| Name | bodo JSON |
| Version |
2025.4
JSON |
| download |
| home_page | None |
| Summary | High-Performance Python Compute Engine for Data and AI |
| upload_time | 2025-04-07 21:42:43 |
| maintainer | None |
| docs_url | None |
| author | Bodo.ai |
| requires_python | <3.13,>=3.10 |
| license | None |
| keywords |
data
analytics
cluster
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!--
NOTE: the example in this file is covered by tests in bodo/tests/test_quickstart_docs.py. Any changes to the examples in this file should also update the corresponding unit test.
-->
<h3 align="center">
<a href="https://docs.bodo.ai/latest/" target="_blank"><b>Docs</b></a>
·
<a href="https://bodocommunity.slack.com/join/shared_invite/zt-qwdc8fad-6rZ8a1RmkkJ6eOX1X__knA#/shared-invite/email" target="_blank"><b>Slack</b></a>
·
<a href="https://www.bodo.ai/benchmarks/" target="_blank"><b>Benchmarks</b></a>
</h3>
# Bodo: High-Performance Python Compute Engine for Data and AI
Bodo is a cutting edge compute engine for large scale Python data processing. Powered by an innovative auto-parallelizing just-in-time compiler, Bodo transforms Python programs into highly optimized, parallel binaries without requiring code rewrites, which makes Bodo [20x to 240x faster](https://github.com/bodo-ai/Bodo/tree/main/benchmarks/nyc_taxi) compared to alternatives!
<img src="benchmarks/img/nyc-taxi-benchmark.png" alt="NYC Taxi Benchmark" width="500"/>
Unlike traditional distributed computing frameworks, Bodo:
- Seamlessly supports native Python APIs like Pandas and NumPy.
- Eliminates runtime overheads common in driver-executor models by leveraging Message Passing Interface (MPI) tech for true distributed execution.
## Goals
Bodo makes Python run much (much!) faster than it normally does!
1. **Exceptional Performance:**
Deliver HPC-grade performance and scalability for Python data workloads as if the code was written in C++/MPI, whether running on a laptop or across large cloud clusters.
2. **Easy to Use:**
Easily integrate into Python workflows with a simple decorator, and support native Pandas and NumPy APIs.
3. **Interoperable:**
Compatible with regular Python ecosystem, and can selectively speed up only the functions that are Bodo supported.
4. **Integration with Modern Data Infrastructure:**
Provide robust support for industry-leading data platforms like Apache Iceberg and Snowflake, enabling smooth interoperability with existing ecosystems.
## Non-goals
1. *Full Python Language Support:*
We are currently focused on a targeted subset of Python used for data-intensive and computationally heavy workloads, rather than supporting the entire Python syntax and all library APIs.
2. *Non-Data Workloads:*
Prioritize applications in data engineering, data science, and AI/ML. Bodo is not designed for general-purpose use cases that are non-data-centric.
3. *Real-time Compilation:*
While compilation time is improving, Bodo is not yet optimized for scenarios requiring very short compilation times (e.g., workloads with execution times of only a few seconds).
## Key Features
- Automatic optimization & parallelization of Python programs using Pandas and NumPy.
- Linear scalability from laptops to large-scale clusters and supercomputers.
- Advanced scalable I/O support for Iceberg, Snowflake, Parquet, CSV, and JSON with automatic filter pushdown and column pruning for optimized data access.
- High performance SQL Engine that is natively integrated into Python.
See Bodo documentation to learn more: https://docs.bodo.ai/
## Installation
Note: Bodo requires Python 3.10, 3.11, or 3.12.
Bodo can be installed using Pip or Conda:
```bash
pip install -U bodo
```
or
```bash
conda create -n Bodo python=3.12 -c conda-forge
conda activate Bodo
conda install bodo -c bodo.ai -c conda-forge
```
Bodo works with Linux x86, both Mac x86 and Mac ARM, and Windows right now. We will have Linux ARM support (and more) coming soon!
## Example Code
Here is an example Pandas code that reads and processes a sample Parquet dataset with Bodo.
```python
import pandas as pd
import numpy as np
import bodo
import time
# Generate sample data
NUM_GROUPS = 30
NUM_ROWS = 20_000_000
df = pd.DataFrame({
"A": np.arange(NUM_ROWS) % NUM_GROUPS,
"B": np.arange(NUM_ROWS)
})
df.to_parquet("my_data.pq")
@bodo.jit(cache=True)
def computation():
t1 = time.time()
df = pd.read_parquet("my_data.pq")
df2 = pd.DataFrame({"A": df.apply(lambda r: 0 if r.A == 0 else (r.B // r.A), axis=1)})
df2.to_parquet("out.pq")
print("Execution time:", time.time() - t1)
computation()
```
## How to Contribute
Please read our latest [project contribution guide](CONTRIBUTING.md).
## Getting involved
You can join our community and collaborate with other contributors by joining our [Slack channel](https://bodocommunity.slack.com/join/shared_invite/zt-qwdc8fad-6rZ8a1RmkkJ6eOX1X__knA#/shared-invite/email) – we’re excited to hear your ideas and help you get started!
[](https://codecov.io/github/bodo-ai/Bodo)
Raw data
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"description": "<!--\nNOTE: the example in this file is covered by tests in bodo/tests/test_quickstart_docs.py. Any changes to the examples in this file should also update the corresponding unit test.\n -->\n\n\n\n<h3 align=\"center\">\n <a href=\"https://docs.bodo.ai/latest/\" target=\"_blank\"><b>Docs</b></a>\n · \n <a href=\"https://bodocommunity.slack.com/join/shared_invite/zt-qwdc8fad-6rZ8a1RmkkJ6eOX1X__knA#/shared-invite/email\" target=\"_blank\"><b>Slack</b></a>\n · \n <a href=\"https://www.bodo.ai/benchmarks/\" target=\"_blank\"><b>Benchmarks</b></a>\n</h3>\n\n# Bodo: High-Performance Python Compute Engine for Data and AI\n\nBodo is a cutting edge compute engine for large scale Python data processing. Powered by an innovative auto-parallelizing just-in-time compiler, Bodo transforms Python programs into highly optimized, parallel binaries without requiring code rewrites, which makes Bodo [20x to 240x faster](https://github.com/bodo-ai/Bodo/tree/main/benchmarks/nyc_taxi) compared to alternatives!\n\n<img src=\"benchmarks/img/nyc-taxi-benchmark.png\" alt=\"NYC Taxi Benchmark\" width=\"500\"/>\n\nUnlike traditional distributed computing frameworks, Bodo:\n- Seamlessly supports native Python APIs like Pandas and NumPy.\n- Eliminates runtime overheads common in driver-executor models by leveraging Message Passing Interface (MPI) tech for true distributed execution.\n\n## Goals\n\nBodo makes Python run much (much!) faster than it normally does!\n\n1. **Exceptional Performance:**\nDeliver HPC-grade performance and scalability for Python data workloads as if the code was written in C++/MPI, whether running on a laptop or across large cloud clusters.\n\n2. **Easy to Use:**\nEasily integrate into Python workflows with a simple decorator, and support native Pandas and NumPy APIs.\n\n3. **Interoperable:**\nCompatible with regular Python ecosystem, and can selectively speed up only the functions that are Bodo supported.\n\n4. **Integration with Modern Data Infrastructure:**\nProvide robust support for industry-leading data platforms like Apache Iceberg and Snowflake, enabling smooth interoperability with existing ecosystems.\n\n\n## Non-goals\n\n1. *Full Python Language Support:*\nWe are currently focused on a targeted subset of Python used for data-intensive and computationally heavy workloads, rather than supporting the entire Python syntax and all library APIs.\n\n2. *Non-Data Workloads:*\nPrioritize applications in data engineering, data science, and AI/ML. Bodo is not designed for general-purpose use cases that are non-data-centric.\n\n3. *Real-time Compilation:*\nWhile compilation time is improving, Bodo is not yet optimized for scenarios requiring very short compilation times (e.g., workloads with execution times of only a few seconds).\n\n\n## Key Features\n\n- Automatic optimization & parallelization of Python programs using Pandas and NumPy.\n- Linear scalability from laptops to large-scale clusters and supercomputers.\n- Advanced scalable I/O support for Iceberg, Snowflake, Parquet, CSV, and JSON with automatic filter pushdown and column pruning for optimized data access.\n- High performance SQL Engine that is natively integrated into Python.\n\nSee Bodo documentation to learn more: https://docs.bodo.ai/\n\n\n## Installation\n\nNote: Bodo requires Python 3.10, 3.11, or 3.12.\n\nBodo can be installed using Pip or Conda:\n\n```bash\npip install -U bodo\n```\n\nor\n\n```bash\nconda create -n Bodo python=3.12 -c conda-forge\nconda activate Bodo\nconda install bodo -c bodo.ai -c conda-forge\n```\n\nBodo works with Linux x86, both Mac x86 and Mac ARM, and Windows right now. We will have Linux ARM support (and more) coming soon!\n\n## Example Code\n\nHere is an example Pandas code that reads and processes a sample Parquet dataset with Bodo.\n\n\n```python\nimport pandas as pd\nimport numpy as np\nimport bodo\nimport time\n\n# Generate sample data\nNUM_GROUPS = 30\nNUM_ROWS = 20_000_000\n\ndf = pd.DataFrame({\n \"A\": np.arange(NUM_ROWS) % NUM_GROUPS,\n \"B\": np.arange(NUM_ROWS)\n})\ndf.to_parquet(\"my_data.pq\")\n\n@bodo.jit(cache=True)\ndef computation():\n t1 = time.time()\n df = pd.read_parquet(\"my_data.pq\")\n df2 = pd.DataFrame({\"A\": df.apply(lambda r: 0 if r.A == 0 else (r.B // r.A), axis=1)})\n df2.to_parquet(\"out.pq\")\n print(\"Execution time:\", time.time() - t1)\n\ncomputation()\n```\n\n## How to Contribute\n\nPlease read our latest [project contribution guide](CONTRIBUTING.md).\n\n## Getting involved\n\nYou can join our community and collaborate with other contributors by joining our [Slack channel](https://bodocommunity.slack.com/join/shared_invite/zt-qwdc8fad-6rZ8a1RmkkJ6eOX1X__knA#/shared-invite/email) \u2013 we\u2019re excited to hear your ideas and help you get started!\n\n[](https://codecov.io/github/bodo-ai/Bodo)",
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