dbt has a number of classes it uses to represent objects in a data warehouse, parts of a dbt project, and the results of a command.

These classes are often useful when building advanced dbt models and macros.

The Relation object is used to interpolate schema and names into SQL code with appropriate quoting. This object should always be used instead of interpolating values with {{ schema }}.{{ table }} directly. Quoting of the Relation object can be configured using the [quoting config][quoting].

A Relation can be created by calling the create class method on the Relation class.

class Relation:
  def create(database=None, schema=None, identifier=None,
             type=None):
  """
    database (optional): The name of the database for this relation
    schema (optional): The name of the schema (or dataset, if on BigQuery) for this relation
    identifier (optional): The name of the identifier for this relation
    type (optional): Metadata about this relation, eg: "table", "view", "cte"
  """

{% set relation = api.Relation.create(schema='snowplow', identifier='events') %}

-- Return the `database` for this relation
{{ relation.database }}

-- Return the `schema` (or dataset) for this relation
{{ relation.schema }}

-- Return the `identifier` for this relation
{{ relation.identifier }}

-- Return relation name without the database
{{ relation.include(database=false) }}

-- Return true if the relation is a table
{{ relation.is_table }}

-- Return true if the relation is a <Term id="table" />
{{ relation.is_view }}

-- Return true if the relation is a cte
{{ relation.is_cte }}

The Column object is used to encode information about a column in a relation.

class Column(object):
  def __init__(self, column, dtype, char_size=None, numeric_size=None):
    """
      column: The name of the column represented by this object
      dtype: The data type of the column (database-specific)
      char_size: If dtype is a variable width character type, the size of the column, or else None
      numeric_size: If dtype is a fixed precision numeric type, the size of the column, or else None
   """


# Example Usage:
col = Column('name', 'varchar', 255)
col.is_string() # True
col.is_numeric() # False
col.is_number() # False
col.is_float() # False
col.string_type() # character varying (255)

• name: Returns the name of the column
• quoted: Returns the name of the column wrapped in quotes
• data_type: Returns the data type of the column

The is_number and is_float instance methods were added dbt v0.16.0

• is_string(): Returns True if the column is a String type (eg. text, varchar), else False
• is_numeric(): Returns True if the column is a fixed-precision Numeric type (eg. numeric), else False
• is_number(): Returns True if the column is a number-y type (eg. numeric, int, float, or similar), else False
• is_float(): Returns True if the column is a float type (eg. float, float64, or similar), else False
• string_size(): Returns the width of the column if it is a string type, else, an exception is raised

• string_type(size): Returns a database-useable representation of the string type (eg. character varying(255))
• numeric_type(dtype, size): Returns a database-useable representation of the numeric type (eg. numeric(12, 4))

The Column type is overridden as a BigQueryColumn in BigQuery dbt projects. This object works the same as the Column type described above, with the exception of extra properties and methods:

• fields: Returns the list of subfields contained within a field (if the column is a STRUCT)
• mode: Returns the "mode" of the column, eg. REPEATED

flatten(): Return a flattened list of BigQueryColumns in which subfields are expanded into their own columns. For example, this nested field:

[{"hits": {"pageviews": 1, "bounces": 0}}]

will be expanded to:

[{"hits.pageviews": 1, "hits.bounces": 0}]

• v0.19.0: The Result object significantly changed its schema. See https://schemas.getdbt.com/dbt/run-results/v1.json for the full specification.

The execution of a resource in dbt generates a Result object. This object contains information about the executed node, timing, status, and metadata returned by the adapter. At the end of an invocation, dbt records these objects in run_results.json.

• node: Full object representation of the dbt resource (model, seed, snapshot, test) executed, including its unique_id
• status: dbt's interpretation of runtime success, failure, or error
• thread_id: Which thread executed this node? E.g. Thread-1
• execution_time: Total time spent executing this node
• timing: Array that breaks down execution time into steps (often compile + execute)
• adapter_response: Dictionary of metadata returned from the database, which varies by adapter. E.g. success code, number of rows_affected, total bytes_processed, etc.
• message: How dbt will report this result on the CLI, based on information returned from the database