shibudb-client 1.0.11

A comprehensive Python client for ShibuDb database

shibudb-client-python

ShibuDb client library for python

ShibuDb Python Client

A comprehensive Python client for ShibuDb database that supports authentication, key-value operations, vector similarity search (insert, search, get, delete), space management, and connection pooling.

Features

• 🔐 Authentication & User Management: Secure login with role-based access control
• 🔑 Key-Value Operations: Traditional key-value storage with PUT, GET, DELETE operations
• 🧮 Vector Similarity Search: Insert, get, delete, and search vectors with multiple index types and metrics
• 🏷️ Metadata Filtering: Declare indexed metadata fields on a Flat vector space and pre-filter searches with a Filter builder or SQL-like WHERE string
• 🗂️ Space Management: Create, delete, and manage different storage spaces
• 🛡️ Error Handling: Comprehensive error handling with custom exceptions
• 📊 Connection Management: Automatic connection handling with context managers
• 🔗 Connection Pooling: High-performance connection pooling for concurrent operations

Installation

Prerequisites

1. ShibuDb Server: Ensure the ShibuDb server is running

   # Start the server (requires sudo)
   sudo shibudb start 4444
   

2. Python Requirements: The client uses only standard library modules

   • Python 3.7+
   • No external dependencies required

Setup

1. Clone or download the client files:

   # Copy the client files to your project
   cp shibudb_client.py your_project/
   

2. Import the client:

   from shibudb_client import ShibuDbClient, User, Filter, MetadataFieldSpec, connect
   

Quick Start

Basic Connection and Authentication

from shibudb_client import ShibuDbClient

# Create client and authenticate
client = ShibuDbClient("localhost", 4444)
client.authenticate("admin", "admin")

# Use context manager for automatic cleanup
with ShibuDbClient("localhost", 4444) as client:
    client.authenticate("admin", "admin")
    # Your operations here

Connection Pooling

from shibudb_client import create_connection_pool

# Create a connection pool
pool = create_connection_pool(
    host="localhost",
    port=4444,
    username="admin",
    password="admin",
    min_size=2,
    max_size=10
)

# Use pooled connections
with pool.get_connection() as client:
    response = client.list_spaces()
    print(f"Available spaces: {response}")

# Pool automatically manages connections
pool.close()

Key-Value Operations

# Create and use a space
client.create_space("mytable", "key-value")
client.use_space("mytable")

# Basic operations
client.put("name", "John Doe")
response = client.get("name")
print(response["value"])  # "John Doe"

client.delete("name")

Vector Operations

# Create a vector space
client.create_space("vectors", "vector", dimension=128, index_type="Flat", metric="L2")
client.use_space("vectors")

# Insert vectors
client.insert_vector(1, [0.1, 0.2, 0.3, ...])
client.insert_vector(2, [0.4, 0.5, 0.6, ...])

# Search for similar vectors
results = client.search_topk([0.1, 0.2, 0.3, ...], k=5)
print(results["message"])  # Search results

# Range search
results = client.range_search([0.1, 0.2, 0.3, ...], radius=0.5)

# Get a vector by ID
client.get_vector(1)

# Delete a vector by ID
client.delete_vector(1)

Metadata Filtering (Flat spaces)

Declare indexed metadata fields on a Flat vector space, attach metadata when inserting, and pre-filter searches so similarity is only computed over matching vectors. This is only supported for the Flat index type.

from shibudb_client import ShibuDbClient, Filter

with ShibuDbClient("localhost", 4444) as client:
    client.authenticate("admin", "admin")

    # 1. Declare indexed metadata fields at space creation (Flat only).
    #    Field types: "string" | "int" | "float".
    client.create_space(
        "products",
        engine_type="vector",
        dimension=4,
        index_type="Flat",
        metric="L2",
        indexed_metadata_fields={
            "user_id": "string",
            "category": "string",
            "price": "float",
            "year": "int",
        },
    )
    client.use_space("products")

    # 2. Attach metadata on insert (upsert: re-inserting an ID updates both
    #    the vector and its metadata).
    client.insert_vector(1, [0.1, 0.1, 0.1, 0.1],
                         metadata={"user_id": "alice", "category": "books", "price": 12.5, "year": 2020})
    client.insert_vector(2, [0.2, 0.2, 0.2, 0.2],
                         metadata={"user_id": "bob", "category": "books", "price": 40, "year": 2022})
    client.insert_vector(3, [0.15, 0.15, 0.15, 0.15],
                         metadata={"user_id": "alice", "category": "toys", "price": 5, "year": 2023})

    # 3a. Filtered search using the Filter builder.
    f = (Filter.eq("user_id", "alice") | Filter.eq("user_id", "bob")) & Filter.lt("price", 40)
    results = client.search_topk([0.1, 0.1, 0.1, 0.1], k=10, filter=f)

    # 3b. ...or with a SQL-like WHERE string (mirrors the CLI --where syntax).
    results = client.search_topk([0.1, 0.1, 0.1, 0.1], k=10,
                                 where="(user_id=alice OR user_id=bob) AND price<40")

    # Range search supports filtering too.
    results = client.range_search([0.1, 0.1, 0.1, 0.1], radius=1.0, where="user_id=alice")

Building filters

The Filter builder mirrors the server's filter operators:

Filter.eq("user_id", "alice")          # field == value
Filter.ne("user_id", "bob")            # field != value  (NOT eq)
Filter.gt("price", 10)                 # > (numeric fields only)
Filter.gte("price", 10)                # >=
Filter.lt("price", 40)                 # <
Filter.lte("price", 40)                # <=
Filter.in_("category", ["books", "toys"])   # field IN (...)
Filter.between("year", 2021, 2023)     # inclusive range (numeric fields only)

# Compose with & (AND), | (OR), ~ (NOT), or the explicit helpers
Filter.and_(a, b, c)
Filter.or_(a, b)
Filter.not_(a)
combined = (Filter.eq("user_id", "alice") & Filter.gte("year", 2021)) | ~Filter.eq("category", "books")

WHERE string grammar

Filter.parse(...) (and the where= argument) accept a SQL-like expression. Keywords (AND, OR, NOT, IN, BETWEEN) are case-insensitive:

Category	Syntax	Notes
Equality	field = value, field != value	!= compiles to NOT(field = value)
Comparison	field > value, >=, <, <=	numeric (int/float) fields only
Membership	field IN (v1, v2, ...)	matches any listed value
Range	field BETWEEN low AND high	inclusive; numeric fields only
Composition	AND, OR, NOT, ( ... )	parentheses for grouping

Bare words (alice) and quoted strings ('alice', "alice") are treated as strings; numeric literals (40, 12.5) are numbers. Quote a numeric-looking string field value to force a string, e.g. user_id='123'.

Notes & limits

• Metadata filtering requires index_type="Flat"; declaring indexed_metadata_fields on a non-Flat space is rejected by the server.
• Filtering on an undeclared field returns an error from the server.
• Numeric values are stored as float64; integers larger than 2^53 may lose precision — use a string field for such identifiers.

API Reference

ShibuDbClient

Constructor

ShibuDbClient(host="localhost", port=4444, timeout=30)

Authentication

client.authenticate(username: str, password: str) -> Dict[str, Any]

Space Management

client.create_space(name: str, engine_type: str, dimension: Optional[int] = None, 
                   index_type: str = "Flat", metric: str = "L2",
                   indexed_metadata_fields: Union[None, Dict[str, str], List] = None,
                   enable_wal: Optional[bool] = None) -> Dict[str, Any]
client.delete_space(name: str) -> Dict[str, Any]
client.list_spaces() -> Dict[str, Any]
client.use_space(name: str) -> Dict[str, Any]

Key-Value Operations

client.put(key: str, value: str, space: Optional[str] = None) -> Dict[str, Any]
client.get(key: str, space: Optional[str] = None) -> Dict[str, Any]
client.delete(key: str, space: Optional[str] = None) -> Dict[str, Any]

Vector Operations

client.insert_vector(vector_id: int, vector: List[float], space: Optional[str] = None,
                     metadata: Optional[Dict[str, Any]] = None) -> Dict[str, Any]
client.get_vector(vector_id: int, space: Optional[str] = None) -> Dict[str, Any]
client.delete_vector(vector_id: int, space: Optional[str] = None) -> Dict[str, Any]
client.search_topk(query_vector: List[float], k: int = 1, space: Optional[str] = None,
                   filter: Union[None, Filter, Dict] = None, where: Optional[str] = None) -> Dict[str, Any]
client.range_search(query_vector: List[float], radius: float, space: Optional[str] = None,
                    filter: Union[None, Filter, Dict] = None, where: Optional[str] = None) -> Dict[str, Any]

metadata, filter, and where are only supported on Flat vector spaces created with indexed_metadata_fields. See Metadata Filtering.

User Management (Admin Only)

client.create_user(user: User) -> Dict[str, Any]
client.update_user_password(username: str, new_password: str) -> Dict[str, Any]
client.update_user_role(username: str, new_role: str) -> Dict[str, Any]
client.update_user_permissions(username: str, permissions: Dict[str, str]) -> Dict[str, Any]
client.delete_user(username: str) -> Dict[str, Any]
client.get_user(username: str) -> Dict[str, Any]

Data Models

User

@dataclass
class User:
    username: str
    password: str
    role: str = "user"
    permissions: Dict[str, str] = None

SpaceInfo

@dataclass
class SpaceInfo:
    name: str
    engine_type: str
    dimension: Optional[int] = None
    index_type: Optional[str] = None
    metric: Optional[str] = None
    indexed_metadata_fields: Optional[List[MetadataFieldSpec]] = None

MetadataFieldSpec

@dataclass
class MetadataFieldSpec:
    name: str
    type: str = "string"   # "string" | "int" | "float"

Filter

Builder for metadata filter expressions. See Building filters and WHERE string grammar.

Filter.eq(field, value); Filter.ne(field, value)
Filter.gt(field, value); Filter.gte(field, value)
Filter.lt(field, value); Filter.lte(field, value)
Filter.in_(field, values); Filter.between(field, low, high)
Filter.and_(*filters); Filter.or_(*filters); Filter.not_(f)
Filter.parse(where_string)        # SQL-like WHERE -> Filter
f.to_dict()                       # raw filter AST
# operators: a & b, a | b, ~a

Exceptions

• ShibuDbError: Base exception for all client errors
• AuthenticationError: Raised when authentication fails
• ConnectionError: Raised when connection fails
• QueryError: Raised when query execution fails
• PoolExhaustedError: Raised when connection pool is exhausted
• FilterError: Raised when a metadata filter expression is invalid

Examples

Complete Example

from shibudb_client import ShibuDbClient, User

def main():
    # Connect and authenticate
    with ShibuDbClient("localhost", 4444) as client:
        client.authenticate("admin", "admin")
        
        # Create spaces
        client.create_space("users", "key-value")
        client.create_space("embeddings", "vector", dimension=128)
        
        # Store user data
        client.use_space("users")
        client.put("user1", "Alice Johnson")
        client.put("user2", "Bob Smith")
        
        # Store embeddings
        client.use_space("embeddings")
        client.insert_vector(1, [0.1, 0.2, 0.3, ...])
        client.insert_vector(2, [0.4, 0.5, 0.6, ...])
        
        # Search for similar embeddings
        results = client.search_topk([0.1, 0.2, 0.3, ...], k=5)
        print(f"Search results: {results}")

if __name__ == "__main__":
    main()

Error Handling

from shibudb_client import ShibuDbClient, AuthenticationError, ConnectionError, QueryError

try:
    client = ShibuDbClient("localhost", 4444)
    client.authenticate("admin", "admin")
    
    # Your operations here
    
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except ConnectionError as e:
    print(f"Connection failed: {e}")
except QueryError as e:
    print(f"Query failed: {e}")
finally:
    client.close()

Advanced Usage

from shibudb_client import ShibuDbClient, User

# Create admin user
admin_user = User(
    username="admin",
    password="adminpass",
    role="admin"
)

# Create regular user with permissions
user = User(
    username="user1",
    password="userpass",
    role="user",
    permissions={"mytable": "read", "vectortable": "write"}
)

with ShibuDbClient("localhost", 4444) as client:
    client.authenticate("admin", "admin")
    
    # Create users
    client.create_user(user)
    
    # Create spaces for different purposes
    client.create_space("users", "key-value")
    client.create_space("products", "key-value")
    client.create_space("embeddings", "vector", dimension=256)
    client.create_space("recommendations", "vector", dimension=512)
    
    # Store data in different spaces
    client.use_space("users")
    client.put("user1", "Alice Johnson")
    
    client.use_space("embeddings")
    client.insert_vector(1, [0.1, 0.2, 0.3, ...])
    
    # Search for recommendations
    query_vector = [0.1, 0.2, 0.3, ...]
    results = client.search_topk(query_vector, k=10)

Running Examples

Simple Test

python simple_test.py

Comprehensive Examples

python example.py

Connection Pooling Examples

python pooling_example.py

Comprehensive Connection Pooling Tests

python comprehensive_pool_test.py

Connection Pooling

The ShibuDb client supports connection pooling for high-performance concurrent operations. Connection pooling provides:

• Connection Reuse: Efficiently reuse database connections
• Concurrent Operations: Support for multiple simultaneous operations
• Automatic Health Checks: Background health monitoring of connections
• Configurable Pool Size: Adjustable minimum and maximum pool sizes
• Timeout Handling: Configurable connection acquisition timeouts

Pool Configuration

from shibudb_client import create_connection_pool, ConnectionConfig

# Create pool with custom configuration
pool = create_connection_pool(
    host="localhost",
    port=4444,
    username="admin",
    password="admin",
    min_size=2,              # Minimum connections in pool
    max_size=10,             # Maximum connections in pool
    acquire_timeout=30,      # Timeout for acquiring connection (seconds)
    health_check_interval=60 # Health check interval (seconds)
)

Using Connection Pools

# Basic usage
with pool.get_connection() as client:
    response = client.list_spaces()
    print(f"Spaces: {response}")

# Concurrent operations
import threading
from concurrent.futures import ThreadPoolExecutor

def worker(worker_id):
    with pool.get_connection() as client:
        client.create_space(f"space_{worker_id}", "key-value")
        client.use_space(f"space_{worker_id}")
        client.put(f"key_{worker_id}", f"value_{worker_id}")
        return client.get(f"key_{worker_id}")

# Run concurrent workers
with ThreadPoolExecutor(max_workers=5) as executor:
    futures = [executor.submit(worker, i) for i in range(5)]
    for future in as_completed(futures):
        result = future.result()
        print(f"Result: {result}")

Pool Statistics

# Get pool statistics
stats = pool.get_stats()
print(f"Pool size: {stats['pool_size']}")
print(f"Active connections: {stats['active_connections']}")
print(f"Min size: {stats['min_size']}")
print(f"Max size: {stats['max_size']}")

Error Handling with Pools

from shibudb_client import PoolExhaustedError

try:
    with pool.get_connection() as client:
        # Your operations here
        pass
except PoolExhaustedError as e:
    print(f"Pool exhausted: {e}")
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except ConnectionError as e:
    print(f"Connection failed: {e}")

Engine Types

Key-Value Engine

• Traditional key-value storage
• Supports PUT, GET, DELETE operations
• No dimension required

Vector Engine

• Vector operations: insert (insert_vector), get by ID (get_vector), delete by ID (delete_vector), and similarity search (search_topk, range_search)
• Metadata filtering (Flat index only): declare indexed_metadata_fields at creation, attach metadata on insert, and pre-filter searches with a Filter/where expression
• Multiple index types:
  • Flat: Exact search (default); the only index type that supports metadata filtering
  • HNSW: Hierarchical Navigable Small World
  • IVF: Inverted File Index
  • IVF with PQ: Product Quantization
• Distance metrics:
  • L2: Euclidean distance (default)
  • IP: Inner product
  • COS: Cosine similarity

Security

• Authentication Required: All operations require valid credentials
• Role-Based Access: Admin and user roles with different permissions
• Space-Level Permissions: Read/write permissions per space
• Connection Security: TCP-based communication with timeout handling

Troubleshooting

Common Issues

1. Connection Failed

   • Ensure ShibuDb server is running: sudo shibudb start 4444
   • Check server port and host settings
   • Verify firewall settings

2. Authentication Failed

   • Verify username and password
   • Ensure user exists in the system
   • Check user permissions

3. Space Not Found

   • Use list_spaces() to see available spaces
   • Create space before using: create_space()
   • Use use_space() to switch to a space

4. Vector Dimension Mismatch

   • Ensure vector dimension matches space dimension
   • Check space creation parameters
   • Verify vector format (comma-separated floats)

Debug Mode

Enable debug logging:

import logging
logging.basicConfig(level=logging.DEBUG)

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

License

This client is provided as-is for use with ShibuDb database.