path: root/python-dask-sql.spec

%global _empty_manifest_terminate_build 0
Name:		python-dask-sql
Version:	2023.4.0
Release:	1
Summary:	SQL query layer for Dask
License:	MIT
URL:		https://github.com/dask-contrib/dask-sql/
Source0:	https://mirrors.aliyun.com/pypi/web/packages/95/ba/82ec4a5f7e766f66c22b3a5d447a458fe09702a0e965a978b8cea422dff1/dask_sql-2023.4.0.tar.gz


%description
## Example
For this example, we use some data loaded from disk and query them with a SQL command from our python code.
Any pandas or dask dataframe can be used as input and ``dask-sql`` understands a large amount of formats (csv, parquet, json,...) and locations (s3, hdfs, gcs,...).
```python
import dask.dataframe as dd
from dask_sql import Context
# Create a context to hold the registered tables
c = Context()
# Load the data and register it in the context
# This will give the table a name, that we can use in queries
df = dd.read_csv("...")
c.create_table("my_data", df)
# Now execute a SQL query. The result is again dask dataframe.
result = c.sql("""
    SELECT
        my_data.name,
        SUM(my_data.x)
    FROM
        my_data
    GROUP BY
        my_data.name
""", return_futures=False)
# Show the result
print(result)
```
## Quickstart
Have a look into the [documentation](https://dask-sql.readthedocs.io/en/latest/) or start the example notebook on [binder](https://mybinder.org/v2/gh/dask-contrib/dask-sql-binder/main?urlpath=lab).
> `dask-sql` is currently under development and does so far not understand all SQL commands (but a large fraction).
We are actively looking for feedback, improvements and contributors!
## Installation
`dask-sql` can be installed via `conda` (preferred) or `pip` - or in a development environment.
### With `conda`
Create a new conda environment or use your already present environment:
    conda create -n dask-sql
    conda activate dask-sql
Install the package from the `conda-forge` channel:
    conda install dask-sql -c conda-forge
### With `pip`
You can install the package with
    pip install dask-sql
### For development
If you want to have the newest (unreleased) `dask-sql` version or if you plan to do development on `dask-sql`, you can also install the package from sources.
    git clone https://github.com/dask-contrib/dask-sql.git
Create a new conda environment and install the development environment:
    conda env create -f continuous_integration/environment-3.9-dev.yaml
It is not recommended to use `pip` instead of `conda` for the environment setup.
After that, you can install the package in development mode
    pip install -e ".[dev]"
The Rust DataFusion bindings are built as part of the `pip install`.
If changes are made to the Rust source in `dask_planner/`, another build/install must be run to recompile the bindings:
    python setup.py build install
This repository uses [pre-commit](https://pre-commit.com/) hooks. To install them, call
    pre-commit install
## Testing
You can run the tests (after installation) with
    pytest tests
GPU-specific tests require additional dependencies specified in `continuous_integration/gpuci/environment.yaml`.
These can be added to the development environment by running
```
conda env update -n dask-sql -f continuous_integration/gpuci/environment.yaml
```
And GPU-specific tests can be run with
```
pytest tests -m gpu --rungpu
```
## SQL Server
`dask-sql` comes with a small test implementation for a SQL server.
Instead of rebuilding a full ODBC driver, we re-use the [presto wire protocol](https://github.com/prestodb/presto/wiki/HTTP-Protocol).
It is - so far - only a start of the development and missing important concepts, such as
authentication.
You can test the sql presto server by running (after installation)
    dask-sql-server
or by using the created docker image
    docker run --rm -it -p 8080:8080 nbraun/dask-sql
in one terminal. This will spin up a server on port 8080 (by default)
that looks similar to a normal presto database to any presto client.
You can test this for example with the default [presto client](https://prestosql.io/docs/current/installation/cli.html):
    presto --server localhost:8080
Now you can fire simple SQL queries (as no data is loaded by default):
    => SELECT 1 + 1;

%package -n python3-dask-sql
Summary:	SQL query layer for Dask
Provides:	python-dask-sql
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
BuildRequires:	python3-cffi
BuildRequires:	gcc
BuildRequires:	gdb
%description -n python3-dask-sql
## Example
For this example, we use some data loaded from disk and query them with a SQL command from our python code.
Any pandas or dask dataframe can be used as input and ``dask-sql`` understands a large amount of formats (csv, parquet, json,...) and locations (s3, hdfs, gcs,...).
```python
import dask.dataframe as dd
from dask_sql import Context
# Create a context to hold the registered tables
c = Context()
# Load the data and register it in the context
# This will give the table a name, that we can use in queries
df = dd.read_csv("...")
c.create_table("my_data", df)
# Now execute a SQL query. The result is again dask dataframe.
result = c.sql("""
    SELECT
        my_data.name,
        SUM(my_data.x)
    FROM
        my_data
    GROUP BY
        my_data.name
""", return_futures=False)
# Show the result
print(result)
```
## Quickstart
Have a look into the [documentation](https://dask-sql.readthedocs.io/en/latest/) or start the example notebook on [binder](https://mybinder.org/v2/gh/dask-contrib/dask-sql-binder/main?urlpath=lab).
> `dask-sql` is currently under development and does so far not understand all SQL commands (but a large fraction).
We are actively looking for feedback, improvements and contributors!
## Installation
`dask-sql` can be installed via `conda` (preferred) or `pip` - or in a development environment.
### With `conda`
Create a new conda environment or use your already present environment:
    conda create -n dask-sql
    conda activate dask-sql
Install the package from the `conda-forge` channel:
    conda install dask-sql -c conda-forge
### With `pip`
You can install the package with
    pip install dask-sql
### For development
If you want to have the newest (unreleased) `dask-sql` version or if you plan to do development on `dask-sql`, you can also install the package from sources.
    git clone https://github.com/dask-contrib/dask-sql.git
Create a new conda environment and install the development environment:
    conda env create -f continuous_integration/environment-3.9-dev.yaml
It is not recommended to use `pip` instead of `conda` for the environment setup.
After that, you can install the package in development mode
    pip install -e ".[dev]"
The Rust DataFusion bindings are built as part of the `pip install`.
If changes are made to the Rust source in `dask_planner/`, another build/install must be run to recompile the bindings:
    python setup.py build install
This repository uses [pre-commit](https://pre-commit.com/) hooks. To install them, call
    pre-commit install
## Testing
You can run the tests (after installation) with
    pytest tests
GPU-specific tests require additional dependencies specified in `continuous_integration/gpuci/environment.yaml`.
These can be added to the development environment by running
```
conda env update -n dask-sql -f continuous_integration/gpuci/environment.yaml
```
And GPU-specific tests can be run with
```
pytest tests -m gpu --rungpu
```
## SQL Server
`dask-sql` comes with a small test implementation for a SQL server.
Instead of rebuilding a full ODBC driver, we re-use the [presto wire protocol](https://github.com/prestodb/presto/wiki/HTTP-Protocol).
It is - so far - only a start of the development and missing important concepts, such as
authentication.
You can test the sql presto server by running (after installation)
    dask-sql-server
or by using the created docker image
    docker run --rm -it -p 8080:8080 nbraun/dask-sql
in one terminal. This will spin up a server on port 8080 (by default)
that looks similar to a normal presto database to any presto client.
You can test this for example with the default [presto client](https://prestosql.io/docs/current/installation/cli.html):
    presto --server localhost:8080
Now you can fire simple SQL queries (as no data is loaded by default):
    => SELECT 1 + 1;

%package help
Summary:	Development documents and examples for dask-sql
Provides:	python3-dask-sql-doc
%description help
## Example
For this example, we use some data loaded from disk and query them with a SQL command from our python code.
Any pandas or dask dataframe can be used as input and ``dask-sql`` understands a large amount of formats (csv, parquet, json,...) and locations (s3, hdfs, gcs,...).
```python
import dask.dataframe as dd
from dask_sql import Context
# Create a context to hold the registered tables
c = Context()
# Load the data and register it in the context
# This will give the table a name, that we can use in queries
df = dd.read_csv("...")
c.create_table("my_data", df)
# Now execute a SQL query. The result is again dask dataframe.
result = c.sql("""
    SELECT
        my_data.name,
        SUM(my_data.x)
    FROM
        my_data
    GROUP BY
        my_data.name
""", return_futures=False)
# Show the result
print(result)
```
## Quickstart
Have a look into the [documentation](https://dask-sql.readthedocs.io/en/latest/) or start the example notebook on [binder](https://mybinder.org/v2/gh/dask-contrib/dask-sql-binder/main?urlpath=lab).
> `dask-sql` is currently under development and does so far not understand all SQL commands (but a large fraction).
We are actively looking for feedback, improvements and contributors!
## Installation
`dask-sql` can be installed via `conda` (preferred) or `pip` - or in a development environment.
### With `conda`
Create a new conda environment or use your already present environment:
    conda create -n dask-sql
    conda activate dask-sql
Install the package from the `conda-forge` channel:
    conda install dask-sql -c conda-forge
### With `pip`
You can install the package with
    pip install dask-sql
### For development
If you want to have the newest (unreleased) `dask-sql` version or if you plan to do development on `dask-sql`, you can also install the package from sources.
    git clone https://github.com/dask-contrib/dask-sql.git
Create a new conda environment and install the development environment:
    conda env create -f continuous_integration/environment-3.9-dev.yaml
It is not recommended to use `pip` instead of `conda` for the environment setup.
After that, you can install the package in development mode
    pip install -e ".[dev]"
The Rust DataFusion bindings are built as part of the `pip install`.
If changes are made to the Rust source in `dask_planner/`, another build/install must be run to recompile the bindings:
    python setup.py build install
This repository uses [pre-commit](https://pre-commit.com/) hooks. To install them, call
    pre-commit install
## Testing
You can run the tests (after installation) with
    pytest tests
GPU-specific tests require additional dependencies specified in `continuous_integration/gpuci/environment.yaml`.
These can be added to the development environment by running
```
conda env update -n dask-sql -f continuous_integration/gpuci/environment.yaml
```
And GPU-specific tests can be run with
```
pytest tests -m gpu --rungpu
```
## SQL Server
`dask-sql` comes with a small test implementation for a SQL server.
Instead of rebuilding a full ODBC driver, we re-use the [presto wire protocol](https://github.com/prestodb/presto/wiki/HTTP-Protocol).
It is - so far - only a start of the development and missing important concepts, such as
authentication.
You can test the sql presto server by running (after installation)
    dask-sql-server
or by using the created docker image
    docker run --rm -it -p 8080:8080 nbraun/dask-sql
in one terminal. This will spin up a server on port 8080 (by default)
that looks similar to a normal presto database to any presto client.
You can test this for example with the default [presto client](https://prestosql.io/docs/current/installation/cli.html):
    presto --server localhost:8080
Now you can fire simple SQL queries (as no data is loaded by default):
    => SELECT 1 + 1;

%prep
%autosetup -n dask_sql-2023.4.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-dask-sql -f filelist.lst
%dir %{python3_sitearch}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 2023.4.0-1
- Package Spec generated