gem-hunter

dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

dbt is the T in ELT. Organize, cleanse, denormalize, filter, rename, and pre-aggregate the raw data in your warehouse so that it's ready for analysis.

The dbt-greenplum package contains the code enabling dbt to work with Greenplum. This adapter based on postgres-adapter with a bit difference for a greenplum specific features

Easiest way to start use dbt-greenplum is to install it using pip pip install dbt-greenplum==<version>

Where <version> is same as your dbt version

Available versions:

• 0.19.2
• 1.0.4
• 1.2.0
• 1.4.0
• 1.5.0

You can specify following settings:

• Storage type
  • heap
  • appendoptimized
• Distribution
  • distributed randomly by defaut
  • distributed by (column, [ ... ] ) by setting up distributed_by parameter in the model config
  • distributed replicated by setting up distributed_replicated=true parameter in the model config
• Table orientation
  • orientation=colum by default
  • orientation=row by setting up orientation parameter in row in the model config
• Compress type, level and blocksize with default values

   compresstype=ZLIB,
   compresslevel=1,
   blocksize=32768

  You can also specify blocksize, compresstype, compresslevel in the model config
• appendoptimized preference by default is true, also you can override it by setting up appendoptimized field in the model config
• Partitions (see "Partition" chapter below)
• Additional incremental strategy
  • truncate-insert by setting up incremental_strategy="truncate+insert" parameter in the model config or +incremental_strategy: truncate_insert in the dbt_project.yml

To create heap table set appendoptimized parameter value to false

{{
   config(
      ...
      materialized='table',
      appendoptimized=false
      ...
   )
}}

select 1 as "id"

will produce following SQL code

create  table "<db_name>"."<schema_name>"."<table_name>"
with (
   appendoptimized=false
) as (
   select 1 as "id"
)
DISTRIBUTED RANDOMLY;

You can use appendopimized or appendonly(legacy) to create appendoptimized table

Such model definition

{{
    config(
        materialized='table',
        distributed_by='id',
        appendoptimized=true,
        orientation='column',
        compresstype='ZLIB',
        compresslevel=1,
        blocksize=32768
    )
}}

with source_data as (

    select 1 as id
    union all
    select null as id

)

select *
from source_data

will produce following sql code

create  table "dvault"."dv"."my_first_dbt_model__dbt_tmp"
with (
    appendoptimized=true,
    blocksize=32768,
    orientation=column,
    compresstype=ZLIB,
    compresslevel=1
)
as (
  with source_data as (
      select 1 as id
      union all
      select null as id
    )
  select *
  from source_data
)  
distributed by (id);

  
alter table "dvault"."dv"."my_first_dbt_model__dbt_tmp" rename to "my_first_dbt_model";

Greenplum does not support partitions with create table as construction, so you need to build model in two steps

• create table schema
• insert data

To implement partitions into you dbt-model you need to specify on of the following config parameters:

• fields_string - definition of columns name, type and constraints
• one of following way to configure partitions
  • raw_partition by default
  • partition_type, partition_column, partition_spec
  • partition_type, partition_column, partition_start, partition_end, partition_every
  • partition_type, partition_column, partition_values
• default_partition_name - name of default partition 'other' by default

Let consider examples of definition model with partitions

• using raw_partition parameter

  {% set fields_string %}
       id int4 null,
       incomingdate timestamp NULL
  {% endset %}
  
  
  {% set raw_partition %}
      PARTITION BY RANGE (incomingdate)
      (
          START ('2021-01-01'::timestamp) INCLUSIVE
          END ('2023-01-01'::timestamp) EXCLUSIVE
          EVERY (INTERVAL '1 day'),
          DEFAULT PARTITION extra
      );
  {% endset %}
  
  {{
      config(
          materialized='table',
          distributed_by='id',
          appendoptimized=true,
          orientation='column',
          compresstype='ZLIB',
          compresslevel=1,
          blocksize=32768,
          fields_string=fields_string,
          raw_partition=raw_partition,
          default_partition_name='other_data'
      )
  }}
  
  with source_data as (
  
      select
          1 as id,
          '2022-02-22'::timestamp as incomingdate
      union all
      select
          null as id,
          '2022-02-25'::timestamp as incomingdate
  )
  select *
  from source_data

  will produce following sql code

  create table if not exists "database"."schema"."my_first_dbt_model__dbt_tmp" (
      id int4 null,
      incomingdate timestamp NULL
  )
  with (
      appendoptimized=true,
      blocksize=32768,
      orientation=column,
      compresstype=ZLIB,
      compresslevel=1
  )
  DISTRIBUTED BY (id)
  PARTITION BY RANGE (incomingdate)
  (
      START ('2021-01-01'::timestamp) INCLUSIVE
      END ('2023-01-01'::timestamp) EXCLUSIVE
      EVERY (INTERVAL '1 day'),
      DEFAULT PARTITION extra
  );
  
  insert into "database"."schema"."my_first_dbt_model__dbt_tmp" (
      with source_data as (
  
          select
              1 as id,
              '2022-02-22'::timestamp as incomingdate
          union all
          select
              null as id,
              '2022-02-25'::timestamp as incomingdate
      )
      select *
      from source_data
  );
  alter table "dvault"."dv"."my_first_dbt_model" rename to "my_first_dbt_model__dbt_backup";
  drop table if exists "dvault"."dv"."my_first_dbt_model__dbt_backup" cascade;
  alter table "database"."schema"."my_first_dbt_model__dbt_tmp" rename to "my_first_dbt_model";

• Same result you can get using partition_type, partition_column, partition_spec parameters

  {% set fields_string %}
      id int4 null,
      incomingdate timestamp NULL
  {% endset %}
  
  {%- set partition_type = 'RANGE' -%}
  {%- set partition_column = 'incomingdate' -%}
  {% set partition_spec %}
      START ('2021-01-01'::timestamp) INCLUSIVE
      END ('2023-01-01'::timestamp) EXCLUSIVE
      EVERY (INTERVAL '1 day'),
      DEFAULT PARTITION extra
  {% endset %}
  
  {{
      config(
          materialized='table',
          distributed_by='id',
          appendoptimized=true,
          orientation='column',
          compresstype='ZLIB',
          compresslevel=1,
          blocksize=32768,
          fields_string=fields_string,
          partition_type=partition_type,
          partition_column=partition_column,
          partition_spec=partition_spec,
          default_partition_name='other_data'
      )
  }}
  
  with source_data as (
  
      select
          1 as id,
          '2022-02-22'::timestamp as incomingdate
      union all
      select
          null as id,
          '2022-02-25'::timestamp as incomingdate
  )
  select *
  from source_data

• also, you can use third way

  {% set fields_string %}
      id int4 null,
      incomingdate timestamp NULL
  {% endset %}
  
  
  {%- set partition_type = 'RANGE' -%}
  {%- set partition_column = 'incomingdate' -%}
  {%- set partition_start = "'2021-01-01'::timestamp" -%}
  {%- set partition_end = "'2022-01-01'::timestamp" -%}
  {%- set partition_every = '1 day' -%}
  
  
  {{
      config(
          materialized='table',
          distributed_by='id',
          appendoptimized=true,
          orientation='column',
          compresstype='ZLIB',
          compresslevel=1,
          blocksize=32768,
          fields_string=fields_string,
          partition_type=partition_type,
          partition_column=partition_column,
          partition_start=partition_start,
          partition_end=partition_end,
          partition_every=partition_every,
          default_partition_name='other_data'
      )
  }}
  
  with source_data as (
  
      select
          1 as id,
          '2022-02-22'::timestamp as incomingdate
      union all
      select
          null as id,
          '2022-02-25'::timestamp as incomingdate
  )
  select *
  from source_data

• example of partition_type LIST is coming soon

Too check generate sql script use -d option: dbt -d run <...> -m <models>

If you want implement complex partition logic with subpartition or something else use raw_partition parameter

You can use this incremental strategy to safely reload models without cascade dropping dependent objects due to Greenplum's transactional TRUNCATE operation realization

Model definition example:

{{
   config(
      ...
      materialized='incremental',
      incremental_strategy='truncate+insert',
      ...
   )
}}

select *
from source_data

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