Generate a Kinesis stream with hotspots using an AWS SDK

The following code example shows how to generate a Kinesis stream with hotspots.

Python

SDK for Python (Boto3)

Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository.

import json
from pprint import pprint
import random
import time
import boto3

STREAM_NAME = "ExampleInputStream"


def get_hotspot(field, spot_size):
    hotspot = {
        "left": field["left"] + random.random() * (field["width"] - spot_size),
        "width": spot_size,
        "top": field["top"] + random.random() * (field["height"] - spot_size),
        "height": spot_size,
    }
    return hotspot


def get_record(field, hotspot, hotspot_weight):
    rectangle = hotspot if random.random() < hotspot_weight else field
    point = {
        "x": rectangle["left"] + random.random() * rectangle["width"],
        "y": rectangle["top"] + random.random() * rectangle["height"],
        "is_hot": "Y" if rectangle is hotspot else "N",
    }
    return {"Data": json.dumps(point), "PartitionKey": "partition_key"}


def generate(
    stream_name, field, hotspot_size, hotspot_weight, batch_size, kinesis_client
):
    """
    Generates points used as input to a hotspot detection algorithm.
    With probability hotspot_weight (20%), a point is drawn from the hotspot;
    otherwise, it is drawn from the base field. The location of the hotspot
    changes for every 1000 points generated.
    """
    points_generated = 0
    hotspot = None
    while True:
        if points_generated % 1000 == 0:
            hotspot = get_hotspot(field, hotspot_size)
        records = [
            get_record(field, hotspot, hotspot_weight) for _ in range(batch_size)
        ]
        points_generated += len(records)
        pprint(records)
        kinesis_client.put_records(StreamName=stream_name, Records=records)

        time.sleep(0.1)


if __name__ == "__main__":
    generate(
        stream_name=STREAM_NAME,
        field={"left": 0, "width": 10, "top": 0, "height": 10},
        hotspot_size=1,
        hotspot_weight=0.2,
        batch_size=10,
        kinesis_client=boto3.client("kinesis"),
    )