poetry 下运行 dbt（qbit）

头像
qbit
     重庆
    阅读 9 分钟

    前言

    • dbt 是 Data Build Tool 的简称，目前由 dbt labs 公司负责开发和维护，公司的前身是Fishtown Analytics。
    • dbt 主要处理 ETL 中的 T（transform）

    • dbt 主要分为两部分

      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 个人觉得 dbt Core 的官方入门教程不友好，一开始就要求谷歌的 BigQuery 服务、GitHub 账号等，其实 dbt Core 可以不联网完全离线使用。
    • 本文主要参照：【大数据架构之旅】2 从零起步学 dbt

    技术栈

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

    poetry 项目配置

    • .../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 项目文件夹 指 .../test_dbt/
    • 本文中的 dbt 项目文件夹 指 .../test_dbt/dbt_demo/

    创建数据库与初始化项目

    • 在 PostgreSQL 里面新建数据库 dbt-demo

    • 初始化 dbt 项目，在 .../test_dbt/ 目录下执行以下命令

      poetry run dbt init dbt_demo

    • 按提示执行完后，.../test_dbt/ 的目录结构如下

      $ 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，中括号里面的内容按自己的实际情况填写

      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
    • 下文命令若无特殊说明，都在 .../test_dbt/dbt_demo/ 目录下运行

    自带模型

    • 进入 .../test_dbt/dbt_demo/ 目录后运行以下命令检查环境信息

      $ 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
    • 检查 PostgreSQL 数据库，应该可以看到一张名为 my_first_dbt_model 的表，和一个名为 my_second_dbt_model 的视图

    外部数据

    创建底表

    • 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 目录下创建文件 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 类型是为了避免 dbt 自动推断类型出错
    • 关于数据列的类型，可参考官方文档 column_types

    • 执行以下命令导入基础数据

      $ 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
    • 检查 PostgreSQL 数据库，应该可以看到新建了 2 张表：hour 和 day

    自定义模型

    • 创建模型目录 .../test_dbt/dbt_demo/models/bike_share，下面 sql 文件中 ref('day') 是引用的上面导入的 day数据库表

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

      /* 季节趋势表 */

{{ 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 目录内创建 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 目录内创建 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

    文档生成和查看

    • 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，并打开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
      image.png
    • 点击右下角按钮查看血缘关系图（Lineage Graph）
      image.png

    目录结构

    • 最后再看一眼目录结构

      $ tree ./dbt_demo/ -L 3
./dbt_demo/
├── analyses
├── dbt_packages
├── dbt_project.yml
├── logs
│   └── dbt.log
├── macros
├── models
│   ├── bike_share
│   │   ├── month_trend.sql
│   │   ├── season_trend.sql
│   │   ├── weather_trend.sql
│   │   └── weekday_trend.sql
│   └── example
│       ├── my_first_dbt_model.sql
│       ├── my_second_dbt_model.sql
│       └── schema.yml
├── README.md
├── seeds
│   ├── bike_share.yml
│   ├── day.csv
│   └── hour.csv
├── snapshots
├── target
│   ├── catalog.json
│   ├── compiled
│   │   └── dbt_demo
│   ├── graph.gpickle
│   ├── index.html
│   ├── manifest.json
│   ├── partial_parse.msgpack
│   ├── run
│   │   └── dbt_demo
│   └── run_results.json
└── tests
    本文出自 qbit snap
    python教程数据sql
    qbit
    268 声望279 粉丝
    « 上一篇
    pandas 将函数应用到列（qbit）
    下一篇 »
    Java 与 Python 国密算法 SM2 交互使用（qbit）

    引用和评论

    推荐阅读
    头像
    Windows 下启动 gradio 信息: 用提供的模式无法找到文件。（qbit）

    qbit

    头像
    将 Python 和 Rust 融合在一起，为 pyQuil® 4.0 带来和谐

    Swift社区赞 4阅读 748

    头像
    Ubuntu20.04 从源代码编译安装 python3.10

    universe_king赞 2阅读 10.7k评论 1

    头像
    docker 打包 opencv-python，libGL.so.1 报错解决方法

    universe_king赞 1阅读 5.8k

    头像
    Python装饰器：让你的代码优雅又高效的秘密武器

    LLLibra146赞 3阅读 768

    头像
    从javascript到python(一):基本环境搭建

    深圳飘赞 2阅读 4.6k

    头像
    浅读TiKV源码：Coprocessor

    泊浮目赞 1阅读 1.3k

    0 条评论
    得票最新