Next, create the following docker-compose.yml file to obtain Confluent Platform:
---
version: '2'
services:
zookeeper:
image: confluentinc/cp-zookeeper:6.0.0
hostname: zookeeper
container_name: zookeeper
ports:
- "2181:2181"
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_TICK_TIME: 2000
broker:
image: confluentinc/cp-kafka:6.0.0
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_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
schema-registry:
image: confluentinc/cp-schema-registry:6.0.0
hostname: schema-registry
container_name: schema-registry
depends_on:
- broker
ports:
- "8081:8081"
environment:
SCHEMA_REGISTRY_HOST_NAME: schema-registry
SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: 'broker:9092'
ksqldb-server:
image: confluentinc/ksqldb-server:0.11.0
hostname: ksqldb-server
container_name: ksqldb-server
depends_on:
- broker
- schema-registry
ports:
- "8088:8088"
environment:
KSQL_CONFIG_DIR: "/etc/ksqldb"
KSQL_LOG4J_OPTS: "-Dlog4j.configuration=file:/etc/ksqldb/log4j.properties"
KSQL_BOOTSTRAP_SERVERS: "broker:9092"
KSQL_HOST_NAME: ksqldb-server
KSQL_LISTENERS: "http://0.0.0.0:8088"
KSQL_CACHE_MAX_BYTES_BUFFERING: 0
KSQL_KSQL_SCHEMA_REGISTRY_URL: "http://schema-registry:8081"
ksqldb-cli:
image: confluentinc/ksqldb-cli:0.11.0
container_name: ksqldb-cli
depends_on:
- broker
- ksqldb-server
entrypoint: /bin/sh
tty: true
environment:
KSQL_CONFIG_DIR: "/etc/ksqldb"
volumes:
- ./src:/opt/app/src
- ./test:/opt/app/test
And launch it by running:
docker-compose up -d
3
Write the program interactively using the CLI
To begin developing interactively, open up the KSQL CLI:
docker exec -it ksqldb-cli ksql http://ksqldb-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 KEY, TIMESTAMP VARCHAR, READING BIGINT)
WITH (KAFKA_TOPIC = 'TEMPERATURE_READINGS',
VALUE_FORMAT = 'JSON',
TIMESTAMP = 'TIMESTAMP',
TIMESTAMP_FORMAT = 'yyyy-MM-dd HH:mm:ss',
PARTITIONS = 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', 'UTC') AS START_PERIOD,
TIMESTAMPTOSTRING(WINDOWEND, 'HH:mm:ss', 'UTC') 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
EMIT CHANGES
LIMIT 3;
This query should produce the following output:
+--------------------+--------------------+--------------------+--------------------+
|ID |START_PERIOD |END_PERIOD |AVG_READING |
+--------------------+--------------------+--------------------+--------------------+
|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 properly. Now let’s create some continuous queries to implement this scenario.
CREATE TABLE TRIGGERED_ALERTS AS
SELECT
ID AS KEY,
AS_VALUE(ID) AS ID,
TIMESTAMPTOSTRING(WINDOWSTART, 'HH:mm:ss', 'UTC') AS START_PERIOD,
TIMESTAMPTOSTRING(WINDOWEND, 'HH:mm:ss', 'UTC') 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
WHERE ID IS NOT NULL
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
EMIT CHANGES
LIMIT 3;
The output should look similar to:
+--------------------+--------------------+--------------------+--------------------+
|ID |START_PERIOD |END_PERIOD |AVG_READING |
+--------------------+--------------------+--------------------+--------------------+
|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:
Key format: JSON or KAFKA_STRING
Value format: JSON or KAFKA_STRING
rowtime: 2020/01/15 02:30:30.000 Z, key: 1, value: {"START_PERIOD":"02:25:00","END_PERIOD":"02:35:00","AVG_READING":42}
rowtime: 2020/01/15 02:30:30.000 Z, key: 1, value: {"START_PERIOD":"02:30:00","END_PERIOD":"02:40:00","AVG_READING":40}
rowtime: 2020/01/15 02:35:30.000 Z, key: 1, value: {"START_PERIOD":"02:30:00","END_PERIOD":"02:40:00","AVG_READING":42}
Topic printing ceased
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 KEY, TIMESTAMP VARCHAR, READING BIGINT)
WITH (KAFKA_TOPIC = 'TEMPERATURE_READINGS',
VALUE_FORMAT = 'JSON',
TIMESTAMP = 'TIMESTAMP',
TIMESTAMP_FORMAT = 'yyyy-MM-dd HH:mm:ss',
PARTITIONS = 1);
CREATE TABLE TRIGGERED_ALERTS AS
SELECT
ID AS KEY,
AS_VALUE(ID) AS ID,
TIMESTAMPTOSTRING(WINDOWSTART, 'HH:mm:ss', 'UTC') AS START_PERIOD,
TIMESTAMPTOSTRING(WINDOWEND, 'HH:mm:ss', 'UTC') 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
WHERE ID IS NOT NULL
PARTITION BY ID;
