Important

You are viewing documentation for an older version of Confluent Platform. For the latest, click here.

Scala¶

In this tutorial, you will run a Scala client application that produces messages to and consumes messages from an Apache Kafka® cluster.

After you run the tutorial, view the provided source code and use it as a reference to develop your own Kafka client application.

Prerequisites¶

Kafka Cluster¶

You can use this tutorial with a Kafka cluster in any environment:
- In Confluent Cloud
- On your local host
- Any remote Kafka cluster
If you are running on Confluent Cloud, you must have access to a Confluent Cloud cluster
- The first 20 users to sign up for Confluent Cloud and use promo code C50INTEG will receive an additional $50 free usage (details)

Setup¶

Clone the confluentinc/examples GitHub repository and check out the 5.5.15-post branch.

git clone https://github.com/confluentinc/examples
cd examples
git checkout 5.5.15-post

Change directory to the example for Scala.
```
cd clients/cloud/scala/
```
Create a local file (for example, at $HOME/.confluent/java.config) with configuration parameters to connect to your Kafka cluster. Starting with one of the templates below, customize the file with connection information to your cluster. Substitute your values for {{ BROKER_ENDPOINT }}, {{CLUSTER_API_KEY }}, and {{ CLUSTER_API_SECRET }} (see Connecting Clients to Confluent Cloud for instructions on how to create or find those values).
- Template configuration file for Confluent Cloud
```
# Kafka
bootstrap.servers={{ BROKER_ENDPOINT }}
security.protocol=SASL_SSL
sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username="{{ CLUSTER_API_KEY }}" password="{{ CLUSTER_API_SECRET }}";
ssl.endpoint.identification.algorithm=https
sasl.mechanism=PLAIN
```
- Template configuration file for local host
```
# Kafka
bootstrap.servers=localhost:9092
```

Basic Producer and Consumer¶

In this example, the producer application writes Kafka data to a topic in your Kafka cluster. If the topic does not already exist in your Kafka cluster, the producer application will use the Kafka Admin Client API to create the topic. Each record written to Kafka has a key representing a username (for example, alice) and a value of a count, formatted as json (for example, {"count": 0}). The consumer application reads the same Kafka topic and keeps a rolling sum of the count as it processes each record.

Consume Records¶

Build the client examples:
```
sbt clean compile
```

Run the consumer:

sbt “runMain io.confluent.examples.clients.scala.Consumer $HOME/.confluent/java.config test1”

You should see:

<snipped>

Polling
....
<snipped>

View the consumer code.

Kafka Streams¶

In a new window, run the Streams app:
```
cd examples/clients/cloud/scala
```
Build the client examples:
```
sbt clean compile
```

Run the streams app:

sbt "runMain io.confluent.examples.clients.scala.Streams $HOME/.confluent/java.config  test1"

View the Kafka Streams code.

Produce Records¶

In new a window, run the Kafka producer application to write records to the Kafka cluster:

sbt "runMain io.confluent.examples.clients.scala.Producer $HOME/.confluent/java.config test1"

You see the following output:

<snipped>
Produced record at test1-0@120
Produced record at test1-0@121
Produced record at test1-0@122
Produced record at test1-0@123
Produced record at test1-0@124
Produced record at test1-0@125
Produced record at test1-0@126
Produced record at test1-0@127
Produced record at test1-0@128
Produced record at test1-0@129
Wrote ten records to test1
[success] Total time: 6 s, completed 10-Dec-2018 16:50:13

In the consumer window, verify you see the following output:

<snipped>
Polling
Consumed record with key alice and value {"count":1}, and updated total count to 1
Consumed record with key alice and value {"count":2}, and updated total count to 3
Consumed record with key alice and value {"count":3}, and updated total count to 6
Consumed record with key alice and value {"count":4}, and updated total count to 10
Consumed record with key alice and value {"count":5}, and updated total count to 15
Consumed record with key alice and value {"count":6}, and updated total count to 21
Consumed record with key alice and value {"count":7}, and updated total count to 28
Consumed record with key alice and value {"count":8}, and updated total count to 36
Consumed record with key alice and value {"count":9}, and updated total count to 45
Consumed record with key alice and value {"count":10}, and updated total count to 55
Polling

In the streams app, verify you see the following output:

[Consumed record]: alice, 1
[Consumed record]: alice, 2
[Consumed record]: alice, 3
[Consumed record]: alice, 4
[Consumed record]: alice, 5
[Consumed record]: alice, 6
[Consumed record]: alice, 7
[Consumed record]: alice, 8
[Consumed record]: alice, 9
[Consumed record]: alice, 10
[Running count]: alice, 1
[Running count]: alice, 3
[Running count]: alice, 6
[Running count]: alice, 10
[Running count]: alice, 15
[Running count]: alice, 21
[Running count]: alice, 28
[Running count]: alice, 36
[Running count]: alice, 45
[Running count]: alice, 55

When you are done, press CTRL-C in both windows to stop the Consumer and Streams.
View the producer code.