Step 1: Create the Input and Output Streams - Amazon Kinesis Data Analytics for SQL Applications Developer Guide

Step 1: Create the Input and Output Streams

Before you create an Amazon Kinesis Data Analytics application for the Hotspots example, you create two Kinesis data streams. Configure one of the streams as the streaming source for your application, and the other stream as the destination where Kinesis Data Analytics persists your application output.

Step 1.1: Create the Kinesis Data Streams

In this section, you create two Kinesis data streams: ExampleInputStream and ExampleOutputStream.

Create these data streams using the console or the AWS CLI.

  • To create the data streams using the console:

    1. Sign in to the AWS Management Console and open the Kinesis console at

    2. Choose Data Streams in the navigation pane.

    3. Choose Create Kinesis stream, and create a stream with one shard named ExampleInputStream.

    4. Repeat the previous step, creating a stream with one shard named ExampleOutputStream.

  • To create data streams using the AWS CLI:

    • Create streams (ExampleInputStream and ExampleOutputStream) using the following Kinesis create-stream AWS CLI command. To create the second stream, which the application will use to write output, run the same command, changing the stream name to ExampleOutputStream.

      $ aws kinesis create-stream \ --stream-name ExampleInputStream \ --shard-count 1 \ --region us-west-2 \ --profile adminuser $ aws kinesis create-stream \ --stream-name ExampleOutputStream \ --shard-count 1 \ --region us-west-2 \ --profile adminuser

Step 1.2: Write Sample Records to the Input Stream

In this step, you run Python code to continuously generate sample records and write to the ExampleInputStream stream.

{"x": 7.921782426109737, "y": 8.746265312709893, "is_hot": "N"} {"x": 0.722248626580026, "y": 4.648868803193405, "is_hot": "Y"}
  1. Install Python and pip.

    For information about installing Python, see the Python website.

    You can install dependencies using pip. For information about installing pip, see Installation on the pip website.

  2. Run the following Python code. This code does the following:

    • Generates a potential hotspot somewhere in the (X, Y) plane.

    • Generates a set of 1,000 points for each hotspot. Of these points, 20 percent are clustered around the hotspot. The rest are generated randomly within the entire space.

    • The put-record command writes the JSON records to the stream.


    Do not upload this file to a web server because it contains your AWS credentials.

    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'))

Next Step

Step 2: Create the Kinesis Data Analytics Application