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|
%global _empty_manifest_terminate_build 0
Name: python-bcpy
Version: 0.1.8
Release: 1
Summary: Microsoft SQL Server bcp (Bulk Copy) wrapper
License: MIT License
URL: https://github.com/titan550/bcpy
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f5/55/2ce7ad290d4907cd420424a8e685c2b39c453cebda3aa0e549a424fddf9d/bcpy-0.1.8.tar.gz
BuildArch: noarch
%description
# bcpy
<table>
<tr>
<td>Latest Release</td>
<td>
<a href="https://pypi.org/project/bcpy/">
<img src="https://img.shields.io/pypi/v/bcpy.svg" alt="latest release" />
</a>
</td>
</tr>
<tr>
<td>License</td>
<td>
<a href="https://github.com/titan550/bcpy/blob/master/LICENSE">
<img src="https://img.shields.io/pypi/l/bcpy.svg" alt="license" />
</a>
</td>
</tr>
<tr>
<td>Build Status (master)</td>
<td>
<a href="https://travis-ci.org/titan550/bcpy">
<img src="https://api.travis-ci.org/titan550/bcpy.svg?branch=master" alt="travis build status" />
</a>
</td>
</tr>
</table>
## What is it?
This package is a wrapper for Microsoft's SQL Server bcp utility. Current database drivers available in Python are not fast enough for transferring millions of records (yes, I have tried [pyodbc fast_execute_many](https://github.com/mkleehammer/pyodbc/wiki/Features-beyond-the-DB-API#fast_executemany)). Despite the IO hits, the fastest option by far is saving the data to a CSV file in file system (preferably /dev/shm tmpfs) and using the bcp utility to transfer the CSV file to SQL Server.
## How Can I Install It?
1. Make sure your computeer has the [requirements](#requirements).
1. You can download and install this package from PyPI repository by running the command below.
```bash
pip install bcpy
```
## Examples
Following examples show you how to load (1) flat files and (2) DataFrame objects to SQL Server using this package.
### Flat File
Following example assumes that you have a comma separated file with no qualifier in path 'tests/data1.csv'. The code below sends the the file to SQL Server.
```python
import bcpy
sql_config = {
'server': 'sql_server_hostname',
'database': 'database_name',
'username': 'test_user',
'password': 'test_user_password1234'
}
sql_table_name = 'test_data1'
csv_file_path = 'tests/data1.csv'
flat_file = bcpy.FlatFile(qualifier='', path=csv_file_path)
sql_table = bcpy.SqlTable(sql_config, table=sql_table_name)
flat_file.to_sql(sql_table)
```
### DataFrame
The following example creates a DataFrame with 100 rows and 4 columns populated with random data and then it sends it to SQL Server.
```python
import bcpy
import numpy as np
import pandas as pd
sql_config = {
'server': 'sql_server_hostname',
'database': 'database_name',
'username': 'test_user',
'password': 'test_user_password1234'
}
table_name = 'test_dataframe'
df = pd.DataFrame(np.random.randint(-100, 100, size=(100, 4)),
columns=list('ABCD'))
bdf = bcpy.DataFrame(df)
sql_table = bcpy.SqlTable(sql_config, table=table_name)
bdf.to_sql(sql_table)
```
## Requirements
You need a working version of Microsoft bcp installed in your system. Your PATH environment variable should contain the directory of the bcp utility. Following are the installation tutorials for different operating systems.
- [Dockerfile (Ubuntu 18.04)](./bcp.Dockerfile)
- [Linux](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools)
- [Mac](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools?view=sql-server-2017#macos)
- [Windows](https://docs.microsoft.com/en-us/sql/tools/bcp-utility)
%package -n python3-bcpy
Summary: Microsoft SQL Server bcp (Bulk Copy) wrapper
Provides: python-bcpy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-bcpy
# bcpy
<table>
<tr>
<td>Latest Release</td>
<td>
<a href="https://pypi.org/project/bcpy/">
<img src="https://img.shields.io/pypi/v/bcpy.svg" alt="latest release" />
</a>
</td>
</tr>
<tr>
<td>License</td>
<td>
<a href="https://github.com/titan550/bcpy/blob/master/LICENSE">
<img src="https://img.shields.io/pypi/l/bcpy.svg" alt="license" />
</a>
</td>
</tr>
<tr>
<td>Build Status (master)</td>
<td>
<a href="https://travis-ci.org/titan550/bcpy">
<img src="https://api.travis-ci.org/titan550/bcpy.svg?branch=master" alt="travis build status" />
</a>
</td>
</tr>
</table>
## What is it?
This package is a wrapper for Microsoft's SQL Server bcp utility. Current database drivers available in Python are not fast enough for transferring millions of records (yes, I have tried [pyodbc fast_execute_many](https://github.com/mkleehammer/pyodbc/wiki/Features-beyond-the-DB-API#fast_executemany)). Despite the IO hits, the fastest option by far is saving the data to a CSV file in file system (preferably /dev/shm tmpfs) and using the bcp utility to transfer the CSV file to SQL Server.
## How Can I Install It?
1. Make sure your computeer has the [requirements](#requirements).
1. You can download and install this package from PyPI repository by running the command below.
```bash
pip install bcpy
```
## Examples
Following examples show you how to load (1) flat files and (2) DataFrame objects to SQL Server using this package.
### Flat File
Following example assumes that you have a comma separated file with no qualifier in path 'tests/data1.csv'. The code below sends the the file to SQL Server.
```python
import bcpy
sql_config = {
'server': 'sql_server_hostname',
'database': 'database_name',
'username': 'test_user',
'password': 'test_user_password1234'
}
sql_table_name = 'test_data1'
csv_file_path = 'tests/data1.csv'
flat_file = bcpy.FlatFile(qualifier='', path=csv_file_path)
sql_table = bcpy.SqlTable(sql_config, table=sql_table_name)
flat_file.to_sql(sql_table)
```
### DataFrame
The following example creates a DataFrame with 100 rows and 4 columns populated with random data and then it sends it to SQL Server.
```python
import bcpy
import numpy as np
import pandas as pd
sql_config = {
'server': 'sql_server_hostname',
'database': 'database_name',
'username': 'test_user',
'password': 'test_user_password1234'
}
table_name = 'test_dataframe'
df = pd.DataFrame(np.random.randint(-100, 100, size=(100, 4)),
columns=list('ABCD'))
bdf = bcpy.DataFrame(df)
sql_table = bcpy.SqlTable(sql_config, table=table_name)
bdf.to_sql(sql_table)
```
## Requirements
You need a working version of Microsoft bcp installed in your system. Your PATH environment variable should contain the directory of the bcp utility. Following are the installation tutorials for different operating systems.
- [Dockerfile (Ubuntu 18.04)](./bcp.Dockerfile)
- [Linux](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools)
- [Mac](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools?view=sql-server-2017#macos)
- [Windows](https://docs.microsoft.com/en-us/sql/tools/bcp-utility)
%package help
Summary: Development documents and examples for bcpy
Provides: python3-bcpy-doc
%description help
# bcpy
<table>
<tr>
<td>Latest Release</td>
<td>
<a href="https://pypi.org/project/bcpy/">
<img src="https://img.shields.io/pypi/v/bcpy.svg" alt="latest release" />
</a>
</td>
</tr>
<tr>
<td>License</td>
<td>
<a href="https://github.com/titan550/bcpy/blob/master/LICENSE">
<img src="https://img.shields.io/pypi/l/bcpy.svg" alt="license" />
</a>
</td>
</tr>
<tr>
<td>Build Status (master)</td>
<td>
<a href="https://travis-ci.org/titan550/bcpy">
<img src="https://api.travis-ci.org/titan550/bcpy.svg?branch=master" alt="travis build status" />
</a>
</td>
</tr>
</table>
## What is it?
This package is a wrapper for Microsoft's SQL Server bcp utility. Current database drivers available in Python are not fast enough for transferring millions of records (yes, I have tried [pyodbc fast_execute_many](https://github.com/mkleehammer/pyodbc/wiki/Features-beyond-the-DB-API#fast_executemany)). Despite the IO hits, the fastest option by far is saving the data to a CSV file in file system (preferably /dev/shm tmpfs) and using the bcp utility to transfer the CSV file to SQL Server.
## How Can I Install It?
1. Make sure your computeer has the [requirements](#requirements).
1. You can download and install this package from PyPI repository by running the command below.
```bash
pip install bcpy
```
## Examples
Following examples show you how to load (1) flat files and (2) DataFrame objects to SQL Server using this package.
### Flat File
Following example assumes that you have a comma separated file with no qualifier in path 'tests/data1.csv'. The code below sends the the file to SQL Server.
```python
import bcpy
sql_config = {
'server': 'sql_server_hostname',
'database': 'database_name',
'username': 'test_user',
'password': 'test_user_password1234'
}
sql_table_name = 'test_data1'
csv_file_path = 'tests/data1.csv'
flat_file = bcpy.FlatFile(qualifier='', path=csv_file_path)
sql_table = bcpy.SqlTable(sql_config, table=sql_table_name)
flat_file.to_sql(sql_table)
```
### DataFrame
The following example creates a DataFrame with 100 rows and 4 columns populated with random data and then it sends it to SQL Server.
```python
import bcpy
import numpy as np
import pandas as pd
sql_config = {
'server': 'sql_server_hostname',
'database': 'database_name',
'username': 'test_user',
'password': 'test_user_password1234'
}
table_name = 'test_dataframe'
df = pd.DataFrame(np.random.randint(-100, 100, size=(100, 4)),
columns=list('ABCD'))
bdf = bcpy.DataFrame(df)
sql_table = bcpy.SqlTable(sql_config, table=table_name)
bdf.to_sql(sql_table)
```
## Requirements
You need a working version of Microsoft bcp installed in your system. Your PATH environment variable should contain the directory of the bcp utility. Following are the installation tutorials for different operating systems.
- [Dockerfile (Ubuntu 18.04)](./bcp.Dockerfile)
- [Linux](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools)
- [Mac](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools?view=sql-server-2017#macos)
- [Windows](https://docs.microsoft.com/en-us/sql/tools/bcp-utility)
%prep
%autosetup -n bcpy-0.1.8
%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-bcpy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.8-1
- Package Spec generated
|
