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sql Task

About

The sql task provides you with different options for working with SQL queries. It lets you execute a SQL script with one or many statements. This is useful for executing UPDATE statements for example. It also lets you write a SELECT statement and SAYN then automates the data processing (i.e. table or view creation, incremental load, etc.) for you.

Info

The old sql and autosql tasks have been combined into one task class, differentiated with the materialisation parameter. The old autosql task still exists for backwards compatibility.

Defining sql Tasks

An sql task group is defined as follows:

project.yaml

...

groups:
  core:
    type: sql
    file_name: "core/*.sql"
    materialisation: table
    destination:
      table: "{{ task.name }}"

...

tasks/base.yaml

task_sql:
  type: sql
  file_name: sql_task.sql
  materialisation: script

An sql task is defined by the following attributes:

  • type: sql.
  • file_name: the path to a file within the sql folder of the project's root. When defining sql groups in project.yaml this property needs to be a glob expression, for example group/*.sql.
  • materialisation: this should be either script, table, view or incremental. script will execute the code unmodified (after jinja compilation), table will create a table, view will create a view. incremental will create a table and will load the data incrementally based on a delete key (see more detail on incremental below).
  • destination: is the name of the object that will be created. It is defined like so schema.table (similarly to the src macro; look bellow). The schema part of the parameter is optional. The final compiled value is affected by schema_prefix, schema_suffix and schema_override as specified in database objects.
  • tmp_schema: the (optional) schema which will be used to store any necessary temporary object created in the process. The final compiled value is affected by schema_prefix, schema_suffix and schema_override as specified in database objects.
  • schema:
  • table: The final compiled value is affected by table_prefix, table_suffix and table_override as specified in database objects.
  • db: the (optional) destination database.
  • delete_key: specifies the incremental process delete key. This is for incremental materialisation only.

Info

By default the task is executed in the database defined by default_db in project.yaml. db can be specified to change this, in which case the connection specified needs to:

  • Be a credential from the required_credentials list in project.yaml.
  • Be defined in your settings.yaml.
  • Be one of the supported databases.

Setting Dependencies With sql

With sql tasks, you should use the src and out macro in your SELECT statements to implicitly create task dependencies.

src in sql query

SELECT field1
     , field2
  FROM {{ src('my_table') }} l

out in sql query

CREATE OR REPLACE {{ out('my_table') }} AS
(
  SELECT field1
       , field2
    FROM {{ src('my_other_table') }} l
}

By using the {{ src('my_table') }} in your FROM clause, you are effectively telling SAYN that your task depends on the my_table table (or view). As a result, SAYN will look for the task that produces my_table and set it as a parent of this sql task automatically. Similarly, by using {{ out('table') }} anywhere in the script you can retrieve the full name of the table to be created. In this way, you also tell SAYN the output of the SQL script.

Info

The out macro is only applicable to the script materialisation, as in the other cases you won't need to access the output table as SAYN handles table creation for you.

Tip

When using the src macro, you can pass a structure formatted as schema.table such as {{ src('my_schema.my_table') }}. In this case, SAYN interprets the first element as the schema, the second element as the table or view. If you use schema_prefix and / or table_prefix in your project settings, SAYN will then prepend the schema_prefix to the schema value and table_prefix to the table value. For example, if your schema_prefix is set to analytics and table_prefix to up then {{ src('my_schema.my_table') }} will compile analytics_my_schema.up_my_table.

Advanced Configuration

If you need to amend the configuration (e.g. materialisation) of a specific sql task within a group, you can overload the values specified in the YAML group definition. To do this, we simply call config from a Jinja tag within the sql file of the task:

sql with config

{{ config(materialisation='view') }}

SELECT ...

The above code will override the value of materialisation setting defined in YAML to make this model a view. All other parameters described above in this page are also available to overload with config except file_name and name. Other properties are available for overloading for advanced use cases: parents, outputs and sources.

Using sql In incremental Mode

sql tasks support loads incrementally, which is extremely useful for large data volumes when full refresh (materialisation: table) would be infeasible.

We set an sql task as incremental by: 1. Setting materialisation to incremental 2. Defining a delete_key

sql in incremental mode

...

task_sql_incremental:
  type: sql
  file_name: task_sql_incremental.sql
  materialisation: incremental
  destination:
    tmp_schema: analytics_staging
    schema: analytics_models
    table: task_sql
  delete_key: dt
...

When using incremental, SAYN will do the following in the background:

  1. Create a temporary table based on the incremental logic from the SAYN query.
  2. Delete from the final table those records for which the delete_key value is in the temporary table.
  3. Insert the contents of the temporary table into the final table.

In order to make the SELECT statement incremental, SAYN provides the following arguments:

  • full_load: a flag defaulting to False and controlled by the -f flag in the SAYN command. If -f is passed to the sayn command, the final table will be replaced with the temporary one in step 2 above, rather than performing a merge of the data.
  • start_dt: a date defaulting to "yesterday" and controlled by the -s flag in the SAYN command.
  • end_dt: a date defaulting to "yesterday" and controlled by the -e flag in the SAYN command.

SQL using incremental arguments

SELECT dt
     , field2
     , COUNT(1) AS c
  FROM table
 WHERE dt BETWEEN {{ start_dt }} AND {{ end_dt }}
 GROUP BY 1,2

Defining columns

Sql tasks accept a columns field in the task definition that affects the table creation by enforcing types and column order.

Attention

Each supported database might have specific DDL related to it. Below are the DDLs that SAYN supports across all databases. For DDLs related to specific databases see the database-specific pages.

CREATE TABLE DDLs

SAYN also lets you control the CREATE TABLE statement if you need more specification. This is done with:

  • columns: the list of columns including their definitions.
  • table_properties: database specific properties that affect table creation (indexes, cluster, sorting, etc.).
  • post_hook: SQL statements executed right after the table/view creation.

columns can define the following attributes:

  • name: the column name.
  • type: the column type.
  • tests: list of keywords that constraint a specific column
  • unique: enforces a unique constraint on the column.
  • not_null: enforces a non null constraint on the column.
  • allowed_values: list allowed values for the column.

table_properties can define the following attributes (database specific): * indexes: * sorting: specify the sorting for the table * distribution_key: specify the type of distribution. * partitioning: specify the partitioning model for the table. * clustering: specify the clustering for the table.

Attention

Each supported database might have specific table_properties related to it; see the database-specific pages for further details and examples.

Attention

If the a primary key is defined in both the columns and indexes DDL entries, the primary key will be set as part of the CREATE TABLE statement only.

sql with columns

...

task_sql:
  type: sql
  file_name: task_sql.sql
  materialisation: table
  destination:
    tmp_schema: analytics_staging
    schema: analytics_models
    table: task_sql
  ddl:
    columns:
      - name: x
        type: int
        primary: True
      - name: y
        type: varchar
        unique: True
    permissions:
      role_name: SELECT
...