Enter your Schema Registry API key:
Enter your Schema Registry API secret:
What is the simplest way to write messages to and read messages from Kafka, using (de)serializers and Schema Registry?
With Confluent Cloud, you can use the Confluent CLI to produce and consume messages. In the following code snippet, substitute the topic name and schema file path.
Producer:
confluent kafka topic produce orders-avro --value-format avro --schema orders-avro-schema.json --parse-key
Consumer:
confluent kafka topic consume orders-avro --value-format avro --print-key --delimiter "-" --from-beginning
This tutorial requires access to an Apache Kafka cluster, and the quickest way to get started free is on Confluent Cloud, which provides Kafka as a fully managed service.
After you log in to Confluent Cloud, click Environments in the lefthand navigation, click on Add cloud environment, and name the environment learn-kafka. Using a new environment keeps your learning resources separate from your other Confluent Cloud resources.
From the Billing & payment section in the menu, apply the promo code CC100KTS to receive an additional $100 free usage on Confluent Cloud (details). To avoid having to enter a credit card, add an additional promo code CONFLUENTDEV1. With this promo code, you will not have to enter a credit card for 30 days or until your credits run out.
Click on LEARN and follow the instructions to launch a Kafka cluster and enable Schema Registry.
This tutorial has some steps for Kafka topic management and producing and consuming events, for which you can use the Confluent Cloud Console or the Confluent CLI. Follow the instructions here to install the Confluent CLI, and then follow these steps connect the CLI to your Confluent Cloud cluster.
Create a Kafka topic called orders-avro in Confluent Cloud.
confluent kafka topic create orders-avro --partitions 1
This should yield the following output:
Created topic "orders-avro".
We are going to use Schema Registry (managed by Confluent Cloud) to control our record format. The first step is creating a schema definition which we will use when producing new records.
Create the following orders-avro-schema.json file:
{
"type": "record",
"namespace": "io.confluent.tutorial",
"name": "OrderDetail",
"fields": [
{"name": "number", "type": "long", "doc": "The order number."},
{"name": "date", "type": "long", "logicalType": "date", "doc": "The date the order was submitted."},
{"name": "shipping_address", "type": "string", "doc": "The shipping address."},
{"name": "subtotal", "type": "double", "doc": "The amount without shipping cost and tax."},
{"name": "shipping_cost", "type": "double", "doc": "The shipping cost."},
{"name": "tax", "type": "double", "doc": "The applicable tax."},
{"name": "grand_total", "type": "double", "doc": "The order grand total ."}
]
}
Next, let’s open up a consumer to read records from the new topic.
From the same terminal you used to create the topic above, run the following command to start a console consumer with the ccloud CLI:
confluent kafka topic consume orders-avro --value-format avro --print-key --delimiter "-" --from-beginning
You will be prompted for the Confluent Cloud Schema Registry credentials as shown below. Enter the values you got from when you enabled Schema Registry in the Confluent Cloud Console.
Enter your Schema Registry API key:
Enter your Schema Registry API secret:The consumer will start up and block waiting for records, you won’t see any output until after the next step.
Now we are going to produce records to our new topic using the schema created a few steps back. Open a second terminal window and start the producer:
confluent kafka topic produce orders-avro --value-format avro --schema orders-avro-schema.json
The producer will start with some information and then wait for you to enter input.
Successfully registered schema with ID 100001
Starting Kafka Producer. ^C or ^D to exit
Below are example records in JSON format with each line representing a single record. In this case we are producing records in Avro format, however, first they are passed to the producer in JSON and the producer converts them to Avro based on the orders-avro-schema.json schema prior to sending them to Kafka.
Copy each line and paste it into the producer terminal, or copy-paste all of them into the terminal and hit enter.
{"number":1,"date":18500,"shipping_address":"ABC Sesame Street,Wichita, KS. 12345","subtotal":110.00,"tax":10.00,"grand_total":120.00,"shipping_cost":0.00}
{"number":2,"date":18501,"shipping_address":"123 Cross Street,Irving, CA. 12345","subtotal":5.00,"tax":0.53,"grand_total":6.53,"shipping_cost":1.00}
{"number":3,"date":18502,"shipping_address":"5014 Pinnickinick Street, Portland, WA. 97205","subtotal":93.45,"tax":9.34,"grand_total":102.79,"shipping_cost":0.00}
{"number":4,"date":18503,"shipping_address":"4082 Elmwood Avenue, Tempe, AX. 85281","subtotal":50.00,"tax":1.00,"grand_total":51.00,"shipping_cost":0.00}
{"number":5,"date":18504,"shipping_address":"123 Cross Street,Irving, CA. 12345","subtotal":33.00,"tax":3.33,"grand_total":38.33,"shipping_cost":2.00}
As you produce records you can observe them in the consumer terminal.
Kafka works with key-value pairs, but so far you’ve only sent records with values only. Well to be fair you’ve sent key-value pairs, but the keys are null.
Sometimes you’ll need to send a valid key in addition to the value from the command line.
To enable sending full key-value pairs from the command line you add two properties to your Confluent CLI, parse-key and delimiter
Let’s try to send some full key-value records now. If your previous producer is still running close it with Ctrl-C and run the following command to start a new console producer:
confluent kafka topic produce orders-avro --value-format avro --schema orders-avro-schema.json --parse-key
Then enter these records either one at time or copy-paste all of them into the terminal and hit enter:
6:{"number":6,"date":18505,"shipping_address":"9182 Shipyard Drive, Raleigh, NC. 27609","subtotal":72.00,"tax":3.00,"grand_total":75.00,"shipping_cost":0.00}
7:{"number":7,"date":18506,"shipping_address":"644 Lagon Street, Chicago, IL. 07712","subtotal":11.00,"tax":1.00,"grand_total":14.00,"shipping_cost":2.00}
Next, let’s open up a consumer to read records from the new topic.
From the same terminal you used to create the topic above, run the following command to start a console consumer with the confluent CLI:
confluent kafka topic consume orders-avro --value-format avro --print-key --delimiter "-" --from-beginning
After the consumer starts you should see the following output in a few seconds:
null-{"number":1,"date":18500,"shipping_address":"ABC Sesame Street,Wichita, KS. 12345","subtotal":110.00,"tax":10.00,"grand_total":120.00,"shipping_cost":0.00}
null-{"number":2,"date":18501,"shipping_address":"123 Cross Street,Irving, CA. 12345","subtotal":5.00,"tax":0.53,"grand_total":6.53,"shipping_cost":1.00}
null-{"number":3,"date":18502,"shipping_address":"5014 Pinnickinick Street, Portland, WA. 97205","subtotal":93.45,"tax":9.34,"grand_total":102.79,"shipping_cost":0.00}
null-{"number":4,"date":18503,"shipping_address":"4082 Elmwood Avenue, Tempe, AX. 85281","subtotal":50.00,"tax":1.00,"grand_total":51.00,"shipping_cost":0.00}
null-{"number":5,"date":18504,"shipping_address":"123 Cross Street,Irving, CA. 12345","subtotal":33.00,"tax":3.33,"grand_total":38.33,"shipping_cost":2.00}
6-{"number":6,"date":18505,"shipping_address":"9182 Shipyard Drive, Raleigh, NC. 27609","subtotal":72.00,"tax":3.00,"grand_total":75.00,"shipping_cost":0.00}
7-{"number":7,"date":18506,"shipping_address":"644 Lagon Street, Chicago, IL. 07712","subtotal":11.00,"tax":1.00,"grand_total":14.00,"shipping_cost":2.00}
Since we kept the --from-beginning property, you’ll see all the records sent to the topic. You’ll notice the results before you sent keys are formatted as null-<value>.
You may try another tutorial, but if you don’t plan on doing other tutorials, use the Confluent Cloud Console or CLI to destroy all of the resources you created. Verify they are destroyed to avoid unexpected charges.