Kafka custom Avro schema and limitations
When creating a Kafka dataset, you have the possibility to enter a custom Avro schema which is then used when reading/writing from the selected topic.
Find below a list of different actions you can perform in Talend Pipeline Designer and their impact on reading/writing from/to the Kafka dataset.
| Action on the dataset | Consequence in the application |
|---|---|
|
Fetch sample from a new topic (no records) in a Kafka dataset with no schema |
The sample is empty |
|
Fetch sample from a new topic (no records) in a Kafka dataset with a valid schema |
The sample is empty |
|
Fetch sample from an existing topic in a Kafka dataset with no schema |
The sample is empty |
|
Fetch sample from an existing topic in a Kafka dataset with a binary compatible schema |
The sample is displayed |
|
Fetch sample from an existing topic in a Kafka dataset with a non compatible schema |
An error is displayed |
|
Run a pipeline that writes to a new topic in a Kafka dataset without schema |
The pipeline is executed without any issues, the records are persisted using the schema of the latest component before the Kafka component |
|
Run a pipeline that writes to a new topic in a Kafka dataset with a schema that is compatible with the pipeline data |
The pipeline is executed without any issues, the records are persisted using the dataset schema |
|
Run a pipeline that writes to a new topic in a Kafka dataset with a schema that is not compatible with the pipeline |
The pipeline fails with an exception |
|
Run a pipeline that reads from an existing topic in a Kafka dataset with a binary compatible schema |
The pipeline is executed without any issues |
|
Run a pipeline that reads from an existing topic in a Kafka dataset with a non compatible schema |
The pipeline fails with an exception |
Did this page help you?
If you find any issues with this page or its content – a typo, a missing step, or a technical error – let us know how we can improve!