# libCacheSim Python Binding
[](https://github.com/cacheMon/libCacheSim-python/actions/workflows/build.yml)
[](docs.libcachesim.com/python)
libCacheSim is fast with the features from [underlying libCacheSim lib](https://github.com/1a1a11a/libCacheSim):
- **High performance** - over 20M requests/sec for a realistic trace replay
- **High memory efficiency** - predictable and small memory footprint
- **Parallelism out-of-the-box** - uses the many CPU cores to speed up trace analysis and cache simulations
libCacheSim is flexible and easy to use with:
- **Seamless integration** with [open-source cache dataset](https://github.com/cacheMon/cache_dataset) consisting of thousands traces hosted on S3
- **High-throughput simulation** with the [underlying libCacheSim lib](https://github.com/1a1a11a/libCacheSim)
- **Detailed cache requests** and other internal data control
- **Customized plugin cache development** without any compilation
## Installation
### Quick Install
Binary installers for the latest released version are available at the [Python Package Index (PyPI)](https://pypi.org/project/libcachesim).
```bash
pip install libcachesim
```
Visit our [documentation](https://cachemon.github.io/libCacheSim-python/getting_started/quickstart/) to learn more.
### Installation from sources
If there are no wheels suitable for your environment, consider building from source.
```bash
git clone https://github.com/cacheMon/libCacheSim-python.git
cd libCacheSim-python
bash scripts/install.sh
```
Run all tests to ensure the package works.
```bash
python -m pytest tests/
```
## Quick Start
### Cache Simulation
With libcachesim installed, you can start cache simulation for some eviction algorithm and cache traces:
```python
import libcachesim as lcs
# Step 1: Open a trace hosted on S3 (find more via https://github.com/cacheMon/cache_dataset)
URI = "s3://cache-datasets/cache_dataset_oracleGeneral/2007_msr/msr_hm_0.oracleGeneral.zst"
reader = lcs.TraceReader(
trace = URI,
trace_type = lcs.TraceType.ORACLE_GENERAL_TRACE,
reader_init_params = lcs.ReaderInitParam(ignore_obj_size=False)
)
# Step 2: Initialize cache
cache = lcs.S3FIFO(
cache_size=1024*1024,
# Cache specific parameters
small_size_ratio=0.2,
ghost_size_ratio=0.8,
move_to_main_threshold=2,
)
# Step 3: Process entire trace efficiently (C++ backend)
req_miss_ratio, byte_miss_ratio = cache.process_trace(reader)
print(f"Request miss ratio: {req_miss_ratio:.4f}, Byte miss ratio: {byte_miss_ratio:.4f}")
# Step 3.1: Process the first 1000 requests
cache = lcs.S3FIFO(
cache_size=1024 * 1024,
# Cache specific parameters
small_size_ratio=0.2,
ghost_size_ratio=0.8,
move_to_main_threshold=2,
)
req_miss_ratio, byte_miss_ratio = cache.process_trace(reader, start_req=0, max_req=1000)
print(f"Request miss ratio: {req_miss_ratio:.4f}, Byte miss ratio: {byte_miss_ratio:.4f}")
```
## Plugin System
libCacheSim allows you to develop your own cache eviction algorithms and test them via the plugin system without any C/C++ compilation required.
### Plugin Cache Overview
The `PluginCache` allows you to define custom caching behavior through Python callback functions. You need to implement these callback functions:
| Function | Signature | Description |
|----------|-----------|-------------|
| `init_hook` | `(common_cache_params: CommonCacheParams) -> Any` | Initialize your data structure |
| `hit_hook` | `(data: Any, request: Request) -> None` | Handle cache hits |
| `miss_hook` | `(data: Any, request: Request) -> None` | Handle cache misses |
| `eviction_hook` | `(data: Any, request: Request) -> int` | Return object ID to evict |
| `remove_hook` | `(data: Any, obj_id: int) -> None` | Clean up when object removed |
| `free_hook` | `(data: Any) -> None` | [Optional] Final cleanup |
### Example: Implementing LRU via Plugin System
```python
from collections import OrderedDict
from typing import Any
from libcachesim import PluginCache, LRU, CommonCacheParams, Request, SyntheticReader
def init_hook(_: CommonCacheParams) -> Any:
return OrderedDict()
def hit_hook(data: Any, req: Request) -> None:
data.move_to_end(req.obj_id, last=True)
def miss_hook(data: Any, req: Request) -> None:
data.__setitem__(req.obj_id, req.obj_size)
def eviction_hook(data: Any, _: Request) -> int:
return data.popitem(last=False)[0]
def remove_hook(data: Any, obj_id: int) -> None:
data.pop(obj_id, None)
def free_hook(data: Any) -> None:
data.clear()
plugin_lru_cache = PluginCache(
cache_size=128,
cache_init_hook=init_hook,
cache_hit_hook=hit_hook,
cache_miss_hook=miss_hook,
cache_eviction_hook=eviction_hook,
cache_remove_hook=remove_hook,
cache_free_hook=free_hook,
cache_name="Plugin_LRU",
)
reader = SyntheticReader(
num_objects=1000, num_of_req=10000, obj_size=1, alpha=1.0, dist="zipf"
)
req_miss_ratio, byte_miss_ratio = plugin_lru_cache.process_trace(reader)
```
By defining custom hook functions for cache initialization, hit, miss, eviction, removal, and cleanup, users can easily prototype and test their own cache eviction algorithms.
### Getting Help
- Check [project documentation](https://docs.libcachesim.com/python) for detailed guides
- Open issues on [GitHub](https://github.com/cacheMon/libCacheSim-python/issues/new/choose)
- Review [examples](/examples) in the main repository
---
## Reference
<details>
<summary> Please cite the following papers if you use libCacheSim. </summary>
```
@inproceedings{yang2020-workload,
author = {Juncheng Yang and Yao Yue and K. V. Rashmi},
title = {A large-scale analysis of hundreds of in-memory cache clusters at Twitter},
booktitle = {14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20)},
year = {2020},
isbn = {978-1-939133-19-9},
pages = {191--208},
url = {https://www.usenix.org/conference/osdi20/presentation/yang},
publisher = {USENIX Association},
}
@inproceedings{yang2023-s3fifo,
title = {FIFO Queues Are All You Need for Cache Eviction},
author = {Juncheng Yang and Yazhuo Zhang and Ziyue Qiu and Yao Yue and K.V. Rashmi},
isbn = {9798400702297},
publisher = {Association for Computing Machinery},
booktitle = {Symposium on Operating Systems Principles (SOSP'23)},
pages = {130–149},
numpages = {20},
year={2023}
}
@inproceedings{yang2023-qdlp,
author = {Juncheng Yang and Ziyue Qiu and Yazhuo Zhang and Yao Yue and K.V. Rashmi},
title = {FIFO Can Be Better than LRU: The Power of Lazy Promotion and Quick Demotion},
year = {2023},
isbn = {9798400701955},
publisher = {Association for Computing Machinery},
doi = {10.1145/3593856.3595887},
booktitle = {Proceedings of the 19th Workshop on Hot Topics in Operating Systems (HotOS23)},
pages = {70–79},
numpages = {10},
}
```
If you used libCacheSim in your research, please cite the above papers.
</details>
---
## License
See [LICENSE](LICENSE) for details.
---
Raw data
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"description": "# libCacheSim Python Binding\n\n[](https://github.com/cacheMon/libCacheSim-python/actions/workflows/build.yml)\n[](docs.libcachesim.com/python)\n\n\nlibCacheSim is fast with the features from [underlying libCacheSim lib](https://github.com/1a1a11a/libCacheSim):\n\n- **High performance** - over 20M requests/sec for a realistic trace replay\n- **High memory efficiency** - predictable and small memory footprint\n- **Parallelism out-of-the-box** - uses the many CPU cores to speed up trace analysis and cache simulations\n\nlibCacheSim is flexible and easy to use with:\n\n- **Seamless integration** with [open-source cache dataset](https://github.com/cacheMon/cache_dataset) consisting of thousands traces hosted on S3\n- **High-throughput simulation** with the [underlying libCacheSim lib](https://github.com/1a1a11a/libCacheSim)\n- **Detailed cache requests** and other internal data control\n- **Customized plugin cache development** without any compilation\n\n## Installation\n\n### Quick Install\n\nBinary installers for the latest released version are available at the [Python Package Index (PyPI)](https://pypi.org/project/libcachesim).\n\n```bash\npip install libcachesim\n```\n\nVisit our [documentation](https://cachemon.github.io/libCacheSim-python/getting_started/quickstart/) to learn more.\n\n### Installation from sources\n\nIf there are no wheels suitable for your environment, consider building from source.\n\n```bash\ngit clone https://github.com/cacheMon/libCacheSim-python.git\ncd libCacheSim-python\nbash scripts/install.sh\n```\n\nRun all tests to ensure the package works.\n\n```bash\npython -m pytest tests/\n```\n\n## Quick Start\n\n### Cache Simulation\n\nWith libcachesim installed, you can start cache simulation for some eviction algorithm and cache traces:\n\n```python\nimport libcachesim as lcs\n\n# Step 1: Open a trace hosted on S3 (find more via https://github.com/cacheMon/cache_dataset)\nURI = \"s3://cache-datasets/cache_dataset_oracleGeneral/2007_msr/msr_hm_0.oracleGeneral.zst\"\nreader = lcs.TraceReader(\n trace = URI,\n trace_type = lcs.TraceType.ORACLE_GENERAL_TRACE,\n reader_init_params = lcs.ReaderInitParam(ignore_obj_size=False)\n)\n\n# Step 2: Initialize cache\ncache = lcs.S3FIFO(\n cache_size=1024*1024,\n # Cache specific parameters\n small_size_ratio=0.2,\n ghost_size_ratio=0.8,\n move_to_main_threshold=2,\n)\n\n# Step 3: Process entire trace efficiently (C++ backend)\nreq_miss_ratio, byte_miss_ratio = cache.process_trace(reader)\nprint(f\"Request miss ratio: {req_miss_ratio:.4f}, Byte miss ratio: {byte_miss_ratio:.4f}\")\n\n# Step 3.1: Process the first 1000 requests\ncache = lcs.S3FIFO(\n cache_size=1024 * 1024,\n # Cache specific parameters\n small_size_ratio=0.2,\n ghost_size_ratio=0.8,\n move_to_main_threshold=2,\n)\nreq_miss_ratio, byte_miss_ratio = cache.process_trace(reader, start_req=0, max_req=1000)\nprint(f\"Request miss ratio: {req_miss_ratio:.4f}, Byte miss ratio: {byte_miss_ratio:.4f}\")\n```\n\n## Plugin System\n\nlibCacheSim allows you to develop your own cache eviction algorithms and test them via the plugin system without any C/C++ compilation required.\n\n### Plugin Cache Overview\n\nThe `PluginCache` allows you to define custom caching behavior through Python callback functions. You need to implement these callback functions:\n\n| Function | Signature | Description |\n|----------|-----------|-------------|\n| `init_hook` | `(common_cache_params: CommonCacheParams) -> Any` | Initialize your data structure |\n| `hit_hook` | `(data: Any, request: Request) -> None` | Handle cache hits |\n| `miss_hook` | `(data: Any, request: Request) -> None` | Handle cache misses |\n| `eviction_hook` | `(data: Any, request: Request) -> int` | Return object ID to evict |\n| `remove_hook` | `(data: Any, obj_id: int) -> None` | Clean up when object removed |\n| `free_hook` | `(data: Any) -> None` | [Optional] Final cleanup |\n\n### Example: Implementing LRU via Plugin System\n\n```python\nfrom collections import OrderedDict\nfrom typing import Any\n\nfrom libcachesim import PluginCache, LRU, CommonCacheParams, Request, SyntheticReader\n\ndef init_hook(_: CommonCacheParams) -> Any:\n return OrderedDict()\n\ndef hit_hook(data: Any, req: Request) -> None:\n data.move_to_end(req.obj_id, last=True)\n\ndef miss_hook(data: Any, req: Request) -> None:\n data.__setitem__(req.obj_id, req.obj_size)\n\ndef eviction_hook(data: Any, _: Request) -> int:\n return data.popitem(last=False)[0]\n\ndef remove_hook(data: Any, obj_id: int) -> None:\n data.pop(obj_id, None)\n\ndef free_hook(data: Any) -> None:\n data.clear()\n\nplugin_lru_cache = PluginCache(\n cache_size=128,\n cache_init_hook=init_hook,\n cache_hit_hook=hit_hook,\n cache_miss_hook=miss_hook,\n cache_eviction_hook=eviction_hook,\n cache_remove_hook=remove_hook,\n cache_free_hook=free_hook,\n cache_name=\"Plugin_LRU\",\n)\n\nreader = SyntheticReader(\n num_objects=1000, num_of_req=10000, obj_size=1, alpha=1.0, dist=\"zipf\"\n)\nreq_miss_ratio, byte_miss_ratio = plugin_lru_cache.process_trace(reader)\n```\n\nBy defining custom hook functions for cache initialization, hit, miss, eviction, removal, and cleanup, users can easily prototype and test their own cache eviction algorithms.\n\n### Getting Help\n\n- Check [project documentation](https://docs.libcachesim.com/python) for detailed guides\n- Open issues on [GitHub](https://github.com/cacheMon/libCacheSim-python/issues/new/choose)\n- Review [examples](/examples) in the main repository\n\n---\n## Reference\n<details>\n<summary> Please cite the following papers if you use libCacheSim. </summary>\n\n```\n@inproceedings{yang2020-workload,\n author = {Juncheng Yang and Yao Yue and K. V. Rashmi},\n title = {A large-scale analysis of hundreds of in-memory cache clusters at Twitter},\n booktitle = {14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20)},\n year = {2020},\n isbn = {978-1-939133-19-9},\n pages = {191--208},\n url = {https://www.usenix.org/conference/osdi20/presentation/yang},\n publisher = {USENIX Association},\n}\n\n@inproceedings{yang2023-s3fifo,\n title = {FIFO Queues Are All You Need for Cache Eviction},\n author = {Juncheng Yang and Yazhuo Zhang and Ziyue Qiu and Yao Yue and K.V. Rashmi},\n isbn = {9798400702297},\n publisher = {Association for Computing Machinery},\n booktitle = {Symposium on Operating Systems Principles (SOSP'23)},\n pages = {130\u2013149},\n numpages = {20},\n year={2023}\n}\n\n@inproceedings{yang2023-qdlp,\n author = {Juncheng Yang and Ziyue Qiu and Yazhuo Zhang and Yao Yue and K.V. Rashmi},\n title = {FIFO Can Be Better than LRU: The Power of Lazy Promotion and Quick Demotion},\n year = {2023},\n isbn = {9798400701955},\n publisher = {Association for Computing Machinery},\n doi = {10.1145/3593856.3595887},\n booktitle = {Proceedings of the 19th Workshop on Hot Topics in Operating Systems (HotOS23)},\n pages = {70\u201379},\n numpages = {10},\n}\n```\nIf you used libCacheSim in your research, please cite the above papers.\n\n</details>\n\n---\n\n## License\nSee [LICENSE](LICENSE) for details.\n\n---\n",
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