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Generate Batch and Event Data

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Creating a data generator for Kafka topic with matching records in a CSV file.

Requirements

  • 5 minutes
  • Git
  • Gradle
  • Docker

Get Started

First, we will clone the data-caterer-example repo which will already have the base project setup required.

git clone git@github.com:pflooky/data-caterer-example.git

Kafka Setup

If you don't have your own Kafka up and running, you can set up and run an instance configured in the docker folder via.

cd docker
docker-compose up -d kafka
docker exec docker-kafkaserver-1 kafka-topics --bootstrap-server localhost:9092 --list

Let's create a task for inserting data into the account-topic that is already defined underdocker/data/kafka/setup_kafka.sh.

Plan Setup

Create a new Java or Scala class.

  • Java: src/main/java/com/github/pflooky/plan/MyAdvancedBatchEventJavaPlanRun.java
  • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedBatchEventPlanRun.scala

Make sure your class extends PlanRun.

import com.github.pflooky.datacaterer.java.api.PlanRun;
...

public class MyAdvancedBatchEventJavaPlanRun extends PlanRun {
    {
        var kafkaTask = new AdvancedKafkaJavaPlanRun().getKafkaTask();
    }
}

import com.github.pflooky.datacaterer.api.PlanRun
...

class MyAdvancedBatchEventPlanRun extends PlanRun {
  val kafkaTask = new AdvancedKafkaPlanRun().kafkaTask
}

We will borrow the Kafka task that is already defined under the class AdvancedKafkaPlanRun or AdvancedKafkaJavaPlanRun. You can go through the Kafka guide here for more details.

Schema

Let us set up the corresponding schema for the CSV file where we want to match the values that are generated for the Kafka messages.

var kafkaTask = new AdvancedKafkaJavaPlanRun().getKafkaTask();

var csvTask = csv("my_csv", "/opt/app/data/csv/account")
        .schema(
                field().name("account_number"),
                field().name("year"),
                field().name("name"),
                field().name("payload")
        );

val kafkaTask = new AdvancedKafkaPlanRun().kafkaTask

val csvTask = csv("my_csv", "/opt/app/data/csv/account")
  .schema(
    field.name("account_number"),
    field.name("year"),
    field.name("name"),
    field.name("payload")
)

This is a simple schema where we want to use the values and metadata that is already defined in the kafkaTask to determine what the data will look like for the CSV file. Even if we defined some metadata here, it would be overridden when we define our foreign key relationships.

Foreign Keys

From the above CSV schema, we see note the following against the Kafka schema:

  • account_number in CSV needs to match with the account_id in Kafka
    • We see that account_id is referred to in the key column as field.name("key").sql("content.account_id")
  • year needs to match with content.year in Kafka, which is a nested field
    • We can only do foreign key relationships with top level fields, not nested fields. So we define a new column called tmp_year which will not appear in the final output for the Kafka messages but is used as an intermediate step field.name("tmp_year").sql("content.year").omit(true)
  • name needs to match with content.details.name in Kafka, also a nested field
    • Using the same logic as above, we define a temporary column called tmp_name which will take the value of the nested field but will be omitted field.name("tmp_name").sql("content.details.name").omit(true)
  • payload represents the whole JSON message sent to Kafka, which matches to value column

Our foreign keys are therefore defined like below. Order is important when defining the list of columns. The index needs to match with the corresponding column in the other data source.

var myPlan = plan().addForeignKeyRelationship(
        kafkaTask, List.of("key", "tmp_year", "tmp_name", "value"),
        List.of(Map.entry(csvTask, List.of("account_number", "year", "name", "payload")))
);

var conf = configuration()
      .generatedReportsFolderPath("/opt/app/data/report");

execute(myPlan, conf, kafkaTask, csvTask);

val myPlan = plan.addForeignKeyRelationship(
    kafkaTask, List("key", "tmp_year", "tmp_name", "value"),
    List(csvTask -> List("account_number", "year", "name", "payload"))
)

val conf = configuration.generatedReportsFolderPath("/opt/app/data/report")

execute(myPlan, conf, kafkaTask, csvTask)

Run

Let's try run.

cd ..
./run.sh
#input class MyAdvancedBatchEventJavaPlanRun or MyAdvancedBatchEventPlanRun
#after completing
docker exec docker-kafkaserver-1 kafka-console-consumer --bootstrap-server localhost:9092 --topic account-topic --from-beginning

It should look something like this.

{"account_id":"ACC03093143","year":2023,"amount":87990.37196728592,"details":{"name":"Nadine Heidenreich Jr.","first_txn_date":"2021-11-09","updated_by":{"user":"YfEyJCe8ohrl0j IfyT","time":"2022-09-26T20:47:53.404Z"}},"transactions":[{"txn_date":"2021-11-09","amount":97073.7914706189}]}
{"account_id":"ACC08764544","year":2021,"amount":28675.58758765888,"details":{"name":"Delila Beer","first_txn_date":"2021-05-19","updated_by":{"user":"IzB5ksXu","time":"2023-01-26T20:47:26.389Z"}},"transactions":[{"txn_date":"2021-10-01","amount":80995.23818711648},{"txn_date":"2021-05-19","amount":92572.40049217848},{"txn_date":"2021-12-11","amount":99398.79832225188}]}
{"account_id":"ACC62505420","year":2023,"amount":96125.3125884202,"details":{"name":"Shawn Goodwin","updated_by":{"user":"F3dqIvYp2pFtena4","time":"2023-02-11T04:38:29.832Z"}},"transactions":[]}

Let's also check if there is a corresponding record in the CSV file.

$ cat docker/sample/csv/account/part-0000* | grep ACC03093143
ACC03093143,2023,Nadine Heidenreich Jr.,"{\"account_id\":\"ACC03093143\",\"year\":2023,\"amount\":87990.37196728592,\"details\":{\"name\":\"Nadine Heidenreich Jr.\",\"first_txn_date\":\"2021-11-09\",\"updated_by\":{\"user\":\"YfEyJCe8ohrl0j IfyT\",\"time\":\"2022-09-26T20:47:53.404Z\"}},\"transactions\":[{\"txn_date\":\"2021-11-09\",\"amount\":97073.7914706189}]}"

Great! The account, year, name and payload look to all match up.

Additional Topics

Order of execution

You may notice that the events are generated first, then the CSV file. This is because as part of the execute function, we passed in the kafkaTask first, before the csvTask. You can change the order of execution by passing in csvTask before kafkaTask into the execute function.