mooncake-api

name

mooncake-api

description

Help users work with the Mooncake Python APIs for distributed storage and high-performance data transfer. Use when working with Mooncake Store (distributed KV cache), Transfer Engine (RDMA/TCP transfers), service setup (master, metadata server), PyTorch tensors in the Store, zero-copy/buffer management, batch operations and replication, or Mooncake EP / Backend (Expert Parallelism). Trigger on questions about MooncakeDistributedStore, TransferEngine, put/get, put_tensor, register_buffer, ReplicateConfig, or the mooncake.store / mooncake.engine / mooncake.pg Python modules.

Mooncake Python API Skill

Use this skill to help users work with Mooncake Python APIs for distributed storage and high-performance data transfer.

When to Use This Skill

Use this skill when users ask about:

• Using Mooncake Store for distributed KV cache storage
• Using Transfer Engine for RDMA/TCP data transfers
• Setting up Mooncake services (master, metadata server)
• Working with PyTorch tensors in Mooncake Store
• Zero-copy operations and buffer management
• Batch operations and replication configuration
• Mooncake EP (Expert Parallelism) and Mooncake Backend
• Troubleshooting Mooncake Python API issues

Routing Guidance

• For most vLLM and SGLang users, start from docs/source/getting_started/quick-start.md.
• For PD disaggregation, direct users to the SGLang/vLLM integration guides listed in Quick Start. Those guides own the serving-framework configuration.
• For Mooncake Store integrations, direct users to the SGLang/vLLM Store setup guides listed in Quick Start. Do not duplicate mooncake_master startup commands in general API answers unless the user is working outside those frameworks.
• For direct low-level Transfer Engine usage, use docs/source/design/transfer-engine/index.md#using-transfer-engine-in-your-projects.
• For API signatures and method details, use the Python API references under docs/source/python-api-reference/.

Core Components

1. Mooncake Store (Distributed KV Cache)

Import:

from mooncake.store import MooncakeDistributedStore, ReplicateConfig

Basic Setup:

store = MooncakeDistributedStore()
store.setup(
    "localhost",                          # local_hostname
    "http://localhost:8080/metadata",     # metadata_server
    512*1024*1024,                        # global_segment_size (512MB)
    128*1024*1024,                        # local_buffer_size (128MB)
    "tcp",                                # protocol ("tcp" or "rdma")
    "",                                   # rdma_devices (empty for auto-select)
    "localhost:50051"                     # master_server_address
)

Common Operations:

# Put/Get
store.put("key", b"value")
data = store.get("key")

# Batch operations
store.put_batch(["key1", "key2"], [b"val1", b"val2"])
values = store.get_batch(["key1", "key2"])

# Check existence
exists = store.is_exist("key")  # Returns 1 (exists), 0 (not exists), -1 (error)

# Remove
store.remove("key")
store.remove_by_regex("^prefix_.*")
store.remove_all()

# Cleanup
store.close()

Zero-Copy Operations (Advanced):

import numpy as np

# Create and register buffer
buffer = np.zeros(100*1024*1024, dtype=np.uint8)
buffer_ptr = buffer.ctypes.data
store.register_buffer(buffer_ptr, buffer.nbytes)

# Zero-copy put
store.put_from("key", buffer_ptr, buffer.nbytes)

# Zero-copy get
recv_buffer = np.empty(100*1024*1024, dtype=np.uint8)
recv_ptr = recv_buffer.ctypes.data
store.register_buffer(recv_ptr, recv_buffer.nbytes)
bytes_read = store.get_into("key", recv_ptr, recv_buffer.nbytes)

# Cleanup
store.unregister_buffer(buffer_ptr)
store.unregister_buffer(recv_ptr)

PyTorch Tensor Operations:

import torch

# Simple tensor operations
tensor = torch.randn(100, 100)
store.put_tensor("my_tensor", tensor)
retrieved = store.get_tensor("my_tensor")

# Batch tensor operations
tensors = [torch.randn(100, 100) for _ in range(3)]
store.batch_put_tensor(["t1", "t2", "t3"], tensors)
retrieved_tensors = store.batch_get_tensor(["t1", "t2", "t3"])

# Tensor Parallelism (TP) support
store.put_tensor_with_tp("model_weights", tensor, tp_rank=0, tp_size=4, split_dim=0)
shard = store.get_tensor_with_tp("model_weights", tp_rank=0, tp_size=4)

Replication Configuration:

config = ReplicateConfig()
config.replica_num = 3              # Number of replicas
config.with_soft_pin = True         # Keep in memory longer
config.preferred_segment = "host:port"  # Preferred location

store.put("key", b"value", config)

2. Transfer Engine (High-Performance Data Transfer)

Import:

from mooncake.engine import TransferEngine, TransferOpcode, TransferNotify

Basic Setup:

engine = TransferEngine()
engine.initialize(
    "127.0.0.1:12345",      # local_hostname
    "127.0.0.1:2379",       # metadata_server (or "etcd://...")
    "tcp",                  # protocol ("tcp" or "rdma")
    ""                      # device_name (empty for all devices)
)

Buffer Management:

# Allocate managed buffer
buffer_size = 1024 * 1024  # 1MB
buffer_addr = engine.allocate_managed_buffer(buffer_size)

# Write/read bytes
data = b"Hello, Transfer Engine!"
engine.write_bytes_to_buffer(buffer_addr, data, len(data))
read_data = engine.read_bytes_from_buffer(buffer_addr, len(data))

# Free buffer
engine.free_managed_buffer(buffer_addr, buffer_size)

Data Transfer Operations:

# Synchronous write
result = engine.transfer_sync_write(
    "target_host:port",     # target_hostname
    local_buffer_addr,      # buffer
    remote_buffer_addr,     # peer_buffer_address
    data_length             # length
)

# Synchronous read
result = engine.transfer_sync_read(
    "target_host:port",
    local_buffer_addr,
    remote_buffer_addr,
    data_length
)

# Asynchronous write
batch_id = engine.transfer_submit_write(
    "target_host:port",
    local_buffer_addr,
    remote_buffer_addr,
    data_length
)

# Check status
status = engine.transfer_check_status(batch_id)
# Returns: 1 (completed), 0 (in progress), -1 (failed), -2 (timeout)

Batch Transfer Operations:

# Batch synchronous write
local_addrs = [addr1, addr2, addr3]
remote_addrs = [remote1, remote2, remote3]
lengths = [len1, len2, len3]

result = engine.batch_transfer_sync_write(
    "target_host:port",
    local_addrs,
    remote_addrs,
    lengths
)

# Batch asynchronous operations
batch_id = engine.batch_transfer_async_write(
    "target_host:port",
    local_addrs,
    remote_addrs,
    lengths
)

# Wait for completion
result = engine.get_batch_transfer_status([batch_id])

Memory Registration (for RDMA):

import numpy as np

buffer = np.ones(1024*1024, dtype=np.uint8)
buffer_ptr = buffer.ctypes.data
buffer_size = buffer.nbytes

# Register memory
engine.register_memory(buffer_ptr, buffer_size)

# Use buffer for transfers...

# Unregister when done
engine.unregister_memory(buffer_ptr)

3. Mooncake EP & Backend (Expert Parallelism)

Mooncake Backend (Fault-Tolerant Collectives):

import torch
import torch.distributed as dist
from mooncake import pg

# Initialize with fault tolerance
active_ranks = torch.ones((world_size,), dtype=torch.int32, device="cuda")
dist.init_process_group(
    backend="mooncake",
    rank=rank,
    world_size=world_size,
    pg_options=pg.MooncakeBackendOptions(active_ranks),
)

# Use standard PyTorch distributed APIs
dist.all_gather(...)
dist.all_reduce(...)

# Check for failures
assert active_ranks.all()  # Verify no ranks are broken

Mooncake EP (Expert Parallelism):

from mooncake.mooncake_ep_buffer import Buffer
import torch.distributed as dist

# Calculate buffer size
num_ep_buffer_bytes = Buffer.get_ep_buffer_size_hint(
    num_max_dispatch_tokens_per_rank=1024,
    hidden=4096,
    num_ranks=8,
    num_experts=64
)

# Create buffer (must be Mooncake Backend process group)
buffer = Buffer(group=dist.group.WORLD, num_ep_buffer_bytes=num_ep_buffer_bytes)

# Dispatch/combine operations
active_ranks = torch.ones((num_ranks,), dtype=torch.int32, device="cuda")
buffer.dispatch(..., active_ranks=active_ranks, timeout_us=1000000)
buffer.combine(..., active_ranks=active_ranks, timeout_us=1000000)

Starting Services

Start Master Service (with HTTP metadata server)

mooncake_master \
  --enable_http_metadata_server=true \
  --http_metadata_server_host=0.0.0.0 \
  --http_metadata_server_port=8080 \
  --default_kv_lease_ttl=5000

Using External etcd (Production)

# Start etcd
etcd --listen-client-urls http://0.0.0.0:2379 \
     --advertise-client-urls http://0.0.0.0:2379

# Start master
mooncake_master --default_kv_lease_ttl=5000

Environment Variables

Transfer Engine

• MC_METADATA_SERVER: Metadata server URL
• MC_FORCE_TCP: Force TCP transport (set to "true")
• MC_LOG_LEVEL: Logging level (0=INFO, 1=WARNING, 2=ERROR)
• MC_MS_AUTO_DISC: Enable RDMA device auto-discovery (set to "1")
• MC_MS_FILTERS: Filter RDMA devices (e.g., "mlx5_0,mlx5_2")
• MC_TRANSFER_TIMEOUT: Transfer timeout in seconds (default: 30)

Mooncake Store

• MC_STORE_CLUSTER_ID: Cluster identifier (default: "mooncake")
• MC_STORE_USE_HUGEPAGE: Enable hugepage support
• MC_STORE_MEMCPY: Enable local memcpy optimization (set to "1")
• MC_STORE_CLIENT_METRIC: Enable client metrics (enabled by default)
• MC_YLT_LOG_LEVEL: Log level (trace/debug/info/warn/error/critical)
• MOONCAKE_STORE_CHECKSUM: Enable diagnostic object-level CRC-64 checks (set to "1" before creating any writer or reader client)

Common Patterns

Pattern 1: Simple KV Store

from mooncake.store import MooncakeDistributedStore

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

# Store and retrieve
store.put("config", b'{"model": "llama-7b"}')
config = store.get("config")

store.close()

Pattern 2: High-Performance Tensor Storage

import torch
from mooncake.store import MooncakeDistributedStore, ReplicateConfig

store = MooncakeDistributedStore()
store.setup("localhost", "http://localhost:8080/metadata",
            512*1024*1024, 128*1024*1024, "rdma", "mlx5_0", "localhost:50051")

# Configure replication
config = ReplicateConfig()
config.replica_num = 2
config.with_soft_pin = True

# Store tensor with replication
tensor = torch.randn(1000, 1000)
store.put_tensor("weights", tensor, config)

# Retrieve
retrieved = store.get_tensor("weights")

store.close()

Pattern 3: Zero-Copy Batch Operations

import numpy as np
from mooncake.store import MooncakeDistributedStore

store = MooncakeDistributedStore()
store.setup("localhost", "http://localhost:8080/metadata",
            512*1024*1024, 16*1024*1024, "rdma", "", "localhost:50051")

# Prepare buffers
num_buffers = 10
buffers = [np.random.randn(1024*1024).astype(np.float32) for _ in range(num_buffers)]
buffer_ptrs = [buf.ctypes.data for buf in buffers]
sizes = [buf.nbytes for buf in buffers]

# Register all buffers
for ptr, size in zip(buffer_ptrs, sizes):
    store.register_buffer(ptr, size)

# Batch put
keys = [f"tensor_{i}" for i in range(num_buffers)]
results = store.batch_put_from(keys, buffer_ptrs, sizes)

# Batch get
recv_buffers = [np.empty(1024*1024, dtype=np.float32) for _ in range(num_buffers)]
recv_ptrs = [buf.ctypes.data for buf in recv_buffers]

for ptr, size in zip(recv_ptrs, sizes):
    store.register_buffer(ptr, size)

results = store.batch_get_into(keys, recv_ptrs, sizes)


 ptr  buffer_ptrs + recv_ptrs:
    store.unregister_buffer(ptr)

store.close()

Pattern 4: Transfer Engine Direct Transfer

from mooncake.engine import TransferEngine
import numpy as np

# Setup engines on both nodes
engine = TransferEngine()
engine.initialize("127.0.0.1:12345", "127.0.0.1:2379", "tcp", "")

# Allocate and register buffer
buffer = np.ones(1024*1024, dtype=np.uint8)
buffer_ptr = buffer.ctypes.data
engine.register_memory(buffer_ptr, buffer.nbytes)

# Get remote buffer address (from peer via metadata exchange)
remote_addr = engine.get_first_buffer_address("target_host:port")

# Transfer data
data = b"Hello from Transfer Engine!"
engine.write_bytes_to_buffer(buffer_ptr, data, len(data))
result = engine.transfer_sync_write("target_host:port", buffer_ptr, remote_addr, len(data))

# Cleanup
engine.unregister_memory(buffer_ptr)

Error Handling

All methods return status codes:

• 0: Success
• Negative values: Error codes

Common checks:

# Store operations
result = store.put("key", b"value")
if result != 0:
    print(f"Put failed with error code: {result}")

# Existence check
exists = store.is_exist("key")
if exists == 1:
    print("Key exists")
elif exists == 0:
    print("Key not found")
else:
    print("Error checking existence")

# Transfer operations
result = engine.transfer_sync_write(...)
if result == 0:
    print("Transfer successful")
else:
    print(f"Transfer failed with code: {result}")

Troubleshooting

Connection Issues

# Force TCP for testing without RDMA
import os
os.environ["MC_FORCE_TCP"] = "true"

# Enable verbose logging
os.environ["MC_LOG_LEVEL"] = "0"
os.environ["MC_YLT_LOG_LEVEL"] = "debug"

Memory Issues

# Check buffer registration before zero-copy ops
result = store.register_buffer(buffer_ptr, size)
if result != 0:
    raise RuntimeError(f"Failed to register buffer: {result}")

Corrupted Data or Garbled Output

# Set this before importing Mooncake or creating any Store client.
import os
os.environ["MOONCAKE_STORE_CHECKSUM"] = "1"

from mooncake.store import MooncakeDistributedStore

Enable the switch on every writer and reader client process, then reproduce with full-object put/upsert and get operations. Treat CHECKSUM_MISMATCH (-801) as a failed read and do not use the destination buffer. Objects without checksum metadata and range reads are not verified. This mode scans object data, stages GPU buffers to host memory, and disables the local hot cache, so use it only for diagnosis.

Service Connectivity

# Check master is running
curl http://localhost:50051

# Check metadata server
curl http://localhost:8080/metadata

Best Practices

1. Always close stores: Call store.close() when done
2. Register buffers for zero-copy: Required for RDMA operations
3. Use batch operations: Better throughput for multiple operations
4. Configure replication: Use ReplicateConfig for important data
5. Use soft pinning: For frequently accessed objects
6. Choose protocol wisely: TCP for dev/test, RDMA for production
7. Monitor leases: Objects have TTL, renew if needed
8. Handle errors: Check return codes and handle failures

Quick Reference

Mooncake Store Methods

• setup(): Initialize store
• put(), get(): Basic operations
• put_batch(), get_batch(): Batch operations
• put_from(), get_into(): Zero-copy operations
• put_tensor(), get_tensor(): PyTorch tensors
• register_buffer(), unregister_buffer(): Buffer management
• is_exist(), remove(): Metadata operations
• close(): Cleanup

Transfer Engine Methods

• initialize(): Setup engine
• allocate_managed_buffer(), free_managed_buffer(): Buffer allocation
• transfer_sync_write(), transfer_sync_read(): Synchronous transfers
• transfer_submit_write(), transfer_check_status(): Async transfers
• batch_transfer_sync_write(), batch_transfer_sync_read(): Batch sync
• batch_transfer_async_write(), get_batch_transfer_status(): Batch async
• register_memory(), unregister_memory(): Memory registration
• write_bytes_to_buffer(), read_bytes_from_buffer(): Buffer I/O

Documentation Links

• Full API Reference: https://kvcache-ai.github.io/Mooncake/
• Quick Start: docs/source/getting_started/quick-start.md
• Mooncake Store: docs/source/python-api-reference/mooncake-store.md
• Transfer Engine: docs/source/python-api-reference/transfer-engine.md
• Transfer Engine direct usage: docs/source/design/transfer-engine/index.md#using-transfer-engine-in-your-projects
• EP Backend: docs/source/python-api-reference/ep-backend.md