sqlmesh.core.config.model
1from __future__ import annotations 2 3import typing as t 4 5from sqlmesh.core.config.base import BaseConfig 6from sqlmesh.core.model.kind import ModelKind, model_kind_validator 7from sqlmesh.utils.date import TimeLike 8 9 10class ModelDefaultsConfig(BaseConfig): 11 """A config object for default values applied to model definitions. 12 13 Args: 14 kind: The model kind. 15 dialect: The SQL dialect that the model's query is written in. 16 cron: A cron string specifying how often the model should be refreshed, leveraging the 17 [croniter](https://github.com/kiorky/croniter) library. 18 owner: The owner of the model. 19 start: The earliest date that the model will be backfilled for. If this is None, 20 then the date is inferred by taking the most recent start date of its ancestors. 21 The start date can be a static datetime or a relative datetime like "1 year ago" 22 batch_size: The maximum number of intervals that can be run per backfill job. If this is None, 23 then backfilling this model will do all of history in one job. If this is set, a model's backfill 24 will be chunked such that each individual job will only contain jobs with max `batch_size` intervals. 25 storage_format: The storage format used to store the physical table, only applicable in certain engines. 26 (eg. 'parquet') 27 """ 28 29 kind: t.Optional[ModelKind] 30 dialect: t.Optional[str] 31 cron: t.Optional[str] 32 owner: t.Optional[str] 33 start: t.Optional[TimeLike] 34 batch_size: t.Optional[int] 35 storage_format: t.Optional[str] 36 37 _model_kind_validator = model_kind_validator
11class ModelDefaultsConfig(BaseConfig): 12 """A config object for default values applied to model definitions. 13 14 Args: 15 kind: The model kind. 16 dialect: The SQL dialect that the model's query is written in. 17 cron: A cron string specifying how often the model should be refreshed, leveraging the 18 [croniter](https://github.com/kiorky/croniter) library. 19 owner: The owner of the model. 20 start: The earliest date that the model will be backfilled for. If this is None, 21 then the date is inferred by taking the most recent start date of its ancestors. 22 The start date can be a static datetime or a relative datetime like "1 year ago" 23 batch_size: The maximum number of intervals that can be run per backfill job. If this is None, 24 then backfilling this model will do all of history in one job. If this is set, a model's backfill 25 will be chunked such that each individual job will only contain jobs with max `batch_size` intervals. 26 storage_format: The storage format used to store the physical table, only applicable in certain engines. 27 (eg. 'parquet') 28 """ 29 30 kind: t.Optional[ModelKind] 31 dialect: t.Optional[str] 32 cron: t.Optional[str] 33 owner: t.Optional[str] 34 start: t.Optional[TimeLike] 35 batch_size: t.Optional[int] 36 storage_format: t.Optional[str] 37 38 _model_kind_validator = model_kind_validator
A config object for default values applied to model definitions.
Arguments:
- kind: The model kind.
- dialect: The SQL dialect that the model's query is written in.
- cron: A cron string specifying how often the model should be refreshed, leveraging the croniter library.
- owner: The owner of the model.
- start: The earliest date that the model will be backfilled for. If this is None, then the date is inferred by taking the most recent start date of its ancestors. The start date can be a static datetime or a relative datetime like "1 year ago"
- batch_size: The maximum number of intervals that can be run per backfill job. If this is None,
then backfilling this model will do all of history in one job. If this is set, a model's backfill
will be chunked such that each individual job will only contain jobs with max
batch_size
intervals. - storage_format: The storage format used to store the physical table, only applicable in certain engines. (eg. 'parquet')
Inherited Members
- pydantic.main.BaseModel
- BaseModel
- parse_obj
- parse_raw
- parse_file
- from_orm
- construct
- copy
- schema
- schema_json
- validate
- update_forward_refs