duckdb_cuda_join
Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (16.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: kathan3
- License: mit
- Language: C++
- Default Branch: main
- Size: 305 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
DuckDB
DuckDB is a high-performance analytical database system. It is designed to be fast, reliable, portable, and easy to use. DuckDB provides a rich SQL dialect, with support far beyond basic SQL. DuckDB supports arbitrary and nested correlated subqueries, window functions, collations, complex types (arrays, structs, maps), and several extensions designed to make SQL easier to use.
DuckDB is available as a standalone CLI application and has clients for Python, R, Java, Wasm, etc., with deep integrations with packages such as pandas and dplyr.
For more information on using DuckDB, please refer to the DuckDB documentation.
Installation
If you want to install DuckDB, please see our installation page for instructions.
Data Import
For CSV files and Parquet files, data import is as simple as referencing the file in the FROM clause:
sql
SELECT * FROM 'myfile.csv';
SELECT * FROM 'myfile.parquet';
Refer to our Data Import section for more information.
SQL Reference
The documentation contains a SQL introduction and reference.
Development
For development, DuckDB requires CMake, Python3 and a C++11 compliant compiler. Run make in the root directory to compile the sources. For development, use make debug to build a non-optimized debug version. You should run make unit and make allunit to verify that your version works properly after making changes. To test performance, you can run BUILD_BENCHMARK=1 BUILD_TPCH=1 make and then perform several standard benchmarks from the root directory by executing ./build/release/benchmark/benchmark_runner. The details of benchmarks are in our Benchmark Guide.
Please also refer to our Build Guide and Contribution Guide.
Support
See the Support Options page.
Owner
- Name: Kathan Mistry
- Login: kathan3
- Kind: user
- Location: Ahmedabad
- Website: https://www.linkedin.com/in/kathan-mistry-92b9a4197/
- Repositories: 3
- Profile: https://github.com/kathan3
Hello.I'm currently a graduate student at Indian Institute of Science,Bangalore in Computer Science and Automation department.
GitHub Events
Total
- Push event: 6
Last Year
- Push event: 6
Dependencies
- openssl *
- openssl *
- adbc_driver_manager * development
- cxxheaderparser * development
- fsspec * development
- mypy * development
- numpy >=1.14 development
- pandas * development
- pcpp * development
- polars * development
- psutil >=5.9.0 development
- pyarrow >=8.0 development
- pybind11 >=2.6.0 development
- pyspark * development
- pytest * development
- pytest-timeout * development
- requests >=2.26 development
- setuptools >=58 development
- setuptools_scm >=6.3 development
- tensorflow * development
- torch * development