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Applies to: 
Databricks Runtime 18.2 and above
Important
This feature is in Beta.
Returns the canonical representation of an IPv4 or IPv6 address.
For the corresponding SQL function, see ip_host function.
Syntax
from pyspark.databricks.sql import functions as dbf
dbf.ip_host(col=<col>)
Parameters
| Parameter | Type | Description |
|---|---|---|
col |
pyspark.sql.Column or str |
A STRING or BINARY value representing a valid IPv4 or IPv6 address. |
Examples
Example 1: Validate an IPv4 address.
from pyspark.databricks.sql import functions as dbf
df = spark.createDataFrame([('192.168.1.5',)], ['ipv4'])
df.select(dbf.ip_host('ipv4').alias('result')).collect()
[Row(result='192.168.1.5')]
Example 2: Canonicalize an IPv6 address.
from pyspark.databricks.sql import functions as dbf
df = spark.createDataFrame([('2001:0DB8:0000:0000:0000:0000:0000:0001',)], ['ipv6'])
df.select(dbf.ip_host('ipv6').alias('result')).collect()
[Row(result='2001:db8::1')]
Example 3: Validate an IPv4-mapped IPv6 address.
from pyspark.databricks.sql import functions as dbf
df = spark.createDataFrame([('::ffff:192.0.2.128',)], ['ip'])
df.select(dbf.ip_host('ip').alias('result')).collect()
[Row(result='::ffff:192.0.2.128')]
Example 4: Validate an IPv4 address in binary format. The input is the binary representation of the IPv4 address 192.168.1.5.
from pyspark.databricks.sql import functions as dbf
from pyspark.sql.functions import hex
df = spark.createDataFrame([(bytearray([0xC0, 0xA8, 0x01, 0x05]),)], ['ip'])
df.select(hex(dbf.ip_host('ip')).alias('result')).collect()
[Row(result='C0A80105')]
Example 5: None input returns None.
from pyspark.databricks.sql import functions as dbf
df = spark.createDataFrame([(None,)], 'ip: string')
df.select(dbf.ip_host('ip').alias('result')).collect()
[Row(result=None)]