DBT Doris Adapter

DBT Doris Adapter

DBT(Data Build Tool) is a component that focuses on doing T (Transform) in ELT (extraction, loading, transformation) - the "transformation data" link The dbt-doris adapter is developed based on dbt-core 1.5.0 and relies on the mysql-connector-python driver to convert data to doris.

git: https://github.com/apache/doris/tree/master/extension/dbt-doris

version

doris	python	dbt-core
>=1.2.5	>=3.8,<=3.10	>=1.5.0

dbt-doris adapter Instructions

dbt-doris adapter install

use pip install:

pip install dbt-doris

check version:

dbt --version

if command not found: dbt:

ln -s /usr/local/python3/bin/dbt /usr/bin/dbt

dbt-doris adapter project init

dbt init 

Users need to prepare the following information to init dbt project

name	default	meaning
project		project name
database		Enter the corresponding number to select the adapter（选择 doris）
host		doris host
port	9030	doris MySQL Protocol Port
schema		In dbt-doris, it is equivalent to database, Database name
username		doris username
password		doris password
threads	1	Parallelism in dbt-doris (setting a parallelism that does not match the cluster capability will increase the risk of dbt running failure)

dbt-doris adapter run

For dbt run documentation, please refer to here. Go to the project directory and execute the default dbt model:

dbt run 

model：my_first_dbt_model和my_second_dbt_model

They are materialized table and view respectively. then login to doris to view the data results and table creation statements of my_first_dbt_model and my_second_dbt_model.

dbt-doris adapter Materialization

dbt-doris Materialization support three:

1. view
2. table
3. incremental

View

Using view as the materialization, Models will be rebuilt as views each time they are run through the create view as statement. (By default, the materialization method of dbt is view)

Advantages: No extra data is stored, and views on top of the source data will always contain the latest records.
Disadvantages: View queries that perform large transformations or are nested on top of other views are slow.
Recommendation: Usually start with the view of the model and only change to another materialization if there are performance issues. Views are best suited for models that do not undergo major transformations, such as renaming, column changes.

config：

models:
  <resource-path>:
    +materialized: view

Or write in the model file

{{ config(materialized = "view") }}

Table

When using the table materialization mode, your model is rebuilt as a table at each run with a create table as select statement. For the tablet materialization of dbt, dbt-doris uses the following steps to ensure the atomicity of data changes:

1. first create a temporary table: create table this_table_temp as {{ model sql}}.
2. Determine whether this_table does not exist, that is, it is created for the first time, execute rename, and change the temporary table to the final table.
3. if already exists, then alter table this_table REPLACE WITH TABLE this_table_temp PROPERTIES('swap' = 'False')，This operation can exchange the table name and delete the this_table_temp temporary table,this guarantees the atomicity of this operation through the transaction mechanism of the Doris.

Advantages: table query speed will be faster than view.
Disadvantages: The table takes a long time to build or rebuild, additional data will be stored, and incremental data synchronization cannot be performed.
Recommendation: It is recommended to use the table materialization method for models queried by BI tools or models with slow operations such as downstream queries and conversions.

config:

models:
  <resource-path>:
    +materialized: table
    +duplicate_key: [ <column-name>, ... ],
    +replication_num: int,
    +partition_by: [ <column-name>, ... ],
    +partition_type: <engine-type>,
    +partition_by_init: [<pertition-init>, ... ]
    +distributed_by: [ <column-name>, ... ],
    +buckets: int | 'auto',
    +properties: {<key>:<value>,...}

Or write in the model file:

{{ config(
    materialized = "table",
    duplicate_key = [ "<column-name>", ... ],
    replication_num = "<int>"
    partition_by = [ "<column-name>", ... ],
    partition_type = "<engine-type>",
    partition_by_init = ["<pertition-init>", ... ]
    distributed_by = [ "<column-name>", ... ],
    buckets = "<int>" | "auto",
    properties = {"<key>":"<value>",...}
      ...
    ]
) }}

The details of the above configuration items are as follows:

item	description	Required?
materialized	The materialized form of the table (Doris Duplicate table)	Required
duplicate_key	Doris Duplicate key	Optional
replication_num	Number of table replicas	Optional
partition_by	Table partition column	Optional
partition_type	Table partition type, range or list.(default: RANGE)	Optional
partition_by_init	Initialized table partitions	Optional
distributed_by	Table distributed column	Optional
buckets	Bucket size	Optional
properties	Doris table properties	Optional

Incremental

Based on the incremental model results of the last run of dbt, records are incrementally inserted or updated into the table. There are two ways to realize the increment of doris. incremental_strategy has two incremental strategies:

• insert_overwrite: Depends on the doris unique model. If there is an incremental requirement, specify the materialization as incremental when initializing the data of the model, and aggregate by specifying the aggregation column to achieve incremental data coverage.
• append: Depends on the doris duplicate model, it only appends incremental data and does not involve modifying any historical data. So no need to specify unique_key.

Advantages: Significantly reduces build time by only converting new records.
Disadvantages: incremental mode requires additional configuration, which is an advanced usage of dbt, and requires the support of complex scenarios and the adaptation of corresponding components.
Recommendation: The incremental model is best for event-based scenarios or when dbt runs become too slow

config:

models:
  <resource-path>:
    +materialized: incremental
    +incremental_strategy: <strategy>
    +unique_key: [ <column-name>, ... ],
    +replication_num: int,
    +partition_by: [ <column-name>, ... ],
    +partition_type: <engine-type>,
    +partition_by_init: [<pertition-init>, ... ]
    +distributed_by: [ <column-name>, ... ],
    +buckets: int | 'auto',
    +properties: {<key>:<value>,...}

Or write in the model file:

{{ config(
    materialized = "incremental",
    incremental_strategy = "<strategy>"
    unique_key = [ "<column-name>", ... ],
    replication_num = "<int>"
    partition_by = [ "<column-name>", ... ],
    partition_type = "<engine-type>",
    partition_by_init = ["<pertition-init>", ... ]
    distributed_by = [ "<column-name>", ... ],
    buckets = "<int>" | "auto",
    properties = {"<key>":"<value>",...}
      ...
    ]
) }}

The details of the above configuration items are as follows:

item	description	Required?
materialized	The materialized form of the table (Doris Duplicate/Unique table)	Required
incremental_strategy	Incremental_strategy	Optional
unique_key	Doris Unique key	Optional
replication_num	Number of table replicas	Optional
partition_by	Table partition column	Optional
partition_type	Table partition type, range or list.(default: RANGE)	Optional
partition_by_init	Initialized table partitions	Optional
distributed_by	Table distributed column	Optional
buckets	Bucket size	Optional
properties	Doris table properties	Optional

dbt-doris adapter seed

seed is a functional module used to load data files such as csv. It is a way to load files into the library and participate in model building, but there are the following precautions:

1. Seeds should not be used to load raw data (for example, large CSV exports from a production database).
2. Since seeds are version controlled, they are best suited to files that contain business-specific logic, for example a list of country codes or user IDs of employees.
3. Loading CSVs using dbt's seed functionality is not performant for large files. Consider using streamload to load these CSVs into doris.

Users can see the seeds directory under the dbt project directory, upload the csv file and seed configuration file in it and run

 dbt seed --select seed_name

Common seed configuration file writing method supports the definition of column types:

seeds:
  seed_name: 
    config: 
      schema: demo_seed 
      full_refresh: true
      replication_num: 1
      column_types:
        id: bigint
        phone: varchar(32)
        ip: varchar(15)
        name: varchar(20)
        cost: DecimalV3(19,10)