Vertica configurations

Configuration of Incremental Models

Using the on_schema_change config parameter

You can use on_schema_change parameter with values ignore, fail and append_new_columns. Value sync_all_columns is not supported at this time.

Configuring the ignore (default) parameter

Source code

vertica_incremental.sql

    
    {{config(materialized = 'incremental',on_schema_change='ignore')}} 
    
    select * from {{ ref('seed_added') }}

Run code

vertica_incremental.sql

    
      insert into "VMart"."public"."merge" ("id", "name", "some_date")
    (
        select "id", "name", "some_date"
        from "merge__dbt_tmp"
    )

Configuring the fail parameter

Source code

vertica_incremental.sql

      {{config(materialized = 'incremental',on_schema_change='fail')}} 
      
      
      select * from {{ ref('seed_added') }}

Run code

vertica_incremental.sql

    
            The source and target schemas on this incremental model are out of sync!
              They can be reconciled in several ways:
                - set the `on_schema_change` config to either append_new_columns or sync_all_columns, depending on your situation.
                - Re-run the incremental model with `full_refresh: True` to update the target schema.
                - update the schema manually and re-run the process.

              Additional troubleshooting context:
                 Source columns not in target: {{ schema_changes_dict['source_not_in_target'] }}
                 Target columns not in source: {{ schema_changes_dict['target_not_in_source'] }}
                 New column types: {{ schema_changes_dict['new_target_types'] }}

Configuring the apppend_new_columns parameter

Source code

vertica_incremental.sql

    
   
{{ config( materialized='incremental', on_schema_change='append_new_columns') }}



    select * from  public.seed_added

Run code

vertica_incremental.sql

    
          insert into "VMart"."public"."over" ("id", "name", "some_date", "w", "w1", "t1", "t2", "t3")
          (
                select "id", "name", "some_date", "w", "w1", "t1", "t2", "t3"
                from "over__dbt_tmp"
          )

Using the incremental_strategy config ​parameter

Append strategy (default):

Insert new records without updating or overwriting any existing data. append only adds the new records based on the condition specified in the is_incremental() conditional block.

Source code

vertica_incremental.sql

{{ config(  materialized='incremental',     incremental_strategy='append'  ) }} 


    select * from  public.product_dimension


    {% if is_incremental() %} 
    
        where product_key > (select max(product_key) from {{this }}) 
    
    
    {% endif %}

Run code

vertica_incremental.sql

    
   insert into "VMart"."public"."samp" (

        "product_key", "product_version", "product_description", "sku_number", "category_description", 
        "department_description", "package_type_description", "package_size", "fat_content", "diet_type",
        "weight", "weight_units_of_measure", "shelf_width", "shelf_height", "shelf_depth", "product_price",
        "product_cost", "lowest_competitor_price", "highest_competitor_price", "average_competitor_price", "discontinued_flag")
    (
          select "product_key", "product_version", "product_description", "sku_number", "category_description", "department_description", "package_type_description", "package_size", "fat_content", "diet_type", "weight", "weight_units_of_measure", "shelf_width", "shelf_height", "shelf_depth", "product_price", "product_cost", "lowest_competitor_price", "highest_competitor_price", "average_competitor_price", "discontinued_flag"

          from "samp__dbt_tmp"
    )

Merge strategy:

Match records based on a unique_key; update old records, insert new ones. (If no unique_key is specified, all new data is inserted, similar to append.) The unique_key config parameter is required for using the merge strategy, the value accepted by this parameter is a single table column.

Source code

vertica_incremental.sql

      {{ config( materialized = 'incremental', incremental_strategy = 'merge',  unique_key='promotion_key'   )  }}
      
      
          select * FROM  public.promotion_dimension

Run code

vertica_incremental.sql

       

      merge into "VMart"."public"."samp" as DBT_INTERNAL_DEST using "samp__dbt_tmp" as DBT_INTERNAL_SOURCE
          on DBT_INTERNAL_DEST."promotion_key" = DBT_INTERNAL_SOURCE."promotion_key"
  
        when matched then update set
        "promotion_key" = DBT_INTERNAL_SOURCE."promotion_key", "price_reduction_type" = DBT_INTERNAL_SOURCE."price_reduction_type", "promotion_media_type" = DBT_INTERNAL_SOURCE."promotion_media_type", "display_type" = DBT_INTERNAL_SOURCE."display_type", "coupon_type" = DBT_INTERNAL_SOURCE."coupon_type", "ad_media_name" = DBT_INTERNAL_SOURCE."ad_media_name", "display_provider" = DBT_INTERNAL_SOURCE."display_provider", "promotion_cost" = DBT_INTERNAL_SOURCE."promotion_cost", "promotion_begin_date" = DBT_INTERNAL_SOURCE."promotion_begin_date", "promotion_end_date" = DBT_INTERNAL_SOURCE."promotion_end_date"
        
        when not matched then insert
          ("promotion_key", "price_reduction_type", "promotion_media_type", "display_type", "coupon_type",
           "ad_media_name", "display_provider", "promotion_cost", "promotion_begin_date", "promotion_end_date")
        values
        (
          DBT_INTERNAL_SOURCE."promotion_key", DBT_INTERNAL_SOURCE."price_reduction_type", DBT_INTERNAL_SOURCE."promotion_media_type", DBT_INTERNAL_SOURCE."display_type", DBT_INTERNAL_SOURCE."coupon_type", DBT_INTERNAL_SOURCE."ad_media_name", DBT_INTERNAL_SOURCE."display_provider", DBT_INTERNAL_SOURCE."promotion_cost", DBT_INTERNAL_SOURCE."promotion_begin_date", DBT_INTERNAL_SOURCE."promotion_end_date"
        )

Using the merge_update_columns config parameter

The merge_update_columns config parameter is passed to only merge the columns specified and it accepts list of table columns.

Source code

vertica_incremental.sql

    {{ config( materialized = 'incremental', incremental_strategy='merge', unique_key = 'id', merge_update_columns = ["id","name","salary"] )}}
    
        select * from {{ref('seed_tc1')}}

Run code

vertica_incremental.sql

        merge into "VMart"."public"."test_merge" as DBT_INTERNAL_DEST using "test_merge__dbt_tmp" as DBT_INTERNAL_SOURCE on  DBT_INTERNAL_DEST."id" = DBT_INTERNAL_SOURCE."id"
        
        when matched then update set
          "id" = DBT_INTERNAL_SOURCE."id", "names" = DBT_INTERNAL_SOURCE."names", "salary" = DBT_INTERNAL_SOURCE."salary"
        
        when not matched then insert
        ("id", "names", "salary")
        values
        (
          DBT_INTERNAL_SOURCE."id", DBT_INTERNAL_SOURCE."names", DBT_INTERNAL_SOURCE."salary"
        )

delete+insert strategy:

Through the delete+insert incremental strategy, you can instruct dbt to use a two-step incremental approach. It will first delete the records detected through the configured is_incremental() block and then re-insert them. The unique_key is a required parameter for using delete+instert strategy which specifies how to update the records when there is duplicate data. The value accepted by this parameter is a single table column.

Source code

vertica_incremental.sql

    {{ config( materialized = 'incremental', incremental_strategy = 'delete+insert',  unique_key='date_key'   )  }}


          select * FROM  public.date_dimension

Run code

vertica_incremental.sql

        delete from "VMart"."public"."samp"
            where (
                date_key) in (
                select (date_key)
                from "samp__dbt_tmp"
            );

        insert into "VMart"."public"."samp" (
             "date_key", "date", "full_date_description", "day_of_week", "day_number_in_calendar_month", "day_number_in_calendar_year", "day_number_in_fiscal_month", "day_number_in_fiscal_year", "last_day_in_week_indicator", "last_day_in_month_indicator", "calendar_week_number_in_year", "calendar_month_name", "calendar_month_number_in_year", "calendar_year_month", "calendar_quarter", "calendar_year_quarter", "calendar_half_year", "calendar_year", "holiday_indicator", "weekday_indicator", "selling_season")
        (
            select "date_key", "date", "full_date_description", "day_of_week", "day_number_in_calendar_month", "day_number_in_calendar_year", "day_number_in_fiscal_month", "day_number_in_fiscal_year", "last_day_in_week_indicator", "last_day_in_month_indicator", "calendar_week_number_in_year", "calendar_month_name", "calendar_month_number_in_year", "calendar_year_month", "calendar_quarter", "calendar_year_quarter", "calendar_half_year", "calendar_year", "holiday_indicator", "weekday_indicator", "selling_season"
            from "samp__dbt_tmp"
        );

insert_overwrite strategy:

Vertica doesn’t support overwrite by default. so, when user specifies insert_overwrite strategy then it behaves as delete+insert.

This strategy may accept partition_by_string and partitions parameter (optional)

Provide these parameter when you want to overwrite a part of the table.

If both the partition_by_string and partition parameter are not provided then insert_overwrite strategy truncate the target table and insert the source table data into target

partition_by_string accepts string value of a any one specific column_name based on which partitioning of the table data takes place.

partitions parameter (optional) accepts a list of group names in the partition table.

To understand more on partition by clause check here

Note:

If you want to pass partitions parameter then you have to partition the table by passing partition_by_string parameter.

Source code

vertica_incremental.sql

{{config(materialized = 'incremental',incremental_strategy = 'insert_overwrite',partition_by_string='YEAR(cc_open_date)',partitions=['2023'])}}


        select * from online_sales.call_center_dimension

Run code

vertica_incremental.sql

        select PARTITION_TABLE('online_sales.update_call_center_dimension');

        SELECT DROP_PARTITIONS('online_sales.update_call_center_dimension', '2023', '2023');
      
        SELECT PURGE_PARTITION('online_sales.update_call_center_dimension', '2023');
      
        insert into "VMart"."online_sales"."update_call_center_dimension"

        ("call_center_key", "cc_closed_date", "cc_open_date", "cc_name", "cc_class", "cc_employees",
       
        "cc_hours", "cc_manager", "cc_address", "cc_city", "cc_state", "cc_region")
      
        (

            select "call_center_key", "cc_closed_date", "cc_open_date", "cc_name", "cc_class", "cc_employees",
        
            "cc_hours", "cc_manager", "cc_address", "cc_city", "cc_state", "cc_region"

            from "update_call_center_dimension__dbt_tmp"
        );

Optimization options for table materialization

There are multiple optimizations that can be used when materializing models as tables. Each config parameter applies a Vertica specific clause in the generated CREATE TABLE DDL.

For more information see Vertica options for table optimization.

You can configure these optimizations in your model SQL file as described in the examples below:

Configuring the ORDER BY clause

To leverage the ORDER BY clause of the CREATE TABLE statement use the order_by config param in your model.

Using the order_by config parameter

Source code

vertica_incremental.sql

        {{ config(  materialized='table',  order_by='product_key') }} 
    
        select * from public.product_dimension

Run code

vertica_incremental.sql

        create  table  "VMart"."public"."order_s__dbt_tmp" as 
            
             ( select * from public.product_dimension)
              
                 order by product_key;

Configuring the SEGMENTED BY clause

To leverage the SEGMENTED BY clause of the CREATE TABLE statement, use the segmented_by_string or segmented_by_all_nodes config parameters in your model. By default ALL NODES are used to segment tables, so the ALL NODES clause in the SQL statement will be added when using segmented_by_string config parameter. You can disable ALL NODES using no_segmentation parameter.

To learn more about segmented by clause check here.

Using the segmented_by_string config parameter

segmented_by_string config parameter can be used to segment projection data using a SQL expression like hash segmentation.

Source code

vertica_incremental.sql

   
        {{ config( materialized='table', segmented_by_string='product_key'  )  }}  
        
        
        select * from public.product_dimension

Run code

vertica_incremental.sql

      create  table
        
        "VMart"."public"."segmented_by__dbt_tmp"
        
        as (select * from public.product_dimension)
          
             segmented by product_key  ALL NODES;

Using the segmented_by_all_nodes config parameter

segmented_by_all_nodes config parameter can be used to segment projection data for distribution across all cluster nodes.

Note:

If you want to pass segmented_by_all_nodes parameter then you have to segment the table by passing segmented_by_string parameter.

Source code

vertica_incremental.sql

        {{ config( materialized='table', segmented_by_string='product_key' ,segmented_by_all_nodes='True' )  }}  
        
            select * from public.product_dimension

Run code

vertica_incremental.sql

          
        create  table   "VMart"."public"."segmented_by__dbt_tmp" as
              
          (select * from public.product_dimension)
                  
            segmented by product_key  ALL NODES;

Configuring the UNSEGMENTED ALL NODES clause

To leverage theUNSEGMENTED ALL NODES clause of the CREATE TABLE statement, use the no_segmentation config parameters in your model.

Using the no_segmentation config parameter

Source code

vertica_incremental.sql

      
     {{config(materialized='table',no_segmentation='true')}}


          select * from public.product_dimension

Run code

vertica_incremental.sql

       
  
           create  table
                      "VMart"."public"."ww__dbt_tmp"
    
                   INCLUDE SCHEMA PRIVILEGES as (
    
                select * from public.product_dimension )
                
                        UNSEGMENTED ALL NODES ;
    
  

Configuring the PARTITION BY clause

To leverage the PARTITION BY clause of the CREATE TABLE statement, use the partition_by_string, partition_by_active_count or the partition_by_group_by_string config parameters in your model.

To learn more about partition by clause check here

Using the partition_by_string config parameter

partition_by_string (optinal) accepts a string value of a any one specific column_name based on which partitioning of the table data takes place.

Source code

vertica_incremental.sql

      
      {{ config( materialized='table', partition_by_string='employee_age' )}} 
    
      
        select * FROM public.employee_dimension

Run code

vertica_incremental.sql

        create table "VMart"."public"."test_partition__dbt_tmp" as 
        
        ( select * FROM public.employee_dimension); 
        
        alter table "VMart"."public"."test_partition__dbt_tmp"
         
        partition BY employee_age

Using the partition_by_active_count config parameter

partition_by_active_count (optional) specifies how many partitions are active for this table. It accepts an integer value.

Note:

If you want to pass partition_by_active_count parameter then you have to partition the table by passing partition_by_string parameter.

Source code

vertica_incremental.sql

    {{ config( materialized='table', 
    partition_by_string='employee_age',    
    partition_by_group_by_string="""
                                  CASE WHEN employee_age < 5 THEN 1
                                  WHEN employee_age>50 THEN 2
                                  ELSE 3 END""",
    
    partition_by_active_count = 2) }}


      select * FROM public.employee_dimension
 
 

Run code

vertica_incremental.sql

    
    create  table "VMart"."public"."test_partition__dbt_tmp" as
      
      ( select * FROM public.employee_dimension );
          
          alter table "VMart"."public"."test_partition__dbt_tmp" partition BY employee_ag  
          
            group by CASE WHEN employee_age < 5 THEN 1
        
        WHEN employee_age>50 THEN 2
        
        ELSE 3 END
        
        SET ACTIVEPARTITIONCOUNT 2  ;

Using the partition_by_group_by_string config parameter

partition_by_group_by_string parameter(optional) accepts a string, in which user should specify each group cases as a single string.

This is derived from the partition_by_string value.

partition_by_group_by_string parameter is used to merge partitions into separate partition groups.

:::info Note:

If you want to pass partition_by_group_by_string parameter then you have to partition the table by passing partition_by_string parameter.

:::

Source code

vertica_incremental.sql

    {{config(materialized='table',
    partition_by_string='number_of_children', 
    partition_by_group_by_string="""
                                  CASE WHEN number_of_children <= 2 THEN 'small_family'
                                  ELSE 'big_family' END""")}}
select * from public.customer_dimension

Run code

vertica_incremental.sql

      create  table "VMart"."public"."test_partition__dbt_tmp"  INCLUDE SCHEMA PRIVILEGES as 
    
        ( select * from public.customer_dimension ) ; 
        
      alter table "VMart"."public"."test_partition__dbt_tmp" 
      partition BY number_of_children
      group by CASE WHEN number_of_children <= 2 THEN 'small_family'
                                             ELSE 'big_family' END  ;

Configuring the KSAFE clause

To leverage the KSAFE clause of the CREATE TABLE statement, use the ksafe config parameter in your model.

Source code

vertica_incremental.sql

{{  config(  materialized='table',    ksafe='1'   ) }} 
        
          select * from  public.product_dimension

Run code

vertica_incremental.sql

        create  table "VMart"."public"."segmented_by__dbt_tmp" as 
  
        (select * from  public.product_dimension ) 
            ksafe 1;