关于python:poetry-下运行-dbtqbit

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dbt 是 Data Build Tool 的简称，目前由 dbt labs 公司负责开发和保护，公司的前身是 Fishtown Analytics。
dbt 次要解决 ETL 中的 T（transform）

dbt 次要分为两局部

dbt Core: cli 命令行工具
dbt Cloud: 云服务

dbt Core 的 github 地址是：https://github.com/dbt-labs/d…
qbit 集体感觉 dbt Core 的官网入门教程不敌对，一开始就要求谷歌的 BigQuery 服务、GitHub 账号等。
本文次要参照：【大数据架构之旅】2 从零起步学 dbt

Windows    10
Python     3.8.10
poetry     1.3.1
git        2.35.1.windows.2
PostgreSQL 15.1

.../test_dbt/pyproject.toml

[tool.poetry]
name = "test-dbt"
version = "0.1.0"
description = ""authors = ["qbit <q@bit.cn>"]
readme = "README.md"
packages = [{include = "test_dbt"}]

[[tool.poetry.source]]
name = "aliyun"
url = "https://mirrors.aliyun.com/pypi/simple/"
default = true

[tool.poetry.dependencies]
python = "^3.8"
dbt-core = "~1.3.1"
dbt-postgres = "~1.3.1"

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

本文中的 poetry 我的项目文件夹 指 .../test_dbt/
本文中的 dbt 我的项目文件夹 指 .../test_dbt/dbt_demo/

在 PostgreSQL 外面新建数据库 dbt-demo
初始化 dbt 我的项目，在 .../test_dbt/ 目录下执行以下命令
```
poetry run dbt init dbt_demo
```

按提醒执行完后，.../test_dbt/ 的目录构造如下

$ tree
.
├── dbt_demo
│   ├── analyses
│   ├── dbt_project.yml
│   ├── macros
│   ├── models
│   │   └── example
│   │       ├── my_first_dbt_model.sql
│   │       ├── my_second_dbt_model.sql
│   │       └── schema.yml
│   ├── README.md
│   ├── seeds
│   ├── snapshots
│   └── tests
├── logs
│   └── dbt.log
├── poetry.lock
└── pyproject.toml

在个人用户目录下找到文件 ~/.dbt/profiles.yml，qbit 的绝对路径是 C:\Users\qbit\.dbt\profiles.yml，将以下内容复制粘贴到文件，中括号外面的内容按本人的理论状况填写

dbt_demo:
outputs:

  dev:
    type: postgres
    threads: 1
    host: [host]
    port: [port]
    user: [dev_username]
    pass: [dev_password]
    dbname: dbt_demo
    schema: dev_schema

  prod:
    type: postgres
    threads: 1
    host: [host]
    port: [port]
    user: [prod_username]
    pass: [prod_password]
    dbname: dbt_demo
    schema: prod_schema

target: dev

下文命令若无非凡阐明，都在 .../test_dbt/dbt_demo/ 目录下运行

进入 .../test_dbt/dbt_demo/ 目录后运行以下命令查看环境信息

$ poetry run dbt debug
06:41:09  Running with dbt=1.3.1
dbt version: 1.3.1
python version: 3.8.10
python path: D:\Python3Project\test_dbt\.venv\Scripts\python.exe
os info: Windows-10-10.0.19045-SP0
Using profiles.yml file at C:\Users\qbit\.dbt\profiles.yml
Using dbt_project.yml file at D:\Python3Project\test_dbt\dbt_demo\dbt_project.yml

Configuration:
profiles.yml file [OK found and valid]
dbt_project.yml file [OK found and valid]

Required dependencies:
 - git [OK found]

Connection:
host: 192.168.1.52
port: 5432
user: postgres
database: dbt_demo
schema: dev_schema
search_path: None
keepalives_idle: 0
sslmode: None
Connection test: [OK connection ok]

All checks passed!

生成自带模型

$ poetry run dbt run
06:47:04  Running with dbt=1.3.1
06:47:04  Partial parse save file not found. Starting full parse.
06:47:04  Found 2 models, 4 tests, 0 snapshots, 0 analyses, 289 macros, 0 operations, 0 seed files, 0 sources, 0 exposures, 0 metrics
06:47:04
06:47:05  Concurrency: 1 threads (target='dev')
06:47:05
06:47:05  1 of 2 START sql table model dev_schema.my_first_dbt_model ..................... [RUN]
06:47:05  1 of 2 OK created sql table model dev_schema.my_first_dbt_model ................ [SELECT 2 in 0.15s]
06:47:05  2 of 2 START sql view model dev_schema.my_second_dbt_model ..................... [RUN]
06:47:05  2 of 2 OK created sql view model dev_schema.my_second_dbt_model ................ [CREATE VIEW in 0.09s]
06:47:05
06:47:05  Finished running 1 table model, 1 view model in 0 hours 0 minutes and 0.53 seconds (0.53s).
06:47:05
06:47:05  Completed successfully
06:47:05
06:47:05  Done. PASS=2 WARN=0 ERROR=0 SKIP=0 TOTAL=2

查看 PostgreSQL 数据库，应该能够看到一张名为 my_first_dbt_model 的表，和一个名为 my_second_dbt_model 的视图

从加州大学欧文分校下载共享单车的数据集，将 hour.csv 和 day.csv 放到 .../test_dbt/dbt_demo/seeds 文件夹下。

在 .../test_dbt/dbt_demo/seeds 目录下创立文件 bike_share.yml，内容如下：

version: 2
seeds:
- name: hour
  config:
    column_types: 
      dteday: date
- name: day
  config:
    column_types:
      dteday: date

这里指定 date 类型是为了防止 dbt 主动推断类型出错
对于数据列的类型，可参考官网文档 column_types

执行以下命令导入根底数据

$ poetry run dbt seed
07:19:26  Running with dbt=1.3.1
07:19:26  Found 2 models, 4 tests, 0 snapshots, 0 analyses, 289 macros, 0 operations, 2 seed files, 0 sources, 0 exposures, 0 metrics
07:19:26
07:19:26  Concurrency: 1 threads (target='dev')
07:19:26
07:19:26  1 of 2 START seed file dev_schema.day .......................................... [RUN]
07:19:31  1 of 2 OK loaded seed file dev_schema.day ...................................... [INSERT 731 in 4.54s]
07:19:31  2 of 2 START seed file dev_schema.hour ......................................... [RUN]
07:21:24  2 of 2 OK loaded seed file dev_schema.hour ..................................... [INSERT 17379 in 112.83s]
07:21:24
07:21:24  Finished running 2 seeds in 0 hours 1 minutes and 57.65 seconds (117.65s).
07:21:24
07:21:24  Completed successfully
07:21:24
07:21:24  Done. PASS=2 WARN=0 ERROR=0 SKIP=0 TOTAL=2

查看 PostgreSQL 数据库，应该能够看到新建了 2 张表：hour 和 day

创立模型目录 .../test_dbt/dbt_demo/models/bike_share，上面 sql 文件中 ref('day') 是援用的下面导入的 day 数据库表

在 .../models/bike_share 目录内创立 weather_trend.sql，内容如下

/* 天气趋势表 */

{{config(materialized='table') }}

with weather_trend as (select weathersit, min(temp*47+8) as min_temp, max(temp*47+8) as max_temp, avg(temp*47+8) as mean_temp, sum(casual) as total_casual, sum(registered) as total_registered, sum(cnt) as total_cnt
  from {{ref('day') }}
  group by weathersit
)

在 .../models/bike_share 目录内创立 season_trend.sql，内容如下

/* 节令趋势表 */

{{config(materialized='table') }}

with season_trend as (select season, min(temp*47+8) as min_temp, max(temp*47+8) as max_temp, avg(temp*47+8) as mean_temp, sum(casual) as total_casual, sum(registered) as total_registered, sum(cnt) as total_cnt
  from {{ref('day') }}
  group by season
)

在 .../models/bike_share 目录内创立 month_trend.sql，内容如下

/* 月度趋势表 */

{{config(materialized='table') }}

with month_trend as (select mnth, min(temp*47+8) as min_temp, max(temp*47+8) as max_temp, avg(temp*47+8) as mean_temp, sum(casual) as total_casual, sum(registered) as total_registered, sum(cnt) as total_cnt
  from {{ref('day') }}
  group by mnth
)

在 .../models/bike_share 目录内创立 weekday_trend.sql，内容如下

/* 星期几趋势表 */

{{config(materialized='table') }}

with weekday_trend as (select weekday, min(temp*47+8) as min_temp, max(temp*47+8) as max_temp, avg(temp*47+8) as mean_temp, sum(casual) as total_casual, sum(registered) as total_registered, sum(cnt) as total_cnt
  from {{ref('day') }}
  group by weekday
)

执行以下命令生成模型

$ poetry run dbt run
07:43:59  Running with dbt=1.3.1
07:43:59  Found 6 models, 4 tests, 0 snapshots, 0 analyses, 289 macros, 0 operations, 2 seed files, 0 sources, 0 exposures, 0 metrics
07:43:59
07:43:59  Concurrency: 1 threads (target='dev')
07:43:59
07:43:59  1 of 6 START sql table model dev_schema.month_trend ............................ [RUN]
07:44:00  1 of 6 OK created sql table model dev_schema.month_trend ....................... [SELECT 12 in 0.15s]
07:44:00  2 of 6 START sql table model dev_schema.my_first_dbt_model ..................... [RUN]
07:44:00  2 of 6 OK created sql table model dev_schema.my_first_dbt_model ................ [SELECT 2 in 0.09s]
07:44:00  3 of 6 START sql table model dev_schema.season_trend ........................... [RUN]
07:44:00  3 of 6 OK created sql table model dev_schema.season_trend ...................... [SELECT 4 in 0.07s]
07:44:00  4 of 6 START sql table model dev_schema.weather_trend .......................... [RUN]
07:44:00  4 of 6 OK created sql table model dev_schema.weather_trend ..................... [SELECT 3 in 0.08s]
07:44:00  5 of 6 START sql table model dev_schema.weekday_trend .......................... [RUN]
07:44:00  5 of 6 OK created sql table model dev_schema.weekday_trend ..................... [SELECT 7 in 0.07s]
07:44:00  6 of 6 START sql view model dev_schema.my_second_dbt_model ..................... [RUN]
07:44:00  6 of 6 OK created sql view model dev_schema.my_second_dbt_model ................ [CREATE VIEW in 0.07s]
07:44:00
07:44:00  Finished running 5 table models, 1 view model in 0 hours 0 minutes and 0.78 seconds (0.78s).
07:44:00
07:44:00  Completed successfully
07:44:00
07:44:00  Done. PASS=6 WARN=0 ERROR=0 SKIP=0 TOTAL=6

运行以下命令生成文档

$ poetry run dbt docs generate
07:49:09  Running with dbt=1.3.1
07:49:09  Found 6 models, 4 tests, 0 snapshots, 0 analyses, 289 macros, 0 operations, 2 seed files, 0 sources, 0 exposures, 0 metrics
07:49:09
07:49:09  Concurrency: 1 threads (target='dev')
07:49:09
07:49:09  Done.
07:49:10  Building catalog
07:49:10  Catalog written to D:\Python3Project\test_dbt\dbt_demo\target\catalog.json

运行以下命令，会启动 http 服务，并关上默认浏览器查看文档

$ poetry run dbt docs serve
07:49:53  Running with dbt=1.3.1
07:49:53  Serving docs at 0.0.0.0:8080
07:49:53  To access from your browser, navigate to:  http://localhost:8080
07:49:53
07:49:53
07:49:53  Press Ctrl+C to exit.
127.0.0.1 - - [28/Dec/2022 15:49:54] "GET / HTTP/1.1" 200 -
127.0.0.1 - - [28/Dec/2022 15:49:54] "GET /manifest.json?cb=1672213794801 HTTP/1.1" 200 -
127.0.0.1 - - [28/Dec/2022 15:49:54] "GET /catalog.json?cb=1672213794801 HTTP/1.1" 200 -

网页界面
点击右下角按钮查看血缘关系图（Lineage Graph）

本文出自 qbit snap

正文完

python

发表至： python

2022-12-28

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关于python:poetry-下运行-dbtqbit

前言

技术栈

poetry 我的项目配置

创立数据库与初始化我的项目

自带模型

内部数据

创立底表

自定义模型

文档生成和查看

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