Example: Tumbling Window Using ROWTIME - Amazon Kinesis Data Analytics for SQL Applications Developer Guide
Step 1: Create a Kinesis Data StreamStep 2: Create the Kinesis Data Analytics Application

After careful consideration, we have decided to discontinue Amazon Kinesis Data Analytics for SQL applications in two steps:

1. From October 15, 2025, you will not be able to create new Kinesis Data Analytics for SQL applications.

2. We will delete your applications starting January 27, 2026. You will not be able to start or operate your Amazon Kinesis Data Analytics for SQL applications. Support will no longer be available for Amazon Kinesis Data Analytics for SQL from that time. For more information, see Amazon Kinesis Data Analytics for SQL Applications discontinuation.

Example: Tumbling Window Using ROWTIME

When a windowed query processes each window in a non-overlapping manner, the window is referred to as a tumbling window. For details, see Tumbling Windows (Aggregations Using GROUP BY). This Amazon Kinesis Data Analytics example uses the ROWTIME column to create tumbling windows. The ROWTIME column represents the time the record was read by the application.

In this example, you write the following records to a Kinesis data stream. 

{"TICKER": "TBV", "PRICE": 33.11}
{"TICKER": "INTC", "PRICE": 62.04}
{"TICKER": "MSFT", "PRICE": 40.97}
{"TICKER": "AMZN", "PRICE": 27.9}
...

You then create a Kinesis Data Analytics application in the AWS Management Console, with the Kinesis data stream as the streaming source. The discovery process reads sample records on the streaming source and infers an in-application schema with two columns (TICKER and PRICE) as shown following.

Console screenshot showing the in-application schema with price and ticker columns.

You use the application code with the MIN and MAX functions to create a windowed aggregation of the data. Then you insert the resulting data into another in-application stream, as shown in the following screenshot:

Console screenshot showing the resulting data in an in-application stream.

In the following procedure, you create a Kinesis Data Analytics application that aggregates values in the input stream in a tumbling window based on ROWTIME.

Step 1: Create a Kinesis Data Stream

Create an Amazon Kinesis data stream and populate the records as follows:

  1. Sign in to the AWS Management Console and open the Kinesis console at https://console.aws.amazon.com/kinesis.

  2. Choose Data Streams in the navigation pane.

  3. Choose Create Kinesis stream, and then create a stream with one shard. For more information, see Create a Stream in the Amazon Kinesis Data Streams Developer Guide.

  4. To write records to a Kinesis data stream in a production environment, we recommend using either the Kinesis Client Library or Kinesis Data Streams API. For simplicity, this example uses the following Python script to generate records. Run the code to populate the sample ticker records. This simple code continuously writes a random ticker record to the stream. Keep the script running so that you can generate the application schema in a later step.

     
import datetime
import json
import random
import boto3

STREAM_NAME = "ExampleInputStream"


def get_data():
    return {
        "EVENT_TIME": datetime.datetime.now().isoformat(),
        "TICKER": random.choice(["AAPL", "AMZN", "MSFT", "INTC", "TBV"]),
        "PRICE": round(random.random() * 100, 2),
    }


def generate(stream_name, kinesis_client):
    while True:
        data = get_data()
        print(data)
        kinesis_client.put_record(
            StreamName=stream_name, Data=json.dumps(data), PartitionKey="partitionkey"
        )


if __name__ == "__main__":
    generate(STREAM_NAME, boto3.client("kinesis"))

Step 2: Create the Kinesis Data Analytics Application

Create a Kinesis Data Analytics application as follows:

  1. Open the Managed Service for Apache Flink console at https://console.aws.amazon.com/kinesisanalytics.

  2. Choose Create application, enter an application name, and choose Create application.

  3. On the application details page, choose Connect streaming data to connect to the source.

  4. On the Connect to source page, do the following:

    1. Choose the stream that you created in the preceding section.

    2. Choose Discover Schema. Wait for the console to show the inferred schema and samples records that are used to infer the schema for the in-application stream created. The inferred schema has two columns.

    3. Choose Save schema and update stream samples. After the console saves the schema, choose Exit.

    4. Choose Save and continue.

  5. On the application details page, choose Go to SQL editor. To start the application, choose Yes, start application in the dialog box that appears.

  6. In the SQL editor, write the application code, and verify the results as follows:

    1. Copy the following application code and paste it into the editor.

      CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (TICKER VARCHAR(4), MIN_PRICE REAL, MAX_PRICE REAL);

CREATE OR REPLACE PUMP "STREAM_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM"
    SELECT STREAM TICKER, MIN(PRICE), MAX(PRICE)
        FROM "SOURCE_SQL_STREAM_001"
        GROUP BY TICKER, 
            STEP("SOURCE_SQL_STREAM_001".ROWTIME BY INTERVAL '60' SECOND);

    2. Choose Save and run SQL.

      On the Real-time analytics tab, you can see all the in-application streams that the application created and verify the data.