A lightweight, GPU accelerated, SQL engine built on the RAPIDS.ai ecosystem.
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BlazingSQL is a GPU accelerated SQL engine built on top of the RAPIDS ecosystem. RAPIDS is based on the Apache Arrow columnar memory format, and 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 library for data science workloads.
Check out our 5-min quick start notebook 
Please reference our docs to find out how to install BlazingSQL.
Querying a CSV file in Amazon S3 with BlazingSQL:
For example:
from blazingsql import BlazingContext
bc = BlazingContext()
bc.s3('dir_name', bucket_name='bucket_name', access_key_id='access_key', secret_key='secret_key')
# Create Table from CSV
bc.create_table('taxi', '/dir_name/taxi.csv')
# Query
result = bc.sql('SELECT count(*) FROM taxi GROUP BY year(key)').get()
result_gdf = result.columns
#Print GDF
print(result_gdf)- Getting Started Guide - Google Colab
- Netflow Demo - Google Colab
- Taxi cuML Linear Regression - Google Colab
You can find our full documentation at the following site
See build instructions.
Have questions or feedback? Post a new github issue.
Please see our guide for contributing to BlazingSQL.
Feel free to join our Slack chat room: RAPIDS Slack Channel
You may also email us at info@blazingsql.com or find out more details on the BlazingSQL site
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.
The GPU version of Apache Arrow 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.