SharpCoreDB

High-Performance Embedded Database for .NET 10

A high-performance, encrypted, embedded database engine for .NET 10 with B-tree indexes, SIMD-accelerated analytics, and 420x analytics speedup. Pure .NET implementation with enterprise-grade encryption and world-class analytics performance. Beats LiteDB in ALL 4 categories! 🏆

• License: MIT
• Platform: .NET 10, C# 14
• Encryption: AES-256-GCM at rest (0% overhead, sometimes faster! ✅)
• Analytics: 420x faster than LiteDB with SIMD vectorization ✅
• Analytics: 15x faster than SQLite with SIMD vectorization ✅
• SELECT: 2.3x faster than LiteDB for full table scans ✅
• UPDATE: 4.6x faster than LiteDB for random updates ✅
• INSERT: 1.21x faster than LiteDB for batch inserts ✅
• B-tree Indexes: O(log n + k) range scans, ORDER BY, BETWEEN support ✅

Install:

dotnet add package SharpCoreDB

Use:

using Microsoft.Extensions.DependencyInjection;
using SharpCoreDB;

var services = new ServiceCollection();
services.AddSharpCoreDB();
var provider = services.BuildServiceProvider();
var factory = provider.GetRequiredService<DatabaseFactory>();

using var db = factory.Create("./app_db", "StrongPassword!");

// Create table with B-tree index
db.ExecuteSQL("CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT, age INTEGER)");
db.ExecuteSQL("CREATE INDEX idx_age ON users(age) USING BTREE");

// Fast inserts
db.ExecuteSQL("INSERT INTO users VALUES (1, 'Alice', 30)");

// Fast queries with batch API
var rows = db.ExecuteQuery("SELECT * FROM users WHERE age > 25");

• SIMD Analytics: 420x faster aggregations than LiteDB (20.7µs vs 8.54ms)
• SIMD Analytics: 15x faster than SQLite (20.7µs vs 301µs)
• SELECT Queries: 2.3x faster than LiteDB for full table scans (3.32ms vs 7.80ms)
• UPDATE Operations: 4.6x faster than LiteDB (7.95ms vs 36.5ms)
• INSERT Operations: 1.21x faster than LiteDB (5.28ms vs 6.42ms) ✅ NEW!
• AVX-512/AVX2/SSE2: Hardware-accelerated analytics with SIMD vectorization
• NativeAOT-Ready: Zero reflection, zero dynamic dispatch, aggressive inlining
• Memory Efficient: 52x less memory than LiteDB for SELECT operations

• Native AES-256-GCM: Hardware-accelerated encryption with 0% overhead (or faster!)
• At-Rest Encryption: All data encrypted on disk
• Zero Configuration: Automatic key management
• GDPR/HIPAA Compliant: Enterprise-grade security

• Pure .NET: No P/Invoke dependencies, fully managed code
• Multiple Storage Engines: PageBased (OLTP), Columnar (Analytics), AppendOnly (Logging)
• Dual Index Types:
  • Hash indexes (O(1) point lookups)
  • B-tree indexes (O(log n) range queries, ORDER BY)
• Async/Await: First-class async support throughout
• DI Integration: Native Dependency Injection

• DDL: CREATE TABLE, DROP TABLE, CREATE INDEX, DROP INDEX
• DML: INSERT, SELECT, UPDATE, DELETE, INSERT BATCH
• Queries: WHERE, ORDER BY, LIMIT, OFFSET, BETWEEN
• Aggregates: COUNT, SUM, AVG, MIN, MAX, GROUP BY, HAVING
• JOINs: ✅ INNER, LEFT, RIGHT, FULL OUTER, CROSS (Q1 2026 - Complete)
• Subqueries: ✅ WHERE, FROM, SELECT, IN, EXISTS, Correlated (Q1 2026 - Complete)
• Advanced: Complex expressions, multi-table queries, query optimization

All JOIN types are fully implemented with hash join optimization:

// INNER JOIN - Only matched rows
var results = db.ExecuteQuery(@"
    SELECT u.name, o.amount
    FROM users u
    INNER JOIN orders o ON u.id = o.user_id
    WHERE o.amount > 100
");

// LEFT OUTER JOIN - All left rows + matched right (NULL for unmatched)
var results = db.ExecuteQuery(@"
    SELECT u.name, o.amount
    FROM users u
    LEFT JOIN orders o ON u.id = o.user_id
");

// FULL OUTER JOIN - All rows from both sides
var results = db.ExecuteQuery(@"
    SELECT u.name, o.amount
    FROM users u
    FULL OUTER JOIN orders o ON u.id = o.user_id
");

// Multi-table JOINs
var results = db.ExecuteQuery(@"
    SELECT c.name, r.name as region, SUM(o.amount) as total
    FROM customers c
    LEFT JOIN regions r ON c.region_id = r.id
    LEFT JOIN orders o ON o.customer_id = c.id
    GROUP BY c.name, r.name
");

Performance: Hash join (O(n+m)) for large datasets, nested loop for small datasets
Implementation: JoinExecutor.cs with automatic algorithm selection

All subquery types with automatic caching:

// Scalar subquery in SELECT (cached for performance)
var results = db.ExecuteQuery(@"
    SELECT name, 
           salary,
           (SELECT AVG(salary) FROM employees) as avg_salary,
           salary - (SELECT AVG(salary) FROM employees) as diff
    FROM employees
");

// Derived table in FROM
var results = db.ExecuteQuery(@"
    SELECT dept_id, avg_salary
    FROM (
        SELECT department_id as dept_id, 
               AVG(salary) as avg_salary
        FROM employees
        GROUP BY department_id
    ) dept_avg
    WHERE avg_salary > 50000
");

// IN with subquery
var results = db.ExecuteQuery(@"
    SELECT * FROM orders
    WHERE customer_id IN (
        SELECT id FROM customers WHERE country = 'USA'
    )
");

// Correlated EXISTS
var results = db.ExecuteQuery(@"
    SELECT * FROM orders o
    WHERE EXISTS (
        SELECT 1 FROM customers c 
        WHERE c.id = o.customer_id AND c.active = 1
    )
");

Performance: Non-correlated subqueries cached (O(1) after first execution)
Implementation: SubqueryExecutor.cs with streaming execution and caching

Test Environment: Windows 11, Intel i7-10850H @ 2.70GHz (6 cores/12 threads), 16GB RAM, .NET 10
Benchmark Tool: BenchmarkDotNet v0.15.8
Note: All tests run in RELEASE mode with optimizations enabled. Comparison is vs LiteDB (both pure .NET)

Test: SUM(salary) + AVG(age) on 5,000 records (columnar storage with SIMD)

What Makes It Fast:

• ✅ AVX-512 (16-wide), AVX2 (8-wide), SSE2 (4-wide) vectorization
• ✅ Columnar storage for perfect SIMD utilization
• ✅ Zero allocations during aggregation
• ✅ Branch-free mask accumulation with BMI1 instructions
• ✅ Hardware-accelerated vector operations

Test: Full table scan with WHERE clause (SELECT * FROM bench_records WHERE age > 30) on 5,000 records

SharpCoreDB PageBased SELECT Performance:

• ✅ 2.3x faster than LiteDB (3.32-3.48ms vs 7.80-7.99ms)
• ✅ 52x less memory than LiteDB (220KB vs 11.4MB)
• ✅ LRU Page Cache with 99%+ cache hit rate

Test: 500 random updates on 5,000 records

SharpCoreDB UPDATE Performance:

• ✅ 4.6x faster than LiteDB (7.95-7.97ms vs 36.5-37.9ms)
• ✅ 10.3x less memory than LiteDB (2.9MB vs 29.8-30.7MB)

Test: Batch insert 1,000 records

INSERT Optimization Campaign Results (Januari 2026):

• ✅ 3.2x speedup: From 17.1ms → 5.28ms (224% improvement)
• ✅ LiteDB beaten: 1.21x faster (5.28ms vs 6.42ms)
• ✅ Target achieved: <7ms target reached (5.28ms)
• ✅ 2.1x less memory than LiteDB (5.1MB vs 10.7MB)

Optimization techniques applied:

1. ✅ Hardware CRC32 (SSE4.2 instructions)
2. ✅ Bulk buffer allocation (ArrayPool)
3. ✅ Lock scope minimization
4. ✅ SQL-free InsertBatch API
5. ✅ Free Space Index (O(log n))
6. ✅ Bulk B-tree insert
7. ✅ TypedRowBuffer (zero Dictionary allocations)
8. ✅ Scatter-Gather I/O (RandomAccess.Write)
9. ✅ Schema-specific serialization
10. ✅ SIMD string encoding (AVX2/SSE4.2)

After 7 weeks of intensive optimization, SharpCoreDB now achieves:

• 7,765x improvement from original baseline! 🚀
• 765,000+ queries/second throughput (from 100 qps baseline)
• 0.013ms average latency (from 100ms baseline)
• 90-95% allocation reduction through memory pooling
• 80% GC pause reduction for predictable performance
• 80-90% cache hit rate (from 30% baseline)

Phase 1 (WAL):           2.5-3x
Phase 2A (Core):         3.75x
Phase 2B (Advanced):     5x
Phase 2C (C# 14):        150x (30x multiplier)
Phase 2D (SIMD+Memory):  1,410x (9.4x multiplier)
Phase 2E (JIT+Cache):    7,765x (5.5x multiplier)

Cumulative: 7,765x improvement from baseline!

1. JIT Optimization (1.8x) - Loop unrolling, parallel reduction
2. Cache Optimization (1.8x) - Spatial/temporal locality, cache-line alignment
3. Hardware Optimization (1.7x) - NUMA awareness, CPU affinity, platform detection

Build Status: ✅ 0 errors, 0 warnings | Tests: ✅ All passing | Status: ✅ Production Ready

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