Mooncake Store Python API

Mooncake Store Python API#

Installation#

PyPI Package#

Install the Mooncake Transfer Engine package from PyPI, which includes both Mooncake Transfer Engine and Mooncake Store Python bindings:

pip install mooncake-transfer-engine

📦 Package Details: https://pypi.org/project/mooncake-transfer-engine/

Required Services#

The following services need to be started separately:

  • mooncake_master - Central coordination service managing cluster state and data distribution

  • mooncake_http_metadata_server - HTTP-based metadata server for Transfer Engine coordination

  • mooncake_store_service.py - Python service for running Mooncake Store as standalone process with REST API

Quick Start#

Start Required Services#

Before using the store, start the master and metadata services:

# Start master service (default port 50051)
mooncake_master

Start the HTTP metadata server (default port 8080) for transfer engine metadata:

# Start HTTP metadata server (default port 8080) 
mooncake_http_metadata_server

Hello World Example#

from mooncake.store import MooncakeDistributedStore

# 1. Create store instance
store = MooncakeDistributedStore()

# 2. Setup with all required parameters
store.setup(
    "localhost:12345",           # Your node's address
    "http://localhost:8080/metadata",    # HTTP metadata server
    512*1024*1024,          # 512MB segment size
    128*1024*1024,          # 128MB local buffer
    "tcp",                             # Use TCP (RDMA for high performance)
    "",                            # Empty for TCP, specify device for RDMA
    "localhost:50051"        # Master service
)

# 3. Store data
store.put("hello_key", b"Hello, Mooncake Store!")

# 4. Retrieve data
data = store.get("hello_key")
print(data.decode())  # Output: Hello, Mooncake Store!

# 5. Clean up
store.close()

Basic API Usage#

Simple Get/Put Operations#

Click to expand: Complete Get/Put example with NumPy arrays 
import numpy as np
import json
from mooncake.store import MooncakeDistributedStore

# 1. Initialize
store = MooncakeDistributedStore()
store.setup("localhost:12345",
            "http://localhost:8080/metadata",
            512*1024*1024,
            128*1024*1024,
            "tcp",
            "",
            "localhost:50051")
print("Store ready.")

# 2. Store data
store.put("config", b'{"model": "llama-7b", "temperature": 0.7}')
model_weights = np.random.randn(1000, 1000).astype(np.float32)
store.put("weights", model_weights.tobytes())
store.put("cache", b"some serialized cache data")

# 3. Retrieve and verify data
config = json.loads(store.get("config").decode())
weights = np.frombuffer(store.get("weights"), dtype=np.float32).reshape(1000, 1000)

print("Config OK:", config["model"])
print("Weights OK, mean =", round(float(weights.mean()), 4))
print("Cache exists?", bool(store.is_exist("cache")))

# 4. Close
store.close()

Zero-Copy API (Advanced Performance)#

For maximum performance, especially with RDMA networks, use the zero-copy API. This allows direct memory access without intermediate copies.

Memory Registration#

⚠️ Important: register_buffer is required for zero-copy RDMA operations. Without proper buffer registration, undefined behavior and memory corruption may occur.

Zero-copy operations require registering memory buffers with the store:

register_buffer()#

Register a memory buffer for direct RDMA access.

unregister_buffer()#

Unregister a previously registered buffer.

Click to expand: Buffer registration example 
import numpy as np
from mooncake.store import MooncakeDistributedStore

# Initialize store
store = MooncakeDistributedStore()
store.setup("localhost:12345", "http://localhost:8080/metadata", 512*1024*1024, 128*1024*1024, "tcp", "", "localhost:50051")

# Create a large buffer
buffer = np.zeros(100 * 1024 * 1024, dtype=np.uint8)  # 100MB buffer

# Register the buffer for zero-copy operations
buffer_ptr = buffer.ctypes.data
result = store.register_buffer(buffer_ptr, buffer.nbytes)
if result != 0:
    print(f"Failed to register buffer: {result}")
    raise RuntimeError(f"Failed to register buffer: {result}")
print("Buffer registered successfully.")
store.unregister_buffer(buffer_ptr)

Zero-Copy Operations#

Complete Zero-Copy Workflow#

⚠️ Critical: Always register buffers before zero-copy operations. Failure to register buffers will cause undefined behavior and potential memory corruption.

Here’s a complete example showing the full zero-copy workflow with proper buffer management:

Click to expand: Complete zero-copy workflow example 
import numpy as np
from mooncake.store import MooncakeDistributedStore

# Initialize store with RDMA protocol for maximum performance
store = MooncakeDistributedStore()
store.setup("localhost:12345", "http://localhost:8080/metadata", 512*1024*1024, 16*1024*1024, "tcp", "", "localhost:50051")

# Create data to store
original_data = np.random.randn(1000, 1000).astype(np.float32)
buffer_ptr = original_data.ctypes.data
size = original_data.nbytes

# Step 1: Register the buffer
result = store.register_buffer(buffer_ptr, size)
if result != 0:
    raise RuntimeError(f"Failed to register buffer: {result}")

# Step 2: Zero-copy store
result = store.put_from("large_tensor", buffer_ptr, size)
if result == 0:
    print(f"Successfully stored {size} bytes with zero-copy")
else:
    raise RuntimeError(f"Store failed with code: {result}")

# Step 3: Pre-allocate buffer for retrieval
retrieved_data = np.empty((1000, 1000), dtype=np.float32)
recv_buffer_ptr = retrieved_data.ctypes.data
recv_size = retrieved_data.nbytes

# Step 4: Register receive buffer
result = store.register_buffer(recv_buffer_ptr, recv_size)
if result != 0:
    raise RuntimeError(f"Failed to register receive buffer: {result}")

# Step 5: Zero-copy retrieval
bytes_read = store.get_into("large_tensor", recv_buffer_ptr, recv_size)
if bytes_read > 0:
    print(f"Successfully retrieved {bytes_read} bytes with zero-copy")
    # Verify the data
    print(f"Data matches: {np.array_equal(original_data, retrieved_data)}")
else:
    raise RuntimeError(f"Retrieval failed with code: {bytes_read}")

# Step 6: Clean up - unregister both buffers
store.unregister_buffer(buffer_ptr)
store.unregister_buffer(recv_buffer_ptr)
store.close()

put_from()#

Store data directly from a registered buffer (zero-copy).

def put_from(self, key: str, buffer_ptr: int, size: int, config=None) -> int

Parameters:

  • key: Object identifier

  • buffer_ptr: Memory address (from ctypes.data or similar)

  • size: Number of bytes to store

  • config: Optional replication configuration

get_into()#

Retrieve data directly into a registered buffer (zero-copy).

def get_into(self, key: str, buffer_ptr: int, size: int) -> int

Parameters:

  • key: Object identifier to retrieve

  • buffer_ptr: Memory address of pre-allocated buffer

  • size: Size of the buffer (must be >= object size)

Returns: Number of bytes read, or negative on error

ReplicateConfig Configuration#

The ReplicateConfig class allows you to control data replication behavior when storing objects in Mooncake Store. This configuration is essential for ensuring data reliability, performance optimization, and storage placement control.

Class Definition#

from mooncake.store import ReplicateConfig

# Create a configuration instance
config = ReplicateConfig()

Properties#

replica_num#

Type: int Default: 1 Description: Specifies the total number of replicas to create for the stored object.

config = ReplicateConfig()
config.replica_num = 3  # Store 3 copies of the data

with_soft_pin#

Type: bool Default: False Description: Enables soft pinning for the stored object. Soft pinned objects are prioritized to remain in memory during eviction - they are only evicted when memory is insufficient and no other objects are eligible for eviction. This is useful for frequently accessed or important objects like system prompts.

config = ReplicateConfig()
config.with_soft_pin = True  # Keep this object in memory longer

preferred_segment#

Type: str Default: "" (empty string) Description: Specifies a preferred segment (node) for data allocation. This is typically the hostname:port of a target server.

config = ReplicateConfig()

# Preferred replica location ("host:port")
config.preferred_segment = "node1_ip:12345"    # pin to node1

# Alternatively, pin to the local host
config.preferred_segment = self.get_hostname()

# Optional: speed up local transfers
#   export MC_STORE_MEMCPY=1

Non-Zero-Copy API (Simple Usage)#

For simpler use cases, use the standard API without memory registration:

Basic Operations#

Click to expand: Non-zero-copy API examples 
from mooncake.store import MooncakeDistributedStore

# Initialize (same as zero-copy)
store = MooncakeDistributedStore()
store.setup("localhost:12345", "http://localhost:8080/metadata", 512*1024*1024, 128*1024*1024, "tcp", "", "localhost:50051")

# Simple put/get (automatic memory management)
data = b"Hello, World!" * 1000  # ~13KB
store.put("message", data)

retrieved = store.get("message")
print(retrieved == data)  # True

# Batch operations
keys = ["key1", "key2", "key3"]
values = [b"value1", b"value2", b"value3"]

store.put_batch(keys, values)
retrieved = store.get_batch(keys)
print("Retrieved all keys successfully:", retrieved == values)

Performance Notes#

Choose the appropriate API based on your use case:

Zero-copy API is beneficial when:

  • Working with large data transfers

  • RDMA network infrastructure is available and configured

  • Direct memory access patterns fit your application design

Non-zero-copy API is suitable for:

  • Development and prototyping phases

  • Applications without specific performance requirements

Batch operations can improve throughput for:

  • Multiple related operations performed together

  • Scenarios where network round-trip reduction is beneficial

get_buffer Buffer Protocol#

The get_buffer method returns a BufferHandle object that implements the Python buffer protocol:

Click to expand: Buffer protocol usage example 
# Get buffer with buffer protocol support
buffer = store.get_buffer("large_object")
if buffer:
    # Access as numpy array without copy
    import numpy as np
    arr = np.array(buffer, copy=False)

    # Direct memory access
    ptr = buffer.ptr()  # Memory address
    size = buffer.size()  # Buffer size in bytes

    # Use with other libraries that accept buffer protocol
    print(f"Buffer size: {len(buffer)} bytes")

    # The buffer is automatically freed
---

Full API Reference#

Class: MooncakeDistributedStore#

The main class for interacting with Mooncake Store.

Constructor#

store = MooncakeDistributedStore()

Creates a new store instance. No parameters required.

setup()#

Initialize distributed resources and establish network connections.

def setup(
    self,
    local_hostname: str,
    metadata_server: str,
    global_segment_size: int = 16777216,
    local_buffer_size: int = 1073741824,
    protocol: str = "tcp",
    rdma_devices: str = "",
    master_server_addr: str,
) -> int

Parameters:

  • local_hostname (str): Required. Local hostname and port (e.g., “localhost:12345”)

  • metadata_server (str): Required. Metadata server address (e.g., “http://localhost:8080/metadata”)

  • global_segment_size (int): Memory segment size in bytes for mounting (default: 16MB = 16777216)

  • local_buffer_size (int): Local buffer size in bytes (default: 1GB = 1073741824)

  • protocol (str): Network protocol - “tcp” or “rdma” (default: “tcp”)

  • rdma_devices (str): Hardware device identifier for RDMA (e.g., “mlx5_0”, “ib0”). Leave empty for TCP.

  • master_server_addr (str): Required. Master server address (e.g., “localhost:50051”)

Returns:

  • int: Status code (0 = success, non-zero = error code)

Example:

Click to expand: Setup examples for TCP and RDMA 
# TCP initialization
store.setup("localhost:12345", "http://localhost:8080/metadata", 1024*1024*1024, 128*1024*1024, "tcp", "", "localhost:50051")

# RDMA initialization
store.setup("localhost:12345", "http://localhost:8080/metadata", 512*1024*1024, 128*1024*1024, "rdma", "mlx5_0", "localhost:50051")

put()#

Store binary data in the distributed storage.

def put(self, key: str, value: bytes, config: ReplicateConfig = None) -> int

Parameters:

  • key (str): Unique object identifier

  • value (bytes): Binary data to store

  • config (ReplicateConfig, optional): Replication configuration

Returns:

  • int: Status code (0 = success, non-zero = error code)

Example:

Click to expand: Put operation examples 
# Simple put
store.put("my_key", b"Hello, World!")

# Put with replication config
config = ReplicateConfig()
config.replica_num = 2
store.put("important_data", b"Critical information", config)

get()#

Retrieve binary data from distributed storage.

def get(self, key: str) -> bytes

Parameters:

  • key (str): Object identifier to retrieve

Returns:

  • bytes: Retrieved binary data

Raises:

  • Returns empty bytes if key doesn’t exist

Example:

Click to expand: Get operation example 
data = store.get("my_key")
if data:
    print(f"Retrieved: {data.decode()}")
else:
    print("Key not found")

put_batch()#

Store multiple objects in a single batch operation.

def put_batch(self, keys: List[str], values: List[bytes], config: ReplicateConfig = None) -> int

Parameters:

  • keys (List[str]): List of object identifiers

  • values (List[bytes]): List of binary data to store

  • config (ReplicateConfig, optional): Replication configuration for all objects

Returns:

  • int: Status code (0 = success, non-zero = error code)

Example:

Click to expand: Batch put example 
keys = ["key1", "key2", "key3"]
values = [b"value1", b"value2", b"value3"]
result = store.put_batch(keys, values)

get_batch()#

Retrieve multiple objects in a single batch operation.

def get_batch(self, keys: List[str]) -> List[bytes]

Parameters:

  • keys (List[str]): List of object identifiers to retrieve

Returns:

  • List[bytes]: List of retrieved binary data

Example:

Click to expand: Batch get example 
keys = ["key1", "key2", "key3"]
values = store.get_batch(keys)
for key, value in zip(keys, values):
    print(f"{key}: {len(value)} bytes")

remove()#

Delete an object from the storage system.

def remove(self, key: str) -> int

Parameters:

  • key (str): Object identifier to remove

Returns:

  • int: Status code (0 = success, non-zero = error code)

Example:

result = store.remove("my_key")
if result == 0:
    print("Successfully removed")

remove_by_regex()#

Remove objects from the storage system whose keys match a regular expression.

def remove_by_regex(self, regex: str) -> int

Parameters:

  • regex (str): The regular expression to match against object keys.

Returns:

  • int: The number of objects removed, or a negative value on error.

Example:

# Remove all keys starting with "user_session_"
count = store.remove_by_regex("^user_session_.*")
if count >= 0:
    print(f"Removed {count} objects")

remove_all()#

Remove all objects from the storage system.

def remove_all(self) -> int

Returns:

  • int: Number of objects removed, or -1 on error

Example:

count = store.remove_all()
print(f"Removed {count} objects")

is_exist()#

Check if an object exists in the storage system.

def is_exist(self, key: str) -> int

Parameters:

  • key (str): Object identifier to check

Returns:

  • int:

    • 1: Object exists

    • 0: Object doesn’t exist

    • -1: Error occurred

Example:

exists = store.is_exist("my_key")
if exists == 1:
    print("Object exists")
elif exists == 0:
    print("Object not found")
else:
    print("Error checking existence")

batch_is_exist()#

Check existence of multiple objects in a single batch operation.

def batch_is_exist(self, keys: List[str]) -> List[int]

Parameters:

  • keys (List[str]): List of object identifiers to check

Returns:

  • List[int]: List of existence results (1=exists, 0=not exists, -1=error)

Example:

keys = ["key1", "key2", "key3"]
results = store.batch_is_exist(keys)
for key, exists in zip(keys, results):
    status = "exists" if exists == 1 else "not found" if exists == 0 else "error"
    print(f"{key}: {status}")

get_size()#

Get the size of a stored object in bytes.

def get_size(self, key: str) -> int

Parameters:

  • key (str): Object identifier

Returns:

  • int: Size in bytes, or negative value on error

Example:

size = store.get_size("my_key")
if size >= 0:
    print(f"Object size: {size} bytes")
else:
    print("Error getting size or object not found")

get_buffer()#

Get object data as a buffer that implements Python’s buffer protocol.

def get_buffer(self, key: str) -> BufferHandle

Parameters:

  • key (str): Object identifier

Returns:

  • BufferHandle: Buffer object or None if not found

Example:

buffer = store.get_buffer("large_object")
if buffer:
    print(f"Buffer size: {buffer.size()} bytes")
    # Use with numpy without copying
    import numpy as np
    arr = np.array(buffer, copy=False)

put_parts()#

Store data from multiple buffer parts as a single object.

def put_parts(self, key: str, *parts, config: ReplicateConfig = None) -> int

Parameters:

  • key (str): Object identifier

  • *parts: Variable number of bytes-like objects to concatenate

  • config (ReplicateConfig, optional): Replication configuration

Returns:

  • int: Status code (0 = success, non-zero = error code)

Example:

part1 = b"Hello, "
part2 = b"World!"
part3 = b" From Mooncake"
result = store.put_parts("greeting", part1, part2, part3)

get_hostname()#

Get the hostname of the current store instance.

def get_hostname(self) -> str

Returns:

  • str: Hostname and port of this store instance

Example:

hostname = store.get_hostname()
print(f"Store running on: {hostname}")

close()#

Clean up all resources and terminate connections.

def close(self) -> int

Returns:

  • int: Status code (0 = success, non-zero = error code)

Example:

store.close()

Batch Zero-Copy Operations#

batch_put_from()#

Store multiple objects from pre-registered buffers (zero-copy).

def batch_put_from(self, keys: List[str], buffer_ptrs: List[int], sizes: List[int], config: ReplicateConfig = None) -> List[int]

Parameters:

  • keys (List[str]): List of object identifiers

  • buffer_ptrs (List[int]): List of memory addresses

  • sizes (List[int]): List of buffer sizes

  • config (ReplicateConfig, optional): Replication configuration

Returns:

  • List[int]: List of status codes for each operation (0 = success, negative = error)

batch_get_into()#

Retrieve multiple objects into pre-registered buffers (zero-copy).

def batch_get_into(self, keys: List[str], buffer_ptrs: List[int], sizes: List[int]) -> List[int]

Parameters:

  • keys (List[str]): List of object identifiers

  • buffer_ptrs (List[int]): List of memory addresses

  • sizes (List[int]): List of buffer sizes

Returns:

  • List[int]: List of bytes read for each operation (positive = success, negative = error)

⚠️ Buffer Registration Required: All buffers must be registered before batch zero-copy operations.

Example:

Click to expand: Batch zero-copy retrieval example 
# Prepare buffers
keys = ["tensor1", "tensor2", "tensor3"]
buffer_size = 1024 * 1024  # 1MB each
buffers = []
buffer_ptrs = []

for i in range(len(keys)):
    buffer = np.empty(buffer_size, dtype=np.uint8)
    buffers.append(buffer)
    buffer_ptrs.append(buffer.ctypes.data)
    store.register_buffer(buffer.ctypes.data, buffer_size)

# Batch retrieve
sizes = [buffer_size] * len(keys)
results = store.batch_get_into(keys, buffer_ptrs, sizes)

# Check results
for key, result in zip(keys, results):
    if result > 0:
        print(f"Retrieved {key}: {result} bytes")
    else:
        print(f"Failed to retrieve {key}: error {result}")

# Cleanup
for ptr in buffer_ptrs:
    store.unregister_buffer(ptr)

Error Handling#

Most methods return integer status codes:

  • 0: Success

  • Negative values: Error codes (for methods that can return data size)

For methods that return data (get, get_batch, get_buffer, get_tensor):

  • Return the requested data on success

  • Return empty/None on failure or key not found

đź“‹ Complete Error Codes Reference: See Error Code Explanation for detailed descriptions of all error codes and their meanings.

Performance Tips#

  1. Use batch operations when working with multiple objects to reduce network overhead

  2. Use zero-copy APIs (put_from, get_into) for large data transfers

  3. Register buffers once and reuse them for multiple operations

  4. Configure replication appropriately - more replicas provide better availability but use more storage

  5. Use soft pinning for frequently accessed objects to keep them in memory

  6. Choose RDMA protocol when available for maximum performance

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