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Processors

Processors transform, validate, or route messages within a pipeline. They are applied in order between the input and output. Add processors when creating or editing a stream in the UI.

Mapping

Applies Bloblang transformations to messages. This is the most commonly used processor.

FieldTypeDescription
MappingBloblangThe Bloblang mapping to apply (required)

Common patterns:

  • Rename and transform fieldsroot.full_name = this.first_name + " " + this.last_name
  • Convert valuesroot.email = this.email.lowercase()
  • Add timestampsroot.created_at = now()
  • Filter messagesroot = if this.status == "active" { this } else { deleted() }

JSON Schema

Validates messages against a JSON Schema. Messages that fail validation are rejected.

FieldTypeDescription
SchemastringThe JSON Schema to validate against (required)

Paste the full JSON Schema into the Schema field. For example, to require id and email fields, use a schema with "required": ["id", "email"] and define the property types.


Catch

Error handling processor. Applies a list of processors when a previous processing step fails.

FieldTypeDescription
ProcessorsarrayProcessors to apply on error

Add child processors inside Catch that run when upstream processing fails. Commonly used with a Mapping processor to capture the error message via error() and the raw content via content().


Switch

Conditional processing — routes messages to different processors based on conditions.

FieldTypeDescription
CasesarrayList of condition/processor pairs

Each case has:

  • Check — A Bloblang condition (e.g., this.type == "order").
  • Processors — Processors to apply when the condition is true.

Add multiple cases in the UI. Messages are evaluated against each case in order.


Schema Registry Decode

Decodes Avro-encoded messages using a Confluent Schema Registry.

FieldTypeDefaultDescription
URLstringSchema Registry URL (required)
Avro Raw JSONbooleanfalseOutput raw JSON instead of Avro logical types

Supports authentication: Basic Auth, OAuth, and JWT.

tip

This processor is commonly paired with the Kafka input for Avro-encoded topic consumption. See the Kafka to PostgreSQL guide for a complete example.