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Profiling with `whylogs` from a Kafka topic¶

In this example we will show how you can profile and merge different profiles from a Kafka topic. To simplify our example and make it reproducible anywhere, we will create a Kafka topic, generate the data from an existing CSV file and ingest it, consume the messages from the topic and then profile these consumed messages.

NOTE: In order to get this example going, we will use Apache Zookeper and Apache Kafka locally with Docker Compose, so be sure to have it installed and ready in your environment. If you want to read more on how this YAML file was built, check out this blogpost.

To get things going, we will put the services up and create the topic in kafka with the following commands:

$ docker-compose up -d

% docker exec -ti kafka bash

root@kafka: kafka-topics --create --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1 --topic whylogs-stream

If you haven't already, make sure to also install kafka-python and whylogs in your environment by uncommenting the following cell.

In [1]:

# Note: you may need to restart the kernel to use updated packages.
%pip install whylogs
%pip install kafka-python

Generating Data¶

To generate the data, we will fetch a small CSV file from a publicly available s3 endpoint and then use the KafkaProducer to send this data over to the topic we have created above

In [2]:

import json
import os.path
import warnings

import pandas as pd
from kafka import KafkaProducer


warnings.simplefilter("ignore")

producer = KafkaProducer(bootstrap_servers='localhost:9092',
                         value_serializer=lambda v: json.dumps(v).encode('utf-8'))


data_url = "https://whylabs-public.s3.us-west-2.amazonaws.com/datasets/tour/current.csv"
full_data = pd.read_csv(os.path.join(data_url))

for i, row in full_data.iterrows():
    producer.send('whylogs-stream', row.to_dict())

Consuming the messages with KafkaConsumer¶

In [3]:

from kafka import KafkaConsumer, TopicPartition


consumer = KafkaConsumer(bootstrap_servers='localhost:9092', 
                         value_deserializer=lambda x: json.loads(x.decode('utf-8')))

assignments = []
topics=['whylogs-stream']

for topic in topics:
    partitions = consumer.partitions_for_topic(topic)
    for p in partitions:
        print(f'topic {topic} - partition {p}')
        assignments.append(TopicPartition(topic, p))
consumer.assign(assignments)

topic whylogs-stream - partition 0

Profiling with `whylogs`¶

For the sake of simplicity, we will build a pandas.DataFrame from the read messages and then profile and write profiles locally until there aren't more messages in the topic. This is done with our log rotation implementation, which we will see in the code block below. You will also need a directory called "profiles", which is the base where the logger will save profiles to, so let's go ahead and create it as well.

In [4]:

import whylogs as why
import pandas as pd 


try:
    os.mkdir("profiles")
except FileExistsError as e:
    pass

consumer.seek_to_beginning()

total = 0 
with why.logger(mode="rolling", interval=5, when="M", base_name="whylogs-kafka") as logger:
    logger.append_writer("local", base_dir="profiles")
    while True:
        finished = True
        record = consumer.poll(timeout_ms=500, max_records=100, update_offsets=True)
        
        for k,v in record.items():
            print(f'{k} - {len(v)}')
            df = pd.DataFrame([row.value for row in v])
            logger.log(df)
            total += len(v)
            finished = False
            
        if finished:
            print(f"total {total}")
            break

TopicPartition(topic='whylogs-stream', partition=0) - 100
TopicPartition(topic='whylogs-stream', partition=0) - 100
TopicPartition(topic='whylogs-stream', partition=0) - 100
TopicPartition(topic='whylogs-stream', partition=0) - 100
TopicPartition(topic='whylogs-stream', partition=0) - 100
TopicPartition(topic='whylogs-stream', partition=0) - 100
TopicPartition(topic='whylogs-stream', partition=0) - 100
TopicPartition(topic='whylogs-stream', partition=0) - 100
TopicPartition(topic='whylogs-stream', partition=0) - 100
TopicPartition(topic='whylogs-stream', partition=0) - 45
total 945

In [5]:

import whylogs as why
from glob import glob

profiles_binaries = glob("profiles/*")
profiles_list = []

for profile in profiles_binaries:
    profiles_list.append(why.read(profile).view())

In [6]:

from functools import reduce

merged_profile = reduce((lambda x, y: x.merge(y)), profiles_list)

In [7]:

merged_profile.to_pandas()

Out[7]:

	types/integral	types/fractional	types/boolean	types/string	types/object	cardinality/est	cardinality/upper_1	cardinality/lower_1	distribution/mean	distribution/stddev	...	distribution/q_75	distribution/q_90	distribution/q_95	distribution/q_99	counts/n	counts/null	type	frequent_items/frequent_strings	ints/max	ints/min
column
Age	0	945	0	0	0	25.000001	25.001250	25.000000	31.609524	6.747796	...	38.0000	41.000	42.0000	43.0000	945	0	SummaryType.COLUMN	NaN	NaN	NaN
Customer ID	0	0	0	945	0	869.683985	881.067672	858.577213	NaN	NaN	...	NaN	NaN	NaN	NaN	945	0	SummaryType.COLUMN	[FrequentItem(value='C268100', est=3, upper=2,...	NaN	NaN
Gender	0	0	0	945	0	2.000000	2.000100	2.000000	NaN	NaN	...	NaN	NaN	NaN	NaN	945	0	SummaryType.COLUMN	[FrequentItem(value='M', est=489, upper=489, l...	NaN	NaN
Item Price	0	945	0	0	0	705.028228	714.256661	696.024282	79.848148	41.921716	...	116.6000	138.200	145.1000	149.0000	945	0	SummaryType.COLUMN	NaN	NaN	NaN
Product Category	0	0	0	945	0	6.000000	6.000300	6.000000	NaN	NaN	...	NaN	NaN	NaN	NaN	945	0	SummaryType.COLUMN	[FrequentItem(value='Books', est=243, upper=24...	NaN	NaN
Product Subcategory	0	0	0	945	0	18.000001	18.000899	18.000000	NaN	NaN	...	NaN	NaN	NaN	NaN	945	0	SummaryType.COLUMN	[FrequentItem(value='Mens', est=141, upper=141...	NaN	NaN
Quantity	945	0	0	0	0	10.000000	10.000500	10.000000	2.450794	2.279227	...	4.0000	5.000	5.0000	5.0000	945	0	SummaryType.COLUMN	[FrequentItem(value='2.000000', est=183, upper...	5.0	-5.0
Store Type	0	0	0	945	0	4.000000	4.000200	4.000000	NaN	NaN	...	NaN	NaN	NaN	NaN	945	0	SummaryType.COLUMN	[FrequentItem(value='e-Shop', est=392, upper=3...	NaN	NaN
Total Amount	0	945	0	0	0	844.069184	855.117588	833.289540	214.615556	261.215174	...	361.5560	580.346	656.8120	804.4400	945	0	SummaryType.COLUMN	NaN	NaN	NaN
Total Tax	0	945	0	0	0	828.657950	839.504628	818.075123	25.664756	19.314519	...	36.7605	57.834	62.7375	76.5975	945	0	SummaryType.COLUMN	NaN	NaN	NaN
Transaction ID	0	0	0	945	0	935.275741	947.517988	923.331294	NaN	NaN	...	NaN	NaN	NaN	NaN	945	0	SummaryType.COLUMN	[]	NaN	NaN
Transaction Type	0	0	0	945	0	2.000000	2.000100	2.000000	NaN	NaN	...	NaN	NaN	NaN	NaN	945	0	SummaryType.COLUMN	[FrequentItem(value='Purchase', est=859, upper...	NaN	NaN

12 rows × 28 columns

In [8]:

import shutil
shutil.rmtree("profiles")

And voilà! With just a few lines of code we could profile and track incoming messages from a Kafka topic. Hopefully this tutorial will get you going for your existing streaming pipelines. If there are any other integrations you wanted to see, or maybe see how other users are getting the most out of whylogs, please check out our community Slack.

🚩 Create a free WhyLabs account to get more value out of whylogs!

Profiling with whylogs from a Kafka topic¶

Generating Data¶

Consuming the messages with KafkaConsumer¶

Profiling with whylogs¶

Profiling with `whylogs` from a Kafka topic¶

Profiling with `whylogs`¶