• A Confluent Cloud account
• The Confluent CLI installed on your machine
• Python 3.8 or later
• Java 21, e.g., follow the OpenJDK installation instructions here if you don't have Java. Validate that java -version shows version 21. Note: Java is required since the Flink Python API uses Py4J to communicate with the JVM under the hood.
• Clone the confluentinc/tutorials GitHub repository (if you haven't already) and navigate to the tutorials directory:

  git clone git@github.com:confluentinc/tutorials.git
  cd tutorials

If you already have the Confluent Cloud resources required to populate a Table API client configuration file, e.g., from running a different tutorial, you may skip to the next step after creating or copying the properties file as documented here to tumbling-windows/flink_table_api_python/cloud.properties within the top-level tutorials directory.

If you need to create the Confluent Cloud infrastructure needed to run this tutorial, the confluent-flink-quickstart CLI plugin creates the resources that you need to get started with Confluent Cloud for Apache Flink. Install it by running:

confluent plugin install confluent-flink-quickstart

Run the plugin as follows to create the Confluent Cloud resources needed for this tutorial and generate a Table API client configuration file. Note that you may specify a different cloud provider (gcp or azure) or region. You can find supported regions in a given cloud provider by running confluent flink region list --cloud <CLOUD>.

confluent flink quickstart \
    --name flink_table_api_tutorials \
    --max-cfu 10 \
    --region us-east-1 \
    --cloud aws \
    --table-api-client-config-file ./tumbling-windows/flink_table_api_python/cloud.properties

The plugin should complete in under a minute and will generate a properties file as documented here.

Take a look at the source code in tumbling-windows/flink_table_api_python/tumbling-windows.py. These two lines instantiate a table environment for executing Table API programs against Confluent Cloud:

settings = ConfluentSettings.from_file("./cloud.properties")
env = TableEnvironment.create(settings)

Let's aggregate one of Confluent Cloud's example tables. You can find these tables in the read-only marketplace database of the examples catalog. The source code in this example uses the Table API's Table.window (over a Tumble window) and GroupWindowedTable.group_by methods to aggregate over 2 second windows. The aggregation is a simple count.

table_result = env.from_path("examples.marketplace.orders") \
    .window(
        Tumble.over(lit(2).seconds)
            .on(col('$rowtime'))
            .alias('window')
    ) \
    .group_by(col('window')) \
    .select(
        col('customer_id').count.alias('count'),
        col('window').start.alias('window_start'),
        col('window').end.alias('window_end')
    ) \
    .execute()

Given the table result, we can then materialize (in memory) the rows in the resulting stream by calling ConfluentTools.collect_materialized_limit or ConfluentTools.print_materialized_limit. This line materializes and prints 2 rows from the table result:

ConfluentTools.print_materialized_limit(table_result, 2)

Alternatively, we can use the Table API's TableResult interface directly to collect rows. For example, to print the next two windows:

with table_result.collect() as rows:
    i = 0
    for row in rows:
        print(f"count: {row[0]}, window start: {row[1]}, window end: {row[2]}")
        i += 1
        if i >= 2: break

In order to run the program, first create a Python virtual environment in which to install the required dependencies. E.g., run the following commands to use the venv module. Note: use python3 and pip3 in the following commands if python and pip refer to Python 2 on your system.

cd tumbling-windows/flink_table_api_python/
python -m venv venv; source ./venv/bin/activate;

Install the confluent-flink-table-api-python-plugin package:

pip install confluent-flink-table-api-python-plugin

You can run the example program directly in your IDE by opening the project located at tumbling-windows/flink_table_api_python/, or via the command line:

python tumbling-windows.py

The program will output 2 rows materialized via print_materialized_limit, and then the next window's count directly via the table result. Note that the same TableResult (and its underlying iterator) is used, so the last window that is printed comes right after the first two windows printed. The output will look like this:

+-------+-------------------------+-------------------------+
| count |            window_start |              window_end |
+-------+-------------------------+-------------------------+
|    31 | 2025-03-03 11:23:14.000 | 2025-03-03 11:23:16.000 |
|   103 | 2025-03-03 11:23:16.000 | 2025-03-03 11:23:18.000 |
+-------+-------------------------+-------------------------+
2 rows in set
count: 100, window start: 2025-03-03 11:23:18, window end: 2025-03-03 11:23:20

When you are done, be sure to clean up any Confluent Cloud resources created for this tutorial. Since you created all resources in a Confluent Cloud environment, you can simply delete the environment and most of the resources created for this tutorial (e.g., the Kafka cluster and Flink compute pool) will be deleted. Run the following command in your terminal to get the environment ID of the form env-123456 corresponding to the environment named flink_table_api_tutorials_environment:

confluent environment list

Delete the environment:

confluent environment delete <ENVIRONMENT_ID>

Next, delete the Flink API key. This API key isn't associated with the deleted environment so it needs to be deleted separately. Find the key:

confluent api-key list --resource flink --current-user

And then copy the 16-character alphanumeric key and delete it:

confluent api-key delete <KEY>

Finally, for the sake of housekeeping, delete the Table API client configuration file:

rm tumbling-windows/flink_table_api_python/cloud.properties