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A 100% Pure-Dart converged database engine combining Relational SQL, NoSQL JSON, HNSW Vector RAG, and PL/SQL with zero C dependencies.

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ultsql: three models, one core

🚀 ULTSQL — Ultra-High Performance Converged Multimodal Database Engine #

pub package Dart SDK Flutter License Build Status

📦 Package: ultsql | Flutter package

UltSQL is a ground-up, zero-dependency, 4-in-1 converged database engine written in 100% pure Dart. It seamlessly combines Relational SQL, PL/SQL Procedural Execution, NoSQL Dotted-Path Document Querying, and AI-Native Vector RAG Search into a single, high-throughput storage model with zero native C dependencies or unsafe memory pointers.

🌍 Universal Installation for All Languages & Operating Systems #

ULTSQL can be accessed by any developer, programming language, or operating system:

graph TD
    subgraph "Your Application (Any System)"
        Py[🐍 Python]
        Node[🟢 Node.js / TS]
        Dart[💙 Flutter / Dart]
        CLI[🖥️ Windows / Mac / Linux Executable]
        Docker[🐳 Docker Container]
    end

    subgraph "Package Registries"
        PyPI[PyPI: pip install ultsql]
        NPM[NPM: npm install ultsql]
        Pub[Pub.dev: package:ultsql]
        Releases[GitHub Releases: ultsql.exe]
        Hub[Docker Hub: docker run]
    end

    Py --> PyPI
    Node --> NPM
    Dart --> Pub
    CLI --> Releases
    Docker --> Hub

1. 🐍 Python Developers #

No Dart or Flutter required!

pip install ultsql

from ultsql import UltSQLClient

db = UltSQLClient("http://localhost:8080")
db.insert("users", {"id": 1, "name": "Alice"})
print(db.query("users"))

2. 🟢 Node.js & TypeScript Developers #

No Dart or Flutter required!

npm install ultsql

const { UltSQLClient } = require('ultsql');

const db = new UltSQLClient({ host: 'localhost', port: 8080 });
await db.insert('users', { id: 1, name: 'Alice' });
console.log(await db.query('users'));

3. 🖥️ 1-Line Standalone CLI Installers (Windows, macOS, Linux) #

Zero Dependencies! Automatically downloads binary and adds ultsql to your system PATH:

  • Windows (PowerShell): 
    iwr -useb https://raw.githubusercontent.com/ompatel3158/ULTSQL/main/install.ps1 | iex
  • Linux & macOS (Bash): 
    curl -fsSL https://raw.githubusercontent.com/ompatel3158/ULTSQL/main/install.sh | bash

Once installed, type ultsql serve in any terminal!

4. 🐳 Docker Container (Cloud & Servers) #

docker run -p 8080:8080 -v ./data:/db ompatel3158/ultsql serve --port 8080 --db /db

5. 🔌 PostgreSQL Wire Protocol (psycopg2, node-postgres, JDBC, psql) #

Connect from any language using standard Postgres drivers:

# Start Postgres Wire Server on port 5432
ultsql .pgwire 5432

🌟 Standalone Engine Metrics #

Capability / Benchmark UltSQL Performance Feature Status
In-Memory Batch Write Throughput 1,200,000+ rows/sec (Peak 3.48M/s) ⚡ High-Throughput Memory Engine
B+ Tree Index Build (100K Rows) 17 ms 🏆 Ultra-Fast Sub-Second Indexing
768-Dim HNSW AI Vector RAG 6 ms (100% Recall) 🧠 Native AI Embedded Vector Engine
Network TCP Wire Protocol Server Port 5432 Supported 🌐 Remote Client Network Connections
Self-Healing Corrupted Recovery Auto-Repairs CRC Mismatches 🛠️ Zero-DBA Self-Healing
P2P Offline Device-to-Device Sync LWW-Element-Set CRDT Sync 📲 Local-First P2P Mesh Sync
Universal Direct File SQL Queries CSV, JSON, LOG Files 📁 Zero-ETL Direct Queries
Zero-Knowledge Ciphertext Search Homomorphic XOR Search 🔐 Secure Privacy Enclave

🏛️ System Architecture #

UltSQL uses a multi-layered Volcano-iterator query engine over custom slotted-page disk/memory tables, LRU page caching, B+ Trees, and HNSW vector graphs:

graph TD
    UI[Flutter IDE Console / Client App] -->|SQL / PL-SQL / NL Prompt| Interpreter[Interpreter Engine]
    Interpreter -->|Natural Language AI| NlEngine[NL-to-SQL AI Compiler]
    Interpreter -->|Lexical Analysis| Lexer[Hand-Written Lexer]
    Lexer -->|Tokens| Parser[Hand-Written Parser]
    Parser -->|AST Tree| QueryPlanner[Optimizing Query Planner]
    QueryPlanner -->|Physical Execution Plan| VolcanoEngine[Volcano Iterator Execution Engine]
    
    VolcanoEngine -->|Page Operations| PageCache[LRU Page Cache Buffer]
    PageCache -->|CRC32 Page Verification| Pager[Slotted Page Pager]
    Pager -->|Storage Engines| StorageAdapters
    
    subgraph StorageAdapters[Converters & Adapters]
      MemoryStore[MemoryTable: 1.2M+ rows/sec]
      RowStore[.db: Row-Oriented Slotted Pages]
      ColumnStore[.col_*: Columnar Parquet Store]
      BTreeIndex[.idx: B+ Tree Indexes]
      HnswIndex[.hnsw: HNSW Vector Graph]
      FileAdapter[Universal CSV / JSON / LOG Adapter]
    end
    
    VolcanoEngine -->|Network Server| PgWireServer[TCP Wire Protocol Server]
    VolcanoEngine -->|P2P Mesh| P2pNode[CRDT P2P Peer Node]

📑 Table of Contents #

  1. 🌟 Standalone Engine Metrics
  2. 🏛️ System Architecture
  3. 💎 The 15 Signature Innovations
  4. ⚖️ Storage Modes: Switchable Performance
  5. 🛠️ SQL & PL/SQL Feature Guide
  6. 📄 NoSQL Dotted-Path JSON Querying
  7. 🧠 AI-Native HNSW Vector RAG Search
  8. 🌐 Network TCP Wire Protocol Server
  9. 🛠️ Self-Healing & Auto-Indexing Telemetry
  10. 📲 P2P Offline Device-to-Device Sync
  11. 📁 Direct File SQL Queries (CSV / JSON / LOG)
  12. 🔐 Zero-Knowledge Security Enclave
  13. 📊 Standalone Engine Performance Metrics
  14. 🚀 Getting Started & Installation
  15. 📜 License

💎 The 15 Signature Innovations #

UltSQL introduces 15 signature database innovations engineered specifically for high-throughput client and cloud workloads:

  1. ⚡ 1.2M+ Rows/sec In-Memory Batch Engine: Zero-allocation linear byte array memory ingestion.
  2. 🏆 Ultra-Fast B+ Tree Bulk Indexing: insertSortedBatchSync constructs 100K-row B+ Trees in 17 ms.
  3. 🧠 Native HNSW Vector RAG Graph: Cosine & Euclidean similarity search over 768-dim embeddings in 6 ms.
  4. 🌐 Network TCP Wire Protocol Server: Accepts incoming connections from standard database drivers.
  5. 🛠️ Self-Healing Page Auto-Repair: Auto-detects CRC32 page corruption and rebuilds intact state from WAL logs.
  6. 🤖 Autonomous Telemetry Auto-Indexer: Monitors query scan frequencies and automatically provisions B+ Tree indexes.
  7. 📁 Universal Direct File SQL Adapter: Runs live SQL queries over standard .csv, .json, and .log files without importing into tables.
  8. 🗣️ AI Natural Language to SQL Compiler: Translates natural language prompts into executable SQL statements.
  9. 🔐 Zero-Knowledge Encrypted Enclave: Performs fast ciphertext searches over homomorphically XOR-encrypted data.
  10. 📲 P2P Offline LWW CRDT Sync: Merges peer database changes over local network without central servers.
  11. 📦 Zero-Allocation RowMap Tuple Wrapper: Replaces Dart Map instantiations with zero-allocation array index views.
  12. ⚡ JIT Compiled Expression Expressions: Compiles SQL WHERE conditions into native Dart closure delegates.
  13. 📊 Auto-Optimized Columnar Parquet Store: Automatically converts tables with VECTOR or analytical data into columnar layout.
  14. 🔄 MVCC Multi-Version Concurrency Control: Provides lock-free readers and repeatable read transaction isolation.
  15. 🛡️ AES-256 Transparent Page Encryption: Encrypts storage pages on disk using 256-bit AES-CBC.

⚖️ Storage Modes: Switchable Performance #

Switch between in-memory speed and durable disk storage with a single line of code:

1. ⚡ In-Memory Storage Mode (1,200,000+ rows/sec) #

For high-frequency streaming, real-time AI vector search, and temporary session caches:

final db = Database(':memory:');
await db.init();

2. 💾 Durable Disk Storage Mode (360,000+ rows/sec) #

For persistent local application data with ACID crash safety and auto-healing WAL recovery:

final db = Database('/path/to/app_data/my_database');
await db.init();

3. 🔄 Hybrid Ingest & Snapshot #

final prep = db.prepare("INSERT INTO users VALUES (?, ?, ?);");
prep.executeBatchSync(batchRows);
await db.flushWalSync(); // Flush WAL snapshot to disk

🛠️ SQL & PL/SQL Feature Guide #

Data Definition Language (DDL) & Metadata Inspection #

-- Enhanced DDL with IF NOT EXISTS / IF EXISTS and TRUNCATE
CREATE TABLE IF NOT EXISTS users (
  id UUID PRIMARY KEY,
  name VARCHAR(250),
  active BOOL DEFAULT true,
  created_at TIMESTAMP,
  balance DECIMAL,
  payload BLOB,
  metadata JSON,
  embedding VECTOR
);

-- DDL & Catalog Inspection Commands
DESCRIBE users;
SHOW COLUMNS FROM users;
SHOW SCHEMAS;
PRAGMA table_info('users');

-- Query System Catalog Views
SELECT table_name, column_name, data_type 
FROM information_schema.columns 
WHERE table_name = 'users';

Data Manipulation, UPSERT & Series Generation #

-- Series Generator
SELECT * FROM generate_series(1, 10, 2);

-- Standard DML & Multi-Row Inserts
INSERT INTO users VALUES ('a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11', 'Alice', true, NOW(), 1500.50, NULL, '{"role": "admin"}', '[0.12, 0.85]');

-- UPSERT (ON CONFLICT DO UPDATE / DO NOTHING) & REPLACE INTO
INSERT INTO users (id, name, balance) VALUES ('a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11', 'Alice', 2000.00)
ON CONFLICT (id) DO UPDATE SET balance = 2000.00;

INSERT INTO users (id, name) VALUES ('a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11', 'Alice')
ON CONFLICT DO NOTHING;

REPLACE INTO users VALUES ('a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11', 'Alice Updated', true, NOW(), 2500.00, NULL, '{}', '[0.1, 0.2]');

Casting, Regex & Developer Functions #

-- ANSI CAST & PostgreSQL :: Typecasting
SELECT balance::TEXT, CAST(active AS INT), name::VARCHAR FROM users;

-- ILIKE (Case-Insensitive) & Regex Matching (~ operator and REGEXP_LIKE)
SELECT * FROM users WHERE name ILIKE 'alice%' OR email ~ '^[a-z]+@';
SELECT REGEXP_LIKE('ompatel@google.com', '^[a-z]+@[a-z]+\.[a-z]+$');

-- Developer Scalar & Math Functions
SELECT 
  COALESCE(NULL, 'default_val'),
  NULLIF(10, 10),
  GREATEST(10, 50, 20),
  LEAST(10, 50, 20),
  CONCAT_WS('-', '2026', '08', '06'),
  TYPEOF(100),
  GEN_RANDOM_UUID(),
  ABS(-42), ROUND(3.14159, 2), CEIL(4.2), FLOOR(4.8), POW(2, 3), SQRT(16),
  REPLACE('hello world', 'world', 'ultsql'), SPLIT_PART('a.b.c', '.', 2), INITCAP('hello world'),
  DATE_ADD('2026-08-06', 10), DATE_SUB('2026-08-06', 5), EXTRACT('year', NOW()),
  VERSION();

PL/SQL Procedural Script Execution #

DECLARE
  counter INT := 0;
  total DOUBLE := 0.0;
BEGIN
  DBMS_OUTPUT.PUT_LINE('Starting calculation...');
  
  WHILE counter < 5 LOOP
    counter := counter + 1;
    total := total + (counter * 100.5);
    
    IF counter % 2 = 0 THEN
      DBMS_OUTPUT.PUT_LINE('Iteration ' || counter || ': EVEN total=' || total);
    ELSE
      DBMS_OUTPUT.PUT_LINE('Iteration ' || counter || ': ODD total=' || total);
    END IF;
  END LOOP;

  DBMS_OUTPUT.PUT_LINE('Calculations Completed.');
END;

📄 NoSQL Dotted-Path JSON Querying #

Query nested JSON document attributes directly using standard SQL dotted-path navigation syntax:

-- Query nested JSON properties directly
SELECT name, metadata->>'role' AS user_role, metadata->>'department' AS dept
FROM users
WHERE metadata->>'role' = 'admin';

Create an HNSW index and execute sub-7ms vector similarity queries:

CREATE INDEX idx_products_emb ON products (embedding) USING HNSW;

SELECT name, vector_distance(embedding, '[0.12, 0.85, -0.44]') AS dist
FROM products
ORDER BY dist ASC
LIMIT 5;

🌐 Network TCP Wire Protocol Server #

UltSQL embeds a full Network TCP Wire Protocol server. Connect directly using network database drivers:

final pgServer = PgWireServer(db: db, port: 5432);
await pgServer.start();
print('TCP Wire Protocol Server running on port 5432...');

🛠️ Self-Healing & Auto-Indexing Telemetry #

UltSQL features autonomous telemetry monitoring that auto-repairs corrupted disk pages from WAL logs and provisions B+ Tree indexes based on query scan frequencies:

-- Enable automated self-healing & telemetry index recommendations
SET engine_option enable_autovacuum = true;
SET engine_option auto_create_indexes = true;

📲 P2P Offline Device-to-Device Sync #

Synchronize database states between offline mobile devices using Conflict-Free Replicated Data Types (CRDT):

final localNode = P2pSyncNode(nodeId: 'device_A', db: db);

// Merge peer update record
localNode.applyPeerUpdate(P2pUpdateRecord(
  entityId: 'user_101',
  timestamp: DateTime.now().millisecondsSinceEpoch,
  data: {'name': 'Alice Updated', 'balance': 2000.0},
));

📁 Direct File SQL Queries (CSV / JSON / LOG) #

Execute standard SQL queries directly over external files without ETL or table imports:

final fileAdapter = UniversalFileAdapter();

// Query external CSV file directly using SQL
final csvResults = fileAdapter.queryCsvSync(
  filePath: '/data/logs.csv',
  sqlQuery: "SELECT * FROM file WHERE status = 'ERROR'",
);

🔐 Zero-Knowledge Security Enclave #

Perform fast ciphertext searches over homomorphically XOR-encrypted data without decrypting database records:

-- Query encrypted zero-knowledge enclave data safely
SELECT * FROM confidential_table WHERE zk_match(ciphertext, 'search_key') = true;

📊 Standalone Engine Performance Metrics #

Empirical performance measurements recorded on 100,000 records on local disk:

======================================================
🔥 ULTSQL STANDALONE ENGINE PERFORMANCE (100,000 ROWS) 🔥
======================================================
1. Bulk Insert Throughput (100,000 Rows):
   - UltSQL (Memory Mode): 82 ms (1,219,512 rows/sec)
   - UltSQL (Disk Mode): 278 ms (359,712 rows/sec)

2. B+ Tree Index Build (100,000 Rows):
   - UltSQL: 17 ms (Ultra-Fast B+ Tree Indexing)

3. Multimodal Features:
   - 768-Dim HNSW Vector Search: 6 ms (100% Recall Accuracy)
   - Network TCP Wire Server: Port 5432 Supported
   - Self-Healing Page Repair: CRC Auto-Recovery Supported
   - P2P Device-to-Device Sync: LWW-CRDT Sync Supported
======================================================

Note

Hardware Environment & Benchmark Disclosure: Performance benchmark metrics were tested by Om on an ASUS ROG Strix G16 (2023). Actual performance throughput may vary (better or worse) depending on your device hardware, CPU architecture, memory bandwidth, and disk I/O capabilities.

Test System Specifications:

  • Laptop Model: ASUS ROG Strix G16 (2023)
  • CPU: Intel Core i7-13650HX
  • RAM: 16 GB DDR5 (4800 MT/s)
  • Storage: 1 TB Gen 5 NVMe SSD
  • GPU: NVIDIA GeForce RTX 4050 (6 GB)

🚀 Getting Started & Installation #

Prerequisites #

Installation #

  1. Clone repository: 
    git clone https://github.com/ompatel3158/ULTSQL.git
cd ULTSQL
  2. Install dependencies: 
    flutter pub get
  3. Run the comprehensive test suite: 
    flutter test
  4. Run the interactive UI Console IDE: 
    flutter run

📜 License #

UltSQL is licensed under the BSD 3-Clause License (the official license used by Flutter & Google). Built with ❤️ in pure Dart.

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A 100% Pure-Dart converged database engine combining Relational SQL, NoSQL JSON, HNSW Vector RAG, and PL/SQL with zero C dependencies.

Repository (GitHub)
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License

BSD-3-Clause (license)

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cupertino_icons, ffi, flutter, mongo_dart

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