Python Fake Data Producer for Apache Kafka®

Description

Python Fake Data Producer for Apache Kafka® is a complete demo app allowing you to quickly produce a Python fake Pizza-based streaming dataset and push it to an Apache Kafka® topic. It gives an example on how easy is to create great fake streaming data to feed Apache Kafka.

• Apache Kafka: a distributed streaming platform
• Topic: all Apache Kafka records are organised into topics, you can think of a topic like an event log or a table if you're familiar with databases.
• Apache Kafka Producer: an entity/application that publishes data to Apache Kafka

An Apache Apache Kafka cluster can be created in minutes in any cloud of your choice using Aiven.io console.

For more informations about the code building blogs check the blog post

Installation

This demo app is relying on Faker and kafka-python which the former requiring Python 3.5 and above. The installation can be done via

pip install -r requirements.txt

Usage

The Python code can be run in bash with the following, in SSL security protocol:

python main.py \
  --security-protocol ssl \
  --cert-folder ~/kafkaCerts/ \
  --host kafka-<name>.aivencloud.com \
  --port 13041 \
  --topic-name pizza-orders \
  --nr-messages 0 \
  --max-waiting-time 0 \
  --subject pizza

in SASL_SSL security protocol:

python main.py \
  --security-protocol SASL_SSL \
  --sasl-mechanism SCRAM-SHA-256 \
  --username <USERNAME> \
  --password <PASSWORD> \
  --cert-folder ~/kafkaCerts/ \
  --host kafka-<name>.aivencloud.com \
  --port 13041 \
  --topic-name pizza-orders \
  --nr-messages 0 \
  --max-waiting-time 0 \
  --subject pizza

in PLAINTEXT security protocol:

python main.py \
  --security-protocol plaintext \
  --host your-kafka-broker-host \
  --port 9092 \
  --topic-name pizza-orders \
  --nr-messages 0 \
  --max-waiting-time 0 \
  --subject pizza

Where

• security-protocol: Security protocol for Kafka. PLAINTEXT, SSL or SASL_SSL are supported.
• cert-folder: points to the folder containing the Apache Kafka CA certificate, Access certificate and Access key (see blog post for more)
• host: the Apache Kafka host
• port: the Apache Kafka port
• topic-name: the Apache Kafka topic name to write to (the topic needs to be pre-created or kafka.auto_create_topics_enable parameter enabled)
• nr-messages: the number of messages to send
• max-waiting-time: the maximum waiting time in seconds between messages
• subject: select amongst various subjects: pizza is the default one, but you can generate also userbehaviour, bet, stock, realstock (using the yahoo finance apis), metric, advancedmetric, and rolling.

If successfully connected to a Apache Kafka cluster, the command will output a number of messages (nr-messages parameter) that are been sent to Apache Kafka in the form

{
  "id": 0,
  "shop": "Circular Pi Pizzeria",
  "name": "Jason Brown",
  "phoneNumber": "(510)290-7469",
  "address": "2701 Samuel Summit Suite 938\nRyanbury, PA 62847",
  "pizzas": [{
    "pizzaName": "Diavola",
    "additionalToppings": []
  }, {
    "pizzaName": "Mari & Monti",
    "additionalToppings": ["olives", "garlic", "anchovies"]
  }, {
    "pizzaName": "Diavola",
    "additionalToppings": ["onion", "anchovies", "mozzarella", "olives"]
  }]
}

With

• id: being the order number, starting from 0 until nr-messages -1
• shop: is the pizza shop name receiving the order, you can check and change the full list of shops in the pizza_shop function within pizzaproducer.py
• name: the caller name
• phoneNumber: the caller phone number
• address: the caller address
• pizzas: an array or pizza orders made by
  • pizzaName: the name of the basic pizza in the range from 1 to MAX_NUMBER_PIZZAS_IN_ORDER defined in main.py, the list of available pizzas can be found in the pizza_name function within pizzaproducer.py
  • additionalToppings: an optional number of additional toppings added to the pizza in the range from 0 to MAX_ADDITIONAL_TOPPINGS_IN_PIZZA , the list of available toppings can be found in the pizza_topping function within pizzaproducer.py

Starting your Apache Kafka Service with Aiven.io

If you don't have a Apache Kafka Cluster available, you can easily start one in Aiven.io console.

Once created your account you can start your Apache Kafka service with Aiven.io's cli

Set your variables first:

KAFKA_INSTANCE_NAME=fafka-my
PROJECT_NAME=my-project
CLOUD_REGION=aws-eu-south-1
AIVEN_PLAN_NAME=business-4
DESTINATION_FOLDER_NAME=~/kafkacerts

Parameters:

• KAFKA_INSTANCE_NAME: the name you want to give to the Apache Kafka instance
• PROJECT_NAME: the name of the project created during sing-up
• CLOUD_REGION: the name of the Cloud region where the instance will be created. The list of cloud regions can be found with

avn cloud list

• AIVEN_PLAN_NAME: name of Aiven's plan to use, which will drive the resources available, the list of plans can be found with

avn service plans --project <PROJECT_NAME> -t kafka --cloud <CLOUD_PROVIDER>

• DESTINATION_FOLDER_NAME: local folder where Apache Kafka certificates will be stored (used to login)

You can create the Apache Kafka service with

avn service create  \
  -t kafka $KAFKA_INSTANCE_NAME \
  --project $PROJECT_NAME \
  --cloud  $CLOUD_PROVIDER \
  -p $AIVEN_PLAN_NAME \
  -c kafka_rest=true \
  -c kafka.auto_create_topics_enable=true \
  -c schema_registry=true

---

You can download the required SSL certificates in the <DESTINATION_FOLDER_NAME> with

avn service user-creds-download $KAFKA_SERVICE_NAME \
  --project $PROJECT_NAME    \
  -d $DESTINATION_FOLDER_NAME \
  --username avnadmin

And retrieve the Apache Kafka Service URI with

avn service get $KAFKA_SERVICE_NAME \
  --project $PROJECT_NAME \
  --format '{service_uri}'

The Apache Kafka Service URI is in the form hostname:port and provides the hostname and port needed to execute the code. You can wait for the newly created Apache Kafka instance to be ready with

avn service wait $KAFKA_SERVICE_NAME --project $PROJECT_NAME

For a more detailed description of services and required credentials, check the blog post

No Pizza? No Problem!

The demo app produces pizza data, however is very simple to change the dataset produced to anything else. The code is based on Faker, an Open Source Python library to generate fake data.

To modify the data generated, change the produce_pizza_order function within the main.py file. The output of the function should be two python dictionaries, containing the event key and message

def produce_pizza_order (ordercount = 1):
    message = {
        'name': fake.unique.name(),
        'phoneNumber': fake.phone_number(),
        'address': fake.address()
      }
    key = {'order' = ordercount}
    return message, key

To customise your dataset, you can check Faker's providers in the related doc

Edit: Now with the subject parameter you can start generating:

• fake advancedmetric data, for 100000 different hostname each having 30 different CPUs

Sending: {'hostname': 'hostname30692', 'cpu': 'cpu9', 'usage': 76.83123942281046, 'occurred_at': 1675064924126}
Sending: {'hostname': 'hostname49005', 'cpu': 'cpu4', 'usage': 76.29121084860914, 'occurred_at': 1675064924126}
Sending: {'hostname': 'hostname65485', 'cpu': 'cpu23', 'usage': 98.6179112244911, 'occurred_at': 1675064924126}
Sending: {'hostname': 'hostname58818', 'cpu': 'cpu15', 'usage': 87.8367169647086, 'occurred_at': 1675064924126}

• fake metric data

{'hostname': 'grumpy', 'cpu': 'cpu4', 'usage': 85.2992318980445, 'occurred_at': 1634221377266}
{'hostname': 'sleepy', 'cpu': 'cpu1', 'usage': 97.83137121091504, 'occurred_at': 1634221378192}
{'hostname': 'sneezy', 'cpu': 'cpu3', 'usage': 85.36598989372837, 'occurred_at': 1634221378395}
{'hostname': 'happy', 'cpu': 'cpu4', 'usage': 81.10449127622482, 'occurred_at': 1634221378800}
{'hostname': 'dopey', 'cpu': 'cpu2', 'usage': 84.98778951073432, 'occurred_at': 1634221379306}

• fake userbehaviour data

{'user_id': 8, 'item_id': 25, 'behavior': 'buy', 'view_id': None, 'group_name': 'A', 'occurred_at': '2021-10-14 16:24:57'}
{'user_id': 6, 'item_id': 28, 'behavior': 'buy', 'view_id': None, 'group_name': 'B', 'occurred_at': '2021-10-14 16:24:51'}
{'user_id': 6, 'item_id': 23, 'behavior': 'cart', 'view_id': None, 'group_name': 'B', 'occurred_at': '2021-10-14 16:24:56'}
{'user_id': 9, 'item_id': 26, 'behavior': 'buy', 'view_id': None, 'group_name': 'A', 'occurred_at': '2021-10-14 16:24:52'}
{'user_id': 1, 'item_id': 23, 'behavior': 'buy', 'view_id': None, 'group_name': 'B', 'occurred_at': '2021-10-14 16:24:56'}

• fake stock data

{'stock_name': 'Pita Pan', 'stock_value': 11.311429500055635, 'timestamp': 1634221435718}
{'stock_name': 'Deja Brew', 'stock_value': 9.956550461386884, 'timestamp': 1634221435877}
{'stock_name': 'Thai Tanic', 'stock_value': 27.227119819515632, 'timestamp': 1634221436180}
{'stock_name': 'Lawn & Order', 'stock_value': 20.625166423466904, 'timestamp': 1634221436285}
{'stock_name': 'Indiana Jeans', 'stock_value': 24.598295127977412, 'timestamp': 1634221436491}

• real realstock data (based on yahoo finance apis)

{'stock_name': 'DOGE-USD', 'stock_value': 0.23705412447452545, 'timestamp': 1634221555719}
{'stock_name': 'DOGE-USD', 'stock_value': 0.23705412447452545, 'timestamp': 1634221556098}
{'stock_name': 'ETH-USD', 'stock_value': 3787.759521484375, 'timestamp': 1634221557011}
{'stock_name': 'ETH-USD', 'stock_value': 3787.759521484375, 'timestamp': 1634221557493}
{'stock_name': 'ADA-USD', 'stock_value': 2.2166504859924316, 'timestamp': 1634221557971}

Apache Kafka is either a registered trademark or trademark of the Apache Software Foundation in the United States and/or other countries. Aiven has no affiliation with and is not endorsed by The Apache Software Foundation.