From 8d8ee3957b0dad922b242c6ac9b99389d479ff4a Mon Sep 17 00:00:00 2001 From: Carlos Oliveira Date: Sat, 11 Apr 2026 01:09:22 -0300 Subject: [PATCH 1/2] =?UTF-8?q?docs:=20atualiza=20documenta=C3=A7=C3=A3o?= =?UTF-8?q?=20t=C3=A9cnica=20MkDocs?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/avancado/arquitetura.md | 138 +++++++++++++++++++++ docs/avancado/recorrencia.md | 112 +++++++++++++++++ docs/avancado/seed.md | 38 ++++++ docs/cli/exemplos.md | 207 ++++++++++++++++++++++++++++++++ docs/cli/referencia.md | 101 ++++++++++++++++ docs/frontend/diagrama.md | 49 ++++++++ docs/frontend/faker.md | 219 ++++++++++++++++++++++++++++++++++ docs/frontend/ia.md | 111 +++++++++++++++++ docs/frontend/visao-geral.md | 59 +++++++++ docs/frontend/yaml.md | 79 ++++++++++++ docs/index.md | 66 +++++----- docs/instalacao/docker.md | 89 ++++++++++++++ docs/instalacao/poetry.md | 93 +++++++++++++++ docs/saida/cloud.md | 100 ++++++++++++++++ docs/saida/formatos.md | 92 ++++++++++++++ docs/saida/particionamento.md | 81 +++++++++++++ docs/saida/sql.md | 86 +++++++++++++ docs/schemas/dominios.md | 92 ++++++++++++++ docs/schemas/dtypes.md | 170 ++++++++++++++++++++++++++ docs/schemas/yaml.md | 172 ++++++++++++++++++++++++++ mkdocs.yml | 58 +++++---- 21 files changed, 2156 insertions(+), 56 deletions(-) create mode 100644 docs/avancado/arquitetura.md create mode 100644 docs/avancado/recorrencia.md create mode 100644 docs/avancado/seed.md create mode 100644 docs/cli/exemplos.md create mode 100644 docs/cli/referencia.md create mode 100644 docs/frontend/diagrama.md create mode 100644 docs/frontend/faker.md create mode 100644 docs/frontend/ia.md create mode 100644 docs/frontend/visao-geral.md create mode 100644 docs/frontend/yaml.md create mode 100644 docs/instalacao/docker.md create mode 100644 docs/instalacao/poetry.md create mode 100644 docs/saida/cloud.md create mode 100644 docs/saida/formatos.md create mode 100644 docs/saida/particionamento.md create mode 100644 docs/saida/sql.md create mode 100644 docs/schemas/dominios.md create mode 100644 docs/schemas/dtypes.md create mode 100644 docs/schemas/yaml.md diff --git a/docs/avancado/arquitetura.md b/docs/avancado/arquitetura.md new file mode 100644 index 0000000..981a902 --- /dev/null +++ b/docs/avancado/arquitetura.md @@ -0,0 +1,138 @@ +# Arquitetura + +Visão técnica dos componentes do Dataforge e como eles se comunicam. + +## Diagrama geral + +```mermaid +flowchart LR + subgraph Browser["Navegador (React + Vite)"] + UI[Interface Visual\nApp.tsx] + SR[SchemaReader] + SW[SchemaWriter] + UI --> SR + UI --> SW + end + + subgraph Server["Servidor Vite / Backend Python"] + API_SCHEMAS[GET /api/schemas\nGET /api/schemas/:nome\nPUT /api/schemas/:nome\nDELETE /api/schemas/:nome] + API_AI[POST /api/ai-generate\nPOST /api/ai-models] + API_DB[POST /api/test-db-connection] + end + + subgraph Core["Core Python"] + CLI[dataset-gen CLI\ncli.py] + GEN[DatasetGenerator\ncore/generator.py] + REG[FAKER_REGISTRY\ncore/registry.py] + SCHEMA[DomainSchema / Table / Column\ncore/schema.py] + LOADER[load_schema\nconfig/loader.py] + DOMAINS[DOMAIN_REGISTRY\ndomains/] + end + + subgraph Output["Saída"] + FILES[Arquivos locais\nCSV / JSON / Parquet / Avro] + CLOUD[Nuvem\nGCS / S3 / Azure] + SQL_DB[Banco SQL\nSQLite / PG / MySQL] + end + + subgraph ExternalAI["APIs de IA"] + ANTHROPIC[Anthropic] + OPENAI[OpenAI] + GOOGLE[Google Gemini] + GROQ[Groq] + MISTRAL[Mistral] + TOGETHER[Together AI] + OLLAMA[Ollama local] + end + + UI -- HTTP --> API_SCHEMAS + UI -- HTTP --> API_AI + UI -- HTTP --> API_DB + API_AI --> ANTHROPIC + API_AI --> OPENAI + API_AI --> GOOGLE + API_AI --> GROQ + API_AI --> MISTRAL + API_AI --> TOGETHER + API_AI --> OLLAMA + API_SCHEMAS -- lê/escreve --> SCHEMA_FILES[(src/dataforge/schemas/*.yaml)] + CLI --> LOADER + CLI --> DOMAINS + LOADER --> SCHEMA + DOMAINS --> SCHEMA + CLI --> GEN + GEN --> REG + GEN --> FILES + GEN --> CLOUD + GEN --> SQL_DB +``` + +## Componentes + +### Frontend (React + TypeScript) + +| Arquivo | Responsabilidade | +|---------|-----------------| +| `App.tsx` | Componente raiz; gerencia todo o estado da aplicação | +| `types/schema.ts` | Tipos TypeScript: `Column`, `Table`, `Schema` | +| `services/SchemaReader.ts` | Converte YAML em estado interno React | +| `services/SchemaWriter.ts` | Converte estado interno em YAML | +| `components/TableNode.tsx` | Nó customizado do ReactFlow para renderizar tabelas | + +### Servidor (vite.config.ts) + +O servidor de desenvolvimento do Vite expõe endpoints de API via proxy ou plugin. Os endpoints disponíveis são: + +| Endpoint | Método | Descrição | +|----------|--------|-----------| +| `/api/schemas` | GET | Lista schemas disponíveis | +| `/api/schemas/:nome` | GET | Retorna o YAML de um schema | +| `/api/schemas/:nome` | PUT | Salva um schema no servidor | +| `/api/schemas/:nome` | DELETE | Remove um schema do servidor | +| `/api/ai-generate` | POST | Gera schema via IA | +| `/api/ai-models` | POST | Lista modelos disponíveis para o provider | +| `/api/test-db-connection` | POST | Testa uma connection string SQL | + +### Core Python + +| Módulo | Responsabilidade | +|--------|-----------------| +| `cli.py` | Entry point Click; define todos os comandos e flags | +| `core/schema.py` | Dataclasses: `DomainSchema`, `Table`, `Column`, `ForeignKey` | +| `core/registry.py` | `FAKER_REGISTRY` — mapeamento de dtype para função geradora | +| `core/generator.py` | `DatasetGenerator` — geração com ordenação topológica e pool de PKs | +| `config/loader.py` | Parser YAML com merge de schemas e validação de FKs | +| `domains/` | Classes de domínio pré-configuradas (ecommerce, hr, finance) | +| `uploaders/` | Implementações de upload para GCS, S3 e Azure | +| `loaders/sql_loader.py` | Carga de DataFrames em banco via SQLAlchemy | +| `writers/json_writer.py` | Escrita JSON no modo nested | + +### Integridade referencial + +```mermaid +flowchart TD + A[Schema com FKs] --> B[Ordenação topológica das tabelas] + B --> C[Gerar tabela pai] + C --> D[Registrar PKs no pool] + D --> E[Gerar tabela filha] + E --> F[Amostrar PKs do pai para colunas FK] + F --> G{Mais tabelas?} + G -- Sim --> C + G -- Não --> H[Datasets prontos] +``` + +Tabelas com FK auto-referenciada (ex: `employees.manager_id`) são preenchidas após a geração da própria tabela, usando o pool de PKs recém-criado. + +## Stack de tecnologia + +| Camada | Tecnologia | +|--------|-----------| +| Frontend | React 18, TypeScript, Vite, ReactFlow, Dagre | +| Backend API | Plugin Vite (Node.js) | +| CLI | Python 3.12, Click | +| Geração de dados | Faker, Pandas | +| Formatos | PyArrow (Parquet), fastavro (Avro) | +| Upload nuvem | google-cloud-storage, boto3, azure-storage-blob | +| SQL | SQLAlchemy 2.0, psycopg2-binary, pymysql | +| Build Python | poetry-core 2.0 | +| Container | Docker multi-stage (python:3.12-slim + Node.js 20 LTS) | diff --git a/docs/avancado/recorrencia.md b/docs/avancado/recorrencia.md new file mode 100644 index 0000000..8f383a7 --- /dev/null +++ b/docs/avancado/recorrencia.md @@ -0,0 +1,112 @@ +# Modo Recorrente + +O modo recorrente faz o gerador produzir batches de dados em intervalos regulares, simulando um pipeline de dados contínuo ou incremental. + +## Ativando + +Use a flag `-R` / `--recurrence` com o intervalo em segundos: + +```bash +# Um batch a cada 30 segundos, indefinidamente +docker compose run --rm cli generate -d finance -f json -R 30 + +# Exatamente 10 batches com intervalo de 60 segundos +docker compose run --rm cli generate -d ecommerce -f parquet -R 60 --count 10 +``` + +Para parar manualmente: `Ctrl+C`. + +## Flags do modo recorrente + +| Flag | Padrão | Descrição | +|------|--------|-----------| +| `--recurrence` / `-R` | — | Intervalo em segundos entre batches (ativa o modo) | +| `--count` | `0` | Número de batches (`0` = infinito) | + +## Seed incremental + +Quando `--seed` é usado com modo recorrente, o seed de cada batch é calculado como `seed + batch_index - 1`. Isso garante que cada batch produza dados diferentes mas determinísticos: + +- Batch 1: seed = 42 +- Batch 2: seed = 43 +- Batch 3: seed = 44 + +## Continuidade de int_seq + +Colunas com `dtype: int_seq` são contínuas entre batches. O gerador acumula o offset de linhas por tabela: + +- Batch 1 (100 linhas): IDs 1 a 100 +- Batch 2 (100 linhas): IDs 101 a 200 +- Batch 3 (100 linhas): IDs 201 a 300 + +## Incremento de colunas (`--increment`) + +A flag `--increment` desloca os valores de uma coluna a cada batch, simulando avanço temporal ou numérico: + +``` +--increment tabela:coluna:passo[:unidade] +``` + +### Unidades disponíveis + +| Unidade | Descrição | +|---------|-----------| +| `days` | Dias (padrão) | +| `hours` | Horas | +| `weeks` | Semanas | +| `months` | Meses (≈ 30,44 dias) | +| `years` | Anos (≈ 365,25 dias) | +| `value` | Valor numérico puro | + +### Exemplos + +```bash +# Avança a coluna "created_at" 1 dia por batch +docker compose run --rm cli generate -d ecommerce -f parquet -R 10 \ + --increment "orders:created_at:1:days" + +# Avança o valor de "amount" em 100 a cada batch +docker compose run --rm cli generate -d finance -f csv -R 5 \ + --increment "transactions:amount:100:value" + +# Avança por hora +docker compose run --rm cli generate -d finance -f json -R 3 \ + --increment "transactions:created_at:1:hours" +``` + +O offset aplicado é `passo × (batch_index - 1)`, portanto o batch 1 tem offset zero. + +## Comportamento de escrita por formato + +| Formato | Batch 1 | Batches 2+ | +|---------|---------|-----------| +| CSV | Cria o arquivo | Append no mesmo arquivo | +| JSON flat | Cria o arquivo | Append no mesmo arquivo | +| Parquet | Cria `tabela.parquet` | Cria `tabela_YYYYMMDD_HHMMSS.parquet` | +| Avro | Cria `tabela.avro` | Cria `tabela_YYYYMMDD_HHMMSS.avro` | + +## Comportamento SQL + +- Batch 1: usa o valor de `--if-exists` +- Batches 2+: forçado para `append` + +## Interface visual + +O painel **Run Generator** da interface visual expõe o campo de intervalo de recorrência e número de batches. Os logs de execução aparecem em tempo real e o processo pode ser interrompido pelo botão de parada. + +## Saída no terminal + +``` +Recurrence mode: interval=30.0s | batches=infinite | Ctrl+C to stop +------------------------------------------------------------ + +[Batch 1] 2024-01-15 10:00:00 + [csv] output/ecommerce/orders/orders.csv + [csv] output/ecommerce/customers/customers.csv + Aguardando 30.0s... (Ctrl+C para parar) + +[Batch 2] 2024-01-15 10:00:30 + [csv/append] output/ecommerce/orders/orders.csv (+1000 rows) + [csv/append] output/ecommerce/customers/customers.csv (+500 rows) + Aguardando 30.0s... (Ctrl+C para parar) +``` diff --git a/docs/avancado/seed.md b/docs/avancado/seed.md new file mode 100644 index 0000000..97467ff --- /dev/null +++ b/docs/avancado/seed.md @@ -0,0 +1,38 @@ +# Reprodutibilidade com Seed + +O Dataforge permite gerar o mesmo dataset em execuções distintas usando a flag `--seed`. + +## Como usar + +```bash +docker compose run --rm cli generate -d finance --seed 42 -f csv -o /app/output/run1 +docker compose run --rm cli generate -d finance --seed 42 -f csv -o /app/output/run2 +# Os arquivos em run1/ e run2/ serão idênticos +``` + +## Como funciona + +O seed é passado para o `DatasetGenerator`, que inicializa o gerador do Faker com esse valor. Toda a geração aleatória (valores de colunas, seleção de FKs, valores nulos) usa esse estado pseudoaleatório fixo. + +## Seed em modo recorrente + +Em modo recorrente com `--seed`, cada batch recebe um seed diferente mas determinístico: + +```python +batch_seed = seed + batch_index - 1 +``` + +Portanto, `--seed 100 -R 30 --count 5` produz os batches com seeds 100, 101, 102, 103, 104. Executar o mesmo comando em outro momento gera exatamente os mesmos cinco batches na mesma ordem. + +## Casos de uso + +- **Testes automatizados**: garantir que os fixtures gerados são sempre os mesmos +- **Comparação de pipelines**: validar que dois pipelines produzem o mesmo resultado com os mesmos dados de entrada +- **Debugging reproduzível**: reproduzir exatamente um conjunto de dados que causou um bug + +## Sem seed + +Sem `--seed`, a geração usa entropia aleatória do sistema operacional. Cada execução produz um dataset diferente. + +!!! warning "Reprodutibilidade parcial" + O seed controla a geração de valores pelo Faker e Pandas. A ordem das linhas dentro de cada tabela também é determinística com seed. Porém, se a versão do Faker ou do Python mudar, os mesmos seeds podem produzir valores diferentes. diff --git a/docs/cli/exemplos.md b/docs/cli/exemplos.md new file mode 100644 index 0000000..84fef51 --- /dev/null +++ b/docs/cli/exemplos.md @@ -0,0 +1,207 @@ +# CLI — Exemplos Práticos + +Todos os exemplos usam `docker compose run --rm cli`. Para instalação sem Docker, substitua por `dataset-gen`. + +## Explorando domínios + +```bash +# Listar todos os domínios disponíveis +docker compose run --rm cli list-domains + +# Ver estrutura completa do domínio ecommerce +docker compose run --rm cli schema-info ecommerce + +# Ver estrutura do domínio hr +docker compose run --rm cli schema-info hr +``` + +## Geração básica + +```bash +# Gerar o domínio ecommerce em CSV (padrão) +docker compose run --rm cli generate -d ecommerce + +# Gerar 500 linhas por tabela em CSV +docker compose run --rm cli generate -d ecommerce -r 500 + +# Gerar em múltiplos formatos +docker compose run --rm cli generate -d ecommerce -r 500 -f csv -f parquet + +# Gerar apenas tabelas específicas +docker compose run --rm cli generate -d hr -t employees -t departments -f csv +``` + +## Schema YAML customizado + +```bash +# Usar um schema YAML salvo em src/dataforge/schemas/ +docker compose run --rm cli generate -d custom \ + -c /app/src/dataforge/schemas/transacoes.yaml \ + -f csv + +# Sobrescrever apenas o número de linhas de um domínio existente +docker compose run --rm cli generate -d ecommerce \ + -c /app/src/dataforge/schemas/meu_override.yaml \ + -f parquet +``` + +## Formatos de saída + +```bash +# CSV (padrão) +docker compose run --rm cli generate -d finance -f csv + +# JSON flat (NDJSON — uma linha por registro) +docker compose run --rm cli generate -d finance -f json + +# JSON nested (filhos aninhados dentro dos pais) +docker compose run --rm cli generate -d finance -f json --json-mode nested + +# Parquet com compressão Snappy +docker compose run --rm cli generate -d finance -f parquet + +# Avro com schema inferido +docker compose run --rm cli generate -d finance -f avro + +# Todos os formatos na mesma execução +docker compose run --rm cli generate -d ecommerce -f csv -f json -f parquet -f avro +``` + +## Filtro de colunas + +```bash +# Gerar apenas colunas específicas da tabela orders +docker compose run --rm cli generate -d ecommerce \ + --columns "orders:id,customer_id,status,total" \ + -f csv + +# Filtrar múltiplas tabelas +docker compose run --rm cli generate -d hr \ + --columns "employees:id,name,department_id" \ + --columns "departments:id,name" \ + -f csv +``` + +## Particionamento Hive-style + +```bash +# Particionar todas as tabelas pela coluna "created_at" +docker compose run --rm cli generate -d ecommerce -f parquet \ + --partition-by created_at + +# Particionar tabela específica pela coluna "status" +docker compose run --rm cli generate -d ecommerce -f parquet \ + --partition-by "orders:status" + +# Múltiplas regras de particionamento +docker compose run --rm cli generate -d ecommerce -f parquet \ + --partition-by "orders:status" \ + --partition-by "order_items:order_id" +``` + +Layout de saída com particionamento: + +``` +output/ +└── ecommerce/ + └── orders/ + ├── status=pending/ + │ └── orders.parquet + ├── status=processing/ + │ └── orders.parquet + └── status=delivered/ + └── orders.parquet +``` + +## Upload para nuvem + +```bash +# Google Cloud Storage +docker compose run --rm cli generate -d ecommerce -f parquet \ + --upload gcs \ + --bucket meu-bucket \ + --prefix raw/ecommerce/ \ + --credentials /app/credentials/service-account.json + +# Amazon S3 (credenciais via variáveis de ambiente) +docker compose run --rm \ + -e AWS_ACCESS_KEY_ID=AKIA... \ + -e AWS_SECRET_ACCESS_KEY=... \ + cli generate -d hr -f csv \ + --upload s3 \ + --bucket meu-bucket \ + --prefix data/hr/ + +# Azure Blob Storage +docker compose run --rm \ + -e AZURE_STORAGE_CONNECTION_STRING="DefaultEndpointsProtocol=https;..." \ + cli generate -d finance -f parquet \ + --upload azure \ + --bucket meu-container \ + --prefix datasets/finance/ +``` + +## Carga em banco SQL + +```bash +# SQLite — arquivo criado em output/ no host +docker compose run --rm cli generate -d ecommerce -r 500 \ + --db-url sqlite:////app/output/ecommerce.db + +# PostgreSQL +docker compose run --rm cli generate -d hr -r 1000 \ + --db-url "postgresql+psycopg2://user:pass@host:5432/mydb" + +# Append em vez de substituir +docker compose run --rm cli generate -d finance \ + --db-url sqlite:////app/output/finance.db --if-exists append + +# Schema específico no PostgreSQL +docker compose run --rm cli generate -d hr \ + --db-url "postgresql+psycopg2://user:pass@host/db" \ + --db-schema staging + +# Arquivo + SQL na mesma execução +docker compose run --rm cli generate -d ecommerce -f csv -f parquet \ + --db-url sqlite:////app/output/ecommerce.db +``` + +## Reprodutibilidade com seed + +```bash +# Gerar o mesmo dataset em execuções diferentes +docker compose run --rm cli generate -d finance --seed 42 -f csv -o /app/output/run1 +docker compose run --rm cli generate -d finance --seed 42 -f csv -o /app/output/run2 +# run1/ e run2/ serão idênticos +``` + +## Modo recorrente + +```bash +# Gerar um batch a cada 30 segundos indefinidamente (Ctrl+C para parar) +docker compose run --rm cli generate -d finance -f json -R 30 + +# Gerar exatamente 10 batches com intervalo de 60 segundos +docker compose run --rm cli generate -d ecommerce -f parquet -R 60 --count 10 + +# Modo recorrente com incremento de data (simula streaming temporal) +docker compose run --rm cli generate -d ecommerce -f parquet -R 10 \ + --increment "orders:created_at:1:days" +``` + +## Combinando opções + +```bash +# Pipeline completo: gerar, salvar em CSV + Parquet, fazer upload para GCS e carregar no PostgreSQL +docker compose run --rm cli generate -d ecommerce \ + -r 1000 \ + -f csv -f parquet \ + --seed 2024 \ + --partition-by "orders:status" \ + --upload gcs \ + --bucket meu-bucket \ + --prefix raw/ecommerce/ \ + --credentials /app/credentials/sa.json \ + --db-url "postgresql+psycopg2://user:pass@host:5432/db" \ + --db-schema staging +``` diff --git a/docs/cli/referencia.md b/docs/cli/referencia.md new file mode 100644 index 0000000..b77b463 --- /dev/null +++ b/docs/cli/referencia.md @@ -0,0 +1,101 @@ +# CLI — Referência Completa + +O CLI do Dataforge é o comando `dataset-gen`. Via Docker, ele é acessado com `docker compose run --rm cli`. + +## Comandos disponíveis + +| Comando | Descrição | +|---------|-----------| +| `generate` | Gera datasets sintéticos | +| `list-domains` | Lista os schemas YAML disponíveis em `src/dataforge/schemas/` | +| `schema-info ` | Exibe tabelas, colunas e FKs de um domínio | + +## `generate` + +Gera datasets e opcionalmente escreve em arquivos, faz upload para nuvem ou carrega em banco SQL. + +### Flags + +| Flag | Atalho | Padrão | Tipo | Descrição | +|------|--------|--------|------|-----------| +| `--domain` | `-d` | — | string | Domínio: `ecommerce`, `hr`, `finance` ou `custom` | +| `--config` | `-c` | — | path | Caminho para YAML (obrigatório quando `--domain custom`) | +| `--rows` | `-r` | por tabela | int | Número de linhas por tabela (sobrescreve o padrão do domínio) | +| `--tables` | `-t` | todas | string | Tabelas a incluir (repetível) | +| `--columns` | | todas | string | `"tabela:col1,col2"` — filtro de colunas por tabela (repetível) | +| `--format` | `-f` | `csv` | choice | Formato de saída: `csv`, `json`, `parquet` ou `avro` (repetível) | +| `--output` | `-o` | `./output` | path | Diretório de saída dentro do container | +| `--json-mode` | | `flat` | choice | Modo JSON: `flat` (NDJSON) ou `nested` | +| `--seed` | | — | int | Seed para geração reproduzível | +| `--partition-by` | | — | string | Particionamento Hive-style: `coluna` ou `tabela:coluna` (repetível) | +| `--upload` | | — | choice | Destino de upload: `gcs`, `s3` ou `azure` | +| `--bucket` | | — | string | Nome do bucket/container de destino | +| `--prefix` | | `datasets/` | string | Prefixo/pasta dentro do bucket | +| `--credentials` | | — | path | Caminho para arquivo de credenciais (dentro do container) | +| `--db-url` | | — | string | Connection string SQLAlchemy para carga SQL | +| `--if-exists` | | `replace` | choice | O que fazer se a tabela SQL já existir: `replace`, `append` ou `fail` | +| `--db-schema` | | — | string | Schema do banco de destino | +| `--recurrence` | `-R` | — | float | Intervalo em segundos entre batches (modo contínuo) | +| `--count` | | `0` | int | Número de batches no modo recorrente (`0` = infinito) | +| `--increment` | | — | string | Desloca valores de coluna a cada batch: `tabela:coluna:passo[:unidade]` (repetível) | + +### Unidades para `--increment` + +| Unidade | Descrição | +|---------|-----------| +| `days` | Dias (padrão) | +| `hours` | Horas | +| `weeks` | Semanas | +| `months` | Meses (aproximado: 30,44 dias) | +| `years` | Anos (aproximado: 365,25 dias) | +| `value` | Valor numérico puro | + +## `list-domains` + +Lista os arquivos `.yaml` disponíveis em `src/dataforge/schemas/`: + +```bash +docker compose run --rm cli list-domains +``` + +Saída de exemplo: + +``` + acoes + crm + ecommerce + estoque + finance + frota + manutencao + rh +``` + +## `schema-info` + +Exibe a estrutura de um domínio com tabelas, colunas, tipos e FKs: + +```bash +docker compose run --rm cli schema-info ecommerce +``` + +Saída de exemplo: + +``` +Domain: ecommerce + + Table: categories (default rows: 20) + id: int_seq [PK] + name: str + + Table: customers (default rows: 500) + id: int_seq [PK] + name: name + email: email + ... + + Table: products (default rows: 200) + id: int_seq [PK] + category_id: int [FK->categories.id] + ... +``` diff --git a/docs/frontend/diagrama.md b/docs/frontend/diagrama.md new file mode 100644 index 0000000..b7506e9 --- /dev/null +++ b/docs/frontend/diagrama.md @@ -0,0 +1,49 @@ +# Diagrama Relacional + +O canvas central da interface exibe as tabelas do schema como nós interativos conectados por setas que representam as chaves estrangeiras. + +## Tecnologia + +O diagrama é construído com **ReactFlow**. Cada tabela é um nó do tipo `tableNode` (componente customizado) e cada FK é uma aresta animada. + +## Interações no canvas + +| Ação | Como fazer | +|------|------------| +| Selecionar uma tabela | Clique no nó da tabela | +| Mover uma tabela | Arraste o nó pelo canvas | +| Zoom | Scroll do mouse ou pinch no trackpad | +| Pan (mover o canvas) | Arraste o fundo | +| Fit all | Botão de ajuste nos controles do ReactFlow | + +## Aresta de FK + +Quando uma coluna é marcada como chave estrangeira com tabela e coluna de referência válidas, uma aresta animada é desenhada automaticamente do nó de origem até o nó de destino. + +- Cor: rosa (`#ec4899`) +- Largura: 2px +- Estilo: animado (tracejado móvel) +- Marcador: seta fechada na ponta + +## Auto Layout + +O botão **Auto Layout** reorganiza os nós do canvas usando o algoritmo **Dagre** em modo `LR` (esquerda para direita). + +Parâmetros utilizados: + +| Parâmetro | Valor | +|-----------|-------| +| `rankdir` | `LR` | +| `ranksep` | 250 | +| `nodesep` | 150 | +| Largura do nó | 300px | +| Altura do nó | 100 + (número de colunas × 35) px | + +!!! tip "Quando usar Auto Layout" + Use o Auto Layout após importar um schema YAML com muitas tabelas. A disposição automática facilita a leitura das relações entre tabelas. + +## Persistência de posições + +As posições dos nós são armazenadas no estado interno do React (campo `position` em cada objeto `Table`). Ao exportar o schema como YAML, as posições não são incluídas — o YAML contém apenas a estrutura lógica. + +Ao importar um YAML, as tabelas recebem posições iniciais calculadas como `{ x: 50 + index * 300, y: 100 + (index % 2) * 200 }`. diff --git a/docs/frontend/faker.md b/docs/frontend/faker.md new file mode 100644 index 0000000..57720a7 --- /dev/null +++ b/docs/frontend/faker.md @@ -0,0 +1,219 @@ +# Catálogo Faker + +O **Faker Browser** é um painel de busca visual com todos os métodos do Faker disponíveis na interface. Ele permite explorar e aplicar provedores de dados diretamente às colunas do schema. + +## Acessando + +Clique no botão **Faker Browser** na barra de ações. O painel lateral é aberto com campo de busca e listagem por categoria. + +## Como usar + +1. Abra o Faker Browser. +2. Use o campo de busca para filtrar métodos pelo nome. +3. Clique em um método para aplicá-lo à coluna selecionada no editor. + +O método selecionado é gravado no campo `faker_provider` da coluna. No YAML gerado, aparece como: + +```yaml +columns: + telefone: + dtype: str + faker_provider: phone_number +``` + +## Categorias disponíveis + +### Person + +| Método | Exemplo | +|--------|---------| +| `name` | John Smith | +| `first_name` | John | +| `last_name` | Smith | +| `name_female` | Jane Doe | +| `name_male` | James Doe | +| `prefix` | Mr. | +| `suffix` | Jr. | + +### Internet + +| Método | Exemplo | +|--------|---------| +| `email` | user@example.com | +| `safe_email` | user@example.org | +| `free_email` | user@gmail.com | +| `user_name` | john_doe42 | +| `domain_name` | example.com | +| `url` | https://example.com/path | +| `slug` | my-blog-post | +| `ipv4` | 192.168.1.1 | +| `ipv6` | 2001:db8::1 | +| `mac_address` | 00:1A:2B:3C:4D:5E | +| `http_method` | GET | +| `http_status_code` | 200 | +| `password` | xK9#mP2! | +| `hostname` | srv-01.example.com | +| `tld` | .com | + +### Address + +| Método | Exemplo | +|--------|---------| +| `address` | 123 Main St, Springfield | +| `street_address` | 123 Main St | +| `street_name` | Main Street | +| `city` | Springfield | +| `postcode` | 12345 | +| `country` | United States | +| `country_code` | US | +| `building_number` | 42 | + +### Phone + +| Método | Exemplo | +|--------|---------| +| `phone_number` | +1-555-123-4567 | +| `msisdn` | 14155552671 | +| `country_calling_code` | +1 | + +### Company + +| Método | Exemplo | +|--------|---------| +| `company` | Acme Corp | +| `company_suffix` | LLC | +| `catch_phrase` | Seamless synergy solutions | +| `bs` | leverage core competencies | +| `job` | Software Engineer | + +### Finance + +| Método | Exemplo | +|--------|---------| +| `iban` | GB82WEST12345698765432 | +| `bban` | 20-00-55 73553400 | +| `bank` | Royal Bank | +| `swift` | BOFAUS3N | +| `currency_code` | USD | +| `currency_name` | US Dollar | +| `currency_symbol` | $ | +| `cryptocurrency_code` | BTC | +| `pricetag` | $12.99 | + +### Date / Time + +| Método | Exemplo | +|--------|---------| +| `date` | 2023-04-15 | +| `date_of_birth` | 1990-07-22 | +| `past_date` | 2022-01-10 | +| `future_date` | 2025-09-30 | +| `iso8601` | 2023-04-15T10:30:00 | +| `time` | 14:30:00 | +| `year` | 2023 | +| `month` | 04 | +| `month_name` | April | +| `day_of_week` | Monday | +| `timezone` | America/New_York | +| `unix_time` | 1681554600 | + +### Text + +| Método | Exemplo | +|--------|---------| +| `word` | example | +| `words` | foo bar baz | +| `sentence` | The quick brown fox. | +| `paragraph` | Lorem ipsum dolor sit amet... | +| `text` | Lorem ipsum... | + +### Identity + +| Método | Exemplo | +|--------|---------| +| `ssn` | 123-45-6789 | +| `uuid4` | 550e8400-e29b-41d4-a716-... | +| `md5` | 5d41402abc4b2a76b9719d... | +| `sha1` | aaf4c61ddcc5e8a2d... | +| `sha256` | 2cf24dba5fb0a30e26e8... | + +### Color + +| Método | Exemplo | +|--------|---------| +| `color_name` | MediumAquaMarine | +| `hex_color` | #a2b3c4 | +| `rgb_color` | 162,179,196 | +| `safe_color_name` | blue | + +### File + +| Método | Exemplo | +|--------|---------| +| `file_name` | report.pdf | +| `file_extension` | pdf | +| `file_path` | /home/user/docs/report.pdf | +| `mime_type` | application/pdf | + +### Geo + +| Método | Exemplo | +|--------|---------| +| `latitude` | 48.8566 | +| `longitude` | 2.3522 | +| `coordinate` | 48.8566 | +| `latlng` | (48.8566, 2.3522) | + +### Automotive + +| Método | Exemplo | +|--------|---------| +| `license_plate` | ABC-1234 | +| `vin` | 1HGCM82633A123456 | + +### Barcode + +| Método | Exemplo | +|--------|---------| +| `ean13` | 5901234123457 | +| `ean8` | 96385074 | +| `isbn13` | 978-3-16-148410-0 | +| `isbn10` | 0-306-40615-2 | + +### Credit Card + +| Método | Exemplo | +|--------|---------| +| `credit_card_number` | 4111111111111111 | +| `credit_card_provider` | Visa | +| `credit_card_expire` | 12/26 | +| `credit_card_security_code` | 123 | + +### User Agent + +| Método | Exemplo | +|--------|---------| +| `user_agent` | Mozilla/5.0 (Windows...) | +| `chrome` | Chrome/114.0... | +| `firefox` | Firefox/115.0... | +| `safari` | Safari/537.36... | + +## Usando faker_provider diretamente no YAML + +Qualquer método do Faker pode ser usado diretamente no YAML do schema, sem precisar da interface: + +```yaml +columns: + cep: + dtype: str + faker_provider: postcode + iban: + dtype: str + faker_provider: iban + placa: + dtype: str + faker_provider: license_plate +``` + +!!! note "Prioridade" + Quando `faker_provider` está definido, ele tem prioridade sobre `dtype`. O campo `dtype` ainda é necessário para compatibilidade com o parser, mas o valor gerado vem do método Faker especificado. diff --git a/docs/frontend/ia.md b/docs/frontend/ia.md new file mode 100644 index 0000000..7dd3042 --- /dev/null +++ b/docs/frontend/ia.md @@ -0,0 +1,111 @@ +# Geração com IA + +O recurso **AI Generate** permite descrever um domínio de negócio em linguagem natural e gerar automaticamente o schema YAML com tabelas, colunas, tipos de dados e relações. + +## Fluxo completo + +```mermaid +flowchart LR + A[Usuário descreve o domínio] --> B[Frontend envia POST /api/ai-generate] + B --> C[Backend chama a API da IA escolhida] + C --> D[IA retorna YAML] + D --> E[Backend valida e devolve o YAML] + E --> F[Frontend parse o YAML com SchemaReader] + F --> G[Canvas exibe as tabelas e relações] +``` + +## Acessando o recurso + +Clique no botão **AI Generate** na barra de ações. O modal de geração será aberto. + +## Providers disponíveis + +| Provider | Chave de API | Modelo padrão | Gratuito? | +|----------|-------------|--------------|-----------| +| Anthropic | `sk-ant-api03-…` | `claude-3-5-haiku-20241022` | Não | +| OpenAI | `sk-…` | `gpt-4o-mini` | Não | +| Google | `AIza…` | `gemini-2.0-flash` | Sim (free tier) | +| Groq | `gsk_…` | `llama-3.3-70b-versatile` | Sim (free tier) | +| Mistral | `xxxxxxxx…` | `mistral-small-latest` | Não | +| Together AI | `xxxxxxxx…` | `meta-llama/Llama-3.3-70B-Instruct-Turbo` | Não | +| Ollama | (sem chave) | `llama3.2` | Sim (local) | + +!!! tip "Opções gratuitas recomendadas" + **Google Gemini** (`gemini-2.0-flash`) e **Groq** (`llama-3.3-70b-versatile`) oferecem free tier generoso e são as opções recomendadas para quem não quer configurar cobrança. + + Para uso totalmente offline, o **Ollama** não requer chave de API — basta ter o serviço rodando localmente em `http://localhost:11434`. + +## Configurando o provider + +1. Selecione o provider desejado clicando nos botões coloridos no modal. +2. Informe a chave de API no campo correspondente (exceto para Ollama). +3. Opcionalmente, clique em **Load Models** para buscar a lista de modelos disponíveis na sua conta e selecionar um. +4. Se não selecionar um modelo, o backend usará o modelo padrão do provider. + +As chaves e modelos são persistidos no `localStorage` do navegador com a chave `dataforge_ai_config`. + +## Escrevendo o prompt + +O campo de prompt aceita texto livre em qualquer idioma. Seja específico sobre: + +- Nomes das tabelas desejadas +- Tipos de dados esperados por coluna +- Relações entre tabelas (FK) +- Intervalos de valores (ex: preço entre 10 e 2000) +- Valores fixos possíveis (ex: status: pending/processing/shipped) + +### Exemplo de prompt incluído na interface + +``` +E-commerce with 4 tables: +- customers: full name, email, phone, city, country, registration date +- products: name, category (Electronics/Clothing/Books/Home/Sports), price (10–2000), stock quantity +- orders: linked to customer, order date (last 2 years), status (pending/processing/shipped/delivered/cancelled), total amount +- order_items: linked to order and product, quantity (1–10), unit price +``` + +## Endpoints utilizados + +| Endpoint | Método | Descrição | +|----------|--------|-----------| +| `/api/ai-generate` | POST | Envia o prompt e o provider; recebe o YAML gerado | +| `/api/ai-models` | POST | Busca a lista de modelos disponíveis para o provider/chave | + +### Payload de `/api/ai-generate` + +```json +{ + "provider": "google", + "apiKey": "AIza...", + "model": "gemini-2.0-flash", + "prompt": "..." +} +``` + +### Resposta de `/api/ai-generate` + +```json +{ + "yaml": "domain: ecommerce\ntables:\n ..." +} +``` + +Em caso de erro: + +```json +{ + "error": "mensagem de erro", + "yaml": "yaml parcial (se disponível)" +} +``` + +## Tratamento de erros comuns + +| Erro | Causa | Solução | +|------|-------|---------| +| `API key is required` | Nenhuma chave foi informada | Informe a chave do provider | +| `Map keys must be unique` | A IA gerou colunas duplicadas | Tente gerar novamente ou edite o YAML | +| `AI generation failed` | Erro na API externa | Verifique a chave e o modelo escolhido | + +!!! warning "Chaves de API" + As chaves de API são armazenadas apenas no `localStorage` do seu navegador. Elas são enviadas ao backend somente no momento da chamada e não são persistidas no servidor. diff --git a/docs/frontend/visao-geral.md b/docs/frontend/visao-geral.md new file mode 100644 index 0000000..dcc4074 --- /dev/null +++ b/docs/frontend/visao-geral.md @@ -0,0 +1,59 @@ +# Interface Visual — Visão Geral + +A interface visual roda em `http://localhost:5173` e é a forma principal de uso do Dataforge. Ela permite criar schemas, configurar a geração e executar o CLI sem sair do navegador. + +## Tecnologias + +- **React** com TypeScript +- **ReactFlow** para o canvas do diagrama relacional +- **Dagre** para o algoritmo de layout automático +- **Vite** como servidor de desenvolvimento + +## Funcionalidades principais + +| Funcionalidade | Descrição | +|---------------|-----------| +| Editor de tabelas | Cria, edita e remove tabelas com nome e quantidade de linhas | +| Editor de colunas | Define `dtype`, `min`/`max`, `nullable`, `choices`, `faker_provider` e chaves | +| Diagrama relacional | Visualiza tabelas e setas de FK em tempo real via ReactFlow | +| Auto Layout | Reorganiza o diagrama automaticamente (algoritmo Dagre, direção esquerda-direita) | +| Faker Browser | Catálogo visual de ~100 métodos do Faker com exemplo de valor gerado | +| AI Generate | Gera schema a partir de descrição em linguagem natural | +| Preview YAML | Exibe o YAML do schema atual sem salvar | +| Save as Default | Salva o schema no servidor em `src/dataforge/schemas/` | +| Import YAML | Carrega um arquivo `.yaml` do computador para o canvas | +| Run Generator | Executa o CLI diretamente da interface com configuração visual completa | + +## Painel lateral (editor) + +Ao clicar em uma tabela no canvas, o painel lateral é aberto. Nele é possível: + +- Alterar o nome da tabela +- Definir o número de linhas (`rows`) +- Adicionar, editar e remover colunas +- Definir `dtype`, `min`, `max`, `nullable`, `choices` e `faker_provider` por coluna +- Marcar colunas como chave primária (`PK`) +- Configurar chaves estrangeiras: selecionar tabela e coluna de referência + +## Seletor de domínio + +O seletor no topo da interface carrega schemas salvos no servidor. Ao selecionar um domínio (diferente de `custom`), o canvas é preenchido com as tabelas desse schema. + +Os schemas disponíveis são carregados via `GET /api/schemas` e seus conteúdos via `GET /api/schemas/{nome}`. + +## Barra de ações + +| Botão | Ação | +|-------|------| +| Add Table | Adiciona nova tabela ao canvas | +| Auto Layout | Reorganiza o diagrama (Dagre LR) | +| AI Generate | Abre o modal de geração com IA | +| Faker Browser | Abre o catálogo de métodos Faker | +| Preview YAML | Gera e exibe o YAML do schema atual | +| Import YAML | Abre o seletor de arquivo para importar um `.yaml` | +| Save as Default | Salva o schema atual no servidor | +| Run Generator | Abre o modal de execução do CLI | + +## Conexões salvas de banco + +As conexões de banco de dados são salvas no `localStorage` do navegador com a chave `dataforge_saved_connections`. Cada conexão armazena nome, formulário de campos e URL de conexão avançada. diff --git a/docs/frontend/yaml.md b/docs/frontend/yaml.md new file mode 100644 index 0000000..1dd00c3 --- /dev/null +++ b/docs/frontend/yaml.md @@ -0,0 +1,79 @@ +# Exportar e Importar YAML + +A interface permite exportar o schema atual como YAML e importar um arquivo YAML existente para o canvas. + +## Exportar YAML (Preview) + +O botão **Preview YAML** gera o YAML do schema atual e exibe no painel lateral. + +O YAML é gerado pela classe `SchemaWriter` (`src/dataforge/frontend/src/services/SchemaWriter.ts`): + +- Lê o estado interno do React (domínio, tabelas, colunas) +- Serializa para YAML usando a biblioteca `yaml` +- O resultado é exibido no painel e pode ser copiado ou baixado + +### Estrutura gerada + +```yaml +domain: meu_dominio +tables: + tabela1: + rows: 1000 + columns: + id: + dtype: int_seq + primary_key: true + nome: + dtype: name + email: + dtype: email + nullable: 0.1 +``` + +### Baixar o arquivo + +Clique em **Download YAML** (disponível após o preview) para baixar o arquivo como `{dominio}_schema.yaml`. O download é gerado via `Blob` no navegador, sem chamada ao servidor. + +## Salvar no servidor (Save as Default) + +O botão **Save as Default** salva o schema no servidor em `src/dataforge/schemas/`. Isso permite: + +- Usar o schema via CLI com `-d {nome}` +- Carregar o schema novamente na interface pelo seletor de domínio + +### Como usar + +1. Clique em **Save as Default**. +2. Informe um nome para o schema (apenas letras minúsculas, números, hífens e underscores). +3. Confirme. O schema é enviado via `PUT /api/schemas/{nome}`. + +Após salvar, o schema aparece no seletor de domínio. + +### Deletar um schema salvo + +Para remover um schema salvo no servidor, use o botão de exclusão disponível no seletor de domínio. A operação envia `DELETE /api/schemas/{nome}`. + +!!! warning "Schemas de domínio padrão" + Os schemas dos domínios padrão (`ecommerce`, `hr`, `finance`) são lidos de `src/dataforge/schemas/`. Salvar um schema com o mesmo nome sobrescreve o arquivo original. + +## Importar YAML + +O botão **Import YAML** abre o seletor de arquivo do navegador. Formatos aceitos: `.yaml` e `.yml`. + +O arquivo é lido pela classe `SchemaReader` (`src/dataforge/frontend/src/services/SchemaReader.ts`): + +1. O conteúdo do arquivo é lido como texto via `FileReader`. +2. O texto YAML é parseado com a biblioteca `yaml`. +3. As tabelas e colunas são convertidas para o estado interno do React. +4. O canvas é atualizado com as tabelas importadas. + +### Erros tratados na importação + +| Erro | Causa | Mensagem exibida | +|------|-------|-----------------| +| Chaves duplicadas | IA gerou duas colunas com o mesmo nome | Indica linha e nome da chave duplicada | +| YAML inválido | Sintaxe incorreta | Mensagem do parser YAML | +| Objeto inválido | Arquivo não contém objeto YAML válido | "Formato YAML inválido" | + +!!! tip "Compatibilidade" + O YAML importado deve seguir a mesma estrutura produzida pelo Dataforge. A importação é compatível com qualquer schema gerado pelo CLI ou pela interface. diff --git a/docs/index.md b/docs/index.md index a7016a6..7839267 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,54 +1,50 @@ # Dataforge -**Gerador de datasets sintéticos relacionais para engenharia de dados.** +Dataforge é uma ferramenta para geração de datasets sintéticos **relacionais** com integridade referencial garantida. Está disponível como interface visual no navegador e como linha de comando (CLI). -Dataforge cria conjuntos de dados fictícios com integridade referencial garantida — ideal para testar pipelines de dados, popular bancos de desenvolvimento e criar fixtures de teste sem depender de dados reais. +Casos de uso típicos: ---- +- Popular bancos de dados de desenvolvimento sem usar dados reais +- Criar fixtures para modelos dbt +- Testar pipelines de ingestão e transformação de dados +- Simular volumes de dados antes de ir para produção -## Por que Dataforge? +## Como funciona -| Problema | Solução | -|---|---| -| Dados reais têm PII e não podem ser compartilhados | Dados 100% sintéticos via [Faker](https://faker.readthedocs.io/) | -| Seed manual de banco é trabalhoso | Domínios prontos com um comando | -| Dados sem relação entre tabelas quebram pipelines | Integridade referencial por ordenação topológica | -| Formato fixo não serve para todos os destinos | CSV, JSON, Parquet, Avro — e upload direto para nuvem | +O Dataforge lê um **schema YAML** que descreve tabelas, colunas, tipos de dados e relações entre tabelas. A partir desse schema ele gera DataFrames Pandas e os escreve nos formatos e destinos escolhidos. ---- +A integridade referencial é garantida por dois mecanismos: -## Funcionalidades +1. **Ordenação topológica** — tabelas pai são geradas antes de tabelas filhas. +2. **Pool de PKs** — após gerar cada tabela, seus valores de chave primária ficam disponíveis; colunas com chave estrangeira amostram desse pool com reposição, permitindo múltiplos filhos por pai. -- **Domínios prontos** — ecommerce, RH, finanças e mais -- **Schemas customizados** em YAML -- **Interface visual** para editar schemas sem escrever YAML -- **Integridade referencial** — chaves estrangeiras sempre resolvidas -- **Múltiplos formatos** — CSV, JSON (flat/nested), Parquet, Avro -- **Upload direto** para GCS, S3, Azure Blob Storage -- **Carga em banco** — PostgreSQL, MySQL, SQL Server, SQLite -- **Modo recorrente** — geração contínua de batches incrementais -- **Reprodutibilidade** — seed para resultados idênticos +## Formas de uso ---- +| Modo | Acesso | Ideal para | +|------|--------|------------| +| Interface visual | `http://localhost:5173` | Criar schemas, explorar domínios, executar via GUI | +| CLI Docker | `docker compose run --rm cli generate ...` | Scripts, CI, automação | +| CLI direto (Poetry) | `dataset-gen generate ...` | Desenvolvimento local | -## Início Rápido +## Início rápido ```bash -# Subir a interface visual -docker compose up frontend - -# Gerar 1000 clientes do domínio ecommerce em CSV -docker compose run --rm cli generate -d ecommerce -t customers -r 1000 -f csv +git clone https://github.com/ckoliveiraa/DataForge +cd Dataforge +docker compose up --build -d ``` -Veja o [Guia de Instalação](getting-started/installation.md) e o [Quickstart](getting-started/quickstart.md) para o passo a passo completo. +Acesse `http://localhost:5173` após o build concluir. ---- +## Estrutura de pastas do host -## Arquitetura em uma Linha +Após subir os containers, três pastas ficam mapeadas entre o container e o host: -``` -Schema YAML → DatasetGenerator → [CSV | JSON | Parquet | Avro] → [Local | GCS | S3 | Azure | SQL] -``` +| Pasta | Uso | +|-------|-----| +| `output/` | Arquivos gerados pelo CLI aparecem aqui | +| `credentials/` | Arquivos de credenciais de nuvem (JSON do GCP, etc.) | +| `src/dataforge/schemas/` | Schemas YAML customizados — editáveis sem rebuild | -Cada camada é plugável via registros internos — veja [Arquitetura](development/architecture.md). +!!! note "Pré-requisitos" + Apenas **Docker** e **Docker Compose** instalados na máquina. Não é necessário instalar Python, Node.js ou qualquer outra dependência separadamente. diff --git a/docs/instalacao/docker.md b/docs/instalacao/docker.md new file mode 100644 index 0000000..4bf8dd6 --- /dev/null +++ b/docs/instalacao/docker.md @@ -0,0 +1,89 @@ +# Instalação com Docker + +Esta é a forma recomendada de usar o Dataforge. Requer apenas Docker e Docker Compose instalados. + +## Pré-requisitos + +- Docker Engine 24+ +- Docker Compose v2+ + +Nenhuma instalação de Python, Node.js ou dependências adicionais é necessária. + +## Clonando o repositório + +```bash +git clone https://github.com/ckoliveiraa/DataForge +cd Dataforge +``` + +## Subindo os serviços + +```bash +docker compose up --build -d +``` + +O build leva alguns minutos na primeira execução pois instala todas as dependências Python e Node.js dentro da imagem. + +Após o build, acesse a interface visual em: + +``` +http://localhost:5173 +``` + +## Estrutura do docker-compose.yml + +O arquivo `docker-compose.yml` define dois serviços: + +| Serviço | Container | Uso | +|---------|-----------|-----| +| `frontend` | `dataforge-frontend` | Interface visual + servidor de desenvolvimento Vite | +| `cli` | `dataforge-cli` | CLI standalone, acionado via `docker compose run` | + +O serviço `cli` usa o profile `cli` e só sobe quando chamado explicitamente com `docker compose run --rm cli`. + +## Volumes mapeados + +```yaml +volumes: + - ./src/dataforge/schemas:/app/src/dataforge/schemas # schemas editáveis + - ./credentials:/app/credentials # credenciais de nuvem + - ./output:/app/output # arquivos gerados + - ./src/dataforge:/app/src/dataforge # hot reload do código +``` + +!!! tip "Hot reload" + O código Python e o frontend têm hot reload ativo. Alterações em `src/dataforge/` são refletidas imediatamente sem rebuild da imagem. + +!!! warning "node_modules" + O volume `frontend_node_modules` protege o `node_modules` Linux gerado no build. Isso evita conflitos com binários nativos do Windows no host. + +## Parando os serviços + +```bash +docker compose down +``` + +## Recriando a imagem + +Necessário após atualizar dependências em `pyproject.toml` ou `package.json`: + +```bash +docker compose up --build -d +``` + +## Usando o CLI via Docker + +O CLI é acessado pelo serviço `cli`. Exemplos: + +```bash +# Listar domínios disponíveis +docker compose run --rm cli list-domains + +# Ver estrutura de um domínio +docker compose run --rm cli schema-info ecommerce + +# Gerar dataset +docker compose run --rm cli generate -d ecommerce -f csv +``` + +Os arquivos gerados aparecem em `output/` no host. diff --git a/docs/instalacao/poetry.md b/docs/instalacao/poetry.md new file mode 100644 index 0000000..35cc611 --- /dev/null +++ b/docs/instalacao/poetry.md @@ -0,0 +1,93 @@ +# Instalação sem Docker (Poetry) + +Para quem prefere instalar diretamente no ambiente local. Requer Python 3.12+ e Poetry. + +## Pré-requisitos + +- Python 3.12 ou superior +- [Poetry](https://python-poetry.org/docs/#installation) + +## Clonando e instalando + +```bash +git clone https://github.com/ckoliveiraa/DataForge +cd Dataforge +``` + +### Instalação base (CSV e JSON apenas) + +```bash +poetry install +``` + +### Instalação com todos os extras + +```bash +poetry install -E "parquet avro sql postgres gcp aws azure" +``` + +## Extras disponíveis + +| Extra | Funcionalidade | Pacote instalado | +|-------|----------------|-----------------| +| `parquet` | Formato Parquet com compressão Snappy | `pyarrow>=16.0` | +| `avro` | Formato Avro com schema inferido | `fastavro>=1.9` | +| `sql` | SQLite e bancos via SQLAlchemy | `sqlalchemy>=2.0` | +| `postgres` | PostgreSQL | `sqlalchemy>=2.0`, `psycopg2-binary>=2.9` | +| `mysql` | MySQL / MariaDB | `sqlalchemy>=2.0`, `pymysql>=1.1` | +| `mssql` | SQL Server | `sqlalchemy>=2.0`, `pyodbc>=5.0` | +| `gcp` | Google Cloud Storage | `google-cloud-storage>=2.0` | +| `aws` | Amazon S3 | `boto3>=1.34` | +| `azure` | Azure Blob Storage | `azure-storage-blob>=12.0` | + +## Usando o CLI + +Após instalar, o comando `dataset-gen` fica disponível no terminal: + +```bash +# Equivalente a docker compose run --rm cli list-domains +dataset-gen list-domains + +# Gerar dataset +dataset-gen generate -d ecommerce -f csv -o ./output +``` + +!!! note "Diferença de caminhos" + Sem Docker, use caminhos do host diretamente. O prefixo `/app/` não existe fora do container. + +## Dependências de desenvolvimento + +Para contribuir com o projeto: + +```bash +poetry install --with dev +``` + +Isso instala `ruff`, `pytest`, `pytest-cov` e `pre-commit`. + +### Tarefas disponíveis (taskipy) + +```bash +poetry run task lint # Verifica erros de estilo +poetry run task fmt # Formata o código +poetry run task test # Executa os testes +poetry run task ci # Lint + formato + testes com cobertura +``` + +## Interface visual sem Docker + +A interface visual requer Node.js 20+. Para rodar em modo de desenvolvimento: + +```bash +cd src/dataforge/frontend +npm install +npm run dev +``` + +Acesse `http://localhost:5173`. + +!!! warning "Variável PYTHONPATH" + Defina `PYTHONPATH` para que o servidor Vite encontre o código Python: + ```bash + export PYTHONPATH=/caminho/para/Dataforge/src + ``` diff --git a/docs/saida/cloud.md b/docs/saida/cloud.md new file mode 100644 index 0000000..3bd6ef1 --- /dev/null +++ b/docs/saida/cloud.md @@ -0,0 +1,100 @@ +# Upload em Nuvem + +O Dataforge pode fazer upload dos arquivos gerados diretamente para Google Cloud Storage, Amazon S3 ou Azure Blob Storage. + +!!! note "Arquivos locais preservados" + Os arquivos sempre são escritos em `output/` localmente, mesmo quando o upload está ativo. O upload é uma cópia adicional para a nuvem. + +## Google Cloud Storage + +### Requisitos + +Extra `gcp` instalado (incluído na imagem Docker padrão). + +### Autenticação + +Coloque o arquivo de service account JSON na pasta `credentials/` do projeto. Ela é montada no container em `/app/credentials/`. + +### Comando + +```bash +docker compose run --rm cli generate -d ecommerce -f parquet \ + --upload gcs \ + --bucket meu-bucket \ + --prefix raw/ecommerce/ \ + --credentials /app/credentials/service-account.json +``` + +### Parâmetros + +| Flag | Valor de exemplo | Descrição | +|------|-----------------|-----------| +| `--upload` | `gcs` | Ativa o upload para GCS | +| `--bucket` | `meu-bucket` | Nome do bucket | +| `--prefix` | `raw/ecommerce/` | Prefixo/pasta dentro do bucket | +| `--credentials` | `/app/credentials/sa.json` | Caminho do arquivo de credenciais | + +## Amazon S3 + +### Requisitos + +Extra `aws` instalado (incluído na imagem Docker padrão). + +### Autenticação + +Passe as credenciais via variáveis de ambiente: + +```bash +docker compose run --rm \ + -e AWS_ACCESS_KEY_ID=AKIA... \ + -e AWS_SECRET_ACCESS_KEY=... \ + cli generate -d hr -f csv \ + --upload s3 \ + --bucket meu-bucket \ + --prefix data/hr/ +``` + +Ou adicione as variáveis no serviço `cli` do `docker-compose.yml`. + +## Azure Blob Storage + +### Requisitos + +Extra `azure` instalado (incluído na imagem Docker padrão). + +### Autenticação + +Passe a connection string via variável de ambiente: + +```bash +docker compose run --rm \ + -e AZURE_STORAGE_CONNECTION_STRING="DefaultEndpointsProtocol=https;AccountName=...;AccountKey=...;EndpointSuffix=core.windows.net" \ + cli generate -d finance -f parquet \ + --upload azure \ + --bucket meu-container \ + --prefix datasets/finance/ +``` + +## Caminho de destino + +O caminho completo de cada arquivo no bucket é construído como: + +``` +{prefix}/{dominio}/{tabela}/{arquivo} +``` + +Exemplo com `--prefix raw/ecommerce/` e domínio `ecommerce`: + +``` +raw/ecommerce/ecommerce/orders/orders.parquet +``` + +Com particionamento `--partition-by "orders:status"`: + +``` +raw/ecommerce/ecommerce/orders/status=delivered/orders.parquet +``` + +## Upload com modo recorrente + +Em modo recorrente, cada batch faz upload dos arquivos gerados naquele batch. Para Parquet e Avro, cada batch gera arquivos com timestamp — portanto cada batch resulta em novos objetos no bucket. Para CSV e JSON (append), o arquivo acumulado é re-enviado a cada batch. diff --git a/docs/saida/formatos.md b/docs/saida/formatos.md new file mode 100644 index 0000000..24670b2 --- /dev/null +++ b/docs/saida/formatos.md @@ -0,0 +1,92 @@ +# Formatos de Saída + +O Dataforge suporta quatro formatos de saída. Use a flag `-f` / `--format` para especificar. A flag é repetível para gerar múltiplos formatos na mesma execução. + +## Formatos disponíveis + +| Flag | Formato | Extensão | Notas | +|------|---------|----------|-------| +| `-f csv` | CSV UTF-8 | `.csv` | Padrão; compatível universalmente | +| `-f json` | JSON linha a linha (NDJSON) | `.json` | Use `--json-mode nested` para aninhar filhos nos pais | +| `-f parquet` | Parquet com compressão Snappy | `.parquet` | Requer extra `parquet` (`pyarrow>=16`) | +| `-f avro` | Avro com schema inferido | `.avro` | Requer extra `avro` (`fastavro>=1.9`) | + +## Múltiplos formatos + +```bash +docker compose run --rm cli generate -d ecommerce -f csv -f json -f parquet +``` + +## Layout de saída + +Os arquivos são organizados por domínio e tabela: + +``` +output/ +└── ecommerce/ + ├── categories/ + │ └── categories.csv + ├── customers/ + │ └── customers.csv + ├── products/ + │ └── products.csv + ├── orders/ + │ └── orders.csv + └── order_items/ + └── order_items.csv +``` + +## CSV + +- Codificação: UTF-8 +- Sem índice (coluna de índice Pandas não incluída) +- Separador: vírgula +- Modo recorrente: CSV é **acumulado** (append) nos batches seguintes ao primeiro + +## JSON + +Dois modos disponíveis via `--json-mode`: + +### flat (padrão) + +Cada registro em uma linha separada (NDJSON — newline-delimited JSON): + +```json +{"id": 1, "name": "Alice", "email": "alice@example.com"} +{"id": 2, "name": "Bob", "email": "bob@example.com"} +``` + +### nested + +Registros aninhados: filhos são incorporados dentro dos pais. Usa o `JsonWriter` interno para construir a estrutura hierárquica. + +```json +[ + { + "id": 1, + "name": "Alice", + "orders": [ + { "id": 101, "status": "delivered" } + ] + } +] +``` + +!!! note "JSON e modo recorrente" + No modo flat, o JSON também é acumulado (append) nos batches seguintes ao primeiro, igual ao CSV. No modo nested, cada batch gera um arquivo com timestamp. + +## Parquet + +- Compressão: Snappy +- Schema: inferido pelo PyArrow a partir do DataFrame Pandas +- Modo recorrente: cada batch gera um arquivo com timestamp (`tabela_YYYYMMDD_HHMMSS.parquet`) + +## Avro + +- Schema: inferido automaticamente com mapeamento de tipos: + - `int64` → `long` + - `float64` → `double` + - `bool` → `boolean` + - demais → `string` +- Todos os campos são opcionais (`["null", tipo]`) com `default: null` +- Modo recorrente: cada batch gera um arquivo com timestamp diff --git a/docs/saida/particionamento.md b/docs/saida/particionamento.md new file mode 100644 index 0000000..9da1e19 --- /dev/null +++ b/docs/saida/particionamento.md @@ -0,0 +1,81 @@ +# Particionamento Hive-style + +O Dataforge suporta particionamento de saída no formato Hive: os arquivos são organizados em subpastas nomeadas `coluna=valor`. + +## Como usar + +Use a flag `--partition-by`. Ela aceita dois formatos: + +| Formato | Efeito | +|---------|--------| +| `--partition-by coluna` | Aplica a partição a todas as tabelas | +| `--partition-by tabela:coluna` | Aplica a partição apenas à tabela especificada | + +A flag é repetível para definir regras diferentes por tabela. + +## Exemplos + +```bash +# Particionar todas as tabelas pela coluna "created_at" +docker compose run --rm cli generate -d ecommerce -f parquet \ + --partition-by created_at + +# Particionar apenas a tabela "orders" pela coluna "status" +docker compose run --rm cli generate -d ecommerce -f parquet \ + --partition-by "orders:status" + +# Múltiplas regras +docker compose run --rm cli generate -d ecommerce -f parquet \ + --partition-by "orders:status" \ + --partition-by "order_items:order_id" +``` + +## Layout de saída + +Com `--partition-by "orders:status"`: + +``` +output/ +└── ecommerce/ + └── orders/ + ├── status=cancelled/ + │ └── orders.parquet + ├── status=delivered/ + │ └── orders.parquet + ├── status=pending/ + │ └── orders.parquet + ├── status=processing/ + │ └── orders.parquet + └── status=shipped/ + └── orders.parquet +``` + +## Comportamento por formato + +| Formato | Sem recorrência | Com recorrência | +|---------|-----------------|-----------------| +| CSV | Um arquivo por partição (`tabela.csv`) | Append no mesmo arquivo | +| JSON flat | Um arquivo por partição (`tabela.json`) | Append no mesmo arquivo | +| Parquet | Um arquivo por partição (`tabela.parquet`) | Arquivo com timestamp por batch | +| Avro | Um arquivo por partição (`tabela.avro`) | Arquivo com timestamp por batch | + +## Valores seguros em nomes de pasta + +Caracteres problemáticos nos valores da coluna de partição são sanitizados: + +- `/` → `-` +- Espaços → `_` + +Por exemplo, o valor `"New York"` gera a pasta `city=New_York`. + +## Compatibilidade com ferramentas analíticas + +O layout Hive-style é reconhecido diretamente por: + +- **Apache Spark** — `spark.read.parquet("output/ecommerce/orders/")` +- **AWS Athena** / **Glue** — descoberta automática de partições +- **DuckDB** — `read_parquet("output/ecommerce/orders/**/*.parquet", hive_partitioning=true)` +- **dbt** — uso como source com partições + +!!! tip "Colunas de data" + Particionar por uma coluna de data (`created_at`, `transaction_date`) é o padrão mais comum para datasets temporais. Combine com o modo recorrente e `--increment` para simular pipelines incrementais. diff --git a/docs/saida/sql.md b/docs/saida/sql.md new file mode 100644 index 0000000..b2b191d --- /dev/null +++ b/docs/saida/sql.md @@ -0,0 +1,86 @@ +# Carga em Banco SQL + +Use `--db-url` para carregar os datasets diretamente em um banco de dados relacional via SQLAlchemy. + +## Bancos suportados + +| Banco | Connection string | Extra necessário | +|-------|------------------|-----------------| +| SQLite | `sqlite:////app/output/dados.db` | Incluído (sem extra) | +| PostgreSQL | `postgresql+psycopg2://user:pass@host:5432/db` | `postgres` | +| MySQL / MariaDB | `mysql+pymysql://user:pass@host:3306/db` | `mysql` | +| SQL Server | `mssql+pyodbc://user:pass@host/db?driver=ODBC+Driver+17+for+SQL+Server` | `mssql` | + +!!! note "Docker" + A imagem Docker padrão inclui os extras `sql` e `postgres`. Para MySQL ou SQL Server, é necessário rebuild com os extras adicionais. + +## Flags relacionadas + +| Flag | Padrão | Descrição | +|------|--------|-----------| +| `--db-url` | — | Connection string SQLAlchemy | +| `--if-exists` | `replace` | O que fazer se a tabela já existir: `replace`, `append` ou `fail` | +| `--db-schema` | — | Schema do banco de destino (ex: `staging`, `public`) | + +## Exemplos + +```bash +# SQLite — arquivo criado em output/ no host +docker compose run --rm cli generate -d ecommerce -r 500 \ + --db-url sqlite:////app/output/ecommerce.db + +# PostgreSQL +docker compose run --rm cli generate -d hr -r 1000 \ + --db-url "postgresql+psycopg2://user:pass@host:5432/mydb" + +# Append em vez de substituir +docker compose run --rm cli generate -d finance \ + --db-url sqlite:////app/output/finance.db \ + --if-exists append + +# Schema específico no PostgreSQL +docker compose run --rm cli generate -d hr \ + --db-url "postgresql+psycopg2://user:pass@host/db" \ + --db-schema staging + +# Arquivo + SQL na mesma execução +docker compose run --rm cli generate -d ecommerce -f csv -f parquet \ + --db-url sqlite:////app/output/ecommerce.db +``` + +## Comportamento de `--if-exists` + +| Valor | Descrição | +|-------|-----------| +| `replace` | Dropa e recria a tabela (padrão) | +| `append` | Insere os novos registros sem modificar os existentes | +| `fail` | Lança erro se a tabela já existir | + +## Modo recorrente e SQL + +Em modo recorrente, o primeiro batch usa o valor de `--if-exists`. A partir do segundo batch, o comportamento é forçado para `append` automaticamente, preservando os dados anteriores: + +```python +effective_if_exists = sql_if_exists if batch == 1 else "append" +``` + +## Teste de conexão na interface + +A interface visual inclui um formulário de conexão com botão **Test Connection**. Ele envia `POST /api/test-db-connection` com a URL de conexão e retorna `{ "success": true }` ou `{ "success": false, "error": "mensagem" }`. + +As conexões configuradas podem ser salvas no `localStorage` do navegador para reutilização. + +## Interface de configuração + +Na interface visual, o painel **Run Generator** oferece dois modos para configurar a conexão SQL: + +- **Formulário guiado**: campos separados para tipo, host, porta, banco, usuário e senha. Suporta PostgreSQL, MySQL e SQLite. +- **URL avançada**: campo livre para informar a connection string completa. + +O formulário guiado constrói automaticamente a URL SQLAlchemy: + +| Tipo | URL gerada | +|------|-----------| +| `postgresql` | `postgresql+psycopg2://user:pass@host:5432/db` | +| `mysql` | `mysql+pymysql://user:pass@host:3306/db` | +| `sqlite` | `sqlite:///caminho_do_arquivo.db` | diff --git a/docs/schemas/dominios.md b/docs/schemas/dominios.md new file mode 100644 index 0000000..6f26375 --- /dev/null +++ b/docs/schemas/dominios.md @@ -0,0 +1,92 @@ +# Domínios Prontos + +O Dataforge inclui domínios relacionais pré-configurados. Use `--domain ` no CLI ou selecione no seletor da interface visual. + +## Domínios disponíveis + +Os domínios padrão são implementados como classes Python em `src/dataforge/domains/` e seus schemas YAML ficam em `src/dataforge/schemas/`. + +### E-commerce (`-d ecommerce`) + +Dataset de loja virtual com clientes, produtos, pedidos e itens de pedido. + +| Tabela | Linhas padrão | FK para | +|--------|---------------|---------| +| `categories` | 20 | — | +| `customers` | 500 | — | +| `products` | 200 | `categories.id` | +| `orders` | 1000 | `customers.id` | +| `order_items` | 3000 | `orders.id`, `products.id` | + +```bash +docker compose run --rm cli generate -d ecommerce +docker compose run --rm cli schema-info ecommerce +``` + +### RH (`-d hr`) + +Dataset de recursos humanos com departamentos, cargos, funcionários e salários. + +| Tabela | Linhas padrão | FK para | +|--------|---------------|---------| +| `departments` | 10 | — | +| `job_titles` | 20 | — | +| `employees` | 300 | `departments.id`, `job_titles.id`, `employees.id` (auto-ref: gerente) | +| `salaries` | 300 | `employees.id` | + +```bash +docker compose run --rm cli generate -d hr +docker compose run --rm cli schema-info hr +``` + +### Financeiro (`-d finance`) + +Dataset financeiro com clientes, contas, categorias e transações. + +| Tabela | Linhas padrão | FK para | +|--------|---------------|---------| +| `customers` | 500 | — | +| `categories` | 15 | — | +| `accounts` | 600 | `customers.id` | +| `transactions` | 5000 | `accounts.id`, `categories.id` | + +```bash +docker compose run --rm cli generate -d finance +docker compose run --rm cli schema-info finance +``` + +## Schemas YAML adicionais + +Além dos domínios padrão, o projeto inclui os seguintes schemas YAML em `src/dataforge/schemas/`: + +| Arquivo | Domínio | +|---------|---------| +| `acoes.yaml` | Mercado de ações | +| `crm.yaml` | CRM / gestão de clientes | +| `ecommerce.yaml` | E-commerce (versão YAML) | +| `estoque.yaml` | Controle de estoque | +| `finance.yaml` | Financeiro (versão YAML) | +| `frota.yaml` | Gestão de frota de veículos | +| `manutencao.yaml` | Ordens de manutenção | +| `rh.yaml` | Recursos humanos (versão YAML) | + +Para usar qualquer um desses schemas: + +```bash +docker compose run --rm cli generate -d custom \ + -c /app/src/dataforge/schemas/crm.yaml \ + -f csv +``` + +Ou liste todos com: + +```bash +docker compose run --rm cli list-domains +``` + +## FK auto-referenciada + +O domínio `hr` demonstra FK auto-referenciada: `employees.manager_id` aponta para `employees.id`. O gerador preenche esse campo após gerar a tabela completa (o pool de PKs já está disponível no momento do preenchimento). + +!!! tip "Criando domínios customizados" + Para criar um domínio com dados em português, estrutura específica de negócio ou tipos de colunas não cobertos pelos domínios padrão, crie um arquivo YAML em `src/dataforge/schemas/` e use `--domain custom --config /app/src/dataforge/schemas/seu_arquivo.yaml`. diff --git a/docs/schemas/dtypes.md b/docs/schemas/dtypes.md new file mode 100644 index 0000000..b1202cc --- /dev/null +++ b/docs/schemas/dtypes.md @@ -0,0 +1,170 @@ +# Tipos de Dados (dtype) + +O campo `dtype` de cada coluna define o tipo de dado gerado. Os tipos são resolvidos pelo `FAKER_REGISTRY` em `src/dataforge/core/registry.py`. + +## Tabela de dtypes + +| dtype | Descrição | Suporta `min`/`max` | Padrão de range | +|-------|-----------|---------------------|-----------------| +| `int_seq` | Inteiro sequencial (1, 2, 3...) — ideal para PKs | Não | — | +| `uuid` | UUID v4 | Não | — | +| `int` | Inteiro aleatório | Sim | 0 – 100.000 | +| `float` | Float com 2 casas decimais | Sim | 0 – 10.000 | +| `str` | Palavra genérica | Não | — | +| `bool` | Booleano (`True`/`False`) | Não | — | +| `date` | Data aleatória em formato ISO (`YYYY-MM-DD`) | Sim | Últimos 3 anos até hoje | +| `name` | Nome completo | Não | — | +| `email` | Endereço de e-mail | Não | — | +| `phone` | Número de telefone | Não | — | +| `address` | Endereço completo (sem quebras de linha) | Não | — | +| `city` | Cidade | Não | — | +| `country` | País | Não | — | +| `company` | Nome de empresa | Não | — | +| `text` | Frase/sentença | Não | — | +| `url` | URL | Não | — | +| `currency` | Código de moeda (ex: `BRL`, `USD`) | Não | — | +| `iban` | IBAN bancário | Não | — | + +## Detalhes de implementação + +### `int_seq` + +Gera sequência inteira a partir de `seq_start` (padrão: 1). Em modo recorrente, o offset é acumulado entre batches para garantir continuidade. + +```python +list(range(seq_start, seq_start + n)) +``` + +### `int` + +```python +faker.random_int(min=int(min_value), max=int(max_value)) +``` + +### `float` + +```python +round(faker.pyfloat(min_value=float(min_value), max_value=float(max_value)), 2) +``` + +### `date` + +Aceita strings ISO (`"2020-01-01"`) ou notação relativa do Faker (`"-6m"`, `"-3y"`, `"today"`). + +```python +faker.date_between(start_date=min_value, end_date=max_value).isoformat() +``` + +### `address` + +As quebras de linha do endereço Faker são substituídas por vírgulas: + +```python +faker.address().replace("\n", ", ") +``` + +## Usando `faker_provider` + +Para qualquer método do Faker não listado acima, use `faker_provider`: + +```yaml +cep: + dtype: str + faker_provider: postcode + +iban: + dtype: str + faker_provider: iban + +placa: + dtype: str + faker_provider: license_plate +``` + +!!! note "Prioridade" + Quando `faker_provider` está definido, ele tem prioridade absoluta sobre `dtype`. O campo `dtype` é necessário para o parser, mas o valor gerado vem do método Faker especificado. + +## Usando `choices` + +Para valores fixos (enum), use `choices`: + +```yaml +status: + dtype: str + choices: + - ativo + - inativo + - pendente + +tier: + dtype: str + choices: + - bronze + - silver + - gold + - platinum +``` + +Os valores são sorteados aleatoriamente com reposição. + +## Combinando `choices` com `nullable` + +```yaml +categoria: + dtype: str + nullable: 0.1 + choices: + - A + - B + - C +``` + +10% dos valores serão nulos; os 90% restantes serão sorteados de `choices`. + +## Exemplos práticos + +```yaml +# Chave primária sequencial +id: + dtype: int_seq + primary_key: true + +# Identificador único +uuid: + dtype: uuid + +# Rating de 1 a 5 +rating: + dtype: int + min: 1 + max: 5 + +# Percentual de desconto +desconto: + dtype: float + min: 0 + max: 1 + +# Data de criação nos últimos 2 anos +criado_em: + dtype: date + min: "-2y" + max: "today" + +# Status com valores fixos +status: + dtype: str + choices: + - ativo + - inativo + +# Campo opcional (20% nulo) +observacao: + dtype: text + nullable: 0.2 + +# Código postal via Faker +cep: + dtype: str + faker_provider: postcode +``` diff --git a/docs/schemas/yaml.md b/docs/schemas/yaml.md new file mode 100644 index 0000000..3f9f086 --- /dev/null +++ b/docs/schemas/yaml.md @@ -0,0 +1,172 @@ +# Schema YAML Customizado + +O schema YAML é o formato central do Dataforge. Ele descreve as tabelas, colunas, tipos de dados e relações que o gerador usará para produzir os datasets. + +## Estrutura do arquivo + +```yaml +domain: nome_do_dominio + +tables: + nome_da_tabela: + rows: 1000 + columns: + nome_da_coluna: + dtype: str + # ... outros campos +``` + +## Estrutura mínima funcional + +```yaml +domain: biblioteca + +tables: + autores: + rows: 50 + columns: + id: + dtype: int_seq + primary_key: true + nome: + dtype: name + email: + dtype: email + + livros: + rows: 200 + columns: + id: + dtype: int_seq + primary_key: true + titulo: + dtype: str + preco: + dtype: float + min: 10 + max: 300 + publicado_em: + dtype: date + min: "2000-01-01" + max: "today" + autor_id: + dtype: int + foreign_key: + table: autores + column: id + ativo: + dtype: bool + descricao: + dtype: text + nullable: 0.3 +``` + +## Campos suportados por coluna + +| Campo | Tipo | Obrigatório | Descrição | +|-------|------|-------------|-----------| +| `dtype` | string | Sim | Tipo de dado (ver tabela de dtypes) | +| `primary_key` | bool | Não | Marca a coluna como chave primária | +| `nullable` | float (0–1) | Não | Proporção de valores nulos (ex: `0.2` = 20% nulos) | +| `min` | number / string | Não | Valor mínimo para `int`, `float` ou `date` | +| `max` | number / string | Não | Valor máximo para `int`, `float` ou `date` | +| `foreign_key.table` | string | — | Tabela referenciada pela FK | +| `foreign_key.column` | string | — | Coluna referenciada pela FK | +| `faker_provider` | string | Não | Qualquer método do Faker (ex: `postcode`, `iban`) | +| `choices` | lista | Não | Lista de valores fixos para sortear | + +## Chave estrangeira + +```yaml +order_id: + dtype: int + foreign_key: + table: orders + column: id +``` + +O gerador valida as FKs na carga do schema: se a tabela ou coluna referenciada não existir, um erro é lançado antes da geração. + +## Valores nulos + +```yaml +observacao: + dtype: text + nullable: 0.3 # 30% dos valores serão nulos +``` + +## Valores fixos com choices + +```yaml +status: + dtype: str + choices: + - pending + - processing + - shipped + - delivered + - cancelled +``` + +## Range de valores + +```yaml +# Inteiro entre 1 e 5 +rating: + dtype: int + min: 1 + max: 5 + +# Float entre 0.0 e 1.0 +desconto: + dtype: float + min: 0 + max: 1 + +# Data entre 2020 e hoje +data_contrato: + dtype: date + min: "2020-01-01" + max: "today" + +# Data dos últimos 6 meses (notação relativa do Faker) +data_recente: + dtype: date + min: "-6m" + max: "today" +``` + +## Override de domínio existente + +Para ajustar apenas o número de linhas de um domínio pronto, crie um YAML com o mesmo nome do domínio e liste apenas as tabelas que deseja sobrescrever: + +```yaml +domain: ecommerce + +tables: + customers: + rows: 200 + orders: + rows: 500 + order_items: + rows: 1500 +# Tabelas não listadas usam os defaults do domínio +``` + +Use com: + +```bash +docker compose run --rm cli generate -d ecommerce \ + -c /app/src/dataforge/schemas/meu_override.yaml +``` + +## Onde salvar os schemas + +Salve os arquivos `.yaml` em `src/dataforge/schemas/`. Essa pasta é montada como volume no container, portanto os arquivos ficam disponíveis imediatamente sem rebuild. + +## Validação de FKs + +O loader valida todas as FKs ao carregar o schema. Erros detectados: + +- Tabela referenciada não existe no schema → `FK error in 'tabela.coluna': table 'X' not found` +- Coluna referenciada não existe na tabela → `FK error in 'tabela.coluna': column 'Y' not found in 'X'` diff --git a/mkdocs.yml b/mkdocs.yml index 7ce9e7f..4d35881 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,11 +1,12 @@ site_name: Dataforge -site_description: Gerador de datasets sintéticos relacionais para engenharia de dados +site_description: Gerador de datasets sintéticos relacionais site_author: Carlos Oliveira site_url: https://ckoliveiraa.github.io/DataForge/ repo_name: DataForge repo_url: https://github.com/ckoliveiraa/DataForge edit_uri: edit/main/docs/ +docs_dir: docs theme: name: material language: pt @@ -49,7 +50,11 @@ plugins: markdown_extensions: - admonition - pymdownx.details - - pymdownx.superfences + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format - pymdownx.highlight: anchor_linenums: true line_spans: __span @@ -64,27 +69,38 @@ markdown_extensions: - toc: permalink: true -nav: - - Início: index.md - - Primeiros Passos: - - Instalação: getting-started/installation.md - - Quickstart: getting-started/quickstart.md - - Uso: - - Interface Visual: usage/frontend.md - - CLI - Referência: usage/cli.md - - Domínios Prontos: usage/domains.md - - Schemas Customizados: usage/schemas.md - - Tipos de Dados: usage/data-types.md - - Avançado: - - Modo Recorrente: advanced/recurrence.md - - Upload para Nuvem: advanced/cloud-upload.md - - Carga em Banco SQL: advanced/sql-loading.md - - Particionamento: advanced/partitioning.md - - Desenvolvimento: - - Arquitetura: development/architecture.md - - Contribuindo: development/contributing.md +extra_javascript: + - https://unpkg.com/mermaid@10/dist/mermaid.min.js extra: social: - icon: fontawesome/brands/github link: https://github.com/ckoliveiraa/DataForge + +nav: + - Início: index.md + - Instalação: + - Docker: instalacao/docker.md + - Sem Docker (Poetry): instalacao/poetry.md + - Interface Visual: + - Visão geral: frontend/visao-geral.md + - Geração com IA: frontend/ia.md + - Diagrama relacional: frontend/diagrama.md + - Catálogo Faker: frontend/faker.md + - Exportar e importar YAML: frontend/yaml.md + - CLI: + - Referência completa: cli/referencia.md + - Exemplos práticos: cli/exemplos.md + - Schemas: + - Schema YAML customizado: schemas/yaml.md + - Domínios prontos: schemas/dominios.md + - Tipos de dados (dtype): schemas/dtypes.md + - Saída de dados: + - Formatos: saida/formatos.md + - Particionamento: saida/particionamento.md + - Upload em nuvem: saida/cloud.md + - Carga SQL: saida/sql.md + - Avançado: + - Modo recorrente: avancado/recorrencia.md + - Reprodutibilidade: avancado/seed.md + - Arquitetura: avancado/arquitetura.md From 21d094e46e10ff122d86e920c0538b6585cecce2 Mon Sep 17 00:00:00 2001 From: Carlos Oliveira Date: Sat, 11 Apr 2026 01:19:33 -0300 Subject: [PATCH 2/2] fix: corrigir bugs identificados no bugcheck e crash no Advanced DB MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Backend: - cli.py: validar formato de --columns antes de desempacotar, adicionar .copy() em _apply_increments, detectar múltiplos --partition-by sem prefixo, corrigir list-domains para usar DOMAIN_REGISTRY - loader.py: validar campos obrigatórios (table/column) em FK antes de acessar - test_cli.py: corrigir nome de domínio "rh" -> "hr" no test_list_domains Frontend: - App.tsx: adicionar import de Schema ausente (causava falha de build), converter todos os setRunConfig para forma funcional (r => ...) eliminando stale closure, adicionar destination ao tipo de runConfig, adicionar AbortController no streaming do handleRunCli - SchemaWriter.ts: corrigir revogação prematura de URL no downloadYaml com setTimeout --- .claude/commands/bugcheck.md | 77 + .claude/commands/documentar.md | 382 ++- .gitignore | 2 +- poetry.lock | 2517 ----------------- skills-lock.json | 15 - src/dataforge/cli.py | 20 +- src/dataforge/config/loader.py | 5 + src/dataforge/frontend/src/App.tsx | 84 +- .../frontend/src/services/SchemaReader.ts | 21 +- .../frontend/src/services/SchemaWriter.ts | 2 +- src/dataforge/frontend/vite.config.ts | 33 + tests/test_cli.py | 2 +- 12 files changed, 548 insertions(+), 2612 deletions(-) create mode 100644 .claude/commands/bugcheck.md delete mode 100644 poetry.lock delete mode 100644 skills-lock.json diff --git a/.claude/commands/bugcheck.md b/.claude/commands/bugcheck.md new file mode 100644 index 0000000..5b35382 --- /dev/null +++ b/.claude/commands/bugcheck.md @@ -0,0 +1,77 @@ +Você é um agente de análise de qualidade de código do projeto **Dataforge**. Sua responsabilidade é inspecionar o código-fonte do backend (Python) e do frontend (TypeScript/React) em busca de bugs reais, potenciais falhas de runtime, problemas de lógica e vulnerabilidades. + +## O que ler antes de analisar + +Leia os arquivos abaixo para entender o estado atual do projeto: + +**Backend:** +- `src/dataforge/cli.py` — comandos Click, validação de flags, lógica de entrada +- `src/dataforge/core/generator.py` — geração de dados com Faker e Pandas +- `src/dataforge/core/registry.py` — mapeamento de dtypes para providers Faker +- `src/dataforge/core/schema.py` — modelos de dados: `Column`, `Table`, `ForeignKey`, `DomainSchema` +- `src/dataforge/config/loader.py` — carregamento e merge de YAML com domínios base +- `src/dataforge/domains/` — schemas dos domínios prontos (`ecommerce`, `hr`, `finance`) +- `src/dataforge/api/` — endpoints FastAPI (se existir): `/api/ai-generate`, `/api/ai-models`, `/api/schemas`, `/api/test-db-connection` + +**Frontend:** +- `src/dataforge/frontend/src/App.tsx` — lógica principal, integração com IA, gerenciamento de estado +- `src/dataforge/frontend/src/services/SchemaReader.ts` — parsing de YAML para estado interno +- `src/dataforge/frontend/src/services/SchemaWriter.ts` — serialização do estado para YAML +- `src/dataforge/frontend/src/types/schema.ts` — tipos `Schema`, `Table`, `Column` +- `src/dataforge/frontend/src/components/` — componentes React + +## O que procurar + +### Backend (Python) + +- **KeyError / IndexError / AttributeError** não tratados — acesso a dicionários ou listas sem verificação de existência +- **Validação de entrada ausente** — flags CLI sem validação de tipo, range ou formato antes de uso +- **Condições de corrida ou estado mutável compartilhado** — especialmente em geração paralela ou recorrente +- **Tratamento de exceção genérico** — `except Exception:` sem log ou re-raise que engole erros silenciosamente +- **Referências circulares de FK** — tabelas que se referenciam mutuamente sem detecção de ciclo +- **Problemas de encoding** — abertura de arquivos sem `encoding='utf-8'` explícito +- **Injeção via YAML** — uso de `yaml.load()` sem `Loader=yaml.SafeLoader` +- **Geração de dados inválida** — dtypes sem provider mapeado causando `None` ou crash silencioso +- **Vazamento de credenciais em log** — API keys, connection strings logadas ou expostas em tracebacks + +### Frontend (TypeScript/React) + +- **Ausência de verificação de null/undefined** — acesso a propriedades opcionais sem optional chaining (`?.`) +- **State stale em closures** — `useEffect` e `useCallback` com dependências ausentes ou incorretas no array `[]` +- **Mutação direta de estado** — modificar arrays/objetos do estado sem spread ou immer +- **Race condition em chamadas async** — múltiplas requisições sem cancelamento (falta de `AbortController`) +- **Parsing de YAML sem try/catch** — falha silenciosa ao importar schema inválido +- **Validação de nome de schema fraca** — regex insuficiente permitindo nomes inválidos no servidor +- **localStorage sem try/catch** — acesso ao `localStorage` pode lançar exceção em modo privado +- **Vazamento de memória** — listeners de evento ou timers sem cleanup no `useEffect` +- **Chaves duplicadas no React** — uso de índice de array como `key` em listas dinâmicas + +## Formato do relatório + +Para cada bug encontrado, reportar no seguinte formato: + +### [SEVERIDADE] Título curto do bug + +**Arquivo:** `caminho/do/arquivo.py` (linha aproximada se identificável) +**Categoria:** (ex: KeyError não tratado, Race condition, Null dereference, etc.) +**Descrição:** O que está errado e por que pode causar problema em produção. +**Reprodução:** Como acionar o bug (entrada, sequência de ações, condição). +**Correção sugerida:** Trecho de código corrigido ou descrição objetiva do fix. + +--- + +Use as seguintes severidades: +- **[CRÍTICO]** — crash garantido ou corrupção de dados em uso normal +- **[ALTO]** — falha provável em casos de uso comuns +- **[MÉDIO]** — falha em edge cases ou condições específicas +- **[BAIXO]** — problema cosmético, performance ou manutenibilidade + +## Regras + +- Analise **todo o código lido** — não pule arquivos +- Reporte **apenas bugs reais ou altamente prováveis** — não invente problemas hipotéticos +- **Não sugira refatorações** ou melhorias de estilo que não sejam bugs +- Se um trecho parecer suspeito mas não for bug comprovado, liste em seção separada **"Pontos de atenção"** ao final +- Mantenha todo o relatório em **português** +- Ao final, exiba um **resumo por severidade**: quantos críticos, altos, médios e baixos foram encontrados +- Não adicione emojis diff --git a/.claude/commands/documentar.md b/.claude/commands/documentar.md index f3bf9bf..1e9cf57 100644 --- a/.claude/commands/documentar.md +++ b/.claude/commands/documentar.md @@ -1,70 +1,368 @@ -Você é um agente de documentação do projeto **Dataforge**. Sua responsabilidade é manter o `README.md` atualizado como um **guia de instalação e uso para o usuário final** — alguém que acabou de descobrir o projeto e quer começar a usá-lo. +Você é um agente de documentação técnica do projeto **Dataforge**. Sua responsabilidade é criar ou atualizar a documentação técnica completa do projeto usando **MkDocs** como padrão — arquitetura, instalação via Docker, guia de uso detalhado para que qualquer desenvolvedor consiga replicar o ambiente e utilizar todas as funcionalidades do app, com foco especial na **interface visual** e na **integração com IA**. -O método de uso principal e recomendado é via **Docker / Docker Compose**. Documente esse caminho em primeiro lugar. O uso via `poetry` é alternativo e pode ser coberto de forma resumida. +## Padrão de documentação: MkDocs + +Toda a documentação técnica **deve ser escrita para o MkDocs**. Isso significa: + +1. **Verifique se `mkdocs.yml` existe** na raiz do projeto. Se não existir, crie-o com a configuração base abaixo. +2. **Todos os arquivos de doc ficam em `docs/`** — nunca escreva documentação técnica fora dessa pasta. +3. **Use Material for MkDocs** como tema (já é o padrão do projeto se configurado, caso contrário defina `theme: name: material`). +4. **Cada seção vira uma página separada** em `docs/` (ex: `docs/cli.md`, `docs/docker.md`, `docs/schemas.md`), nunca um único arquivo monolítico. +5. **Atualize o `nav:` do `mkdocs.yml`** para refletir todas as páginas criadas ou atualizadas. + +### Configuração base do `mkdocs.yml` (use se não existir) + +```yaml +site_name: Dataforge +site_description: Gerador de datasets sintéticos relacionais +docs_dir: docs +theme: + name: material + language: pt + palette: + scheme: default + features: + - navigation.tabs + - navigation.sections + - toc.integrate + - content.code.copy + +markdown_extensions: + - admonition + - pymdownx.highlight: + anchor_linenums: true + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format + - pymdownx.tabbed: + alternate_style: true + - tables + - toc: + permalink: true + +extra_javascript: + - https://unpkg.com/mermaid@10/dist/mermaid.min.js + +nav: + - Início: index.md + - Instalação: + - Docker: instalacao/docker.md + - Sem Docker (Poetry): instalacao/poetry.md + - Interface Visual: + - Visão geral: frontend/visao-geral.md + - Geração com IA: frontend/ia.md + - Diagrama relacional: frontend/diagrama.md + - Catálogo Faker: frontend/faker.md + - Exportar e importar YAML: frontend/yaml.md + - CLI: + - Referência completa: cli/referencia.md + - Exemplos práticos: cli/exemplos.md + - Schemas: + - Schema YAML customizado: schemas/yaml.md + - Domínios prontos: schemas/dominios.md + - Tipos de dados (dtype): schemas/dtypes.md + - Saída de dados: + - Formatos: saida/formatos.md + - Particionamento: saida/particionamento.md + - Upload em nuvem: saida/cloud.md + - Carga SQL: saida/sql.md + - Avançado: + - Modo recorrente: avancado/recorrencia.md + - Reprodutibilidade: avancado/seed.md + - Arquitetura: avancado/arquitetura.md +``` + +### Estrutura de arquivos a criar/atualizar + +Verifique quais já existem antes de criar. Atualize os que existirem, crie os que não existirem: + +``` +docs/ +├── index.md ← visão geral e início rápido +├── instalacao/ +│ ├── docker.md ← instalação e configuração Docker (principal) +│ └── poetry.md ← alternativa sem Docker +├── frontend/ +│ ├── visao-geral.md ← o que é a UI, URL, layout geral +│ ├── ia.md ← geração de schema com IA (foco principal) +│ ├── diagrama.md ← diagrama relacional ReactFlow +│ ├── faker.md ← catálogo de providers Faker +│ └── yaml.md ← exportar/importar/salvar YAML na UI +├── cli/ +│ ├── referencia.md ← tabela completa de todos os flags +│ └── exemplos.md ← 10+ exemplos copiáveis +├── schemas/ +│ ├── yaml.md ← estrutura do YAML customizado +│ ├── dominios.md ← ecommerce, hr, finance detalhados +│ └── dtypes.md ← tabela completa de dtypes +├── saida/ +│ ├── formatos.md ← csv, json, parquet, avro +│ ├── particionamento.md ← hive-style, --partition-by +│ ├── cloud.md ← gcs, s3, azure +│ └── sql.md ← sqlite, postgresql, mysql, mssql +└── avancado/ + ├── recorrencia.md ← --recurrence, --count, --increment + ├── seed.md ← reprodutibilidade + └── arquitetura.md ← diagrama Mermaid + descrição de componentes +``` ## O que ler antes de escrever -Leia os arquivos abaixo para entender o estado atual do projeto antes de alterar qualquer coisa: +Leia os arquivos abaixo para entender o estado atual do projeto antes de escrever qualquer coisa: -- `README.md` — estado atual, para preservar o que ainda é válido -- `Dockerfile` — como a imagem é construída (stages, extras instalados, porta exposta) -- `docker-compose.yml` — serviços disponíveis (`frontend` e `cli`), volumes, variáveis de ambiente -- `src/dataforge/cli.py` — todos os flags e opções do comando `dataset-gen` +- `README.md` — visão geral já documentada (não duplique, complemente) +- `Dockerfile` — stages do build, extras instalados, porta exposta +- `docker-compose.yml` — serviços `frontend` e `cli`, volumes, variáveis de ambiente, profiles +- `src/dataforge/cli.py` — todos os comandos (`generate`, `list-domains`, `schema-info`) e todos os flags - `src/dataforge/core/registry.py` — dtypes suportados e seus parâmetros -- `src/dataforge/core/schema.py` — estrutura de Column, Table, ForeignKey -- `src/dataforge/config/loader.py` — campos suportados no YAML customizado -- `src/dataforge/domains/` — domínios prontos disponíveis e suas tabelas -- `src/dataforge/schemas/` — schemas YAML de exemplo (liste os arquivos .yaml existentes) +- `src/dataforge/core/schema.py` — estrutura de `Column`, `Table`, `ForeignKey`, `DomainSchema` +- `src/dataforge/config/loader.py` — campos suportados no YAML, lógica de merge com base domain +- `src/dataforge/domains/` — domínios prontos disponíveis (`ecommerce`, `hr`, `finance`) e suas tabelas +- `src/dataforge/schemas/` — schemas YAML de exemplo +- `pyproject.toml` — extras disponíveis (`parquet`, `avro`, `sql`, `postgres`, `gcp`, `aws`, `azure`, etc.) +- `src/dataforge/frontend/src/App.tsx` — lógica completa da UI, integração com IA, providers suportados +- `src/dataforge/frontend/src/components/TableNode.tsx` — nó do diagrama ReactFlow +- `src/dataforge/frontend/src/services/SchemaReader.ts` — parsing de YAML para estado interno +- `src/dataforge/frontend/src/services/SchemaWriter.ts` — serialização do estado para YAML +- `src/dataforge/frontend/src/types/schema.ts` — tipos `Schema`, `Table`, `Column` + +## Conteúdo de cada página MkDocs + +--- + +### Arquitetura (`docs/avancado/arquitetura.md`) + +Use **Mermaid** para o diagrama de arquitetura — não ASCII. Use `flowchart LR` ou `graph TD`. Exemplo de estrutura esperada: + +```mermaid +flowchart LR + UI["Frontend\n(React + Vite)"] + API["Backend\n(FastAPI / Python)"] + AI["IA Externa\n(Gemini / Groq / etc.)"] + GEN["Gerador\n(Faker + Pandas)"] + OUT["Saída\n(arquivo / SQL / Cloud)"] + + UI -- "prompt + provider" --> API + API -- "API Key + modelo" --> AI + AI -- "YAML gerado" --> API + API -- "schema JSON" --> UI + UI -- "YAML exportado" --> GEN + GEN --> OUT +``` + +Adapte o diagrama com base no que você ler no código — não invente fluxos que não existem. + +Inclua também: +- Descrição de cada componente e sua responsabilidade +- Stack tecnológica: Python 3.12, Click, Faker, Pandas, Vite, React, TypeScript, ReactFlow, dagre +- Como o frontend se comunica com o backend (endpoints `/api/ai-generate`, `/api/ai-models`, `/api/schemas`, `/api/test-db-connection`) + +--- + +### Interface Visual — Visão Geral (`docs/frontend/visao-geral.md`) + +- URL de acesso: `http://localhost:5173` +- Layout geral da interface (painel lateral esquerdo com tabelas, canvas central com diagrama) +- O que é possível fazer: criar tabelas, adicionar colunas, definir FKs, usar domínios prontos, gerar schema com IA, exportar/importar YAML, salvar schema personalizado, deletar schema, testar conexão com banco +- Como navegar entre modos (editor de tabelas × diagrama relacional) + +--- + +### Geração de Schema com IA (`docs/frontend/ia.md`) + +Esta é a página mais importante da seção de frontend. Documente com detalhe: + +#### O que é + +A UI possui um botão "Gerar com IA" (ícone Sparkles) que abre um modal onde o usuário descreve em linguagem natural o domínio que quer gerar. A IA retorna um YAML válido que é automaticamente importado para o canvas. + +#### Providers suportados + +Documente cada provider presente em `AI_PROVIDERS` no `App.tsx`: + +| Provider | Chave de API | Modelo padrão sugerido | Gratuito? | +|--------------|-----------------------|-----------------------------------|-----------------| +| Anthropic | `sk-ant-api03-…` | `claude-3-5-haiku-20241022` | Não | +| OpenAI | `sk-…` | `gpt-4o-mini` | Não | +| Google | `AIza…` | `gemini-2.0-flash` | Sim (free tier) | +| Groq | `gsk_…` | `llama-3.3-70b-versatile` | Sim (free tier) | +| Mistral | `xxxxxxxx…` | `mistral-small-latest` | Não | +| Together AI | `xxxxxxxx…` | `meta-llama/Llama-3.3-70B-Instruct-Turbo` | Não | +| Ollama | (sem chave) | `llama3.2` | Sim (local) | + +#### Recomendação de uso gratuito + +!!! tip "Opções gratuitas recomendadas" + Para começar sem custo, as melhores opções são: + + - **Google Gemini** — obtenha uma chave gratuita em [Google AI Studio](https://aistudio.google.com/). O modelo `gemini-2.0-flash` é rápido, gratuito no tier básico e produz YAMLs de alta qualidade. + - **Groq** — registre-se em [console.groq.com](https://console.groq.com/) e obtenha uma chave gratuita. O modelo `llama-3.3-70b-versatile` oferece geração rápida sem custo. + - **Ollama** — para uso 100% local sem nenhuma chave de API. Instale o Ollama, baixe um modelo como `llama3.2` e configure o endpoint local. + +#### Como usar o modal de IA -## O que atualizar no README +1. Clique no botão **Gerar com IA** (ícone de faísca) no topo da interface +2. Selecione o provider e informe a chave de API (salva no `localStorage` para próximas sessões) +3. Clique em **Carregar modelos** para buscar os modelos disponíveis via API +4. Selecione o modelo desejado +5. Escreva uma descrição do domínio em linguagem natural (inglês ou português) +6. Clique em **Gerar** — o YAML é gerado e importado automaticamente para o canvas -Reescreva ou atualize as seções necessárias para que o README responda, em ordem, às perguntas de um novo usuário: +#### Exemplo de prompt -1. **O que é o Dataforge?** — visão geral em 2-3 frases focada no benefício +``` +E-commerce com 4 tabelas: +- customers: nome completo, email, telefone, cidade, país, data de cadastro +- products: nome, categoria (Electronics/Clothing/Books), preço (10–2000), estoque +- orders: vinculado ao customer, data (últimos 2 anos), status, total +- order_items: vinculado a order e product, quantidade (1–10), preço unitário +``` -2. **Pré-requisitos** — apenas Docker e Docker Compose (nada de Python ou Node local) +#### Fluxo interno -3. **Instalação e início rápido (Docker)** - - `git clone` + `docker compose up --build -d` - - Onde acessar o frontend (porta 5173) - - Estrutura de pastas geradas no host (`output/`, `credentials/`, `schemas/`) +1. O frontend envia `{ provider, apiKey, model, prompt }` para `POST /api/ai-generate` +2. O backend constrói um prompt de sistema que instrui a IA a retornar apenas YAML válido no formato Dataforge +3. O YAML retornado é parseado por `SchemaReader.parseYaml()` e carregado no canvas +4. Erros de chaves duplicadas no YAML gerado são detectados e exibidos com mensagem de orientação -4. **Usando o CLI via Docker** - - Como rodar o `dataset-gen` com `docker compose run --rm cli ` - - Pelo menos 3 exemplos copiáveis com flags diferentes - - Como passar schemas YAML customizados (via volume `schemas/`) - - Como coletar os arquivos gerados (pasta `output/` no host) +#### Persistência de configuração -5. **Interface visual (Frontend)** - - O que é possível fazer pela UI - - Como criar tabelas, colunas, FKs visualmente - - Como exportar/importar YAML pelo editor - - Como usar templates de domínios prontos +As chaves de API, modelos e o provider selecionado são salvos no `localStorage` com a chave `dataforge_ai_config`. Isso evita reconfiguração a cada sessão. -6. **Domínios prontos** — tabela com todos os domínios disponíveis e as tabelas que cada um gera +--- -7. **Schema YAML customizado** — estrutura mínima de um arquivo `.yaml` com exemplos de `dtype`, `min`, `max`, `nullable`, `primary_key`, `foreign_key`, `faker_provider` +### Diagrama Relacional (`docs/frontend/diagrama.md`) -8. **Tipos de dados (`dtype`)** — tabela completa com parâmetros aceitos por cada tipo +- O canvas central usa **ReactFlow** para renderizar as tabelas como nós conectados +- Cada tabela é um nó do tipo `tableNode` (componente `TableNode.tsx`) +- Colunas que são chave primária aparecem com ícone de chave (amarelo) +- Colunas que são chave estrangeira aparecem com ícone de corrente (rosa) +- As arestas (setas) são criadas automaticamente para cada `foreign_key` definida +- O layout automático usa a biblioteca **dagre** para posicionar os nós sem sobreposição +- O usuário pode arrastar e reposicionar os nós livremente +- Ao selecionar um nó, ele é destacado com borda azul e o painel lateral foca na tabela correspondente -9. **Formatos de saída** — CSV, JSON, Parquet, Avro com o flag correspondente +--- -10. **Upload em nuvem (GCS, S3, Azure)** — quais flags usar e como montar credenciais via volume `credentials/` +### Catálogo Faker (`docs/frontend/faker.md`) -11. **Carga em banco SQL** — `--sql-url` e `--sql-if-exists`, com exemplos para SQLite e PostgreSQL +- Botão "Catálogo Faker" (ícone BookOpen) abre um painel lateral +- Exibe todos os providers organizados por categoria com exemplo de valor gerado +- Categorias disponíveis: Person, Internet, Address, Phone, Company, Finance, Date/Time, Text, Identity, Color, File, Geo, Automotive, Barcode, Credit Card, User Agent +- Ao clicar em um provider, ele é copiado para a área de transferência e pode ser colado no campo `faker_provider` de uma coluna -12. **Reprodutibilidade** — `--seed` com exemplo +--- -13. **Instalação alternativa sem Docker** (seção curta no final) — para quem preferir instalar com `poetry` localmente +### Exportar e Importar YAML (`docs/frontend/yaml.md`) + +#### Exportar + +- Botão **Gerar YAML** converte o estado atual (tabelas + colunas + FKs) em YAML via `SchemaWriter.generateYaml()` +- O YAML gerado é exibido em um bloco de código editável na interface +- Botão **Baixar** faz download do arquivo `.yaml` com o nome `{domain}_schema.yaml` + +#### Importar + +- Botão de upload (ícone Upload) permite carregar um arquivo `.yaml` do disco +- O arquivo é parseado por `SchemaReader.readFromFile()` e o canvas é recarregado com as tabelas encontradas + +#### Salvar schema personalizado + +- Campo de texto para nomear o schema (somente letras minúsculas, números, hífens e underscores) +- Botão **Salvar** envia o YAML para `POST /api/schemas/{name}` e salva no servidor +- O schema salvo aparece no seletor de domínios e pode ser carregado em sessões futuras + +#### Deletar schema + +- Botão de lixeira exibe confirmação antes de chamar `DELETE /api/schemas/{domain}` +- Não é possível deletar domínios embutidos (`ecommerce`, `hr`, `finance`) + +#### Usar o YAML exportado no CLI + +O YAML exportado pela UI é 100% compatível com o CLI. Exemplo: + +```bash +docker compose run --rm cli generate --schema meu_schema.yaml --format parquet --rows 5000 +``` + +--- + +### Instalação via Docker (`docs/instalacao/docker.md`) + +- Como subir o ambiente completo +- O que cada stage do Dockerfile faz +- Volumes mapeados e hot reload +- Como acionar o serviço CLI separado (`profiles: [cli]`) + +### Instalação sem Docker (`docs/instalacao/poetry.md`) + +Para quem prefere instalar localmente com Poetry (Python 3.12+ requerido): + +```bash +poetry install -E "parquet avro sql postgres gcp aws azure" +dataset-gen generate --help +``` + +Tabela de extras disponíveis com a funcionalidade que cada um habilita. + +--- + +### CLI — Referência completa (`docs/cli/referencia.md`) + +Documente os três comandos disponíveis com tabela de todos os flags. + +### CLI — Exemplos práticos (`docs/cli/exemplos.md`) + +Pelo menos 10 exemplos copiáveis cobrindo todos os flags principais. + +--- + +### Demais páginas + +Documente conforme necessário com base no código lido: + +- `docs/schemas/yaml.md` — estrutura completa do YAML customizado +- `docs/schemas/dominios.md` — tabelas detalhadas de cada domínio pronto +- `docs/schemas/dtypes.md` — tabela completa de dtypes com parâmetros +- `docs/saida/formatos.md` — csv, json, parquet, avro +- `docs/saida/particionamento.md` — hive-style +- `docs/saida/cloud.md` — gcs, s3, azure +- `docs/saida/sql.md` — bancos suportados, connection strings, if-exists +- `docs/avancado/recorrencia.md` — pipeline de streaming simulado +- `docs/avancado/seed.md` — reprodutibilidade entre runs + +--- ## Regras +- **MkDocs é o padrão obrigatório** — nunca escreva documentação técnica fora da estrutura `docs/` + `mkdocs.yml` +- Sempre verifique se `mkdocs.yml` existe antes de criar; atualize o `nav:` a cada novo arquivo criado +- **Use Mermaid para todos os diagramas de arquitetura** — não use ASCII art +- **A seção de IA é prioritária** — documente todos os providers com exemplos, destaque as opções gratuitas (Google Gemini e Groq) +- Cada página deve ter um `# Título` no topo compatível com o MkDocs Material - Não invente funcionalidades que não existem no código -- Preserve exemplos que ainda são válidos -- Use tabelas markdown para flags, dtypes e domínios +- Use tabelas markdown para flags, dtypes, domínios e campos YAML - Use blocos de código para todos os comandos e exemplos YAML -- Mantenha o README em **português** +- Use admonitions do MkDocs Material quando útil: `!!! note`, `!!! tip`, `!!! warning` +- Mantenha todo o conteúdo em **português** - Não adicione emojis -- Seja objetivo — o README é para quem quer usar, não para quem quer contribuir +- Seja detalhado — este é um guia técnico, não um resumo executivo +- Crie as pastas necessárias dentro de `docs/` antes de escrever os arquivos + +Ao terminar, liste no chat: +- Quais arquivos foram criados ou atualizados +- Se `mkdocs.yml` foi criado ou apenas atualizado +- Se algum comportamento encontrado no código divergia do que o README descrevia + +## Commit automático + +Após escrever toda a documentação, faça commit de todos os arquivos alterados ou criados: + +```bash +git add mkdocs.yml docs/ +git commit -m "docs: atualiza documentação técnica MkDocs" +``` -Ao terminar, liste no chat quais seções foram adicionadas, alteradas ou removidas. +Não inclua co-author na mensagem de commit. 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"computedHash": "6337038fe1fe6bbe1b9f252ab678ee575859190bab6f0f246f4061824eb40875" - } - } -} diff --git a/src/dataforge/cli.py b/src/dataforge/cli.py index 35ba18c..dfcc321 100644 --- a/src/dataforge/cli.py +++ b/src/dataforge/cli.py @@ -37,7 +37,12 @@ def _filter_schema(schema, tables: tuple[str, ...], columns: tuple[str, ...]): if columns: col_map: dict[str, list[str]] = {} for spec in columns: - tname, cols = spec.split(":", 1) + parts = spec.split(":", 1) + if len(parts) != 2: + raise click.UsageError( + f"Invalid --columns '{spec}'. Expected format: table:col1,col2" + ) + tname, cols = parts col_map[tname] = [c.strip() for c in cols.split(",")] for table in schema.tables: if table.name in col_map: @@ -117,6 +122,7 @@ def _apply_increments(datasets: dict, increments: list[dict], batch_index: int) df = datasets[table] offset = step * batch_index + df = df.copy() if unit == "value": df[column] = pd.to_numeric(df[column], errors="coerce") + offset else: @@ -144,6 +150,11 @@ def _parse_partition_by(partition_by: tuple[str, ...]) -> dict[str, str]: table, col = spec.split(":", 1) result[table.strip()] = col.strip() else: + if "*" in result: + raise click.UsageError( + "Multiple --partition-by entries without 'table:' prefix are not allowed. " + "Use 'table:column' format to partition different tables by different columns." + ) result["*"] = spec.strip() return result @@ -523,10 +534,9 @@ def generate( @cli.command("list-domains") def list_domains(): - """List available schema YAMLs.""" - schemas_dir = Path(__file__).parent / "schemas" - for f in sorted(schemas_dir.glob("*.yaml")): - click.echo(f" {f.stem}") + """List available built-in domains.""" + for name in sorted(DOMAIN_REGISTRY): + click.echo(f" {name}") @cli.command("schema-info") diff --git a/src/dataforge/config/loader.py b/src/dataforge/config/loader.py index a2be356..5da95bc 100644 --- a/src/dataforge/config/loader.py +++ b/src/dataforge/config/loader.py @@ -27,6 +27,11 @@ def _parse_schema(raw: dict) -> DomainSchema: fk = None if "foreign_key" in cconf: fk_conf = cconf["foreign_key"] + if "table" not in fk_conf or "column" not in fk_conf: + raise ValueError( + f"FK on '{cname}' is missing required fields. " + f"Expected: foreign_key: {{table: , column: }}" + ) fk = ForeignKey( ref_table=fk_conf["table"], ref_column=fk_conf["column"], diff --git a/src/dataforge/frontend/src/App.tsx b/src/dataforge/frontend/src/App.tsx index bd054c1..6d52033 100644 --- a/src/dataforge/frontend/src/App.tsx +++ b/src/dataforge/frontend/src/App.tsx @@ -135,7 +135,7 @@ const FAKER_CATALOG: { category: string; color: string; methods: { name: string; ]}, ]; -import type { Table, Column } from './types/schema'; +import type { Schema, Table, Column } from './types/schema'; import { SchemaWriter } from './services/SchemaWriter'; import { SchemaReader } from './services/SchemaReader'; import TableNode from './components/TableNode'; @@ -549,7 +549,16 @@ export default function App() { body: JSON.stringify({ provider: aiProvider, apiKey: currentApiKey.trim(), model: currentModel.trim() || undefined, prompt: aiPrompt }), }); const data = await res.json(); - if (!res.ok || data.error) { setAiError(data.error || 'AI generation failed.'); return; } + if (!res.ok || data.error) { + const errMsg = data.error || 'AI generation failed.'; + // If the backend also returned the raw YAML (e.g. duplicate keys), show it so the user can inspect/fix + if (data.yaml) { + setAiError(`${errMsg}\n\nYAML gerado (para referência):\n${data.yaml}`); + } else { + setAiError(errMsg); + } + return; + } const parsed = SchemaReader.parseYaml(data.yaml); setDomain(parsed.domain || 'custom'); const tablesWithPos = parsed.tables.map((t: Table, index: number) => ({ @@ -606,6 +615,7 @@ export default function App() { const [showRunPanel, setShowRunPanel] = useState(false); const [runConfig, setRunConfig] = useState<{ formats: string[], + destination: 'local' | 'cloud' | 'database', outputDir: string, rows: string, uploadTarget: string, @@ -644,8 +654,11 @@ export default function App() { }); const [runLogs, setRunLogs] = useState(''); const [isRunning, setIsRunning] = useState(false); + const runAbortRef = useRef(null); const handleRunCli = async () => { + const controller = new AbortController(); + runAbortRef.current = controller; setIsRunning(true); setRunLogs(''); try { @@ -653,6 +666,7 @@ export default function App() { const res = await fetch('/api/run-cli', { method: 'POST', headers: {'Content-Type': 'application/json'}, + signal: controller.signal, body: JSON.stringify({ yamlStr, formats: runConfig.formats, @@ -703,8 +717,12 @@ export default function App() { } } } catch (e: any) { - setRunLogs(prev => prev + `\nConnection Error: ${e.message || String(e)}`); + if ((e as DOMException).name !== 'AbortError') { + setRunLogs(prev => prev + `\nConnection Error: ${e.message || String(e)}`); + } setIsRunning(false); + } finally { + runAbortRef.current = null; } }; @@ -735,6 +753,16 @@ export default function App() { v{import.meta.env.VITE_APP_VERSION} + (e.currentTarget.style.color = 'var(--text-main)')} + onMouseLeave={e => (e.currentTarget.style.color = 'var(--text-muted)')}> + + Docs + (e.currentTarget.style.color = 'var(--text-main)')} @@ -1162,7 +1190,7 @@ export default function App() { const next = active ? runConfig.formats.filter(f => f !== fmt) : [...runConfig.formats, fmt]; - setRunConfig({ ...runConfig, formats: next.length > 0 ? next : [fmt] }); + setRunConfig(r => ({ ...r, formats: next.length > 0 ? next : [fmt] })); }} style={{ padding: '0.35rem 0.85rem', borderRadius: '6px', border: `1px solid ${active ? '#10b981' : 'rgba(255,255,255,0.1)'}`, background: active ? 'rgba(16,185,129,0.15)' : 'rgba(255,255,255,0.05)', color: active ? '#10b981' : '#94a3b8', cursor: 'pointer', fontSize: '0.85rem', fontWeight: active ? 600 : 400 }}> {fmt.toUpperCase()} @@ -1173,7 +1201,7 @@ export default function App() { {runConfig.formats.includes('json') && (
- setRunConfig(r => ({...r, jsonMode: e.target.value}))} style={{ width: '100%', padding: '0.5rem', background: 'rgba(255,255,255,0.05)', color: 'white', border: '1px solid rgba(255,255,255,0.1)', borderRadius: '6px' }}> @@ -1182,7 +1210,7 @@ export default function App() {
- setRunConfig({...runConfig, rows: e.target.value})} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. 5000" min="1" /> + setRunConfig(r => ({...r, rows: e.target.value}))} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. 5000" min="1" />
@@ -1199,7 +1227,7 @@ export default function App() { const active = runConfig.destination === key; return ( @@ -1211,7 +1239,7 @@ export default function App() { {runConfig.destination === 'local' && (
- setRunConfig({...runConfig, outputDir: e.target.value})} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. output" /> + setRunConfig(r => ({...r, outputDir: e.target.value}))} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. output" />
)} @@ -1220,7 +1248,7 @@ export default function App() {
- setRunConfig(r => ({...r, uploadTarget: e.target.value}))} style={{ width: '100%', padding: '0.5rem', background: 'rgba(255,255,255,0.05)', color: 'white', border: '1px solid rgba(255,255,255,0.1)', borderRadius: '6px' }}> @@ -1229,11 +1257,11 @@ export default function App() {
- setRunConfig({...runConfig, bucket: e.target.value})} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. my-data-lake" /> + setRunConfig(r => ({...r, bucket: e.target.value}))} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. my-data-lake" />
- setRunConfig({...runConfig, prefix: e.target.value})} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. datasets/" /> + setRunConfig(r => ({...r, prefix: e.target.value}))} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. datasets/" />

@@ -1400,7 +1428,7 @@ export default function App() { { setRunConfig({...runConfig, dbUrl: e.target.value}); setDbTestStatus('idle'); setDbTestError(''); }} + onChange={e => { setRunConfig(r => ({...r, dbUrl: e.target.value})); setDbTestStatus('idle'); setDbTestError(''); }} style={{ width: '100%', padding: '0.5rem' }} placeholder="postgresql+psycopg2://user:pass@host:5432/db" /> @@ -1442,7 +1470,7 @@ export default function App() {

- setRunConfig(r => ({...r, ifExists: e.target.value}))} style={{ width: '100%', padding: '0.5rem', background: 'rgba(255,255,255,0.05)', color: 'white', border: '1px solid rgba(255,255,255,0.1)', borderRadius: '6px' }}> @@ -1450,7 +1478,7 @@ export default function App() {
- setRunConfig({...runConfig, dbSchema: e.target.value})} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. public" /> + setRunConfig(r => ({...r, dbSchema: e.target.value}))} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. public" />
@@ -1462,7 +1490,7 @@ export default function App() {

Reproducibility

- setRunConfig({...runConfig, seed: e.target.value})} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. 42" /> + setRunConfig(r => ({...r, seed: e.target.value}))} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. 42" />
@@ -1472,12 +1500,12 @@ export default function App() {
- setRunConfig({...runConfig, recurrence: e.target.value})} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. 60" min="1" /> + setRunConfig(r => ({...r, recurrence: e.target.value}))} style={{ width: '100%', padding: '0.5rem' }} placeholder="e.g. 60" min="1" />
{runConfig.recurrence && (
- setRunConfig({...runConfig, count: e.target.value})} style={{ width: '100%', padding: '0.5rem' }} placeholder="0" min="0" /> + setRunConfig(r => ({...r, count: e.target.value}))} style={{ width: '100%', padding: '0.5rem' }} placeholder="0" min="0" />
)}
@@ -1488,26 +1516,26 @@ export default function App() {
{runConfig.increments.map((inc, idx) => (
- { const next = [...runConfig.increments]; next[idx] = { ...next[idx], table: e.target.value, column: '' }; setRunConfig(r => ({ ...r, increments: next })); }} style={{ flex: '1.2', padding: '0.35rem 0.4rem', background: 'rgba(255,255,255,0.04)', border: '1px solid rgba(255,255,255,0.1)', borderRadius: '6px', color: '#e2e8f0', fontSize: '0.8rem' }}> {tables.map(t => )} - { const next = [...runConfig.increments]; next[idx] = { ...next[idx], column: e.target.value }; setRunConfig(r => ({ ...r, increments: next })); }} style={{ flex: '1.5', padding: '0.35rem 0.4rem', background: 'rgba(255,255,255,0.04)', border: '1px solid rgba(255,255,255,0.1)', borderRadius: '6px', color: inc.column ? '#e2e8f0' : '#475569', fontSize: '0.8rem' }}> {(tables.find(t => t.name === inc.table)?.columns || []).map(c => )} - { const next = [...runConfig.increments]; next[idx] = { ...next[idx], step: e.target.value }; setRunConfig({ ...runConfig, increments: next }); }} + { const next = [...runConfig.increments]; next[idx] = { ...next[idx], step: e.target.value }; setRunConfig(r => ({ ...r, increments: next })); }} style={{ width: '56px', padding: '0.35rem 0.4rem', background: 'rgba(255,255,255,0.04)', border: '1px solid rgba(255,255,255,0.1)', borderRadius: '6px', color: '#e2e8f0', fontSize: '0.8rem' }} placeholder="step" /> - { const next = [...runConfig.increments]; next[idx] = { ...next[idx], unit: e.target.value }; setRunConfig(r => ({ ...r, increments: next })); }} style={{ flex: '1', padding: '0.35rem 0.4rem', background: 'rgba(255,255,255,0.04)', border: '1px solid rgba(255,255,255,0.1)', borderRadius: '6px', color: '#e2e8f0', fontSize: '0.8rem' }}> @@ -1516,7 +1544,7 @@ export default function App() { -
))} @@ -1539,7 +1567,7 @@ export default function App() { const next = active ? runConfig.tablesToInclude.filter(n => n !== t.name) : [...runConfig.tablesToInclude, t.name]; - setRunConfig({ ...runConfig, tablesToInclude: next }); + setRunConfig(r => ({ ...r, tablesToInclude: next })); }} style={{ padding: '0.25rem 0.65rem', borderRadius: '5px', border: `1px solid ${active ? '#60a5fa' : 'rgba(255,255,255,0.1)'}`, background: active ? 'rgba(96,165,250,0.15)' : 'rgba(255,255,255,0.04)', color: active ? '#60a5fa' : '#64748b', cursor: 'pointer', fontSize: '0.8rem', fontWeight: active ? 600 : 400 }}> {t.name} @@ -1548,7 +1576,7 @@ export default function App() { })} {runConfig.tablesToInclude.length > 0 && ( - + )} )} @@ -1565,7 +1593,7 @@ export default function App() { const col = e.target.value; const next = { ...runConfig.partitionByTable }; if (col) next[t.name] = col; else delete next[t.name]; - setRunConfig({ ...runConfig, partitionByTable: next }); + setRunConfig(r => ({ ...r, partitionByTable: next })); }} style={{ flex: 1, padding: '0.35rem 0.5rem', background: 'rgba(255,255,255,0.04)', border: '1px solid rgba(255,255,255,0.1)', borderRadius: '6px', color: runConfig.partitionByTable[t.name] ? '#e2e8f0' : '#475569', fontSize: '0.82rem' }} > @@ -1583,7 +1611,7 @@ export default function App() {