSW indexes, and 118 built-in functions. Compiles to WebAssembly.</p>
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<div class="spotlight-tag">Concurrency</div>
<h2>MVCC Transactions</h2>
<p>Full multi-version concurrency control with snapshot isolation. Readers never block writers. Optimistic concurrency delivers high throughput on mixed workloads with time-travel queries via <code>AS OF</code>.</p>
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<pre class="spotlight-code"><span class="c-cmt">-- Concurrent transactions</span>
<span class="c-kw">BEGIN</span>;
<span class="c-kw">UPDATE</span> accounts <span class="c-kw">SET</span> balance = balance - <span class="c-num">100</span>
<span class="c-kw">WHERE</span> id = <span class="c-num">1</span>;
<span class="c-kw">UPDATE</span> accounts <span class="c-kw">SET</span> balance = balance + <span class="c-num">100</span>
<span class="c-kw">WHERE</span> id = <span class="c-num">2</span>;
<span class="c-kw">COMMIT</span>;
<span class="c-cmt">-- Time-travel query</span>
<span class="c-kw">SELECT</span> * <span class="c-kw">FROM</span> accounts
<span class="c-kw">AS OF</span> <span class="c-str">'2024-01-15 10:30:00'</span>;</pre>
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<h2>Cost-Based Optimizer</h2>
<p>PostgreSQL-style query planning with adaptive execution. The optimizer learns from actual cardinalities and re-optimizes at runtime. Bloom filter propagation, zone map pruning, and semantic query caching reduce redundant work.</p>
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<pre class="spotlight-code"><span class="c-cmt">-- EXPLAIN shows the optimizer at work</span>
<span class="c-kw">EXPLAIN ANALYZE</span>
<span class="c-kw">SELECT</span> u.name, <span class="c-fn">SUM</span>(o.amount)
<span class="c-kw">FROM</span> users u
<span class="c-kw">JOIN</span> orders o <span class="c-kw">ON</span> u.id = o.user_id
<span class="c-kw">WHERE</span> o.amount > <span class="c-num">50</span>
<span class="c-kw">GROUP BY</span> u.name;
<span class="c-dim">-- Hash Join (cost=45.2, rows=1200)
-- Seq Scan on orders (rows=8500)
-- Hash on users (rows=1000)</span></pre>
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<div class="spotlight-tag">Scale</div>
<h2>Parallel Execution</h2>
<p>Rayon work-stealing scheduler automatically parallelizes filters, joins, sorts, and distinct operations. Chunked processing with configurable thresholds keeps small queries fast while large queries scale across all cores.</p>
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<pre class="spotlight-code"><span class="c-cmt">-- Automatic parallel execution</span>
<span class="c-kw">SELECT</span> category, <span class="c-fn">AVG</span>(price)
<span class="c-kw">FROM</span> products
<span class="c-kw">WHERE</span> price > <span class="c-num">10</span>
<span class="c-kw">GROUP BY</span> category;
<span class="c-dim">-- EXPLAIN ANALYZE output:
-- Parallel Seq Scan (workers=8)
-- actual rows=735,000
-- parallel speedup: 6.2x</span></pre>
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<div class="spotlight-tag">AI / ML</div>
<h2>Vector & Semantic Search</h2>
<p>Native VECTOR type with HNSW indexes for sub-linear approximate nearest neighbor search. Three distance metrics (L2, Cosine, Inner Product). Optional <code>EMBED()</code> function converts text to vectors using a built-in sentence-transformer model, no external APIs or Python needed.</p>
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<pre class="spotlight-code"><span class="c-cmt">-- Semantic search with HNSW index</span>
<span class="c-kw">CREATE TABLE</span> docs (
id <span class="c-kw">INTEGER PRIMARY KEY</span>,
content <span class="c-kw">TEXT</span>,
embedding <span class="c-kw">VECTOR</span>(<span class="c-num">384</span>)
);
<span class="c-kw">CREATE INDEX</span> idx_emb <span class="c-kw">ON</span> docs(embedding)
<span class="c-kw">USING HNSW WITH</span> (metric = <span class="c-str">'cosine'</span>);
<span class="c-kw">SELECT</span> content, <span class="c-fn">VEC_DISTANCE_COSINE</span>(
embedding, <span class="c-fn">EMBED</span>(<span class="c-str">'forgot password'</span>)
) <span class="c-kw">AS</span> dist
<span class="c-kw">FROM</span> docs <span class="c-kw">ORDER BY</span> dist <span class="c-kw">LIMIT</span> <span class="c-num">5</span>;</pre>
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<h2 class="section-heading">Everything You Need</h2>
<p class="section-sub">Enterprise-grade features in an embeddable package.</p>
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<h3>Vector & HNSW Indexes</h3>
<p>Native VECTOR type with HNSW, B-tree, Hash, and Bitmap indexes. Optional built-in semantic search.</p>
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<h3>Semantic Query Caching</h3>
<p>Predicate subsumption detects when cached results can answer stricter queries.</p>
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<h3>Window Functions</h3>
<p>ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, NTILE with PARTITION BY and frames.</p>
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<h3>Recursive CTEs</h3>
<p>WITH RECURSIVE for hierarchical queries, graph traversal, and sequence generation.</p>
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<h3>WAL + Crash Recovery</h3>
<p>Write-ahead logging with snapshots ensures durability. Automatic recovery on restart.</p>
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<h3>Full SQL Support</h3>
<p>JOINs, subqueries, ROLLUP/CUBE/GROUPING SETS, UNION/INTERSECT/EXCEPT, and more.</p>
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<p>Stoolap compiles to WebAssembly. Run real SQL queries right now, no installation, no server. Everything runs locally.</p>
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<pre class="hero-term-body"><span class="t-muted">stoolap></span> <span class="t-purple">WITH RECURSIVE</span> fib(n, a, b) <span class="t-purple">AS</span> (
<span class="t-purple">SELECT</span> <span class="t-orange">1</span>, <span class="t-orange">0</span>, <span class="t-orange">1</span>
<span class="t-purple">UNION ALL</span>
<span class="t-purple">SELECT</span> n+<span class="t-orange">1</span>, b, a+b <span class="t-purple">FROM</span> fib
<span class="t-purple">WHERE</span> n < <span class="t-orange">10</span>
)
<span class="t-purple">SELECT</span> n, a <span class="t-purple">AS</span> fibonacci <span class="t-purple">FROM</span> fib;
<span class="t-dim">+----+-----------+
| n | fibonacci |
+----+-----------+
| 1 | 0 |
| 2 | 1 |
| 3 | 1 |
| 4 | 2 |
| 5 | 3 |
+----+-----------+
(10 rows, 0.1ms)</span></pre>
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<pre class="code-pre"><span class="c-kw">use</span> stoolap::Database;
<span class="c-kw">fn</span> <span class="c-fn">main</span>() -> <span class="c-type">Result</span><(), Box<<span class="c-kw">dyn</span> std::error::Error>> {
<span class="c-kw">let</span> db = Database::open(<span class="c-str">"my_database"</span>)?;
db.execute(<span class="c-str">"CREATE TABLE users (
id INTEGER PRIMARY KEY,
name TEXT NOT NULL,
email TEXT UNIQUE
)"</span>, ())?;
db.execute(<span class="c-str">"INSERT INTO users VALUES (1, 'Alice', 'alice@example.com')"</span>, ())?;
<span class="c-kw">for</span> row <span class="c-kw">in</span> db.query(<span class="c-str">"SELECT * FROM users"</span>, ())? {
<span class="c-kw">let</span> row = row?;
<span class="c-kw">let</span> name: String = row.get(<span class="c-num">1</span>)?;
<span class="c-fn">println!</span>(<span class="c-str">"{name}"</span>);
}
<span class="c-type">Ok</span>(())
}</pre>
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<div class="code-panel" data-tab="sql" role="tabpanel" id="panel-sql" aria-labelledby="tab-sql">
<pre class="code-pre"><span class="c-cmt">-- Window functions with CTEs</span>
<span class="c-kw">WITH</span> ranked <span class="c-kw">AS</span> (
<span class="c-kw">SELECT</span>
customer_id,
amount,
<span class="c-fn">ROW_NUMBER</span>() <span class="c-kw">OVER</span> (
<span class="c-kw">PARTITION BY</span> customer_id
<span class="c-kw">ORDER BY</span> amount <span class="c-kw">DESC</span>
) <span class="c-kw">AS</span> rn
<span class="c-kw">FROM</span> orders
<span class="c-kw">WHERE</span> amount > <span class="c-num">1000</span>
)
<span class="c-kw">SELECT</span> * <span class="c-kw">FROM</span> ranked <span class="c-kw">WHERE</span> rn = <span class="c-num">1</span>;
<span class="c-cmt">-- Time-travel queries (unique to Stoolap)</span>
<span class="c-kw">SELECT</span> * <span class="c-kw">FROM</span> users
<span class="c-kw">AS OF</span> <span class="c-str">'2024-01-15 10:30:00'</span>;</pre>
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<a href="{{ '/docs/getting-started/installation/' | relative_url }}">Installation Guide →</a>
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<a href="{{ '/docs/sql-commands/sql-commands/' | relative_url }}">SQL Reference →</a>
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<h2 class="section-heading">How Stoolap Compares</h2>
<p class="section-sub">Feature-by-feature comparison with popular databases</p>
<div class="table-wrap">
<table>
<caption class="sr-only">Feature comparison between Stoolap, SQLite, DuckDB, and PostgreSQL</caption>
<thead>
<tr>
<th>Feature</th>
<th class="hl">Stoolap</th>
<th>SQLite</th>
<th>DuckDB</th>
<th>PostgreSQL</th>
</tr>
</thead>
<tbody>
<tr><td>MVCC Transactions</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td></tr>
<tr><td>AS OF Temporal Queries</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="n" aria-label="No">✗</span></td></tr>
<tr><td>Cost-Based Optimizer</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td></tr>
<tr><td>Adaptive Query Execution</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="n" aria-label="No">✗</span></td></tr>
<tr><td>Semantic Query Caching</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="n" aria-label="No">✗</span></td></tr>
<tr><td>Parallel Query Execution</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td></tr>
<tr><td>Hash / Merge Joins</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td></tr>
<tr><td>Window Functions</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td></tr>
<tr><td>ROLLUP / CUBE / GROUPING SETS</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td></tr>
<tr><td>Recursive CTEs</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td></tr>
<tr><td>Native Vector / HNSW Search</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="n" aria-label="No">✗</span></td></tr>
<tr><td>Pure Rust (Memory Safe)</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="n" aria-label="No">✗</span></td><td><span class="n" aria-label="No">✗</span></td></tr>
<tr><td>Embeddable (No Server)</td><td class="hl"><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td><td><span class="y" aria-label="Yes">✓</span></td><td><span class="n" aria-label="No">✗</span></td></tr>
</tbody>
</table>
</div>
</div>
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<h2 class="section-heading">Modern Architecture</h2>
<p class="section-sub">Designed for performance, scalability, and ease of use</p>
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<h3>Flexible Client Interface</h3>
<p>CLI and Rust API. Embed directly in your application with zero external dependencies.</p>
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<h3>Optimized Query Processing</h3>
<p>SQL parsing, cost-based optimization, and parallel execution for exceptional performance.</p>
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<h3>MVCC Storage Engine</h3>
<p>Row-based version store with B-tree, Hash, Bitmap, and HNSW indexes for transactional integrity and vector search.</p>
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