Mooncake/docs/source/python-api-reference/mooncake-store.md

# 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:

```bash
pip install mooncake-transfer-engine
```

📦 **Package Details**: [https://pypi.org/project/mooncake-transfer-engine/](https://pypi.org/project/mooncake-transfer-engine/)

### Required Service
Only one service is required now:

- `mooncake_master` — Master service which now embeds the HTTP metadata server

## Quick Start

### Start Master (with HTTP enabled)

Enable the built-in HTTP metadata server when starting the master:

```bash
mooncake_master \
  --enable_http_metadata_server=true \
  --http_metadata_server_host=0.0.0.0 \
  --http_metadata_server_port=8080
```
This exposes the metadata endpoint at `http://<host>:<port>/metadata`.

### Hello World Example

```python
from mooncake.store import MooncakeDistributedStore

# 1. Create store instance
store = MooncakeDistributedStore()

# 2. Setup with all required parameters
store.setup(
    "localhost",           # 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)
    "",                            # Leave empty; Mooncake auto-picks RDMA devices when needed
    "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()
```

**RDMA device selection**: Leave `rdma_devices` as `""` to auto-select RDMA NICs. Provide a comma-separated list (e.g. `"mlx5_0,mlx5_1"`) to pin to specific hardware.

Mooncake selects available ports internally at `setup() `, so you do not need to fix specific port numbers in these examples. Internally, ports are chosen from a dynamic range (currently 12300–14300).

#### P2P Hello World (preview)

The following setup uses the new P2P handshake and does not require an HTTP metadata server. This feature is not released yet; use only if you’re testing the latest code.

```python
from mooncake.store import MooncakeDistributedStore

store = MooncakeDistributedStore()
store.setup(
    "localhost",           # Your node's ip address
    "P2PHANDSHAKE",              # P2P handshake (no HTTP metadata)
    512*1024*1024,                # 512MB segment size
    128*1024*1024,                # 128MB local buffer
    "tcp",                       # Use TCP (RDMA for high performance)
    "",                          # Leave empty; Mooncake auto-picks RDMA devices when needed
    "localhost:50051"           # Master service
)

store.put("hello_key", b"Hello, Mooncake Store!")
print(store.get("hello_key").decode())
store.close()
```

## Basic API Usage

### Simple Get/Put Operations

<details>
<summary>Click to expand: Complete Get/Put example with NumPy arrays</summary>

```python
import numpy as np
import json
from mooncake.store import MooncakeDistributedStore

# 1. Initialize
store = MooncakeDistributedStore()
store.setup("localhost",
            "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()
```

</details>

## 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.

<details>
<summary>Click to expand: Buffer registration example</summary>

```python
import numpy as np
from mooncake.store import MooncakeDistributedStore

# Initialize store
store = MooncakeDistributedStore()
store.setup("localhost", "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)
```

</details>

---

### 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:

<details>
<summary>Click to expand: Complete zero-copy workflow example</summary>

```python
import numpy as np
from mooncake.store import MooncakeDistributedStore

# Initialize store with RDMA protocol for maximum performance
store = MooncakeDistributedStore()
store.setup("localhost", "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()
```

</details>

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

```python
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).

```python
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

#### get_into_ranges()
Retrieve multiple byte ranges from multiple objects into registered buffers (zero-copy).

```python
def get_into_ranges(self, buffer_ptrs: List[int], all_keys: List[List[str]], all_dst_offsets: List[List[List[int]]], all_src_offsets: List[List[List[int]]], all_sizes: List[List[List[int]]]) -> List[List[List[int]]]
```

This API is **buffer-major** and supports **multiple fragments per key**.

Think of the input shape as:
- `buffer_ptrs[i]`: the `i`-th destination buffer
- `all_keys[i][j]`: the `j`-th key that writes into buffer `i`
- `all_dst_offsets[i][j][k]`: destination offset of fragment `k` for key `j` in buffer `i`
- `all_src_offsets[i][j][k]`: source offset of fragment `k` inside key `j` for buffer `i`
- `all_sizes[i][j][k]`: byte size of fragment `k`

For each triple `(i, j, k)`, Mooncake reads the source range
`[all_src_offsets[i][j][k], all_src_offsets[i][j][k] + all_sizes[i][j][k])`
from object `all_keys[i][j]`, then writes it into destination buffer
`buffer_ptrs[i]` at offset `all_dst_offsets[i][j][k]`.

This lets one buffer gather interleaved fragments from multiple keys, and lets one key contribute multiple disjoint fragments to the same buffer in a single call.

**Parameters:**
- `buffer_ptrs`: Memory addresses of pre-allocated destination buffers. Every buffer must be registered with `register_buffer()` before calling this API.
- `all_keys`: For each buffer, the ordered list of source object keys to read from.
- `all_dst_offsets`: For each buffer and key, the destination offsets of that key's fragments.
- `all_src_offsets`: For each buffer and key, the source offsets of that key's fragments inside the object.
- `all_sizes`: For each buffer and key, the byte lengths of that key's fragments.

**Shape rules:**
- `len(buffer_ptrs) == len(all_keys) == len(all_dst_offsets) == len(all_src_offsets) == len(all_sizes)`
- For each buffer `i`, `len(all_keys[i]) == len(all_dst_offsets[i]) == len(all_src_offsets[i]) == len(all_sizes[i])`
- For each `(buffer i, key j)`, `len(all_dst_offsets[i][j]) == len(all_src_offsets[i][j]) == len(all_sizes[i][j])`

If a top-level shape or per-key fragment shape does not match, the corresponding result entries are negative error codes.

**Returns:** A nested list of per-buffer, per-key, per-fragment results. `results[i][j][k]` is the number of bytes read for fragment `k`, or a negative value on error.

A successful call can still contain per-fragment failures. For example, if one key is missing but another key in the same buffer is valid, the missing key's fragment result will be negative while the valid fragment can still succeed.

**Typical scenarios:**
- **Partial read from one object:** You only need a slice of a large value, such as a header, metadata block, or a small subrange of a tensor shard. In this case, use one buffer, one key, and one or more fragments under that key.
- **Stitch multiple fragments from one object into one buffer:** You need several non-contiguous ranges from the same object and want to pack them into one destination buffer. In this case, keep a single key entry and place multiple fragments under that key.
- **Stitch data from multiple objects into one buffer:** You want to assemble one logical payload from several keys. In this case, use one destination buffer and list multiple keys under that buffer, with each key contributing one or more fragments.
- **Fill multiple output buffers in one call:** You have several destination buffers, each with its own read plan. In this case, each top-level entry in `buffer_ptrs` and the parallel nested arrays describes one independent destination buffer.

**How to use it for partial reads:**
If you only want part of an object, do not call `get_into()` with the full object buffer size. Instead:
1. Allocate and register a destination buffer sized for the bytes you actually want to materialize.
2. Put that buffer pointer into `buffer_ptrs`.
3. Put the source key into `all_keys`.
4. Set `all_src_offsets` to the start offsets of the object ranges you want.
5. Set `all_sizes` to the lengths of those ranges.
6. Set `all_dst_offsets` to where those ranges should land in your destination buffer.

A useful way to think about the arguments is:
- `buffer_ptrs` answers **where does the data land**
- `all_keys` answers **which object does it come from**
- `all_src_offsets` and `all_sizes` answer **which bytes should be read**
- `all_dst_offsets` answers **where each fragment should be placed in the destination buffer**

If you are extracting a single contiguous slice from one object, the minimal shape is:

```python
results = store.get_into_ranges(
    [buffer_ptr],
    [["my_key"]],
    [[[0]]],
    [[[src_offset]]],
    [[[size]]],
)
```

This means:
- one destination buffer
- one source key for that buffer
- one fragment for that key
- read `size` bytes from `my_key[src_offset:src_offset + size]`
- write them into `buffer_ptr[0:size]`

If you want to read several disjoint ranges from the same object and pack them together, keep the same key and add more fragments under it. For example:

```python
results = store.get_into_ranges(
    [buffer_ptr],
    [["my_key"]],
    [[[0, 16, 40]]],
    [[[128, 4096, 8192]]],
    [[[8, 12, 4]]],
)
```

This reads three fragments from `my_key` and places them into the same destination buffer at offsets `0`, `16`, and `40`. This pattern is useful when you want to assemble only the needed pieces of a large object without reading the whole value.

If you want to assemble one output buffer from multiple objects, keep one top-level buffer entry and add multiple keys under it. Each key can still contribute one or more fragments. For example, you might put a header from `meta_key` at the front of the buffer, then place a payload slice from `data_key` after it.

**Usage example:**

```python
import ctypes

buffer_size = 32
buffer0 = (ctypes.c_ubyte * buffer_size)()
buffer1 = (ctypes.c_ubyte * buffer_size)()
buffer_ptr0 = ctypes.addressof(buffer0)
buffer_ptr1 = ctypes.addressof(buffer1)

store.register_buffer(buffer_ptr0, buffer_size)
store.register_buffer(buffer_ptr1, buffer_size)

# Buffer 0 reads:
# - from key1: two fragments -> src[1:5] -> dst[0:4], src[30:33] -> dst[20:23]
# - from key2: one fragment  -> src[2:7] -> dst[8:13]
# Buffer 1 reads:
# - from key2: one fragment  -> src[0:6] -> dst[4:10]
# - from key1: one fragment  -> src[10:14] -> dst[16:20]
results = store.get_into_ranges(
    [buffer_ptr0, buffer_ptr1],
    [["key1", "key2"], ["key2", "key1"]],
    [[[0, 20], [8]], [[4], [16]]],
    [[[1, 30], [2]], [[0], [10]]],
    [[[4, 3], [5]], [[6], [4]]],
)

# results == [
#   [[4, 3], [5]],
#   [[6], [4]],
# ]
```

In the example above:
- `results[0][0][0] == 4`: buffer 0, key 0 (`"key1"`), fragment 0 succeeded with 4 bytes
- `results[0][0][1] == 3`: buffer 0, key 0 (`"key1"`), fragment 1 succeeded with 3 bytes
- `results[0][1][0] == 5`: buffer 0, key 1 (`"key2"`), fragment 0 succeeded with 5 bytes

**Common pitfalls:**
- Do not flatten all fragments for a buffer into one list. Fragments must be grouped under their corresponding key.
- `all_dst_offsets`, `all_src_offsets`, and `all_sizes` are 3D, but `all_keys` is 2D.
- Buffer overflow is checked against the registered destination buffer size.
- Source overflow is checked against the source object's size.
- Full-object `get_into()` and ranged `get_into_ranges()` are different APIs; use `get_into()` when you want the whole object into one buffer.

**Current limitation:** true ranged items currently require the selected source replica to be memory-backed. Whole-object reads still follow the normal full-read path, but partial reads through `get_into_ranges()` do not support non-memory replicas.

---

## 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

```python
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.

```python
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.

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

#### with_hard_pin
**Type:** `bool`
**Default:** `False`
**Description:** Enables hard pinning for the stored object. Hard pinned objects will not be evicted. This grants user to manually control the life time of stored objects.

```python
config = ReplicateConfig()
config.with_hard_pin = True  # Keep this object in memory that will not be evicted
```

#### 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.

```python
config = ReplicateConfig()

# Preferred replica location ("host:port")
config.preferred_segment = "localhost:12345"     # pin to a specific machine

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

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

#### prefer_alloc_in_same_node
**Type:** `str`
**Default:** `""` (empty string)
**Description:** Enables the preference for allocating data on the same node. Currently, this only supports `batch_put_from_multi_buffers`. Additionally, it does not support disk segments, and the `replica_num` can only be set to 1.

```python
config = ReplicateConfig()
config.prefer_alloc_in_same_node = "True
```
---

## Non-Zero-Copy API (Simple Usage)

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

### Basic Operations

<details>
<summary>Click to expand: Non-zero-copy API examples</summary>

```python
from mooncake.store import MooncakeDistributedStore

# Initialize (same as zero-copy)
store = MooncakeDistributedStore()
store.setup("localhost", "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)
```

</details>

### 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

---

## Topology & Devices

- Auto-discovery: Disabled by default. For `protocol="rdma"`, you must specify RDMA devices.
- Enable auto-discovery (optional):
  - `MC_MS_AUTO_DISC=1` enables auto-discovery; then `rdma_devices` is not required.
  - Optionally restrict candidates with `MC_MS_FILTERS`, a comma-separated whitelist of NIC names, e.g. `MC_MS_FILTERS=mlx5_0,mlx5_2`.
  - If `MC_MS_AUTO_DISC` is not set or set to `0`, auto-discovery remains disabled and `rdma_devices` is required for RDMA.

Examples:

```bash
# Auto-select with default settings
python - <<'PY'
from mooncake.store import MooncakeDistributedStore as S
s = S()
s.setup("localhost", "http://localhost:8080/metadata", 512*1024*1024, 128*1024*1024, "rdma", "", "localhost:50051")
PY

# Manual device list
unset MC_MS_AUTO_DISC
python - <<'PY'
from mooncake.store import MooncakeDistributedStore as S
s = S()
s.setup("localhost", "http://localhost:8080/metadata", 512*1024*1024, 128*1024*1024, "rdma", "mlx5_0,mlx5_1", "localhost:50051")
PY

# Auto-select with filters
export MC_MS_AUTO_DISC=1
export MC_MS_FILTERS=mlx5_0,mlx5_2
python - <<'PY'
from mooncake.store import MooncakeDistributedStore as S
s = S()
s.setup("localhost", "http://localhost:8080/metadata", 512*1024*1024, 128*1024*1024, "rdma", "", "localhost:50051")
PY
```

## get_buffer Buffer Protocol

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

<details>
<summary>Click to expand: Buffer protocol usage example</summary>

```python
# 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
```

</details>
---

## Full API Reference

### Class: MooncakeDistributedStore

The main class for interacting with Mooncake Store.

#### Constructor
```python
store = MooncakeDistributedStore()
```
Creates a new store instance. No parameters required.

---


#### setup()
Initialize distributed resources and establish network connections.

```python
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" or "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): RDMA device name(s), e.g. `"mlx5_0"` or `"mlx5_0,mlx5_1"`. Leave empty to auto-select NICs. Provide device names to pin the NICs. Always 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:**

<details>
<summary>Click to expand: Setup examples for TCP and RDMA</summary>

```python
# TCP initialization
store.setup("localhost", "http://localhost:8080/metadata", 1024*1024*1024, 128*1024*1024, "tcp", "", "localhost:50051")

# RDMA auto-detect
store.setup("localhost", "http://localhost:8080/metadata", 512*1024*1024, 128*1024*1024, "rdma", "", "localhost:50051")

# RDMA with explicit device list
store.setup("localhost", "http://localhost:8080/metadata", 512*1024*1024, 128*1024*1024, "rdma", "mlx5_0,mlx5_1", "localhost:50051")
```

</details>

---
#### setup_dummy()
Initialize the store with a dummy client for testing purposes.

```python
def setup_dummy(self, mem_pool_size: int, local_buffer_size: int, server_address: str) -> int
```

**Parameters:**
- `mem_pool_size` (int): Memory pool size in bytes
- `local_buffer_size` (int): Local buffer size in bytes
- `server_address` (str): Server address in format "hostname:port"

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Example:**
```python
# Initialize with dummy client
store.setup_dummy(1024*1024*256, 1024*1024*64, "localhost:8080")
```

---

#### put()
Store binary data in the distributed storage.

```python
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:**

<details>
<summary>Click to expand: Put operation examples</summary>

```python
# 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)
```

</details>

---

#### get()
Retrieve binary data from distributed storage.

```python
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:**

<details>
<summary>Click to expand: Get operation example</summary>

```python
data = store.get("my_key")
if data:
    print(f"Retrieved: {data.decode()}")
else:
    print("Key not found")
```

</details>

---

#### put_batch()
Store multiple objects in a single batch operation.

```python
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:**

<details>
<summary>Click to expand: Batch put example</summary>

```python
keys = ["key1", "key2", "key3"]
values = [b"value1", b"value2", b"value3"]
result = store.put_batch(keys, values)
```

</details>

---

#### upsert()

Insert a new object if the key does not exist, or update the existing object in place when possible. They use the same replication configuration model as `put()`.

Upsert binary data in the distributed storage.

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

**Parameters:**
- `key` (str): Unique object identifier
- `value` (bytes): Binary data to insert or update
- `config` (ReplicateConfig, optional): Replication configuration

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Example:**
```python
config = ReplicateConfig()
config.replica_num = 2

rc = store.upsert("weights", b"new-bytes", config)
if rc == 0:
    print("Upsert succeeded")
```

#### upsert_from()

Upsert object data directly from a pre-allocated buffer (zero-copy).

```python
def upsert_from(self, key: str, buffer_ptr: int, size: int, config: ReplicateConfig = None) -> int
```

**Parameters:**
- `key` (str): Object identifier
- `buffer_ptr` (int): Memory address of the source buffer
- `size` (int): Number of bytes to insert or update
- `config` (ReplicateConfig, optional): Replication configuration

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Note:** This is the zero-copy counterpart of `upsert()`. As with
`put_from()`, register the buffer before issuing the request.

#### batch_upsert_from()

Upsert multiple objects directly from pre-allocated buffers.

```python
def batch_upsert_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 source buffer addresses
- `sizes` (List[int]): List of byte lengths for each buffer
- `config` (ReplicateConfig, optional): Replication configuration shared by all objects

**Returns:**
- `List[int]`: List of status codes for each upsert

#### upsert_parts()

Upsert data from multiple buffer parts as a single object (insert or update).

```python
def upsert_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:**
```python
part1 = b"Hello, "
part2 = b"World!"
result = store.upsert_parts("greeting", part1, part2)
```

#### upsert_batch()

Upsert multiple objects in a single batch operation.

```python
def upsert_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 insert or update
- `config` (ReplicateConfig, optional): Replication configuration for all objects

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Example:**
```python
keys = ["key1", "key2", "key3"]
values = [b"value1", b"value2", b"value3"]
result = store.upsert_batch(keys, values)
```

---

#### get_batch()
Retrieve multiple objects in a single batch operation.

```python
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:**

<details>
<summary>Click to expand: Batch get example</summary>

```python
keys = ["key1", "key2", "key3"]
values = store.get_batch(keys)
for key, value in zip(keys, values):
    print(f"{key}: {len(value)} bytes")
```

</details>

---

#### remove()
Delete an object from the storage system.

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

**Parameters:**
- `key` (str): Object identifier to remove

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Example:**
```python
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.

```python
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:**
```python
# 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.

```python
def remove_all(self) -> int
```

**Returns:**
- `int`: Number of objects removed, or -1 on error

**Example:**
```python
count = store.remove_all()
print(f"Removed {count} objects")
```

---

#### batch_remove()
Remove multiple objects by their keys in a single batch operation.

```python
def batch_remove(self, keys: List[str], force: bool = False) -> List[int]
```

**Parameters:**
- `keys` (List[str]): List of object identifiers to remove
- `force` (bool): If True, skip lease and replication task checks (default: False)

**Returns:**
- `List[int]`: List of status codes for each key (0 = success, negative = error code)

**Example:**
```python
# Remove multiple keys in one batch
keys = ["key1", "key2", "key3", "key4", "key5"]
results = store.batch_remove(keys)

# Check results
for key, result in zip(keys, results):
    if result == 0:
        print(f"✓ {key} removed successfully")
    else:
        print(f"✗ {key} failed with error code: {result}")

# Force remove (bypass lease checks)
results = store.batch_remove(keys, force=True)
```

---

#### is_exist()
Check if an object exists in the storage system.

```python
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:**
```python
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.

```python
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:**
```python
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.

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

**Parameters:**
- `key` (str): Object identifier

**Returns:**
- `int`: Size in bytes, or negative value on error

**Example:**
```python
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.

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

**Parameters:**
- `key` (str): Object identifier

**Returns:**
- `BufferHandle`: Buffer object or None if not found

**Example:**
```python
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.

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

---
#### batch_get_buffer()
Get multiple objects as buffers that implement Python's buffer protocol.

```python
def batch_get_buffer(self, keys: List[str]) -> List[BufferHandle]
```

**Parameters:**
- `keys` (List[str]): List of object identifiers to retrieve

**Returns:**
- `List[BufferHandle]`: List of buffer objects, with None for keys not found

**Note:** This function is not supported for dummy client.

**Example:**
```python
buffers = store.batch_get_buffer(["key1", "key2", "key3"])
for i, buffer in enumerate(buffers):
    if buffer:
        print(f"Buffer {i} size: {buffer.size()} bytes")
```

---
#### alloc_from_mem_pool()
Allocate memory from the memory pool.

```python
def alloc_from_mem_pool(self, size: int) -> int
```

**Parameters:**
- `size` (int): Size of memory to allocate in bytes

**Returns:**
- `int`: Memory address as integer, or 0 on failure

---
#### init_all()
Initialize all resources with specified protocol and device.

```python
def init_all(self, protocol: str, device_name: str, mount_segment_size: int = 16777216) -> int
```

**Parameters:**
- `protocol` (str): Network protocol - "tcp" or "rdma"
- `device_name` (str): Device name for the protocol
- `mount_segment_size` (int): Memory segment size in bytes for mounting (default: 16MB = 16777216)

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

---

#### get_hostname()
Get the hostname of the current store instance.

```python
def get_hostname(self) -> str
```

**Returns:**
- `str`: Hostname and port of this store instance

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

---

#### get_replica_desc()
Get descriptors of replicas for a key.

```python
def get_replica_desc(self, key: str) -> List[Replica::Descriptor]
```

**Parameters:**
- `key` (str): mooncake store key

**Returns:**
- `List[Replica::Descriptor]`: List of replica descriptors

**Example:**
```python
descriptors = store.get_replica_desc("mooncake_key")
for desc in descriptors:
    print("Status:", desc.status)
    if desc.is_memory_replica():
        mem_desc = desc.get_memory_descriptor()
        print("Memory buffer desc:", mem_desc.buffer_descriptor)
    elif desc.is_disk_replica():
        disk_desc = desc.get_disk_descriptor()
        print("Disk path:", disk_desc.file_path, "Size:", disk_desc.object_size)
```

---

#### batch_get_replica_desc()
Get descriptors of replicas for a tuple of keys.

```python
def batch_get_replica_desc(self, keys: List[str]) -> Dict[str, List[Replica::Descriptor]]
```

**Parameters:**
- `keys` (List[str]): List of mooncake store keys

**Returns:**
- `Dict[str, List[Replica::Descriptor]]`: Dictionary mapping keys to their list of replica descriptors

**Example:**
```python
descriptors_map = store.batch_get_replica_desc(["key1", "key2"])
for key, desc_list in descriptors_map.items():
    print(f"Replicas for key: {key}")
    for desc in desc_list:
        if desc.is_memory_replica():
            mem_desc = desc.get_memory_descriptor()
            print("Memory buffer desc:", mem_desc.buffer_descriptor)
        elif desc.is_disk_replica():
            disk_desc = desc.get_disk_descriptor()
            print("Disk path:", disk_desc.file_path, "Size:", disk_desc.object_size)
```

---

#### create_copy_task()

Creates an asynchronous copy task to replicate an object to target segments.

```python
def create_copy_task(self, key: str, targets: List[str]) -> Tuple[UUID, int]
```

**Parameters:**
- `key` (str): Object key to copy
- `targets` (List[str]): List of target segment names where replicas should be created

**Returns:**
- `Tuple[UUID, int]`: (task UUID, error code)
  - If successful: (task UUID, 0)
  - If failed: (UUID{0, 0}, error code)

**Example:**
```python
# Create an asynchronous copy task
task_id, error_code = store.create_copy_task("my_key", ["segment1", "segment2"])
if error_code == 0:
    print(f"Copy task created with ID: {task_id}")
    # Query task status later
    response, status = store.query_task(task_id)
    if status == 0:
        print(f"Task status: {response.status}")
else:
    print(f"Failed to create copy task: {error_code}")
```

---

#### create_move_task()

Creates an asynchronous move task to move an object from source segment to target segment.

```python
def create_move_task(self, key: str, source: str, target: str) -> Tuple[UUID, int]
```

**Parameters:**
- `key` (str): Object key to move
- `source` (str): Source segment name where the replica currently exists
- `target` (str): Target segment name where the replica should be moved to

**Returns:**
- `Tuple[UUID, int]`: (task UUID, error code)
  - If successful: (task UUID, 0)
  - If failed: (UUID{0, 0}, error code)

**Example:**
```python
# Create an asynchronous move task
task_id, error_code = store.create_move_task("my_key", "old_segment", "new_segment")
if error_code == 0:
    print(f"Move task created with ID: {task_id}")
    # Query task status later
    response, status = store.query_task(task_id)
    if status == 0:
        print(f"Task status: {response.status}")
else:
    print(f"Failed to create move task: {error_code}")
```

---

#### query_task()

Queries the status of an asynchronous task (copy or move).

```python
def query_task(self, task_id: UUID) -> Tuple[QueryTaskResponse | None, int]
```

**Parameters:**
- `task_id` (UUID): UUID of the task to query

**Returns:**
- `Tuple[QueryTaskResponse | None, int]`: (QueryTaskResponse if success, error code)
  - If successful: (QueryTaskResponse, 0)
  - If failed: (None, error code)

**Example:**
```python
from mooncake.store import MooncakeDistributedStore, TaskStatus
import time

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

# Submit multiple copy tasks
tasks = []
for key in ["key1", "key2", "key3"]:
    task_id, error = store.create_copy_task(key, ["segment1", "segment2"])
    if error == 0:
        tasks.append(task_id)
        print(f"Created copy task {task_id} for {key}")

# Monitor task progress
while tasks:
    completed = []
    for task_id in tasks:
        response, status = store.query_task(task_id)
        if status == 0 and response:
            if response.status == TaskStatus.SUCCESS:
                print(f"Task {task_id} succeeded")
                completed.append(task_id)
            elif response.status == TaskStatus.FAILED:
                print(f"Task {task_id} failed: {response.message}")
                completed.append(task_id)

    # Remove completed tasks
    tasks = [t for t in tasks if t not in completed]

    if tasks:
        time.sleep(1)  # Wait before next check

print("All tasks completed")
store.close()
```

---

#### close()
Clean up all resources and terminate connections.

```python
def close(self) -> int
```

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Example:**
```python
store.close()
```

---
#### put_from_with_metadata()
Store data directly from a registered buffer with metadata (zero-copy).

```python
def put_from_with_metadata(self, key: str, buffer_ptr: int, metadata_buffer_ptr: int, size: int, metadata_size: int, config: ReplicateConfig = None) -> int
```

**Parameters:**
- `key` (str): Object identifier
- `buffer_ptr` (int): Memory address of the main data buffer (from ctypes.data or similar)
- `metadata_buffer_ptr` (int): Memory address of the metadata buffer
- `size` (int): Number of bytes for the main data
- `metadata_size` (int): Number of bytes for the metadata
- `config` (ReplicateConfig, optional): Replication configuration

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Note:** This function is not supported for dummy client.

**Example:**
```python
import numpy as np

# Create data and metadata
data = np.random.randn(1000).astype(np.float32)
metadata = np.array([42, 100], dtype=np.int32)  # example metadata

# Register buffers
data_ptr = data.ctypes.data
metadata_ptr = metadata.ctypes.data
store.register_buffer(data_ptr, data.nbytes)
store.register_buffer(metadata_ptr, metadata.nbytes)

# Store with metadata
result = store.put_from_with_metadata("data_with_metadata", data_ptr, metadata_ptr,
                                     data.nbytes, metadata.nbytes)
if result == 0:
    print("Data with metadata stored successfully")

# Cleanup
store.unregister_buffer(data_ptr)
store.unregister_buffer(metadata_ptr)
```

---
#### pub_tensor()
Publish a PyTorch tensor with configurable replication settings.

```python
def pub_tensor(self, key: str, tensor: torch.Tensor, config: ReplicateConfig = None) -> int
```

**Parameters:**
- `key` (str): Unique object identifier
- `tensor` (torch.Tensor): PyTorch tensor to store
- `config` (ReplicateConfig, optional): Replication configuration

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Note:** This function requires `torch` to be installed and available in the environment.

**Example:**
```python
import torch
from mooncake.store import ReplicateConfig

# Create a tensor
tensor = torch.randn(100, 100)

# Create replication config
config = ReplicateConfig()
config.replica_num = 3
config.with_soft_pin = True

# Publish tensor with replication settings
result = store.pub_tensor("my_tensor", tensor, config)
if result == 0:
    print("Tensor published successfully")
```

---

### PyTorch Tensor Operations (Tensor Parallelism)

These methods provide direct support for storing and retrieving PyTorch tensors. They automatically handle serialization and metadata, and include built-in support for **Tensor Parallelism (TP)** by automatically splitting and reconstructing tensor shards.

⚠️ **Note**: These methods require `torch` to be installed and available in the environment.

#### put_tensor_with_tp()

Put a PyTorch tensor into the store, optionally splitting it into shards for tensor parallelism.
The tensor is chunked immediately and stored as separate keys (e.g., `key_tp_0`, `key_tp_1`...).

```python
def put_tensor_with_tp(self, key: str, tensor: torch.Tensor, tp_rank: int = 0, tp_size: int = 1, split_dim: int = 0) -> int
```

**Parameters:**

  - `key` (str): Base identifier for the tensor.
  - `tensor` (torch.Tensor): The PyTorch tensor to store.
  - `tp_rank` (int): Current tensor parallel rank (default: 0). *Note: The method splits and stores all chunks for all ranks regardless of this value.*
  - `tp_size` (int): Total tensor parallel size (default: 1). If \> 1, the tensor is split into `tp_size` chunks.
  - `split_dim` (int): The dimension to split the tensor along (default: 0).

**Returns:**

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

#### pub_tensor_with_tp()

Publish a PyTorch tensor into the store with configurable replication settings, optionally splitting it into shards for tensor parallelism.
The tensor is chunked immediately and stored as separate keys (e.g., `key_tp_0`, `key_tp_1`...).

```python
def pub_tensor_with_tp(self, key: str, tensor: torch.Tensor, config: ReplicateConfig, tp_rank: int = 0, tp_size: int = 1, split_dim: int = 0) -> int
```

**Parameters:**

  - `key` (str): Base identifier for the tensor.
  - `tensor` (torch.Tensor): The PyTorch tensor to store.
  - `config` (ReplicateConfig): Optional replication configuration.
  - `tp_rank` (int): Current tensor parallel rank (default: 0). *Note: The method splits and stores all chunks for all ranks regardless of this value.*
  - `tp_size` (int): Total tensor parallel size (default: 1). If \> 1, the tensor is split into `tp_size` chunks.
  - `split_dim` (int): The dimension to split the tensor along (default: 0).

**Returns:**

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

#### get_tensor_with_tp()

Get a PyTorch tensor from the store, specifically retrieving the shard corresponding to the given Tensor Parallel rank.

```python
def get_tensor_with_tp(self, key: str, tp_rank: int = 0, tp_size: int = 1, split_dim: int = 0) -> torch.Tensor
```

**Parameters:**

  - `key` (str): Base identifier of the tensor.
  - `tp_rank` (int): The tensor parallel rank to retrieve (default: 0). Fetches key `key_tp_{rank}` if `tp_size > 1`.
  - `tp_size` (int): Total tensor parallel size (default: 1).
  - `split_dim` (int): The dimension used during splitting (default: 0).

**Returns:**

  - `torch.Tensor`: The retrieved tensor (or shard). Returns `None` if not found.

#### batch_put_tensor_with_tp()

Put a batch of PyTorch tensors into the store, splitting each into shards for tensor parallelism.

```python
def batch_put_tensor_with_tp(self, base_keys: List[str], tensors_list: List[torch.Tensor], tp_rank: int = 0, tp_size: int = 1, split_dim: int = 0) -> List[int]
```

**Parameters:**

  - `base_keys` (List[str]): List of base identifiers.
  - `tensors_list` (List[torch.Tensor]): List of tensors to store.
  - `tp_rank` (int): Current rank (default: 0).
  - `tp_size` (int): Total TP size (default: 1).
  - `split_dim` (int): Split dimension (default: 0).

**Returns:**

  - `List[int]`: List of status codes for each tensor operation.

#### batch_pub_tensor_with_tp()

Publish a batch of PyTorch tensors into the store with configurable replication settings, splitting each into shards for tensor parallelism.

```python
def batch_pub_tensor_with_tp(self, base_keys: List[str], tensors_list: List[torch.Tensor], config: ReplicateConfig, tp_rank: int = 0, tp_size: int = 1, split_dim: int = 0) -> List[int]
```

**Parameters:**

  - `base_keys` (List[str]): List of base identifiers.
  - `tensors_list` (List[torch.Tensor]): List of tensors to store.
  - `config` (ReplicateConfig): Optional replication configuration.
  - `tp_rank` (int): Current rank (default: 0).
  - `tp_size` (int): Total tp size (default: 1).
  - `split_dim` (int): Split dimension (default: 0).

**Returns:**

  - `List[int]`: List of status codes for each tensor operation.

#### batch_get_tensor_with_tp()

Get a batch of PyTorch tensor shards from the store for a given Tensor Parallel rank.

```python
def batch_get_tensor_with_tp(self, base_keys: List[str], tp_rank: int = 0, tp_size: int = 1) -> List[torch.Tensor]
```

**Parameters:**

  - `base_keys` (List[str]): List of base identifiers.
  - `tp_rank` (int): The tensor parallel rank to retrieve (default: 0).
  - `tp_size` (int): Total tensor parallel size (default: 1).

**Returns:**

  - `List[torch.Tensor]`: List of retrieved tensors (or shards). Contains `None` for missing keys.

---
#### put_tensor()

Put a PyTorch tensor into the store.

```python
def put_tensor(self, key: str, tensor: torch.Tensor) -> int
```

**Parameters:**
- `key` (str): Object identifier
- `tensor` (torch.Tensor): The PyTorch tensor to store

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Note:** This function requires `torch` to be installed and available in the environment.

**Example:**
```python
import torch
from mooncake.store import MooncakeDistributedStore

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

# Store a tensor
tensor = torch.randn(100, 100)
result = store.put_tensor("my_tensor", tensor)
if result == 0:
    print("Tensor stored successfully")
```

---

#### get_tensor()

Get a PyTorch tensor from the store.

```python
def get_tensor(self, key: str) -> torch.Tensor
```

**Parameters:**
- `key` (str): Object identifier to retrieve

**Returns:**
- `torch.Tensor`: The retrieved tensor. Returns `None` if not found.

**Note:** This function requires `torch` to be installed and available in the environment.

**Example:**
```python
import torch
from mooncake.store import MooncakeDistributedStore

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

# Store a tensor
tensor = torch.randn(100, 100)
store.put_tensor("my_tensor", tensor)

# Retrieve the tensor
retrieved_tensor = store.get_tensor("my_tensor")
if retrieved_tensor is not None:
    print(f"Retrieved tensor with shape: {retrieved_tensor.shape}")
```

---

#### batch_get_tensor()

Get a batch of PyTorch tensors from the store.

```python
def batch_get_tensor(self, keys: List[str]) -> List[torch.Tensor]
```

**Parameters:**
- `keys` (List[str]): List of object identifiers to retrieve

**Returns:**
- `List[torch.Tensor]`: List of retrieved tensors. Contains `None` for missing keys.

**Note:** This function requires `torch` to be installed and available in the environment.

**Example:**
```python
import torch
from mooncake.store import MooncakeDistributedStore

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

# Store tensors
tensor1 = torch.randn(100, 100)
tensor2 = torch.randn(50, 50)
store.put_tensor("tensor1", tensor1)
store.put_tensor("tensor2", tensor2)

# Retrieve multiple tensors
tensors = store.batch_get_tensor(["tensor1", "tensor2", "nonexistent"])
for i, tensor in enumerate(tensors):
    if tensor is not None:
        print(f"Tensor {i} shape: {tensor.shape}")
    else:
        print(f"Tensor {i} not found")
```

---

#### batch_put_tensor()

Put a batch of PyTorch tensors into the store.

```python
def batch_put_tensor(self, keys: List[str], tensors_list: List[torch.Tensor]) -> List[int]
```

**Parameters:**
- `keys` (List[str]): List of object identifiers
- `tensors_list` (List[torch.Tensor]): List of tensors to store

**Returns:**
- `List[int]`: List of status codes for each tensor operation.

**Note:** This function requires `torch` to be installed and available in the environment.

**Example:**
```python
import torch
from mooncake.store import MooncakeDistributedStore

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

# Create tensors
tensors = [torch.randn(100, 100), torch.randn(50, 50), torch.randn(25, 25)]
keys = ["tensor1", "tensor2", "tensor3"]

# Store multiple tensors
results = store.batch_put_tensor(keys, tensors)
for i, result in enumerate(results):
    if result == 0:
        print(f"Tensor {i} stored successfully")
    else:
        print(f"Tensor {i} failed to store with code: {result}")
```

#### batch_pub_tensor()

Pub a batch of PyTorch tensors into the store with configurable replication settings.

```python
def batch_pub_tensor(self, keys: List[str], tensors_list: List[torch.Tensor], config: ReplicateConfig) -> List[int]
```

**Parameters:**
  - `keys` (List[str]): List of object identifiers
  - `tensors_list` (List[torch.Tensor]): List of tensors to store
  - `config` (ReplicateConfig): Optional replication configuration.

**Returns:**
- `List[int]`: List of status codes for each tensor operation.

**Note:** This function requires `torch` to be installed and available in the environment.

---

#### upsert_tensor()

Insert a tensor if its key is missing, or update the existing tensor if the key already exists. The current tensor upsert helpers use the default `ReplicateConfig` and therefore do not take a `config` parameter.

Upsert a PyTorch tensor into the store.

```python
def upsert_tensor(self, key: str, tensor: torch.Tensor) -> int
```

**Parameters:**
- `key` (str): Object identifier
- `tensor` (torch.Tensor): The PyTorch tensor to insert or update

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Note:** This function requires `torch` to be installed and available in the environment.

#### upsert_tensor_from()

Upsert a tensor directly from a pre-allocated buffer. The buffer layout must be
`[TensorMetadata][tensor data]`, matching the layout used by
`get_tensor_into()`.

```python
def upsert_tensor_from(self, key: str, buffer_ptr: int, size: int) -> int
```

**Parameters:**
- `key` (str): Object identifier
- `buffer_ptr` (int): Buffer pointer containing serialized tensor metadata and payload
- `size` (int): Actual serialized byte length of the tensor buffer

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Note:** This function is not supported for dummy client.

#### batch_upsert_tensor_from()

Upsert multiple tensors directly from pre-allocated buffers. Each buffer must
use layout `[TensorMetadata][tensor data]`.

```python
def batch_upsert_tensor_from(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 serialized tensor buffer pointers
- `sizes` (List[int]): List of actual serialized byte lengths

**Returns:**
- `List[int]`: List of status codes for each tensor upsert

#### batch_upsert_tensor()

Upsert a batch of PyTorch tensors into the store (insert or update).

```python
def batch_upsert_tensor(self, keys: List[str], tensors_list: List[torch.Tensor]) -> List[int]
```

**Parameters:**
- `keys` (List[str]): List of object identifiers
- `tensors_list` (List[torch.Tensor]): List of tensors to insert or update

**Returns:**
- `List[int]`: List of status codes for each tensor operation.

**Note:** This function requires `torch` to be installed and available in the environment. Not supported for dummy client.

#### upsert_pub_tensor()

Upsert a PyTorch tensor with configurable replication settings (insert or update).

```python
def upsert_pub_tensor(self, key: str, tensor: torch.Tensor, config: ReplicateConfig = None) -> int
```

**Parameters:**
- `key` (str): Unique object identifier
- `tensor` (torch.Tensor): PyTorch tensor to insert or update
- `config` (ReplicateConfig, optional): Replication configuration

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Note:** This function requires `torch` to be installed and available in the environment. Not supported for dummy client.

**Example:**
```python
import torch
from mooncake.store import ReplicateConfig

tensor = torch.randn(100, 100)

config = ReplicateConfig()
config.replica_num = 2
config.with_soft_pin = True

result = store.upsert_pub_tensor("my_tensor", tensor, config)
if result == 0:
    print("Tensor upserted successfully")
```

#### batch_upsert_pub_tensor()

Batch upsert PyTorch tensors with configurable replication settings (insert or update).

```python
def batch_upsert_pub_tensor(self, keys: List[str], tensors_list: List[torch.Tensor], config: ReplicateConfig = None) -> List[int]
```

**Parameters:**
- `keys` (List[str]): List of object identifiers
- `tensors_list` (List[torch.Tensor]): List of tensors to insert or update
- `config` (ReplicateConfig, optional): Replication configuration

**Returns:**
- `List[int]`: List of status codes for each tensor operation.

**Note:** This function requires `torch` to be installed and available in the environment. Not supported for dummy client.

---

### PyTorch Tensor Operations (Zero Copy)

These methods provide direct support for storing and retrieving PyTorch tensors. They automatically handle serialization and metadata, and include built-in support for **Tensor Parallelism (TP)** by automatically splitting and reconstructing tensor shards.

⚠️ **Note**: These methods require `torch` to be installed and available in the environment.

#### get_tensor_into()

Get a PyTorch tensor from the store directly into a pre-allocated buffer.

```python
def get_tensor_into(self, key: str, buffer_ptr: int, size: int) -> torch.Tensor
```

**Parameters:**

  - `key` (str): Base identifier of the tensor.
  - `buffer_ptr` (int): The buffer pointer pre-allocated for tensor, and the buffer should be registered.
  - `size` (int): The size of buffer.

**Returns:**

  - `torch.Tensor`: The retrieved tensor (or shard). Returns `None` if not found.

#### batch_get_tensor()

Get a batch of PyTorch tensor from the store directly into a pre-allocated buffer.

```python
def batch_get_tensor_into(self, base_keys: List[str], buffer_ptrs: List[int], sizes: List[int]) -> List[torch.Tensor]
```

**Parameters:**

  - `base_keys` (List[str]): List of base identifiers.
  - `buffer_ptrs` (List[int]): List of buffer pointers pre-allocated for tensor; buffers should be registered.
  - `sizes` (List[int]): List of buffer sizes.

**Returns:**

  - `List[torch.Tensor]`: List of retrieved tensors (or shards). Contains `None` for missing keys.

#### get_tensor_with_tp_into()

Get a PyTorch tensor from the store, specifically retrieving the shard corresponding to the given Tensor Parallel rank, directly into the pre-allocated buffer.

```python
def get_tensor_with_tp_into(self, key: str, buffer_ptr: int, size: int, tp_rank: int = 0, tp_size: int = 1, split_dim: int = 0) -> torch.Tensor
```

**Parameters:**

  - `key` (str): Base identifier of the tensor.
  - `buffer_ptr` (int): The buffer pointer pre-allocated for tensor, and the buffer should be registered.
  - `size` (int): The size of buffer.
  - `tp_rank` (int): The tensor parallel rank to retrieve (default: 0). Fetches key `key_tp_{rank}` if `tp_size > 1`.
  - `tp_size` (int): Total tensor parallel size (default: 1).
  - `split_dim` (int): The dimension used during splitting (default: 0).

**Returns:**

  - `torch.Tensor`: The retrieved tensor (or shard). Returns `None` if not found.

#### batch_get_tensor_with_tp_into()

Get a batch of PyTorch tensor shards from the store for a given Tensor Parallel rank, directly into the pre-allocated buffer.

```python
def batch_get_tensor_with_tp_into(self, base_keys: List[str], buffer_ptrs: List[int], sizes: List[int], tp_rank: int = 0, tp_size: int = 1) -> List[torch.Tensor]
```

**Parameters:**

  - `base_keys` (List[str]): List of base identifiers.
  - `buffer_ptrs` (List[int]): List of buffer pointers pre-allocated for tensor; buffers should be registered.
  - `sizes` (List[int]): List of buffer sizes.
  - `tp_rank` (int): The tensor parallel rank to retrieve (default: 0).
  - `tp_size` (int): Total tensor parallel size (default: 1).

**Returns:**

  - `List[torch.Tensor]`: List of retrieved tensors (or shards). Contains `None` for missing keys.

#### put_tensor_from()

Put a PyTorch tensor into the store directly from a pre-allocated buffer (zero-copy). The buffer must contain data in the same layout as produced by `get_tensor_into`: **\[TensorMetadata\]\[tensor data\]**. The buffer is only read during this call; no Python object references it.

```python
def put_tensor_from(self, key: str, buffer_ptr: int, size: int) -> int
```

**Parameters:**

  - `key` (str): Object identifier for the tensor.
  - `buffer_ptr` (int): The buffer pointer; the buffer should be registered. Layout must be \[TensorMetadata\]\[tensor data\].
  - `size` (int): **Actual serialized byte length** of the data in the buffer (metadata + tensor bytes), not the buffer capacity.

**Returns:**

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

#### batch_put_tensor_from()

Put a batch of PyTorch tensors into the store directly from pre-allocated buffers (zero-copy). Each buffer must contain data in the layout **\[TensorMetadata\]\[tensor data\]**, same as `get_tensor_into`.

```python
def batch_put_tensor_from(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 buffer pointers; buffers should be registered.
  - `sizes` (List[int]): List of **actual serialized byte lengths** for each buffer (metadata + tensor bytes), not buffer capacities.

**Returns:**

  - `List[int]`: List of status codes for each tensor operation (0 = success, non-zero = error code).

#### put_tensor_with_tp_from()

Put a **full tensor** into the store directly from a pre-allocated buffer (zero-copy), for use with Tensor Parallelism. This is the zero-copy counterpart of `put_tensor_with_tp()`: the buffer must contain the complete tensor in layout **\[TensorMetadata\]\[tensor data\]**, and Mooncake will split it internally and store all shards under `key_tp_<rank>`.

```python
def put_tensor_with_tp_from(self, key: str, buffer_ptr: int, size: int, tp_rank: int = 0, tp_size: int = 1, split_dim: int = 0) -> int
```

**Parameters:**

  - `key` (str): Base identifier for the tensor.
  - `buffer_ptr` (int): The buffer pointer; the buffer should be registered.
  - `size` (int): **Actual serialized byte length** of the full tensor in the buffer.
  - `tp_rank` (int): Kept for signature compatibility with `put_tensor_with_tp()` (default: 0). It does **not** mean "only write one shard".
  - `tp_size` (int): Total tensor parallel size (default: 1). If 1, equivalent to `put_tensor_from(key, buffer_ptr, size)`.
  - `split_dim` (int): Dimension along which the full tensor is split before storing shards.

**Returns:**

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

#### batch_put_tensor_with_tp_from()

Put a batch of **full tensors** into the store directly from pre-allocated buffers (zero-copy). This is the zero-copy counterpart of `batch_put_tensor_with_tp()`: each buffer contains one full tensor in layout **\[TensorMetadata\]\[tensor data\]**, and Mooncake splits each tensor internally and stores all TP shards.

```python
def batch_put_tensor_with_tp_from(self, base_keys: List[str], buffer_ptrs: List[int], sizes: List[int], tp_rank: int = 0, tp_size: int = 1, split_dim: int = 0) -> List[int]
```

**Parameters:**

  - `base_keys` (List[str]): List of base identifiers.
  - `buffer_ptrs` (List[int]): List of buffer pointers; buffers should be registered.
  - `sizes` (List[int]): List of **actual serialized byte lengths** for each full-tensor buffer.
  - `tp_rank` (int): Kept for signature compatibility with `batch_put_tensor_with_tp()` (default: 0). It does **not** select a single shard to write.
  - `tp_size` (int): Total tensor parallel size (default: 1). If 1, equivalent to `batch_put_tensor_from(base_keys, buffer_ptrs, sizes)`.
  - `split_dim` (int): Dimension along which each full tensor is split before storing shards.

**Returns:**

  - `List[int]`: List of status codes for each tensor operation (0 = success, non-zero = error code).

---

### Batch Zero-Copy Operations

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

```python
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).

```python
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:**

<details>
<summary>Click to expand: Batch zero-copy retrieval example</summary>

```python
# 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)
```

</details>

---

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

```python
def batch_put_from_multi_buffers(self, keys: List[str], all_buffer_ptrs: List[List[int]], all_sizes: List[List[int]],
                                 config: ReplicateConfig = None) -> List[int]
```

**Parameters:**
- `keys` (List[str]): List of object identifiers
- `all_buffer_ptrs` (List[int]): all List of memory addresses
- `sizes` (List[int]): all 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_multi_buffers()

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

```python
def batch_get_into_multi_buffers(self, keys: List[str], all_buffer_ptrs: List[List[int], all_sizes: List[List[int]) ->
List[int]
```

**Parameters:**
- `keys` (List[str]): List of object identifiers
- `all_buffer_ptrs` (List[int]): List of memory addresses
- `all_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:**

<details>
<summary>Click to expand: Batch zero-copy put and get for multiple buffers example</summary>

```python
tensor = torch.ones(10, 61, 128*1024, dtype=torch.int8)
data_ptr = tensor.data_ptr()
store.register_buffer(data_ptr, 10*61*128*1024)

target_tensor = torch.zeros(10, 61, 128*1024, dtype=torch.int8)
target_data_ptr = target_tensor.data_ptr()
store.register_buffer(target_data_ptr, 10*61*128*1024)

all_local_addrs = []
all_remote_addrs = []
all_sizes = []
keys = []
for block_i in range(10):
  local_addrs = []
  remote_addrs = []
  sizes = []
  for _ in range(61):
    local_addrs.append(data_ptr)
    remote_addrs.append(target_data_ptr)
    sizes.append(128*1024)
    data_ptr += 128*1024
    target_data_ptr += 128*1024
  all_local_addrs.append(local_addrs)
  all_remote_addrs.append(remote_addrs)
  all_sizes.append(sizes)
  keys.append(f"kv_{rank}_{block_i}")

config = ReplicateConfig()
config.prefer_alloc_in_same_node = True
store.batch_put_from_multi_buffers(keys, all_local_addrs, all_sizes, config)
store.batch_get_into_multi_buffers(keys, all_remote_addrs, all_sizes, True)

store.unregister_buffer(tensor.data_ptr())
store.unregister_buffer(target_tensor.data_ptr())
```
</details>

## MooncakeHostMemAllocator Class

The `MooncakeHostMemAllocator` class provides host memory allocation capabilities for Mooncake Store operations.

### Class Definition

```python
from mooncake.store import MooncakeHostMemAllocator

# Create an allocator instance
allocator = MooncakeHostMemAllocator()
```

### Methods

#### alloc()
Allocate memory from the host memory pool.

```python
def alloc(self, size: int) -> int
```

**Parameters:**
- `size` (int): Size of memory to allocate in bytes

**Returns:**
- `int`: Memory address as integer, or 0 on failure

**Example:**
```python
allocator = MooncakeHostMemAllocator()
ptr = allocator.alloc(1024 * 1024)  # Allocate 1MB
if ptr != 0:
    print(f"Allocated memory at address: {ptr}")
```

#### free()
Free previously allocated memory.

```python
def free(self, ptr: int) -> int
```

**Parameters:**
- `ptr` (int): Memory address to free

**Returns:**
- `int`: Status code (0 = success, non-zero = error code)

**Example:**
```python
result = allocator.free(ptr)
if result == 0:
    print("Memory freed successfully")
```

---

## bind_to_numa_node Function

The `bind_to_numa_node` function binds the current thread and memory allocation preference to a specified NUMA node.

### Function Definition

```python
from mooncake.store import bind_to_numa_node

# Bind to NUMA node
bind_to_numa_node(node: int)
```

**Parameters:**
- `node` (int): NUMA node number to bind to

**Example:**
```python
from mooncake.store import bind_to_numa_node

# Bind current thread to NUMA node 0
bind_to_numa_node(0)
```

---

---

## 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

📋 **Complete Error Codes Reference**: See [Error Code Explanation](../troubleshooting/error-code.md) 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

---