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Model kinds

This page describes the kinds of models SQLMesh supports, which determine how the data for a model is loaded.

INCREMENTAL_BY_TIME_RANGE

Models of the INCREMENTAL_BY_TIME_RANGE kind are computed incrementally based on a time range. This is an optimal choice for datasets in which records are captured over time and represent immutable facts such as events, logs, or transactions. Using this kind for appropriate datasets typically results in significant cost and time savings.

Only missing time intervals are processed during each execution for INCREMENTAL_BY_TIME_RANGE models. This is in contrast to the FULL model kind, where the entire dataset is recomputed every time the model is executed.

An INCREMENTAL_BY_TIME_RANGE model has two requirements that other models do not: it must know which column contains the time data it will use to filter the data by time range, and it must contain a WHERE clause that filters the upstream data by time.

The name of the column containing time data is specified in the model's MODEL DDL. It is specified ih the DDL kind specification's time_column key. This example shows the MODEL DDL for an INCREMENTAL_BY_TIME_RANGE model that stores time data in the "event_date" column:

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MODEL (
  name db.events,
  kind INCREMENTAL_BY_TIME_RANGE (
    time_column event_date -- This model's time information is stored in the `event_date` column
  )
);

In addition to specifying a time column in the MODEL DDL, the model's query must contain a WHERE clause that filters the upstream records by time range. SQLMesh provides special macros that represent the start and end of the time range being processed: @start_date / @end_date and @start_ds / @end_ds. Refer to Macros for more information.

This example implements a complete INCREMENTAL_BY_TIME_RANGE model that specifies the time column name event_date in the MODEL DDL and includes a SQL WHERE clause to filter records by time range:

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MODEL (
  name db.events,
  kind INCREMENTAL_BY_TIME_RANGE (
    time_column event_date
  )
);

SELECT
  event_date::TEXT as event_date,
  event_payload::TEXT as payload
FROM raw_events
WHERE
  event_date BETWEEN @start_ds AND @end_ds;

Time column

SQLMesh needs to know which column in the model's output represents the timestamp or date associated with each record.

The time column is used to determine which records will be overwritten during data restatement and provides a partition key for engines that support partitioning (such as Apache Spark). The name of the time column is specified in the MODEL DDL kind specification:

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MODEL (
  name db.events,
  kind INCREMENTAL_BY_TIME_RANGE (
    time_column event_date -- This model's time information is stored in the `event_date` column
  )
);

By default, SQLMesh assumes the time column is in the %Y-%m-%d format. For other formats, the default can be overridden with a formatting string: 

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MODEL (
  name db.events,
  kind INCREMENTAL_BY_TIME_RANGE (
    time_column (event_date, '%Y-%m-%d')
  )
);
Note: The time format should be defined using the same SQL dialect as the one used to define the model's query.

SQLMesh also uses the time column to automatically append a time range filter to the model's query at runtime, which prevents records that are not part of the target interval from being stored. This is a safety mechanism that prevents unintentionally overwriting unrelated records when handling late-arriving data.

The required filter you write in the model query's WHERE clause filters the input data as it is read from upstream tables, reducing the amount of data processed by the model. The automatically appended time range filter is applied to the model query's output data to prevent data leakage.

Consider the following model definition, which specifies a WHERE clause filter with the receipt_date column. The model's time_column is a different column event_date, whose filter is automatically added to the model query. This approach is useful when an upstream model's time column is different from the model's time column:

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MODEL (
  name db.events,
  kind INCREMENTAL_BY_TIME_RANGE (
    time_column event_date -- `event_date` is model's time column
  )
);

SELECT
  event_date::TEXT as event_date,
  event_payload::TEXT as payload
FROM raw_events
WHERE
  receipt_date BETWEEN @start_ds AND @end_ds; -- Filter is based on the user-supplied `receipt_date` column

At runtime, SQLMesh will automatically modify the model's query to look like this: 

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SELECT
  event_date::TEXT as event_date,
  event_payload::TEXT as payload
FROM raw_events
WHERE
  receipt_date BETWEEN @start_ds AND @end_ds
  AND event_date BETWEEN @start_ds AND @end_ds; -- `event_date` time column filter automatically added by SQLMesh

Idempotency

It is recommended that queries of models of this kind are idempotent to prevent unexpected results during data restatement.

Note, however, that upstream models and tables can impact a model's idempotency. For example, referencing an upstream model of kind FULL in the model query automatically causes the model to be non-idempotent.

Materialization strategy

Depending on the target engine, models of the INCREMENTAL_BY_TIME_RANGE kind are materialized using the following strategies:

Engine Strategy
Spark INSERT OVERWRITE by time column partition
Databricks INSERT OVERWRITE by time column partition
Snowflake DELETE by time range, then INSERT
BigQuery DELETE by time range, then INSERT
Redshift DELETE by time range, then INSERT
Postgres DELETE by time range, then INSERT
DuckDB DELETE by time range, then INSERT

INCREMENTAL_BY_UNIQUE_KEY

Models of the INCREMENTAL_BY_UNIQUE_KEY kind are computed incrementally based on a unique key.

If a key is missing in the model's table, the new data row is inserted; otherwise, the existing row associated with this key is updated with the new one. This kind is a good fit for datasets that have the following traits:

  • Each record has a unique key associated with it.
  • There is at most one record associated with each unique key.
  • It is appropriate to upsert records, so existing records can be overridden by new arrivals when their keys match.

A Slowly Changing Dimension (SCD) is one approach that fits this description well. See the SCD Type 2 model kind for a specific model kind for SCD Type 2 models.

The name of the unique key column must be provided as part of the MODEL DDL, as in this example: 

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MODEL (
  name db.employees,
  kind INCREMENTAL_BY_UNIQUE_KEY (
    unique_key name
  )
);

SELECT
  name::TEXT as name,
  title::TEXT as title,
  salary::INT as salary
FROM raw_employees;

Composite keys are also supported: 

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MODEL (
  name db.employees,
  kind INCREMENTAL_BY_UNIQUE_KEY (
    unique_key (first_name, last_name)
  )
);

INCREMENTAL_BY_UNIQUE_KEY model kinds can also filter upstream records by time range using a SQL WHERE clause and the @start_date, @end_date or other macros (similar to the INCREMENTAL_BY_TIME_RANGE kind): 

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SELECT
  name::TEXT as name,
  title::TEXT as title,
  salary::INT as salary
FROM raw_employee_events
WHERE
  event_date BETWEEN @start_date AND @end_date;

Note: Models of the INCREMENTAL_BY_UNIQUE_KEY kind are inherently non-idempotent, which should be taken into consideration during data restatement. As a result, partial data restatement is not supported for this model kind, which means that the entire table will be recreated from scratch if restated.

Unique Key Expressions

The unique_key values can either be column names or SQL expressions. For example, if you wanted to create a key that is based on the coalesce of a value then you could do the following:

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MODEL (
  name db.employees,
  kind INCREMENTAL_BY_UNIQUE_KEY (
    unique_key COALESCE("name", '')
  )
);

When Matched Expression

The logic to use when updating columns when a match occurs (the source and target match on the given keys) by default updates all the columns. This can be overriden with custom logic like below:

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MODEL (
  name db.employees,
  kind INCREMENTAL_BY_UNIQUE_KEY (
    unique_key name,
    when_matched WHEN MATCHED THEN UPDATE SET target.salary = COALESCE(source.salary, target.salary)
  )
);

The source and target aliases are required when using the when_matched expression in order to distinguish between the source and target columns.

Note: when_matched is only available on engines that support the MERGE statement. Currently supported engines include:

  • BigQuery
  • Databricks
  • Postgres
  • Snowflake
  • Spark

Materialization strategy

Depending on the target engine, models of the INCREMENTAL_BY_UNIQUE_KEY kind are materialized using the following strategies:

Engine Strategy
Spark not supported
Databricks MERGE ON unique key
Snowflake MERGE ON unique key
BigQuery MERGE ON unique key
Redshift MERGE ON unique key
Postgres MERGE ON unique key
DuckDB DELETE ON matched + INSERT new rows

FULL

Models of the FULL kind cause the dataset associated with a model to be fully refreshed (rewritten) upon each model evaluation.

The FULL model kind is somewhat easier to use than incremental kinds due to the lack of special settings or additional query considerations. This makes it suitable for smaller datasets, where recomputing data from scratch is relatively cheap and doesn't require preservation of processing history. However, using this kind with datasets containing a large volume of records will result in significant runtime and compute costs.

This kind can be a good fit for aggregate tables that lack a temporal dimension. For aggregate tables with a temporal dimension, consider the INCREMENTAL_BY_TIME_RANGE kind instead.

This example specifies a FULL model kind: 

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MODEL (
  name db.salary_by_title_agg,
  kind FULL
);

SELECT
  title,
  AVG(salary)
FROM db.employees
GROUP BY title;

Materialization strategy

Depending on the target engine, models of the FULL kind are materialized using the following strategies:

Engine Strategy
Spark INSERT OVERWRITE
Databricks INSERT OVERWRITE
Snowflake CREATE OR REPLACE TABLE
BigQuery CREATE OR REPLACE TABLE
Redshift DROP TABLE, CREATE TABLE, INSERT
Postgres DROP TABLE, CREATE TABLE, INSERT
DuckDB CREATE OR REPLACE TABLE

VIEW

The model kinds described so far cause the output of a model query to be materialized and stored in a physical table.

The VIEW kind is different, because no data is actually written during model execution. Instead, a non-materialized view (or "virtual table") is created or replaced based on the model's query.

Note: VIEW is the default model kind if kind is not specified.

Note: With this kind, the model's query is evaluated every time the model is referenced in a downstream query. This may incur undesirable compute cost and time in cases where the model's query is compute-intensive, or when the model is referenced in many downstream queries.

This example specifies a VIEW model kind: 

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MODEL (
  name db.highest_salary,
  kind VIEW
);

SELECT
  MAX(salary)
FROM db.employees;

Materialized Views

The VIEW model kind can be configured to represent a materialized view by setting the materialized flag to true: 

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MODEL (
  name db.highest_salary,
  kind VIEW (
    materialized true
  )
);

Note: This flag only applies to engines that support materialized views and is ignored by other engines. Supported engines include:

  • BigQuery
  • Databricks
  • Snowflake

During the evaluation of a model of this kind, the view will be replaced or recreated only if the model's query rendered during evaluation does not match the query used during the previous view creation for this model, or if the target view does not exist. Thus, views are recreated only when necessary in order to realize all the benefits provided by materialized views.

EMBEDDED

Embedded models are a way to share common logic between different models of other kinds.

There are no data assets (tables or views) associated with EMBEDDED models in the data warehouse. Instead, an EMBEDDED model's query is injected directly into the query of each downstream model that references it.

This example specifies a EMBEDDED model kind: 

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MODEL (
  name db.unique_employees,
  kind EMBEDDED
);

SELECT DISTINCT
  name
FROM db.employees;

SEED

The SEED model kind is used to specify seed models for using static CSV datasets in your SQLMesh project.

SCD Type 2

SCD Type 2 is a model kind that supports slowly changing dimensions (SCDs) in your SQLMesh project. SCDs are a common pattern in data warehousing that allow you to track changes to records over time.

SQLMesh achieves this by adding a valid_from and valid_to column to your model. The valid_from column is the timestamp that the record became valid (inclusive) and the valid_to column is the timestamp that the record became invalid (exclusive). The valid_to column is set to NULL for the latest record.

Therefore you can use these models to not only tell you what the latest value is for a given record but also what the values were anytime in the past. Note that maintaining this history does come at a cost of increased storage and compute and this may not be a good fit for sources that change frequently since the history could get very large.

Note: Partial data restatement is not supported for this model kind, which means that the entire table will be recreated from scratch if restated. This may lead to data loss, which is why data restatement is disabled for models of this kind by default.

There are two ways to tracking changes: By Time (Recommended) or By Column.

SCD Type 2 By Time supports sourcing from tables that have an "Updated At" timestamp defined in the table that tells you when a given was last updated. This is the recommended way since this "Updated At" gives you a precise time when the record was last updated and therefore improves the accuracy of the SCD Type 2 table that is produced.

This example specifies a SCD_TYPE_2_BY_TIME model kind: 

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MODEL (
  name db.menu_items,
  kind SCD_TYPE_2_BY_TIME (
    unique_key id,
  )
);

SELECT
  id::INT,
  name::STRING,
  price::DOUBLE,
  updated_at::TIMESTAMP
FROM
  stg.current_menu_items;

SQLMesh will materialize this table with the following structure: 

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TABLE db.menu_items (
  id INT,
  name STRING,
  price DOUBLE,
  updated_at TIMESTAMP,
  valid_from TIMESTAMP,
  valid_to TIMESTAMP
);

The updated_at column name can also be changed by adding the following to your model definition: 

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MODEL (
  name db.menu_items,
  kind SCD_TYPE_2_BY_TIME (
    unique_key id,
    updated_at_name my_updated_at -- Name for `updated_at` column
  )
);

SELECT
    id,
    name,
    price,
    my_updated_at
FROM
    stg.current_menu_items;

SQLMesh will materialize this table with the following structure: 

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TABLE db.menu_items (
  id INT,
  name STRING,
  price DOUBLE,
  my_updated_at TIMESTAMP,
  valid_from TIMESTAMP,
  valid_to TIMESTAMP
);

SCD Type 2 By Column

SCD Type 2 By Column supports sourcing from tables that do not have an "Updated At" timestamp defined in the table. Instead, it will check the columns defined in the columns field to see if their value has changed and if so it will record the valid_from time as the execution time when the change was detected.

This example specifies a SCD_TYPE_2_BY_COLUMN model kind: 

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MODEL (
  name db.menu_items,
  kind SCD_TYPE_2_BY_COLUMN (
    unique_key id,
    columns [name, price]
  )
);

SELECT
  id::INT,
  name::STRING,
  price::DOUBLE,
FROM
  stg.current_menu_items;

SQLMesh will materialize this table with the following structure: 

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TABLE db.menu_items (
  id INT,
  name STRING,
  price DOUBLE,
  valid_from TIMESTAMP,
  valid_to TIMESTAMP
);

Change Column Names

SQLMesh will automatically add the valid_from and valid_to columns to your table. If you would like to specify the names of these columns you can do so by adding the following to your model definition: 

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MODEL (
  name db.menu_items,
  kind SCD_TYPE_2_BY_TIME (
    unique_key id,
    valid_from_name my_valid_from, -- Name for `valid_from` column
    valid_to_name my_valid_to -- Name for `valid_to` column
  )
);

SQLMesh will materialize this table with the following structure: 

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TABLE db.menu_items (
  id INT,
  name STRING,
  price DOUBLE,
  updated_at TIMESTAMP,
  my_valid_from TIMESTAMP,
  my_valid_to TIMESTAMP
);

Deletes

A hard delete is when a record no longer exists in the source table. When this happens,

If invalidate_hard_deletes is set to true (default):

  • valid_to column will be set to the time when the SQLMesh run started that detected the missing record (called execution_time).
  • If the record is added back, then the valid_to column will remain unchanged.

If invalidate_hard_deletes is set to false:

  • valid_to column will continue to be set to NULL (therefore still considered "valid")
  • If the record is added back, then the valid_to column will be set to the valid_from of the new record.

When a record is added back, the new record will be inserted into the table with valid_from set to:

  • SCD_TYPE_2_BY_TIME: the largest of either the updated_at timestamp of the new record or the valid_from timestamp of the deleted record in the SCD Type 2 table
  • SCD_TYPE_2_BY_COLUMN: the execution_time when the record was detected again

One way to think about invalidate_hard_deletes is that, if enabled, deletes are most accurately tracked in the SCD Type 2 table since it records when the delete occurred. As a result though, you can have gaps between records if the there is a gap of time between when it was deleted and added back. If you would prefer to not have gaps, and a result consider missing records in source as still "valid", then you can set invalidate_hard_deletes to false.

Example of SCD Type 2 By Time in Action

Lets say that you started with the following data in your source table:

ID Name Price Updated At
1 Chicken Sandwich 10.99 2020-01-01 00:00:00
2 Cheeseburger 8.99 2020-01-01 00:00:00
3 French Fries 4.99 2020-01-01 00:00:00

The target table, which is currently empty, will be materialized with the following data:

ID Name Price Updated At Valid From Valid To
1 Chicken Sandwich 10.99 2020-01-01 00:00:00 1970-01-01 00:00:00 NULL
2 Cheeseburger 8.99 2020-01-01 00:00:00 1970-01-01 00:00:00 NULL
3 French Fries 4.99 2020-01-01 00:00:00 1970-01-01 00:00:00 NULL

Now lets say that you update the source table with the following data:

ID Name Price Updated At
1 Chicken Sandwich 12.99 2020-01-02 00:00:00
3 French Fries 4.99 2020-01-01 00:00:00
4 Milkshake 3.99 2020-01-02 00:00:00

Summary of Changes:

  • The price of the Chicken Sandwich was increased from $10.99 to $12.99.
  • Cheeseburger was removed from the menu.
  • Milkshakes were added to the menu.

Assuming your pipeline ran at 2020-01-02 11:00:00, target table will be updated with the following data:

ID Name Price Updated At Valid From Valid To
1 Chicken Sandwich 10.99 2020-01-01 00:00:00 1970-01-01 00:00:00 2020-01-02 00:00:00
1 Chicken Sandwich 12.99 2020-01-02 00:00:00 2020-01-02 00:00:00 NULL
2 Cheeseburger 8.99 2020-01-01 00:00:00 1970-01-01 00:00:00 2020-01-02 11:00:00
3 French Fries 4.99 2020-01-01 00:00:00 1970-01-01 00:00:00 NULL
4 Milkshake 3.99 2020-01-02 00:00:00 2020-01-02 00:00:00 NULL

For our final pass, lets say that you update the source table with the following data:

ID Name Price Updated At
1 Chicken Sandwich 14.99 2020-01-03 00:00:00
2 Cheeseburger 8.99 2020-01-03 00:00:00
3 French Fries 4.99 2020-01-01 00:00:00
4 Chocolate Milkshake 3.99 2020-01-02 00:00:00

Summary of changes:

  • The price of the Chicken Sandwich was increased from $12.99 to $14.99 (must be good!)
  • Cheeseburger was added back to the menu with original name and price.
  • Milkshake name was updated to be "Chocolate Milkshake".

Target table will be updated with the following data:

ID Name Price Updated At Valid From Valid To
1 Chicken Sandwich 10.99 2020-01-01 00:00:00 1970-01-01 00:00:00 2020-01-02 00:00:00
1 Chicken Sandwich 12.99 2020-01-02 00:00:00 2020-01-02 00:00:00 2020-01-03 00:00:00
1 Chicken Sandwich 14.99 2020-01-03 00:00:00 2020-01-03 00:00:00 NULL
2 Cheeseburger 8.99 2020-01-01 00:00:00 1970-01-01 00:00:00 2020-01-02 11:00:00
2 Cheeseburger 8.99 2020-01-03 00:00:00 2020-01-03 00:00:00 NULL
3 French Fries 4.99 2020-01-01 00:00:00 1970-01-01 00:00:00 NULL
4 Milkshake 3.99 2020-01-02 00:00:00 2020-01-02 00:00:00 2020-01-03 00:00:00
4 Chocolate Milkshake 3.99 2020-01-03 00:00:00 2020-01-03 00:00:00 NULL

Note: Cheeseburger was deleted from 2020-01-02 11:00:00 to 2020-01-03 00:00:00 meaning if you queried the table during that time range then you would not see Cheeseburger in the menu. This is the most accurate representation of the menu based on the source data provided. If Cheeseburger were added back to the menu with it's original updated at timestamp of 2020-01-01 00:00:00 then the valid_from timestamp of the new record would have been 2020-01-02 11:00:00 resulting in no period of time where the item was deleted. Since in this case the updated at timestamp did not change it is likely the item was removed in error and this again most accurately represents the menu based on the source data.

Example of SCD Type 2 By Column in Action

Lets say that you started with the following data in your source table:

ID Name Price
1 Chicken Sandwich 10.99
2 Cheeseburger 8.99
3 French Fries 4.99

We configure the SCD Type 2 By Column model to check the columns Name and Price for changes

The target table, which is currently empty, will be materialized with the following data:

ID Name Price Valid From Valid To
1 Chicken Sandwich 10.99 1970-01-01 00:00:00 NULL
2 Cheeseburger 8.99 1970-01-01 00:00:00 NULL
3 French Fries 4.99 1970-01-01 00:00:00 NULL

Now lets say that you update the source table with the following data:

ID Name Price
1 Chicken Sandwich 12.99
3 French Fries 4.99
4 Milkshake 3.99

Summary of Changes:

  • The price of the Chicken Sandwich was increased from $10.99 to $12.99.
  • Cheeseburger was removed from the menu.
  • Milkshakes were added to the menu.

Assuming your pipeline ran at 2020-01-02 11:00:00, target table will be updated with the following data:

ID Name Price Valid From Valid To
1 Chicken Sandwich 10.99 1970-01-01 00:00:00 2020-01-02 11:00:00
1 Chicken Sandwich 12.99 2020-01-02 11:00:00 NULL
2 Cheeseburger 8.99 1970-01-01 00:00:00 2020-01-02 11:00:00
3 French Fries 4.99 1970-01-01 00:00:00 NULL
4 Milkshake 3.99 2020-01-02 11:00:00 NULL

For our final pass, lets say that you update the source table with the following data:

ID Name Price
1 Chicken Sandwich 14.99
2 Cheeseburger 8.99
3 French Fries 4.99
4 Chocolate Milkshake 3.99

Summary of changes:

  • The price of the Chicken Sandwich was increased from $12.99 to $14.99 (must be good!)
  • Cheeseburger was added back to the menu with original name and price.
  • Milkshake name was updated to be "Chocolate Milkshake".

Assuming your pipeline ran at 2020-01-03 11:00:00, Target table will be updated with the following data:

ID Name Price Valid From Valid To
1 Chicken Sandwich 10.99 1970-01-01 00:00:00 2020-01-02 11:00:00
1 Chicken Sandwich 12.99 2020-01-02 11:00:00 2020-01-03 11:00:00
1 Chicken Sandwich 14.99 2020-01-03 11:00:00 NULL
2 Cheeseburger 8.99 1970-01-01 00:00:00 2020-01-02 11:00:00
2 Cheeseburger 8.99 2020-01-03 11:00:00 NULL
3 French Fries 4.99 1970-01-01 00:00:00 NULL
4 Milkshake 3.99 2020-01-02 11:00:00 2020-01-03 11:00:00
4 Chocolate Milkshake 3.99 2020-01-03 11:00:00 NULL

Note: Cheeseburger was deleted from 2020-01-02 11:00:00 to 2020-01-03 11:00:00 meaning if you queried the table during that time range then you would not see Cheeseburger in the menu. This is the most accurate representation of the menu based on the source data provided.

Shared Configuration Options

Name Description Type
unique_key Unique key used for identifying rows between source and target List of strings or string
valid_from_name The name of the valid_from column to create in the target table. Default: valid_from string
valid_to_name The name of the valid_to column to create in the target table. Default: valid_to string
invalidate_hard_deletes If set to true, when a record is missing from the source table it will be marked as invalid. Default: true bool

SCD Type 2 By Time Configuration Options

Name Description Type
updated_at_name The name of the column containing a timestamp to check for new or updated records. Default: updated_at string
updated_at_as_valid_from By default, for new rows valid_from is set to 1970-01-01 00:00:00. This changes the behavior to set it to the valid of updated_at when the row is inserted. Default: false bool

SCD Type 2 By Column Configuration Options

Name Description Type
columns The name of the columns to check for changes. * to represent that all columns should be checked. List of strings or string
execution_time_as_valid_from By default, for new rows valid_from is set to 1970-01-01 00:00:00. This changes the behavior to set it to the execution_time of when the pipeline ran. Default: false bool

Querying SCD Type 2 Models

Querying the current version of a record

Although SCD Type 2 models support history, it is still very easy to query for just the latest version of a record. Simply query the model as you would any other table. For example, if you wanted to query the latest version of the menu_items table you would simply run:

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SELECT
    *
FROM
    menu_items
WHERE
    valid_to IS NULL;

One could also create a view on top of the SCD Type 2 model that creates a new is_current column to make it easy for consumers to identify the current record.

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SELECT
    *,
    valid_to IS NULL AS is_current
FROM
    menu_items;

Querying for a specific version of a record at a give point in time

If you wanted to query the menu_items table as it was on 2020-01-02 01:00:00 you would simply run:

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SELECT
    *
FROM
    menu_items
WHERE
    id = 1
    AND '2020-01-02 01:00:00' >= valid_from
    AND '2020-01-02 01:00:00' < COALESCE(valid_to, CAST('2199-12-31 23:59:59+00:00' AS TIMESTAMP));

Example in a join:

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SELECT
    *
FROM
    orders
    INNER JOIN
    menu_items
    ON orders.menu_item_id = menu_items.id
    AND orders.created_at >= menu_items.valid_from
    AND orders.created_at < COALESCE(menu_items.valid_to, CAST('2199-12-31 23:59:59+00:00' AS TIMESTAMP));

A view can be created to do the COALESCE automatically. This, combined with the is_current flag, makes it easier to query for a specific version of a record.

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SELECT
    id,
    name,
    price,
    updated_at,
    valid_from,
    COALESCE(valid_to, CAST('2199-12-31 23:59:59+00:00' AS TIMESTAMP)) AS valid_to
    valid_to IS NULL AS is_current,
FROM
    menu_items;

Furthermore if you want to make it so users can use BETWEEN when querying by making valid_to inclusive you can do the following: 

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SELECT
    id,
    name,
    price,
    updated_at,
    valid_from,
    COALESCE(valid_to, CAST('2200-01-01 00:00:00+00:00' AS TIMESTAMP)) - INTERVAL 1 SECOND AS valid_to
    valid_to IS NULL AS is_current,

Note: The precision of the timestamps in this example is second so I subtract 1 second. Make sure to subtract a value equal to the precision of your timestamps.

Querying for deleted records

One way to identify deleted records is to query for records that do not have a valid_to record of NULL. For example, if you wanted to query for all deleted ids in the menu_items table you would simply run:

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SELECT
    id,
    MAX(CASE WHEN valid_to IS NULL THEN 0 ELSE 1 END) AS is_deleted
FROM
    menu_items
GROUP BY
    id

EXTERNAL

The EXTERNAL model kind is used to specify external models that store metadata about external tables. External models are special; they are not specified in .sql files like the other model kinds. They are optional but useful for propagating column and type information for external tables queried in your SQLMesh project.