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Applies to: 
Databricks Runtime 18.2 and above
Important
This feature is in Beta. Workspace admins can control access to this feature from the Previews page. See Manage Azure Databricks previews.
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)]