How to create hopping windows

Problem:

you have time-series events in a Kafka topic, and you want to group them into fixed-size, possibly-overlapping, contiguous time intervals to identify an specific scenario.

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Example use case:

You want to build an alerting system that automatically detects if the temperature of a room consistently drops. In this tutorial, we'll write a program that monitors a stream of temperature readings and detects when the temperature consistently drops below 45 °F for a period of 10 minutes.

Code example:

KSQL

Try it

1
Initialize the project

To get started, make a new directory anywhere you’d like for this project:

mkdir hopping-windows && cd hopping-windows

Then make the following directories to set up its structure:

mkdir src test

2
Get Confluent Platform

Next, create the following docker-compose.yml file to obtain Confluent Platform:

---
version: '2'

services:
  zookeeper:
    image: confluentinc/cp-zookeeper:5.3.2
    hostname: zookeeper
    container_name: zookeeper
    ports:
      - "2181:2181"
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000

  broker:
    image: confluentinc/cp-enterprise-kafka:5.3.2
    hostname: broker
    container_name: broker
    depends_on:
      - zookeeper
    ports:
      - "29092:29092"
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181'
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:9092,PLAINTEXT_HOST://localhost:29092
      KAFKA_METRIC_REPORTERS: io.confluent.metrics.reporter.ConfluentMetricsReporter
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
      KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
      CONFLUENT_METRICS_REPORTER_BOOTSTRAP_SERVERS: broker:9092
      CONFLUENT_METRICS_REPORTER_ZOOKEEPER_CONNECT: zookeeper:2181
      CONFLUENT_METRICS_REPORTER_TOPIC_REPLICAS: 1
      CONFLUENT_METRICS_ENABLE: 'true'
      CONFLUENT_SUPPORT_CUSTOMER_ID: 'anonymous'

  schema-registry:
    image: confluentinc/cp-schema-registry:5.3.2
    hostname: schema-registry
    container_name: schema-registry
    depends_on:
      - zookeeper
      - broker
    ports:
      - "8081:8081"
    environment:
      SCHEMA_REGISTRY_HOST_NAME: schema-registry
      SCHEMA_REGISTRY_KAFKASTORE_CONNECTION_URL: 'zookeeper:2181'

  ksql-server:
    image: confluentinc/cp-ksql-server:5.3.2
    hostname: ksql-server
    container_name: ksql-server
    depends_on:
      - broker
      - schema-registry
    ports:
      - "8088:8088"
    environment:
      KSQL_CONFIG_DIR: "/etc/ksql"
      KSQL_LOG4J_OPTS: "-Dlog4j.configuration=file:/etc/ksql/log4j-rolling.properties"
      KSQL_BOOTSTRAP_SERVERS: "broker:9092"
      KSQL_HOST_NAME: ksql-server
      KSQL_LISTENERS: "http://0.0.0.0:8088"
      KSQL_CACHE_MAX_BYTES_BUFFERING: 0
      KSQL_KSQL_SCHEMA_REGISTRY_URL: "http://schema-registry:8081"
      KSQL_PRODUCER_INTERCEPTOR_CLASSES: "io.confluent.monitoring.clients.interceptor.MonitoringProducerInterceptor"
      KSQL_CONSUMER_INTERCEPTOR_CLASSES: "io.confluent.monitoring.clients.interceptor.MonitoringConsumerInterceptor"

  ksql-cli:
    image: confluentinc/cp-ksql-cli:5.3.2
    container_name: ksql-cli
    depends_on:
      - broker
      - ksql-server
    entrypoint: /bin/sh
    tty: true
    volumes:
      - ./src:/opt/app/src
      - ./test:/opt/app/test

And launch it by running:

docker-compose up

3
Write the program interactively using the CLI

To begin developing interactively, open up the KSQL CLI:

docker exec -it ksql-cli ksql http://ksql-server:8088

To start off the implementation of this scenario, you need to create a stream that represents the temperature readings coming from the sensors. Since we will be handling time in this scenario, it is important that each reading contains a timestamp indicating when that reading was taken. The field TIMESTAMP will be used for this purpose.

CREATE STREAM TEMPERATURE_READINGS (ID VARCHAR, TIMESTAMP VARCHAR, READING BIGINT)
    WITH (KAFKA_TOPIC = 'TEMPERATURE_READINGS',
          VALUE_FORMAT = 'JSON',
          TIMESTAMP = 'TIMESTAMP',
          TIMESTAMP_FORMAT = 'yyyy-MM-dd HH:mm:ss',
          PARTITIONS = 1, REPLICAS = 1);

Now let’s produce some events that represent temperature readings from a sensor. Note how the timestamps are increasing on intervals of 5 minutes, to indicate that the sensor emits those events every 5 minutes. Moreover, note that we simulate a temperature drop from 02:25 until 02:40. This is the period that our alerting system should focus on.

INSERT INTO TEMPERATURE_READINGS (ID, TIMESTAMP, READING) VALUES ('1', '2020-01-15 02:15:30', 55);
INSERT INTO TEMPERATURE_READINGS (ID, TIMESTAMP, READING) VALUES ('1', '2020-01-15 02:20:30', 50);
INSERT INTO TEMPERATURE_READINGS (ID, TIMESTAMP, READING) VALUES ('1', '2020-01-15 02:25:30', 45);
INSERT INTO TEMPERATURE_READINGS (ID, TIMESTAMP, READING) VALUES ('1', '2020-01-15 02:30:30', 40);
INSERT INTO TEMPERATURE_READINGS (ID, TIMESTAMP, READING) VALUES ('1', '2020-01-15 02:35:30', 45);
INSERT INTO TEMPERATURE_READINGS (ID, TIMESTAMP, READING) VALUES ('1', '2020-01-15 02:40:30', 50);
INSERT INTO TEMPERATURE_READINGS (ID, TIMESTAMP, READING) VALUES ('1', '2020-01-15 02:45:30', 55);
INSERT INTO TEMPERATURE_READINGS (ID, TIMESTAMP, READING) VALUES ('1', '2020-01-15 02:50:30', 60);

Now that you have stream with some events in it, let’s start to leverage them. The first thing to do is set the following properties to ensure that you’re reading from the beginning of the stream:

SET 'auto.offset.reset' = 'earliest';

Now here comes the fun part. We need to create a query capable of detecting when the temperature drops below 45 °F for a period of 10 minutes. However, since our sensor emits readings every 5 minutes, we need to come up with a way to detect this even if the drop goes beyond the interval of 10 minutes. Hence why we are using hopping windows for this scenario. Hopping windows may overlap, thus if the temperature dropped for 10 minutes but the drop was kept up to 5 minutes beyond an given window — the query will be able to detect that as well.

Along with implementing a hopping window, we will base our logic on the average temperature within the detected period, and we will show this period through two different fields called START_PERIOD and END_PERIOD. This may come in handy if your alert system needs to display the specific time period associated with each alert.

SELECT
    ID,
    TIMESTAMPTOSTRING(WindowStart(), 'HH:mm:ss') AS START_PERIOD,
    TIMESTAMPTOSTRING(WindowEnd(), 'HH:mm:ss') AS END_PERIOD,
    SUM(READING)/COUNT(READING) AS AVG_READING
FROM TEMPERATURE_READINGS
WINDOW HOPPING (SIZE 10 MINUTES, ADVANCE BY 5 MINUTES)
GROUP BY ID HAVING SUM(READING)/COUNT(READING) < 45 LIMIT 3;

This query should produce the following output:

1 | 02:25:00 | 02:35:00 | 42
1 | 02:30:00 | 02:40:00 | 40
1 | 02:30:00 | 02:40:00 | 42
Limit Reached
Query terminated

Note that the period where the average temperature fell below 45F was exactly from 02:25 until 02:40. This means that our alerting system is working propertly. Now let’s create some continuous queries to implement this scenario.

CREATE TABLE TRIGGERED_ALERTS AS
    SELECT
        ID,
        TIMESTAMPTOSTRING(WindowStart(), 'HH:mm:ss') AS START_PERIOD,
        TIMESTAMPTOSTRING(WindowEnd(), 'HH:mm:ss') AS END_PERIOD,
        SUM(READING)/COUNT(READING) AS AVG_READING
    FROM TEMPERATURE_READINGS
    WINDOW HOPPING (SIZE 10 MINUTES, ADVANCE BY 5 MINUTES)
    GROUP BY ID HAVING SUM(READING)/COUNT(READING) < 45;

CREATE STREAM RAW_ALERTS (ID VARCHAR, START_PERIOD VARCHAR, END_PERIOD VARCHAR, AVG_READING BIGINT)
    WITH (KAFKA_TOPIC = 'TRIGGERED_ALERTS',
          VALUE_FORMAT = 'JSON');

CREATE STREAM ALERTS AS
    SELECT
        ID,
        START_PERIOD,
        END_PERIOD,
        AVG_READING
    FROM RAW_ALERTS
    PARTITION BY ID;

Note that we called the query that detects the temperature drop as TRIGGERED_ALERTS and we modeled as a table, since we are performing some aggregations in the query. But just like the temperature readings, alerts can happen continuously – therefore we transform the table back to a stream so we can have multiple alerts throughout time. Finally, also note that we rekeyed the stream to use the ID field as key.

SELECT
    ID,
    START_PERIOD,
    END_PERIOD,
    AVG_READING
FROM ALERTS LIMIT 3;

The output should look similar to:

1 | 02:25:00 | 02:35:00 | 42
1 | 02:30:00 | 02:40:00 | 40
1 | 02:30:00 | 02:40:00 | 42
Limit Reached
Query terminated

Finally, let’s see what’s available on the underlying Kafka topic for the table. We can print that out easily.

PRINT 'ALERTS' FROM BEGINNING LIMIT 3;

The output should look similar to:

Format:JSON
{"ROWTIME":1579055430000,"ROWKEY":"1","ID":"1","START_PERIOD":"02:25:00","END_PERIOD":"02:35:00","AVG_READING":42}
{"ROWTIME":1579055430000,"ROWKEY":"1","ID":"1","START_PERIOD":"02:30:00","END_PERIOD":"02:40:00","AVG_READING":40}
{"ROWTIME":1579055730000,"ROWKEY":"1","ID":"1","START_PERIOD":"02:30:00","END_PERIOD":"02:40:00","AVG_READING":42}

4
Write your statements to a file

Now that you have a series of statements that’s doing the right thing, the last step is to put them into a file so that they can be used outside the CLI session. Create a file at src/statements.sql with the following content:

CREATE STREAM TEMPERATURE_READINGS (ID VARCHAR, TIMESTAMP VARCHAR, READING BIGINT)
    WITH (KAFKA_TOPIC = 'TEMPERATURE_READINGS',
          VALUE_FORMAT = 'JSON',
          TIMESTAMP = 'TIMESTAMP',
          TIMESTAMP_FORMAT = 'yyyy-MM-dd HH:mm:ss',
          PARTITIONS = 1, REPLICAS = 1);

CREATE TABLE TRIGGERED_ALERTS AS
    SELECT
        ID,
        TIMESTAMPTOSTRING(WindowStart(), 'HH:mm:ss') AS START_PERIOD,
        TIMESTAMPTOSTRING(WindowEnd(), 'HH:mm:ss') AS END_PERIOD,
        SUM(READING)/COUNT(READING) AS AVG_READING
    FROM TEMPERATURE_READINGS
    WINDOW HOPPING (SIZE 10 MINUTES, ADVANCE BY 5 MINUTES)
    GROUP BY ID HAVING SUM(READING)/COUNT(READING) < 45;

CREATE STREAM RAW_ALERTS (ID VARCHAR, START_PERIOD VARCHAR, END_PERIOD VARCHAR, AVG_READING BIGINT)
    WITH (KAFKA_TOPIC = 'TRIGGERED_ALERTS',
          VALUE_FORMAT = 'JSON');

CREATE STREAM ALERTS AS
    SELECT
        ID,
        START_PERIOD,
        END_PERIOD,
        AVG_READING
    FROM RAW_ALERTS
    PARTITION BY ID;

Test it

1
Create the test data

Create a file at test/input.json with the inputs for testing:

{
  "inputs": [
    {
      "topic": "TEMPERATURE_READINGS",
      "key": "1",
      "value": {
        "ID": "1",
        "TIMESTAMP": "2020-01-15 02:20:30",
        "READING": 50
      }
    },
    {
      "topic": "TEMPERATURE_READINGS",
      "key": "1",
      "value": {
        "ID": "1",
        "TIMESTAMP": "2020-01-15 02:25:30",
        "READING": 45
      }
    },
    {
      "topic": "TEMPERATURE_READINGS",
      "key": "1",
      "value": {
        "ID": "1",
        "TIMESTAMP": "2020-01-15 02:30:30",
        "READING": 40
      }
    },
    {
      "topic": "TEMPERATURE_READINGS",
      "key": "1",
      "value": {
        "ID": "1",
        "TIMESTAMP": "2020-01-15 02:35:30",
        "READING": 45
      }
    },
    {
      "topic": "TEMPERATURE_READINGS",
      "key": "1",
      "value": {
        "ID": "1",
        "TIMESTAMP": "2020-01-15 02:40:30",
        "READING": 50
      }
    },
    {
      "topic": "TEMPERATURE_READINGS",
      "key": "1",
      "value": {
        "ID": "1",
        "TIMESTAMP": "2020-01-15 02:45:30",
        "READING": 55
      }
    },
    {
      "topic": "TEMPERATURE_READINGS",
      "key": "1",
      "value": {
        "ID": "1",
        "TIMESTAMP": "2020-01-15 02:50:30",
        "READING": 60
      }
    }
  ]
}

Similarly, create a file at test/output.json with the expected outputs.

{
  "outputs": [
    {
      "topic": "ALERTS",
      "key": "1",
      "value": {
        "ID": "1",
        "START_PERIOD": "02:25:00",
        "END_PERIOD": "02:35:00",
        "AVG_READING": 42
      },
      "timestamp": 1579055430000
    },
    {
      "topic": "ALERTS",
      "key": "1",
      "value": {
        "ID": "1",
        "START_PERIOD": "02:30:00",
        "END_PERIOD": "02:40:00",
        "AVG_READING": 40
      },
      "timestamp": 1579055430000
    },
    {
      "topic": "ALERTS",
      "key": "1",
      "value": {
        "ID": "1",
        "START_PERIOD": "02:30:00",
        "END_PERIOD": "02:40:00",
        "AVG_READING": 42
      },
      "timestamp": 1579055730000
    }
  ]
}

2
Invoke the tests

Lastly, invoke the tests using the test runner and the statements file that you created earlier:

docker exec ksql-cli ksql-test-runner -i /opt/app/test/input.json -s opt/app/src/statements.sql -o /opt/app/test/output.json

Which should pass:

	 >>> Test passed!

Take it to production

1
Send the statements to the REST API

Launch your statements into production by sending them to the REST API with the following command:

statements=$(< src/statements.sql) && \
    echo '{"ksql":"'$statements'", "streamsProperties": {}}' | \
        curl -X "POST" "http://localhost:8088/ksql" \
             -H "Content-Type: application/vnd.ksql.v1+json; charset=utf-8" \
             -d @- | \
        jq