path: root/python-pysparkip.spec

%global _empty_manifest_terminate_build 0
Name:		python-PySparkIP
Version:	1.2.4
Release:	1
Summary:	An API for working with IP addresses in Apache Spark.
License:	Apache Software License
URL:		https://github.com/jshalaby510/PySparkIP
Source0:	https://mirrors.aliyun.com/pypi/web/packages/12/25/1c587c11b9316b8ecf13c8ec16ddd5c331071702ade401adda4f6b609aeb/PySparkIP-1.2.4.tar.gz
BuildArch:	noarch


%description
[![license](https://img.shields.io/badge/license-Apache_2.0-blue.svg)](https://github.com/jshalaby510/PySparkIP/blob/main/LICENSE)

# PySparkIP
An API for working with IP addresses in Apache Spark. Built on top of [ipaddress](https://docs.python.org/3/library/ipaddress.html).

## Usage
  * pip install PySparkIP
  * from PySparkIP import *

## License
This project is licensed under the Apache License. Please see [LICENSE](LICENSE) file for more details.

## Tutorial
### Initialize
Before using in SparkSQL, initialize PySparkIP by passing `spark` to `PySparkIP`, 
then define `IPAddressUDT()` in the schema.
<br/>
Optionally pass the log level as well (if left unspecified, `PySparkIP` resets 
the log level to "WARN" and gives a warning message).
<br/>
NOTE: Values that are unsuccessfully converted to IP addresses will be converted to "::"
```python
from PySparkIP import *

# Initialize for SparkSQL use (not needed for pure PySpark)
PySparkIP(spark)
# or PySparkIP(spark, "DEBUG"), PySparkIP(spark, "FATAL"), etc if specifying a log level

schema = StructType([StructField("IPAddress", IPAddressUDT())])
ipDF = spark.read.json("ipFile.json", schema=schema)
ipDF.createOrReplaceTempView("IPAddresses")
```

### Functions
**Cast StringType() to IPAddressUDT()**
```python
# PySpark
ipDF = ipDF.select(to_ip('ip_string'))
# SparkSQL
spark.sql("SELECT to_ip(ip_string) FROM IPAddresses")
```

**Check address type**
```python
# Multicast
ipDF.select('*').withColumn("IPColumn", isMulticast("IPAddress"))
spark.sql("SELECT * FROM IPAddresses WHERE isMulticast(IPAddress)")

"""
Other address types:
    isPrivate, isGlobal, isUnspecified, isReserved, 
    isLoopback, isLinkLocal, isIPv4Mapped, is6to4, 
    isTeredo, isIPv4, isIPv6
"""
```

**Output address in different formats**
```python
# Exploded
spark.sql("SELECT explodedIP(IPAddress) FROM IPAddresses")
ipDF.select(explodedIP("IPAddress"))

# Compressed
spark.sql("SELECT compressedIP(IPAddress) FROM IPAddresses")
ipDF.select(compressedIP("IPAddress"))
```

**Sort IP Addresses**
```python
# SparkSQL doesn't support values > LONG_MAX
# To sort IPv6 addresses, use ipAsBinary
# To sort IPv4 addresses, use either ipv4AsNum or ipAsBinary, but ipv4AsNum is more efficient

# Sort IPv4 and IPv6
spark.sql("SELECT * FROM IPAddresses SORT BY ipAsBinary(IPAddress)")
ipDF.select('*').sort(ipAsBinary("IPAddress"))

# Sort ONLY IPv4
spark.sql("SELECT * FROM IPv4 SORT BY ipv4AsNum(IPAddress)")
ipv4DF.select('*').sort(ipv4AsNum("IPAddress"))
```

**IP network functions**
```python
# Network contains
spark.sql("SELECT * FROM IPAddresses WHERE networkContains(IPAddress, '195.0.0.0/16')")
ipDF.select('*').filter("networkContains(IPAddress, '195.0.0.0/16')")
ipDF.select('*').withColumn("netCol", networkContains("192.0.0.0/16")("IPAddress"))

# Or use ipaddress.ip_network objects
net1 = ipaddress.ip_network('::/10')
ipDF.select('*').filter(networkContains(net1)("IPAddress"))
```

**IP Set**
#### Create IP Sets (Note: This functionality also works with add and remove):
```python
# Strings
ipStr = '192.0.0.0'
netStr = '225.0.0.0'
# Tuples, lists, or sets
ip_net_mix = ('::5', '5.0.0.0/8', '111.8.9.7')
# ipaddress objects
ipAddr = ipaddress.ip_address('::')
# Dataframes
ipMulticastDF = spark.sql("SELECT IPAddress FROM IPAddresses WHERE isMulticast(IPAddress)")

""" 
Or use our predefined networks (multicastIPs, privateIPs, 
 publicIPs, reservedIPs, unspecifiedIPs, linkLocalIPs, 
 loopBackIPs, ipv4MappedIPs, ipv4TranslatedIPs, ipv4ipv6TranslatedIPs,
 teredoIPs, sixToFourIPs, or siteLocalIPs)
 """

# Mix them together
ipSet = IPSet(ipStr, '::/16', '2001::', netStr, ip_net_mix, privateIPs)
ipSet2 = IPSet("6::", "9.0.8.7", ipAddr, ipMulticastDF)
# Use other IPSets
ipSet3 = IPSet(ipSet, ipSet2)
# Or just make an empty set
ipSet4 = IPSet()
```
#### Use IP Sets:
```python
# Initialize an IP Set
setOfIPs = {"192.0.0.0", "5422:6622:1dc6:366a:e728:84d4:257e:655a", "::"}
ipSet = IPSet(setOfIPs)

# Use it!
ipDF.select('*').filter("setContains(IPAddress, 'ipSet')")
ipDF.select('*').withColumn("setCol", setContains(ipSet)("IPAddress"))
```

#### Register IP Sets for use in SparkSQL:
Before using IP Sets in SparkSQL, register it by passing it to `PySparkIPSets`
```python
ipSet = IPSet('::')
ipSet2 = IPSet()

# Pass the set, then the set name
PySparkIPSets.add(ipSet, 'ipSet')
PySparkIPSets.add(ipSet2, 'ipSet2')
```
#### Remove IP Sets from registered sets in SparkSQL:
```python
PySparkIPSets.remove('ipSet', 'ipSet2')
```

#### Use IP Sets in SparkSQL:
```python
# Note you have to pass the variable name using SparkSQL, not the actual variable

# Initialize an IP Set
setOfIPs = {"192.0.0.0", "5422:6622:1dc6:366a:e728:84d4:257e:655a", "::"}
ipSet = IPSet(setOfIPs)

# Register it
PySparkIPSets.add(ipSet, 'ipSet')

#Use it!
# Set Contains
spark.sql("SELECT * FROM IPAddresses WHERE setContains(IPAddress, 'ipSet')")

# Show sets available to use
PySparkIPSets.setsAvailable()

# Remove a set
PySparkIPSets.remove('ipSet')

# Clear sets available
PySparkIPSets.clear()
```

#### IP Set functions (outside Spark):
```python
ipSet = IPSet()

# Add
ipSet.add('0.0.0.0', '::/16')

# Remove
ipSet.remove('::/16')

# Contains
ipSet.contains('0.0.0.0')

# Clear
ipSet.clear()

# Show all
ipSet.showAll()

# Union
ipSet2 = ('2001::', '::33', 'ffff::f')
ipSet.union(ipSet2)

# Intersection
ipSet.intersects(ipSet2)

# Diff
ipSet.diff(ipSet2)

# Show All
ipSet.showAll()

# Return All
ipSet.returnAll()

# Is empty
ipSet.isEmpty()

# Compare IPSets
ipSet2 = ('2001::', '::33', 'ffff::f')
ipSet == ipSet2
ipSet != ipSet2

# Return the # of elements in the set
len(ipSet)
```
#### Other operations (outside Spark):
```python
# Nets intersect
net1 = '192.0.0.0/16'
net2 = '192.0.0.0/8'
# or ipaddress.ip_network('192.0.0.0/8')
netsIntersect(net1, net2)
```


%package -n python3-PySparkIP
Summary:	An API for working with IP addresses in Apache Spark.
Provides:	python-PySparkIP
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-PySparkIP
[![license](https://img.shields.io/badge/license-Apache_2.0-blue.svg)](https://github.com/jshalaby510/PySparkIP/blob/main/LICENSE)

# PySparkIP
An API for working with IP addresses in Apache Spark. Built on top of [ipaddress](https://docs.python.org/3/library/ipaddress.html).

## Usage
  * pip install PySparkIP
  * from PySparkIP import *

## License
This project is licensed under the Apache License. Please see [LICENSE](LICENSE) file for more details.

## Tutorial
### Initialize
Before using in SparkSQL, initialize PySparkIP by passing `spark` to `PySparkIP`, 
then define `IPAddressUDT()` in the schema.
<br/>
Optionally pass the log level as well (if left unspecified, `PySparkIP` resets 
the log level to "WARN" and gives a warning message).
<br/>
NOTE: Values that are unsuccessfully converted to IP addresses will be converted to "::"
```python
from PySparkIP import *

# Initialize for SparkSQL use (not needed for pure PySpark)
PySparkIP(spark)
# or PySparkIP(spark, "DEBUG"), PySparkIP(spark, "FATAL"), etc if specifying a log level

schema = StructType([StructField("IPAddress", IPAddressUDT())])
ipDF = spark.read.json("ipFile.json", schema=schema)
ipDF.createOrReplaceTempView("IPAddresses")
```

### Functions
**Cast StringType() to IPAddressUDT()**
```python
# PySpark
ipDF = ipDF.select(to_ip('ip_string'))
# SparkSQL
spark.sql("SELECT to_ip(ip_string) FROM IPAddresses")
```

**Check address type**
```python
# Multicast
ipDF.select('*').withColumn("IPColumn", isMulticast("IPAddress"))
spark.sql("SELECT * FROM IPAddresses WHERE isMulticast(IPAddress)")

"""
Other address types:
    isPrivate, isGlobal, isUnspecified, isReserved, 
    isLoopback, isLinkLocal, isIPv4Mapped, is6to4, 
    isTeredo, isIPv4, isIPv6
"""
```

**Output address in different formats**
```python
# Exploded
spark.sql("SELECT explodedIP(IPAddress) FROM IPAddresses")
ipDF.select(explodedIP("IPAddress"))

# Compressed
spark.sql("SELECT compressedIP(IPAddress) FROM IPAddresses")
ipDF.select(compressedIP("IPAddress"))
```

**Sort IP Addresses**
```python
# SparkSQL doesn't support values > LONG_MAX
# To sort IPv6 addresses, use ipAsBinary
# To sort IPv4 addresses, use either ipv4AsNum or ipAsBinary, but ipv4AsNum is more efficient

# Sort IPv4 and IPv6
spark.sql("SELECT * FROM IPAddresses SORT BY ipAsBinary(IPAddress)")
ipDF.select('*').sort(ipAsBinary("IPAddress"))

# Sort ONLY IPv4
spark.sql("SELECT * FROM IPv4 SORT BY ipv4AsNum(IPAddress)")
ipv4DF.select('*').sort(ipv4AsNum("IPAddress"))
```

**IP network functions**
```python
# Network contains
spark.sql("SELECT * FROM IPAddresses WHERE networkContains(IPAddress, '195.0.0.0/16')")
ipDF.select('*').filter("networkContains(IPAddress, '195.0.0.0/16')")
ipDF.select('*').withColumn("netCol", networkContains("192.0.0.0/16")("IPAddress"))

# Or use ipaddress.ip_network objects
net1 = ipaddress.ip_network('::/10')
ipDF.select('*').filter(networkContains(net1)("IPAddress"))
```

**IP Set**
#### Create IP Sets (Note: This functionality also works with add and remove):
```python
# Strings
ipStr = '192.0.0.0'
netStr = '225.0.0.0'
# Tuples, lists, or sets
ip_net_mix = ('::5', '5.0.0.0/8', '111.8.9.7')
# ipaddress objects
ipAddr = ipaddress.ip_address('::')
# Dataframes
ipMulticastDF = spark.sql("SELECT IPAddress FROM IPAddresses WHERE isMulticast(IPAddress)")

""" 
Or use our predefined networks (multicastIPs, privateIPs, 
 publicIPs, reservedIPs, unspecifiedIPs, linkLocalIPs, 
 loopBackIPs, ipv4MappedIPs, ipv4TranslatedIPs, ipv4ipv6TranslatedIPs,
 teredoIPs, sixToFourIPs, or siteLocalIPs)
 """

# Mix them together
ipSet = IPSet(ipStr, '::/16', '2001::', netStr, ip_net_mix, privateIPs)
ipSet2 = IPSet("6::", "9.0.8.7", ipAddr, ipMulticastDF)
# Use other IPSets
ipSet3 = IPSet(ipSet, ipSet2)
# Or just make an empty set
ipSet4 = IPSet()
```
#### Use IP Sets:
```python
# Initialize an IP Set
setOfIPs = {"192.0.0.0", "5422:6622:1dc6:366a:e728:84d4:257e:655a", "::"}
ipSet = IPSet(setOfIPs)

# Use it!
ipDF.select('*').filter("setContains(IPAddress, 'ipSet')")
ipDF.select('*').withColumn("setCol", setContains(ipSet)("IPAddress"))
```

#### Register IP Sets for use in SparkSQL:
Before using IP Sets in SparkSQL, register it by passing it to `PySparkIPSets`
```python
ipSet = IPSet('::')
ipSet2 = IPSet()

# Pass the set, then the set name
PySparkIPSets.add(ipSet, 'ipSet')
PySparkIPSets.add(ipSet2, 'ipSet2')
```
#### Remove IP Sets from registered sets in SparkSQL:
```python
PySparkIPSets.remove('ipSet', 'ipSet2')
```

#### Use IP Sets in SparkSQL:
```python
# Note you have to pass the variable name using SparkSQL, not the actual variable

# Initialize an IP Set
setOfIPs = {"192.0.0.0", "5422:6622:1dc6:366a:e728:84d4:257e:655a", "::"}
ipSet = IPSet(setOfIPs)

# Register it
PySparkIPSets.add(ipSet, 'ipSet')

#Use it!
# Set Contains
spark.sql("SELECT * FROM IPAddresses WHERE setContains(IPAddress, 'ipSet')")

# Show sets available to use
PySparkIPSets.setsAvailable()

# Remove a set
PySparkIPSets.remove('ipSet')

# Clear sets available
PySparkIPSets.clear()
```

#### IP Set functions (outside Spark):
```python
ipSet = IPSet()

# Add
ipSet.add('0.0.0.0', '::/16')

# Remove
ipSet.remove('::/16')

# Contains
ipSet.contains('0.0.0.0')

# Clear
ipSet.clear()

# Show all
ipSet.showAll()

# Union
ipSet2 = ('2001::', '::33', 'ffff::f')
ipSet.union(ipSet2)

# Intersection
ipSet.intersects(ipSet2)

# Diff
ipSet.diff(ipSet2)

# Show All
ipSet.showAll()

# Return All
ipSet.returnAll()

# Is empty
ipSet.isEmpty()

# Compare IPSets
ipSet2 = ('2001::', '::33', 'ffff::f')
ipSet == ipSet2
ipSet != ipSet2

# Return the # of elements in the set
len(ipSet)
```
#### Other operations (outside Spark):
```python
# Nets intersect
net1 = '192.0.0.0/16'
net2 = '192.0.0.0/8'
# or ipaddress.ip_network('192.0.0.0/8')
netsIntersect(net1, net2)
```


%package help
Summary:	Development documents and examples for PySparkIP
Provides:	python3-PySparkIP-doc
%description help
[![license](https://img.shields.io/badge/license-Apache_2.0-blue.svg)](https://github.com/jshalaby510/PySparkIP/blob/main/LICENSE)

# PySparkIP
An API for working with IP addresses in Apache Spark. Built on top of [ipaddress](https://docs.python.org/3/library/ipaddress.html).

## Usage
  * pip install PySparkIP
  * from PySparkIP import *

## License
This project is licensed under the Apache License. Please see [LICENSE](LICENSE) file for more details.

## Tutorial
### Initialize
Before using in SparkSQL, initialize PySparkIP by passing `spark` to `PySparkIP`, 
then define `IPAddressUDT()` in the schema.
<br/>
Optionally pass the log level as well (if left unspecified, `PySparkIP` resets 
the log level to "WARN" and gives a warning message).
<br/>
NOTE: Values that are unsuccessfully converted to IP addresses will be converted to "::"
```python
from PySparkIP import *

# Initialize for SparkSQL use (not needed for pure PySpark)
PySparkIP(spark)
# or PySparkIP(spark, "DEBUG"), PySparkIP(spark, "FATAL"), etc if specifying a log level

schema = StructType([StructField("IPAddress", IPAddressUDT())])
ipDF = spark.read.json("ipFile.json", schema=schema)
ipDF.createOrReplaceTempView("IPAddresses")
```

### Functions
**Cast StringType() to IPAddressUDT()**
```python
# PySpark
ipDF = ipDF.select(to_ip('ip_string'))
# SparkSQL
spark.sql("SELECT to_ip(ip_string) FROM IPAddresses")
```

**Check address type**
```python
# Multicast
ipDF.select('*').withColumn("IPColumn", isMulticast("IPAddress"))
spark.sql("SELECT * FROM IPAddresses WHERE isMulticast(IPAddress)")

"""
Other address types:
    isPrivate, isGlobal, isUnspecified, isReserved, 
    isLoopback, isLinkLocal, isIPv4Mapped, is6to4, 
    isTeredo, isIPv4, isIPv6
"""
```

**Output address in different formats**
```python
# Exploded
spark.sql("SELECT explodedIP(IPAddress) FROM IPAddresses")
ipDF.select(explodedIP("IPAddress"))

# Compressed
spark.sql("SELECT compressedIP(IPAddress) FROM IPAddresses")
ipDF.select(compressedIP("IPAddress"))
```

**Sort IP Addresses**
```python
# SparkSQL doesn't support values > LONG_MAX
# To sort IPv6 addresses, use ipAsBinary
# To sort IPv4 addresses, use either ipv4AsNum or ipAsBinary, but ipv4AsNum is more efficient

# Sort IPv4 and IPv6
spark.sql("SELECT * FROM IPAddresses SORT BY ipAsBinary(IPAddress)")
ipDF.select('*').sort(ipAsBinary("IPAddress"))

# Sort ONLY IPv4
spark.sql("SELECT * FROM IPv4 SORT BY ipv4AsNum(IPAddress)")
ipv4DF.select('*').sort(ipv4AsNum("IPAddress"))
```

**IP network functions**
```python
# Network contains
spark.sql("SELECT * FROM IPAddresses WHERE networkContains(IPAddress, '195.0.0.0/16')")
ipDF.select('*').filter("networkContains(IPAddress, '195.0.0.0/16')")
ipDF.select('*').withColumn("netCol", networkContains("192.0.0.0/16")("IPAddress"))

# Or use ipaddress.ip_network objects
net1 = ipaddress.ip_network('::/10')
ipDF.select('*').filter(networkContains(net1)("IPAddress"))
```

**IP Set**
#### Create IP Sets (Note: This functionality also works with add and remove):
```python
# Strings
ipStr = '192.0.0.0'
netStr = '225.0.0.0'
# Tuples, lists, or sets
ip_net_mix = ('::5', '5.0.0.0/8', '111.8.9.7')
# ipaddress objects
ipAddr = ipaddress.ip_address('::')
# Dataframes
ipMulticastDF = spark.sql("SELECT IPAddress FROM IPAddresses WHERE isMulticast(IPAddress)")

""" 
Or use our predefined networks (multicastIPs, privateIPs, 
 publicIPs, reservedIPs, unspecifiedIPs, linkLocalIPs, 
 loopBackIPs, ipv4MappedIPs, ipv4TranslatedIPs, ipv4ipv6TranslatedIPs,
 teredoIPs, sixToFourIPs, or siteLocalIPs)
 """

# Mix them together
ipSet = IPSet(ipStr, '::/16', '2001::', netStr, ip_net_mix, privateIPs)
ipSet2 = IPSet("6::", "9.0.8.7", ipAddr, ipMulticastDF)
# Use other IPSets
ipSet3 = IPSet(ipSet, ipSet2)
# Or just make an empty set
ipSet4 = IPSet()
```
#### Use IP Sets:
```python
# Initialize an IP Set
setOfIPs = {"192.0.0.0", "5422:6622:1dc6:366a:e728:84d4:257e:655a", "::"}
ipSet = IPSet(setOfIPs)

# Use it!
ipDF.select('*').filter("setContains(IPAddress, 'ipSet')")
ipDF.select('*').withColumn("setCol", setContains(ipSet)("IPAddress"))
```

#### Register IP Sets for use in SparkSQL:
Before using IP Sets in SparkSQL, register it by passing it to `PySparkIPSets`
```python
ipSet = IPSet('::')
ipSet2 = IPSet()

# Pass the set, then the set name
PySparkIPSets.add(ipSet, 'ipSet')
PySparkIPSets.add(ipSet2, 'ipSet2')
```
#### Remove IP Sets from registered sets in SparkSQL:
```python
PySparkIPSets.remove('ipSet', 'ipSet2')
```

#### Use IP Sets in SparkSQL:
```python
# Note you have to pass the variable name using SparkSQL, not the actual variable

# Initialize an IP Set
setOfIPs = {"192.0.0.0", "5422:6622:1dc6:366a:e728:84d4:257e:655a", "::"}
ipSet = IPSet(setOfIPs)

# Register it
PySparkIPSets.add(ipSet, 'ipSet')

#Use it!
# Set Contains
spark.sql("SELECT * FROM IPAddresses WHERE setContains(IPAddress, 'ipSet')")

# Show sets available to use
PySparkIPSets.setsAvailable()

# Remove a set
PySparkIPSets.remove('ipSet')

# Clear sets available
PySparkIPSets.clear()
```

#### IP Set functions (outside Spark):
```python
ipSet = IPSet()

# Add
ipSet.add('0.0.0.0', '::/16')

# Remove
ipSet.remove('::/16')

# Contains
ipSet.contains('0.0.0.0')

# Clear
ipSet.clear()

# Show all
ipSet.showAll()

# Union
ipSet2 = ('2001::', '::33', 'ffff::f')
ipSet.union(ipSet2)

# Intersection
ipSet.intersects(ipSet2)

# Diff
ipSet.diff(ipSet2)

# Show All
ipSet.showAll()

# Return All
ipSet.returnAll()

# Is empty
ipSet.isEmpty()

# Compare IPSets
ipSet2 = ('2001::', '::33', 'ffff::f')
ipSet == ipSet2
ipSet != ipSet2

# Return the # of elements in the set
len(ipSet)
```
#### Other operations (outside Spark):
```python
# Nets intersect
net1 = '192.0.0.0/16'
net2 = '192.0.0.0/8'
# or ipaddress.ip_network('192.0.0.0/8')
netsIntersect(net1, net2)
```


%prep
%autosetup -n PySparkIP-1.2.4

%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-PySparkIP -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.4-1
- Package Spec generated