Pyspark 在 function 的基础上加入 dataframe

Question

I have 2 dataframes that look like that

networks

+----------------+-------+
|    Network     | VLAN  |
+----------------+-------+
| 192.168.1.0/24 | VLAN1 |
| 192.168.2.0/24 | VLAN2 |
+----------------+-------+

flows

+--------------+----------------+
|  source_ip   | destination_ip |
+--------------+----------------+
| 192.168.1.11 | 192.168.2.13   |
+--------------+----------------+

Ideally I would like to get something like this

+--------------+----------------+-------------+------------------+
|  source_ip   | destination_ip | source_vlan | destination_vlan |
+--------------+----------------+-------------+------------------+
| 192.168.1.11 | 192.168.2.13   | VLAN1       | VLAN2            |
+--------------+----------------+-------------+------------------+

Unfortunately the flows dataframe does not contain the subnetmask. What I have tried so far without pyspark

1. Get a distinct list of subnets (in this example [24])
2. For every subnet and source_ip compute the network ipaddress.ip_network('{}/{}'.format(ip,sub), strict=False)
3. Search if that subnet exists in dataframe "networks" and return VLAN otherwise return empty string

I tried to do a similar approach with pyspark but it does not work as well as I think there might be better ways of doing it?

def get_available_subnets(df):
    split_col = split(df['network'], '/')
    df = df.withColumn('sub', split_col.getItem(1))
    return df.select('sub').distinct()

def get_vlan_by_ip(ip, infoblox, subnets):
    for sub in subnets:
        net = ipaddress.ip_network('{}/{}'.format(ip,sub), strict=False)
        if net:
            search = infoblox.filter(infoblox.network == str(net))

            if not search.head(1).isEmpty():
                return search.first.vlan
    return hashlib.sha1(str.encode(ip)).hexdigest()

subnets = get_available_subnets(infoblox_networks_df).select('sub').rdd.flatMap(lambda x: x).collect()


short = flows_filtered_prepared_df.limit(1000)


partial_vlan_func = partial(get_vlan_by_ip, infoblox=infoblox_networks_df, subnets=subnets)
get_vlan_udf = udf(lambda ip: partial_vlan_func(ip), StringType())

short.select('source_ip', 'destination_ip', get_vlan_udf('source_ip').alias('source_vlan')).show()

Answer 1

This method completely avoids the use of udf , leveraging split and slice , but perhaps there is a better way. The benefit of this approach is that it directly leverages the bits present in the subnet mask and that it's written purely in PySpark .

Context for the solution: IP addresses can be split and masked by the subnet. This means that 8, 16, 24, 32 tell you which parts of the IP matter - this motivates the division by 8 and using the resulting column to slice the IP address ArrayType column once it's split from its original StringType .

NB: pyspark.sql.functions.slice will work in newer version of PySpark >= 2.4 , some older ones need to use f.expr("slice(...)") .

The setup:

flows = spark.createDataFrame([
    (1, "192.168.1.1", "192.168.2.1"),
    (2, "192.168.2.1", "192.168.3.1"), 
    (3, "192.168.3.1", "192.168.1.1"), 
], ['id', 'source_ip', 'destination_ip'] 
)
networks = spark.createDataFrame([
    (1, "192.168.1.0/24", "VLAN1"),
    (2, "192.168.2.0/24", "VLAN2"), 
    (3, "192.168.3.0/24", "VLAN3"), 
], ['id', 'network', 'vlan'] 
)

Some pre-processing:

networks_split = networks.select(
    "*",
    (f.split(f.col("network"), "/")[1] / 8).cast("int").alias("bits"),
    f.split(f.split(f.col("network"), "/")[0], "\.").alias('segmented_ip')
)
networks_split.show()
+---+--------------+-----+----+----------------+
| id|       network| vlan|bits|    segmented_ip|
+---+--------------+-----+----+----------------+
|  1|192.168.1.0/24|VLAN1|   3|[192, 168, 1, 0]|
|  2|192.168.2.0/24|VLAN2|   3|[192, 168, 2, 0]|
|  3|192.168.3.0/24|VLAN3|   3|[192, 168, 3, 0]|
+---+--------------+-----+----+----------------+

networks_masked = networks_split.select(
    "*",
    f.expr("slice(segmented_ip, 1, bits)").alias("masked_bits"),
)
networks_masked.show()
+---+--------------+-----+----+----------------+-------------+
| id|       network| vlan|bits|    segmented_ip|  masked_bits|
+---+--------------+-----+----+----------------+-------------+
|  1|192.168.1.0/24|VLAN1|   3|[192, 168, 1, 0]|[192, 168, 1]|
|  2|192.168.2.0/24|VLAN2|   3|[192, 168, 2, 0]|[192, 168, 2]|
|  3|192.168.3.0/24|VLAN3|   3|[192, 168, 3, 0]|[192, 168, 3]|
+---+--------------+-----+----+----------------+-------------+

flows_split = flows.select(
    "*",
    f.split(f.split(f.col("source_ip"), "/")[0], "\.").alias('segmented_source_ip'),
    f.split(f.split(f.col("destination_ip"), "/")[0], "\.").alias('segmented_destination_ip')
)
flows_split.show()
+---+-----------+--------------+-------------------+------------------------+
| id|  source_ip|destination_ip|segmented_source_ip|segmented_destination_ip|
+---+-----------+--------------+-------------------+------------------------+
|  1|192.168.1.1|   192.168.2.1|   [192, 168, 1, 1]|        [192, 168, 2, 1]|
|  2|192.168.2.1|   192.168.3.1|   [192, 168, 2, 1]|        [192, 168, 3, 1]|
|  3|192.168.3.1|   192.168.1.1|   [192, 168, 3, 1]|        [192, 168, 1, 1]|
+---+-----------+--------------+-------------------+------------------------+

Finally, I crossJoin and filter on the slice based on the bits of my mask, such as:

flows_split.crossJoin(
    networks_masked.select("vlan", "bits", "masked_bits")
).where(
    f.expr("slice(segmented_source_ip, 1, bits)") == f.col("masked_bits")
).show()
+---+-----------+--------------+-------------------+------------------------+-----+----+-------------+
| id|  source_ip|destination_ip|segmented_source_ip|segmented_destination_ip| vlan|bits|  masked_bits|
+---+-----------+--------------+-------------------+------------------------+-----+----+-------------+
|  1|192.168.1.1|   192.168.2.1|   [192, 168, 1, 1]|        [192, 168, 2, 1]|VLAN1|   3|[192, 168, 1]|
|  2|192.168.2.1|   192.168.3.1|   [192, 168, 2, 1]|        [192, 168, 3, 1]|VLAN2|   3|[192, 168, 2]|
|  3|192.168.3.1|   192.168.1.1|   [192, 168, 3, 1]|        [192, 168, 1, 1]|VLAN3|   3|[192, 168, 3]|
+---+-----------+--------------+-------------------+------------------------+-----+----+-------------+

Exactly the same approach can be done for destination_ip , such as:

flows_split.crossJoin(
    networks_masked.select("vlan", "bits", "masked_bits")
).where(
    f.expr("slice(segmented_destination_ip, 1, bits)") == f.col("masked_bits")
).show()
+---+-----------+--------------+-------------------+------------------------+-----+----+-------------+
| id|  source_ip|destination_ip|segmented_source_ip|segmented_destination_ip| vlan|bits|  masked_bits|
+---+-----------+--------------+-------------------+------------------------+-----+----+-------------+
|  1|192.168.1.1|   192.168.2.1|   [192, 168, 1, 1]|        [192, 168, 2, 1]|VLAN2|   3|[192, 168, 2]|
|  2|192.168.2.1|   192.168.3.1|   [192, 168, 2, 1]|        [192, 168, 3, 1]|VLAN3|   3|[192, 168, 3]|
|  3|192.168.3.1|   192.168.1.1|   [192, 168, 3, 1]|        [192, 168, 1, 1]|VLAN1|   3|[192, 168, 1]|
+---+-----------+--------------+-------------------+------------------------+-----+----+-------------+

Finally, you either join the resulting two tables together on source_ip and destination_ip (since you have the vlan information attached as required), or you merge the previous two steps together and crossJoin and filter twice.

Hope this helps!