nexadb 1.0.0

Official Python client for NexaDB - The high-performance, easy-to-use database

NexaDB Python Client

Official Python client for NexaDB - The high-performance, easy-to-use database.

Features

• 3-10x faster than HTTP/REST - Binary protocol with MessagePack encoding
• Persistent TCP connections - No HTTP overhead
• Context manager support - Pythonic with statement
• Type hints - Full type annotations for better IDE support
• Automatic reconnection - Built-in connection management
• Connection pooling - Handle 1000+ concurrent operations

Installation

pip install nexadb

Quick Start

from nexadb import NexaClient

# Using context manager (recommended)
with NexaClient(host='localhost', port=6970) as db:
    # Create document
    user = db.create('users', {
        'name': 'John Doe',
        'email': '[e-mail]'
    })

    # Get document
    found = db.get('users', user['document_id'])

    # Update document
    db.update('users', user['document_id'], {'age': 30})

    # Query documents
    results = db.query('users', {'age': {'$gte': 25}})

    # Delete document
    db.delete('users', user['document_id'])

API Reference

Constructor

db = NexaClient(host='localhost', port=6970, timeout=30)

Parameters:

• host (str) - Server host (default: 'localhost')
• port (int) - Server port (default: 6970)
• timeout (int) - Connection timeout in seconds (default: 30)

Methods

connect()

Connect to NexaDB server.

db = NexaClient()
db.connect()

create(collection, data)

Create document in collection.

result = db.create('users', {
    'name': 'Alice',
    'email': '[e-mail]'
})
# Returns: {'collection': 'users', 'document_id': '...', 'message': '...'}

get(collection, key)

Get document by ID.

user = db.get('users', user_id)
# Returns: {'_id': '...', 'name': 'Alice', ...} or None

update(collection, key, updates)

Update document.

db.update('users', user_id, {
    'age': 30,
    'department': 'Engineering'
})

delete(collection, key)

Delete document.

db.delete('users', user_id)

query(collection, filters, limit=100)

Query documents with filters.

results = db.query('users', {
    'role': 'developer',
    'age': {'$gte': 25}
}, limit=100)
# Returns: [{'_id': '...', 'name': '...', ...}, ...]

batch_write(collection, documents)

Bulk insert documents.

db.batch_write('users', [
    {'name': 'Alice', 'email': '[e-mail]'},
    {'name': 'Bob', 'email': '[e-mail]'},
    {'name': 'Carol', 'email': '[e-mail]'}
])

vector_search(collection, vector, limit=10, dimensions=768)

Vector similarity search (for AI/ML applications).

results = db.vector_search('embeddings', [0.1, 0.2, ...], limit=10)
# Returns: [{'document_id': '...', 'similarity': 0.95, 'document': {...}}, ...]

ping()

Ping server (keep-alive / health check).

pong = db.ping()

disconnect()

Disconnect from server.

db.disconnect()

Context Manager

The recommended way to use NexaClient is with a context manager:

with NexaClient(host='localhost', port=6970) as db:
    # Connection is automatically established
    user = db.create('users', {'name': 'John'})
    # Connection is automatically closed when exiting the block

This ensures proper connection management and cleanup.

Performance

NexaClient uses a custom binary protocol for maximum performance:

Metric	HTTP/REST	NexaDB (Binary)	Improvement
Latency	5-10ms	1-2ms	3-5x faster
Throughput	1K ops/sec	5-10K ops/sec	5-10x faster
Bandwidth	300KB	62KB	80% less

Examples

Basic CRUD

from nexadb import NexaClient

with NexaClient() as db:
    # Create
    user = db.create('users', {
        'name': 'Alice Johnson',
        'email': '[e-mail]',
        'age': 28,
        'role': 'developer'
    })

    user_id = user['document_id']

    # Read
    found = db.get('users', user_id)
    print(f"Found user: {found['name']}")

    # Update
    db.update('users', user_id, {
        'age': 29,
        'department': 'Engineering'
    })

    # Delete
    db.delete('users', user_id)

Batch Operations

with NexaClient() as db:
    # Bulk insert
    users = [
        {'name': 'Alice', 'email': '[e-mail]'},
        {'name': 'Bob', 'email': '[e-mail]'},
        {'name': 'Carol', 'email': '[e-mail]'}
    ]

    result = db.batch_write('users', users)
    print(f"Inserted {result['count']} users")

Querying

with NexaClient() as db:
    # Find all developers aged 25+
    developers = db.query('users', {
        'role': 'developer',
        'age': {'$gte': 25}
    }, limit=10)

    for user in developers:
        print(f"{user['name']} - {user['age']} years old")

Vector Search

import numpy as np
from nexadb import NexaClient

with NexaClient() as db:
    # Generate or load embedding vector
    query_vector = np.random.rand(768).tolist()

    # Search for similar documents
    results = db.vector_search('embeddings', query_vector, limit=5)

    for result in results:
        print(f"Similarity: {result['similarity']:.2f}")
        print(f"Document: {result['document']}")

Requirements

• Python >= 3.7
• msgpack >= 1.0.0
• NexaDB server running on localhost:6970 (or custom host/port)

NexaDB vs MongoDB

Feature	MongoDB	NexaDB
Setup	15 min	2 min (brew install nexadb)
Write speed	~50K/s	~89K/s
Memory	2-4 GB	111 MB
Protocol	Custom binary	Custom binary
Python client	pymongo	nexadb (this package)

License

MIT

Links

• NexaDB GitHub
• Python Client GitHub
• Documentation
• NPM Client

Contributing

Contributions are welcome! Please open an issue or PR on GitHub.

Support

For support, please:

• Open an issue on GitHub
• Email: [e-mail]