dq-doctor 0.3.0

A lightweight data quality checkup CLI — no YAML, no rule syntax to remember.

dq-doctor

Generate data quality reports from your database in minutes — no YAML, no rule syntax to remember.

A lightweight CLI that profiles your database tables, auto-generates quality check rules, runs validations, and outputs an HTML report. One command, zero config.

English | 中文说明

Quick Start

# Install
pip install dq-doctor

# Generate a demo database to try it out
dqdoctor demo

# List tables
dqdoctor tables --db examples/ecommerce/demo.duckdb

# Profile a table
dqdoctor profile --db examples/ecommerce/demo.duckdb --table orders

# Full check: profile + rules + validate + HTML report
dqdoctor check --db examples/ecommerce/demo.duckdb --table orders --out report.html

# Check all tables at once
dqdoctor check --db examples/ecommerce/demo.duckdb --all-tables --out report.html

# Export rules to dbt / Great Expectations / Markdown
dqdoctor export --db examples/ecommerce/demo.duckdb --table orders --format dbt --out schema.yml
dqdoctor export --db examples/ecommerce/demo.duckdb --table orders --format gx --out suite.json
dqdoctor export --db examples/ecommerce/demo.duckdb --table orders --format markdown --out dict.md

That's it. Open report.html in your browser.

What It Does

DuckDB (first-class)
  → Profile table structure & column distributions
  → Auto-generate quality rules (not_null, unique, accepted_values, range, freshness)
  → Execute validations
  → Output HTML report
  → Export to dbt schema.yml / Great Expectations / Markdown

Every rule comes with a human-readable reason — so you know why the rule was suggested, not just what it checks.

Example Output

orders: Rules 14  Passed 14  Failed 0
  PASS not_null on order_id: All 20 rows have non-null 'order_id'.
  PASS unique on order_id: All 20 values in 'order_id' are unique.
  PASS range on total_amount: All 20 values within [45.00, 680.00].
  PASS accepted_values on status: All 20 non-null values in accepted set.
  PASS freshness on created_at: Latest value is 3.0h old (max 24h).

Supported Rules

Rule	How It's Triggered	Example
not_null	Column has zero nulls, or is an identifier field	order_id has no nulls → require not_null
unique	Identifier field with ≥98% distinct rate	user_id is nearly unique → require unique
accepted_values	Category field with ≤20 distinct values	status has 4 values → constrain to that set
range	Numeric column	total_amount in [45.00, 680.00]
freshness	Timestamp field	created_at should be within 24h

Export Formats

# Starter dbt schema.yml with column tests
dqdoctor export --format dbt --out schema.yml

# Great Expectations Expectation Suite JSON
dqdoctor export --format gx --out suite.json

# Markdown data dictionary
dqdoctor export --format markdown --out dict.md

Note: dbt export generates a starter schema.yml structure. You may need to adjust test types (e.g. range) to match your dbt version and packages.

LLM-Enhanced Rules (Experimental)

Pass an LLM API key to get additional business rules beyond the heuristic ones:

dqdoctor check --db demo.duckdb --table orders ^
  --llm-key "sk-xxx" ^
  --llm-base-url "https://api.deepseek.com/v1" ^
  --llm-model "deepseek-chat"

Without --llm-key, dqdoctor runs purely with deterministic heuristic rules. Requires pip install dq-doctor[llm].

CI Mode

Use in CI/CD pipelines — exits with code 1 when failures exceed threshold:

dqdoctor check --db demo.duckdb --table orders --ci --max-failures 0

Why Not Great Expectations / Soda / dbt?

dq-doctor is not a replacement — it's a quick checkup layer that runs before you invest in heavy tooling:

• Great Expectations / Soda: Powerful but require YAML configs, expectation suites, and setup. dqdoctor gives you a first-pass report with zero config.
• dbt tests: Great for ongoing CI, but you need to write tests first. dqdoctor suggests tests for you and can export a starter schema.yml.
• Think of it as: dqdoctor check → discover issues → export to dbt/GX → refine.

中文说明

dqdoctor 是一个轻量级数据质量体检 CLI 工具。你不需要手写 YAML，不需要记 Great Expectations 或 dbt 的规则语法，只需要一行命令，就能对数据库表做 profiling、自动生成质量检查规则、执行校验并输出 HTML 报告。

快速开始

# 安装
pip install dq-doctor

# 生成示例数据库
dqdoctor demo

# 列出所有表
dqdoctor tables --db examples/ecommerce/demo.duckdb

# 对单表做 profiling
dqdoctor profile --db examples/ecommerce/demo.duckdb --table orders

# 完整检查：profiling + 规则生成 + 校验 + HTML 报告
dqdoctor check --db examples/ecommerce/demo.duckdb --table orders --out report.html

# 一次性检查所有表
dqdoctor check --db examples/ecommerce/demo.duckdb --all-tables --out report.html

# 导出规则为 dbt / Great Expectations / Markdown
dqdoctor export --db examples/ecommerce/demo.duckdb --table orders --format dbt --out schema.yml
dqdoctor export --db examples/ecommerce/demo.duckdb --table orders --format gx --out suite.json
dqdoctor export --db examples/ecommerce/demo.duckdb --table orders --format markdown --out dict.md

打开 report.html 即可查看报告。

它做了什么

DuckDB（一等支持）
  → 分析表结构和字段分布
  → 自动生成质量规则（not_null, unique, accepted_values, range, freshness）
  → 执行校验
  → 输出 HTML 报告
  → 导出为 dbt schema.yml / Great Expectations / Markdown

每条规则都有可读的生成原因 — 你不仅知道检查了什么，还知道为什么建议这个规则。

示例输出

orders: Rules 14  Passed 14  Failed 0  Suggested 0
  PASS not_null on order_id: All 20 rows have non-null 'order_id'.
  PASS unique on order_id: All 20 values in 'order_id' are unique.
  PASS range on total_amount: All 20 values within [45.00, 680.00].
  PASS accepted_values on status: All 20 non-null values in accepted set.
  PASS freshness on created_at: Latest value is 3.0h old (max 24h).

支持的规则

规则	触发条件	示例
not_null	字段零空值，或被推断为标识符	order_id 没有空值 → 要求 not_null
unique	标识符字段且唯一率 ≥98%	user_id 近乎唯一 → 要求 unique
accepted_values	分类字段且不同值 ≤20 个	status 有 4 个值 → 约束为这 4 个
range	数值字段	total_amount 在 [45.00, 680.00]
freshness	时间戳字段	created_at 应在 24 小时以内

导出格式

# dbt schema.yml（含 column tests）
dqdoctor export --format dbt --out schema.yml

# Great Expectations Expectation Suite JSON
dqdoctor export --format gx --out suite.json

# Markdown 数据字典
dqdoctor export --format markdown --out dict.md

注意：dbt 导出生成的是 starter 格式的 schema.yml。range 规则使用 dbt_utils.expression_is_true，需要安装 dbt-utils 包。

LLM 增强规则（实验性）

传入 LLM API key 可以在启发式规则之外获得额外的业务规则建议：

dqdoctor check --db demo.duckdb --table orders ^
  --llm-key "sk-xxx" ^
  --llm-base-url "https://api.deepseek.com/v1" ^
  --llm-model "deepseek-chat"

不传 --llm-key 时 dqdoctor 只运行确定性启发式规则。LLM 功能需要 pip install dq-doctor[llm]。

LLM 生成的规则显示为 SUGGEST 状态（未实际校验），与通过/失败的规则分开统计。

CI 模式

在 CI/CD 流水线中使用 — 失败数超过阈值时 exit 1：

dqdoctor check --db demo.duckdb --table orders --ci --max-failures 0

为什么不用 Great Expectations / Soda / dbt？

dq-doctor 不是替代品 — 它是一个快速体检层，在你投入重型工具之前先跑一轮：

• Great Expectations / Soda：功能强大但需要 YAML 配置、Expectation Suite 和初始化。dqdoctor 零配置给你第一轮报告。
• dbt tests：适合持续 CI，但你得先写测试。dqdoctor 帮你建议测试，还能导出 starter schema.yml。
• 定位：dqdoctor check → 发现问题 → 导出到 dbt/GX → 精细化。

适用人群

数据开发工程师、数仓工程师、数据平台实习生。

技术栈

• Python 3.9+
• Typer — CLI 框架
• DuckDB — 嵌入式分析数据库
• Pydantic — 数据模型
• Jinja2 — HTML 报告模板
• Rich — 终端输出

Tech Stack

• Python 3.9+
• Typer — CLI framework
• DuckDB — embedded analytical database
• Pydantic — data models
• Jinja2 — HTML report templates
• Rich — terminal output

Development

git clone https://github.com/pugyy/dq-doctor.git
cd dq-doctor
pip install -e ".[dev]"

# Run tests (66 tests)
pytest tests/ -v

# Lint
ruff check dqdoctor/ tests/

# Try the demo
dqdoctor demo
dqdoctor check --db examples/ecommerce/demo.duckdb --table orders

Roadmap

• PostgreSQL / MySQL connector framework (DuckDB first-class)
• dbt schema.yml native format export
• LLM-enhanced rule suggestions
• PyPI published (v0.2.0)
• Demo GIF
• PostgreSQL / MySQL real integration testing

License

MIT