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https://github.com/blazingdb/blazingsql
BlazingSQL is a lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.
https://github.com/blazingdb/blazingsql
arrow artificial-intelligence blazingsql conda-environment cudf data-science gpu gpu-acceleration gpu-dataframes machine-learning machine-learning-workflow python rapids rapidsai sql sql-engine
Last synced: 29 days ago
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BlazingSQL is a lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.
- Host: GitHub
- URL: https://github.com/blazingdb/blazingsql
- Owner: BlazingDB
- License: apache-2.0
- Created: 2018-09-24T18:25:45.000Z (about 6 years ago)
- Default Branch: branch-21.08
- Last Pushed: 2022-09-16T23:58:37.000Z (about 2 years ago)
- Last Synced: 2024-04-13T23:01:47.693Z (7 months ago)
- Topics: arrow, artificial-intelligence, blazingsql, conda-environment, cudf, data-science, gpu, gpu-acceleration, gpu-dataframes, machine-learning, machine-learning-workflow, python, rapids, rapidsai, sql, sql-engine
- Language: C++
- Homepage: https://blazingsql.com
- Size: 41.4 MB
- Stars: 1,896
- Watchers: 55
- Forks: 179
- Open Issues: 146
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
> A lightweight, GPU accelerated, SQL engine built on the [RAPIDS.ai](https://rapids.ai) ecosystem.
Get Started on app.blazingsql.com
[Getting Started](#getting-started) | [Documentation](https://docs.blazingdb.com) | [Examples](#examples) | [Contributing](#contributing) | [License](LICENSE) | [Blog](https://blog.blazingdb.com) | [Try Now](https://app.blazingsql.com/jupyter/user-redirect/lab/workspaces/auto-b/tree/Welcome_to_BlazingSQL_Notebooks/welcome.ipynb)
BlazingSQL is a GPU accelerated SQL engine built on top of the RAPIDS ecosystem. RAPIDS is based on the [Apache Arrow](http://arrow.apache.org) columnar memory format, and [cuDF](https://github.com/rapidsai/cudf) is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
BlazingSQL is a SQL interface for cuDF, with various features to support large scale data science workflows and enterprise datasets.
* **Query Data Stored Externally** - a single line of code can register remote storage solutions, such as Amazon S3.
* **Simple SQL** - incredibly easy to use, run a SQL query and the results are GPU DataFrames (GDFs).
* **Interoperable** - GDFs are immediately accessible to any [RAPIDS](htts://github.com/rapidsai) library for data science workloads.
Try our 5-min [Welcome Notebook](https://app.blazingsql.com/jupyter/user-redirect/lab/workspaces/auto-b/tree/Welcome_to_BlazingSQL_Notebooks/welcome.ipynb) to start using BlazingSQL and RAPIDS AI.
# Getting Started
Here's two copy + paste reproducable BlazingSQL snippets, keep scrolling to find [example Notebooks](#examples) below.
Create and query a table from a `cudf.DataFrame` with progress bar:
```python
import cudf
df = cudf.DataFrame()
df['key'] = ['a', 'b', 'c', 'd', 'e']
df['val'] = [7.6, 2.9, 7.1, 1.6, 2.2]
from blazingsql import BlazingContext
bc = BlazingContext(enable_progress_bar=True)
bc.create_table('game_1', df)
bc.sql('SELECT * FROM game_1 WHERE val > 4') # the query progress will be shown
```
| | Key | Value |
| - | -:| ---:|
| 0 | a | 7.6 |
| 1 | b | 7.1 |
Create and query a table from a AWS S3 bucket:
```python
from blazingsql import BlazingContext
bc = BlazingContext()
bc.s3('blazingsql-colab', bucket_name='blazingsql-colab')
bc.create_table('taxi', 's3://blazingsql-colab/yellow_taxi/taxi_data.parquet')
bc.sql('SELECT passenger_count, trip_distance FROM taxi LIMIT 2')
```
| | passenger_count | fare_amount |
| - | -:| ---:|
| 0 | 1.0 | 1.1 |
| 1 | 1.0 | 0.7 |
## Examples
| Notebook Title | Description | Try Now |
| -------------- | ----------- | ------- |
| Welcome Notebook | An introduction to BlazingSQL Notebooks and the GPU Data Science Ecosystem. | 
|
| The DataFrame | Learn how to use BlazingSQL and cuDF to create GPU DataFrames with SQL and Pandas-like APIs. | |
| Data Visualization | Plug in your favorite Python visualization packages, or use GPU accelerated visualization tools to render millions of rows in a flash. | |
| Machine Learning | Learn about cuML, mirrored after the Scikit-Learn API, it offers GPU accelerated machine learning on GPU DataFrames. | |
## Documentation
You can find our full documentation at [docs.blazingdb.com](https://docs.blazingdb.com/docs).
# Prerequisites
* [Anaconda or Miniconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html) installed
* OS Support
* Ubuntu 16.04/18.04 LTS
* CentOS 7
* GPU Support
* Pascal or Better
* Compute Capability >= 6.0
* CUDA Support
* 11.0
* 11.2
* 11.4
* Python Support
* 3.7
* 3.8
# Install Using Conda
BlazingSQL can be installed with conda ([miniconda](https://conda.io/miniconda.html), or the full [Anaconda distribution](https://www.anaconda.com/download)) from the [blazingsql](https://anaconda.org/blazingsql/) channel:
## Stable Version
```bash
conda install -c blazingsql -c rapidsai -c nvidia -c conda-forge -c defaults blazingsql python=$PYTHON_VERSION cudatoolkit=$CUDA_VERSION
```
Where $CUDA_VERSION is 11.0, 11.2 or 11.4 and $PYTHON_VERSION is 3.7 or 3.8
*For example for CUDA 11.2 and Python 3.8:*
```bash
conda install -c blazingsql -c rapidsai -c nvidia -c conda-forge -c defaults blazingsql python=3.8 cudatoolkit=11.2
```
## Nightly Version
For nightly version cuda 11+ are only supported, see https://github.com/rapidsai/cudf#cudagpu-requirements
```bash
conda install -c blazingsql-nightly -c rapidsai-nightly -c nvidia -c conda-forge -c defaults blazingsql python=$PYTHON_VERSION cudatoolkit=$CUDA_VERSION
```
Where $CUDA_VERSION is 11.0, 11.2 or 11.4 and $PYTHON_VERSION is 3.7 or 3.8
*For example for CUDA 11.2 and Python 3.8:*
```bash
conda install -c blazingsql-nightly -c rapidsai-nightly -c nvidia -c conda-forge -c defaults blazingsql python=3.8 cudatoolkit=11.2
```
# Build/Install from Source (Conda Environment)
This is the recommended way of building all of the BlazingSQL components and dependencies from source. It ensures that all the dependencies are available to the build process.
## Stable Version
### Install build dependencies
```bash
conda create -n bsql python=$PYTHON_VERSION
conda activate bsql
./dependencies.sh 21.08 $CUDA_VERSION
```
Where $CUDA_VERSION is is 11.0, 11.2 or 11.4 and $PYTHON_VERSION is 3.7 or 3.8
*For example for CUDA 11.2 and Python 3.7:*
```bash
conda create -n bsql python=3.7
conda activate bsql
./dependencies.sh 21.08 11.2
```
### Build
The build process will checkout the BlazingSQL repository and will build and install into the conda environment.
```bash
cd $CONDA_PREFIX
git clone https://github.com/BlazingDB/blazingsql.git
cd blazingsql
git checkout main
export CUDACXX=/usr/local/cuda/bin/nvcc
./build.sh
```
NOTE: You can do `./build.sh -h` to see more build options.
$CONDA_PREFIX now has a folder for the blazingsql repository.
## Nightly Version
### Install build dependencies
For nightly version cuda 11+ are only supported, see https://github.com/rapidsai/cudf#cudagpu-requirements
```bash
conda create -n bsql python=$PYTHON_VERSION
conda activate bsql
./dependencies.sh 21.10 $CUDA_VERSION nightly
```
Where $CUDA_VERSION is 11.0, 11.2 or 11.4 and $PYTHON_VERSION is 3.7 or 3.8
*For example for CUDA 11.2 and Python 3.8:*
```bash
conda create -n bsql python=3.8
conda activate bsql
./dependencies.sh 21.10 11.2 nightly
```
### Build
The build process will checkout the BlazingSQL repository and will build and install into the conda environment.
```bash
cd $CONDA_PREFIX
git clone https://github.com/BlazingDB/blazingsql.git
cd blazingsql
export CUDACXX=/usr/local/cuda/bin/nvcc
./build.sh
```
NOTE: You can do `./build.sh -h` to see more build options.
NOTE: You can perform static analysis with cppcheck with the command `cppcheck --project=compile_commands.json` in any of the cpp project build directories.
$CONDA_PREFIX now has a folder for the blazingsql repository.
#### Storage plugins
To build without the storage plugins (AWS S3, Google Cloud Storage) use the next arguments:
```bash
# Disable all storage plugins
./build.sh disable-aws-s3 disable-google-gs
# Disable AWS S3 storage plugin
./build.sh disable-aws-s3
# Disable Google Cloud Storage plugin
./build.sh disable-google-gs
```
NOTE: By disabling the storage plugins you don't need to install previously AWS SDK C++ or Google Cloud Storage (neither any of its dependencies).
#### SQL providers
To build without the SQL providers (MySQL, PostgreSQL, SQLite) use the next arguments:
```bash
# Disable all SQL providers
./build.sh disable-mysql disable-sqlite disable-postgresql
# Disable MySQL provider
./build.sh disable-mysql
...
```
NOTES:
- By disabling the storage plugins you don't need to install mysql-connector-cpp=8.0.23 libpq=13 sqlite=3 (neither any of its dependencies).
- Currenlty we support only MySQL. but PostgreSQL and SQLite will be ready for the next version!
# Documentation
User guides and public APIs documentation can be found at [here](https://docs.blazingdb.com/docs)
Our internal code architecture can be built using Spinx.
```bash
conda install -c conda-forge doxygen
cd $CONDA_PREFIX
cd blazingsql/docsrc
pip install -r requirements.txt
make doxygen
make html
```
The generated documentation can be viewed in a browser at `blazingsql/docsrc/build/html/index.html`
# Community
## Contributing
Have questions or feedback? Post a [new github issue](https://github.com/blazingdb/blazingsql/issues/new/choose).
Please see our [guide for contributing to BlazingSQL](CONTRIBUTING.md).
## Contact
Feel free to join our channel (#blazingsql) in the RAPIDS-GoAi Slack: [](https://join.slack.com/t/rapids-goai/shared_invite/enQtMjE0Njg5NDQ1MDQxLTJiN2FkNTFkYmQ2YjY1OGI4NTc5Y2NlODQ3ZDdiODEwYmRiNTFhMzNlNTU5ZWJhZjA3NTg4NDZkMThkNTkxMGQ).
You can also email us at [[email protected]]([email protected]) or find out more details on [BlazingSQL.com](https://blazingsql.com).
## License
[Apache License 2.0](LICENSE)
## RAPIDS AI - Open GPU Data Science
The RAPIDS suite of open source software libraries aim to enable execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
## Apache Arrow on GPU
The GPU version of [Apache Arrow](https://arrow.apache.org/) is a common API that enables efficient interchange of tabular data between processes running on the GPU. End-to-end computation on the GPU avoids unnecessary copying and converting of data off the GPU, reducing compute time and cost for high-performance analytics common in artificial intelligence workloads. As the name implies, cuDF uses the Apache Arrow columnar data format on the GPU. Currently, a subset of the features in Apache Arrow are supported.