poetry 下執行 dbt（qbit）

qbit發表於2022-12-30

前言

技術棧

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

poetry 專案配置

  • .../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 專案資料夾 指 .../test_dbt/
  • 本文中的 dbt 專案資料夾 指 .../test_dbt/dbt_demo/

建立資料庫與初始化專案

  • 在 PostgreSQL 裡面新建資料庫 dbt-demo

  • 初始化 dbt 專案，在 .../test_dbt/ 目錄下執行以下命令

    poetry run dbt init dbt_demo

  • 按提示執行完後，.../test_dbt/ 的目錄結構如下

    $ 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

  • 在個人使用者目錄下找到檔案 ~/.dbt/profiles.yml，qbit 的絕對路徑是 C:\Users\qbit\.dbt\profiles.yml，將以下內容複製貼上到檔案，中括號裡面的內容按自己的實際情況填寫

    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
  • 下文命令若無特殊說明，都在 .../test_dbt/dbt_demo/ 目錄下執行

自帶模型

  • 進入 .../test_dbt/dbt_demo/ 目錄後執行以下命令檢查環境資訊

    $ 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!

  • 生成自帶模型

    $ 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
  • 檢查 PostgreSQL 資料庫，應該可以看到一張名為 my_first_dbt_model 的表，和一個名為 my_second_dbt_model 的檢視

外部資料

建立底表

  • 從加州大學歐文分校下載共享單車的資料集，將 hour.csv 和 day.csv 放到 .../test_dbt/dbt_demo/seeds 資料夾下。

  • 在 .../test_dbt/dbt_demo/seeds 目錄下建立檔案 bike_share.yml，內容如下：

    version: 2
seeds:
- name: hour
  config:
    column_types: 
      dteday: date
- name: day
  config:
    column_types:
      dteday: date
  • 這裡指定 date 型別是為了避免 dbt 自動推斷型別出錯
  • 關於資料列的型別，可參考官方文件 column_types

  • 執行以下命令匯入基礎資料

    $ 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
  • 檢查 PostgreSQL 資料庫，應該可以看到新建了 2 張表：hour 和 day

自定義模型

  • 建立模型目錄 .../test_dbt/dbt_demo/models/bike_share，下面 sql 檔案中 ref('day') 是引用的上面匯入的 day資料庫表

  • 在 .../models/bike_share 目錄內建立 weather_trend.sql，內容如下

    /* 天氣趨勢表 */

{{ 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 目錄內建立 season_trend.sql，內容如下

    /* 季節趨勢表 */

{{ 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 目錄內建立 month_trend.sql，內容如下

    /* 月度趨勢表 */

{{ 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 目錄內建立 weekday_trend.sql，內容如下

    /* 星期幾趨勢表 */

{{ 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
)

  • 執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

文件生成和檢視

  • 執行以下命令生成文件

    $ 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

  • 執行以下命令，會啟動 http 服務，並開啟預設瀏覽器檢視文件

    $ 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 -
  • 網頁介面
    image.png
  • 點選右下角按鈕檢視血緣關係圖（Lineage Graph）
    image.png

目錄結構

  • 最後再看一眼目錄結構

    $ 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

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