About model object

model is the dbt graph object (or node) for the current model. It can be used to:

• Access config settings, say, in a post-hook
• Access the path to the model

For example:

{% if model.config.materialized == 'view' %}
  {{ log(model.name ~ " is a view.", info=True) }}
{% endif %}

To view the contents of model for a given model:

Command line interface

If you're using the command line interface (CLI), use log() to print the full contents:

{{ log(model, info=True) }}

dbt Cloud IDE

If you're using the dbt Cloud IDE, compile the following to print the full contents:

{{ model | tojson(indent = 4) }}

Batch properties for microbatch models

Starting in dbt Core v1.9, the model object includes a batch property (model.batch), which provides details about the current batch when executing an incremental microbatch model. This property is only populated during the batch execution of a microbatch model.

The following table describes the properties of the batch object. Note that dbt appends the property to the model and batch objects.

Property	Description	Example
id	The unique identifier for the batch within the context of the microbatch model.	model.batch.id
event_time_start	The start time of the batch's event_time filter (inclusive).	model.batch.event_time_start
event_time_end	The end time of the batch's event_time filter (exclusive).	model.batch.event_time_end

Usage notes

model.batch is only available during the execution of a microbatch model batch. Outside of the microbatch execution, model.batch is None, and its sub-properties aren't accessible.

Example of safeguarding access to batch properties

We recommend to always check if model.batch is populated before accessing its properties. To do this, use an if statement for safe access to batch properties:

{% if model.batch %}
  {{ log(model.batch.id) }}  # Log the batch ID #
  {{ log(model.batch.event_time_start) }}  # Log the start time of the batch #
  {{ log(model.batch.event_time_end) }}  # Log the end time of the batch #
{% endif %}

In this example, the if model.batch statement makes sure that the code only runs during a batch execution. log() is used to print the batch properties for debugging.

Example of log batch details

This is a practical example of how you might use model.batch in a microbatch model to log batch details for the batch.id:

{% if model.batch %}
  {{ log("Processing batch with ID: " ~ model.batch.id, info=True) }}
  {{ log("Batch event time range: " ~ model.batch.event_time_start ~ " to " ~ model.batch.event_time_end, info=True) }}
{% endif %}

In this example, the if model.batch statement makes sure that the code only runs during a batch execution. log() is used to print the batch properties for debugging.

Model structure and JSON schema

To view the structure of models and their definitions:

• Refer to dbt JSON Schema for describing and consuming dbt generated artifacts
• Select the corresponding manifest version under Manifest. For example if you're on dbt v1.8, then you would select Manifest v12
  • The manifest.json version number is related to (but not equal to) your dbt version, so you must use the correct manifest.json version for your dbt version. To find the correct manifest.json version, refer to Manifest and select the dbt version on the top navigation (such as v1.5). This will help you find out which tags are associated with your model.
• Then go to nodes --> Select Additional properties --> CompiledModelNode or view other definitions/objects.

Use the following table to understand how the versioning pattern works and match the Manifest version with the dbt version:

dbt Core version	Manifest version
v1.8	v12
v1.7	v11
v1.6	v10
v1.5	v9
v1.4	v8
v1.3	v7
v1.2	v6
v1.1	v5
v1.0	v4

Related docs

• dbt JSON Schema