生成类型安全的查询结构体用于过滤实体。过滤支持分页、搜索和范围查询，具有编译时安全性。
#[derive(Entity)]
#[entity(table = "products")]
pub struct Product {
#[id]
pub id: Uuid,
#[field(create, update, response)]
#[filter]
pub name: String,
#[field(create, update, response)]
#[filter(like)]
pub description: String,
#[field(create, update, response)]
#[filter(range)]
pub price: i64,
#[field(create, response)]
#[filter]
pub category_id: Uuid,
#[field(response)]
#[auto]
#[filter(range)]
pub created_at: DateTime<Utc>,
}/// 用于过滤Product实体的查询参数。
#[derive(Debug, Clone, Default)]
pub struct ProductQuery {
/// 按精确name匹配过滤。
pub name: Option<String>,
/// 按description模式过滤（ILIKE）。
pub description: Option<String>,
/// 按最低价格过滤。
pub price_from: Option<i64>,
/// 按最高价格过滤。
pub price_to: Option<i64>,
/// 按精确category_id匹配过滤。
pub category_id: Option<Uuid>,
/// 按最早created_at过滤。
pub created_at_from: Option<DateTime<Utc>>,
/// 按最晚created_at过滤。
pub created_at_to: Option<DateTime<Utc>>,
/// 最大结果数。
pub limit: Option<i64>,
/// 跳过的结果数。
pub offset: Option<i64>,
}#[async_trait]
pub trait ProductRepository: Send + Sync {
// ... 标准CRUD方法
/// 使用过滤器查询产品。
async fn query(&self, query: ProductQuery) -> Result<Vec<Product>, Self::Error>;
}SELECT id, name, description, price, category_id, created_at
FROM products
WHERE ($1 IS NULL OR name = $1)
AND ($2 IS NULL OR description ILIKE $2)
AND ($3 IS NULL OR price >= $3)
AND ($4 IS NULL OR price <= $4)
AND ($5 IS NULL OR category_id = $5)
AND ($6 IS NULL OR created_at >= $6)
AND ($7 IS NULL OR created_at <= $7)
ORDER BY created_at DESC
LIMIT $8 OFFSET $9过滤字段等于提供值的记录。
#[filter]
pub status: String,
#[filter(eq)] // 同上
pub category_id: Uuid,生成：
pub status: Option<String>,
pub category_id: Option<Uuid>,SQL：
WHERE status = $1
AND category_id = $2使用不区分大小写的模式匹配（ILIKE）过滤。
#[filter(like)]
pub name: String,
#[filter(like)]
pub description: String,生成：
pub name: Option<String>,
pub description: Option<String>,SQL：
WHERE name ILIKE $1
AND description ILIKE $2用法：
let query = ProductQuery {
name: Some("%widget%".into()), // 包含 "widget"
description: Some("premium%".into()), // 以 "premium" 开头
..Default::default()
};在范围内过滤（包含边界）。
#[filter(range)]
pub price: i64,
#[filter(range)]
pub created_at: DateTime<Utc>,生成：
pub price_from: Option<i64>,
pub price_to: Option<i64>,
pub created_at_from: Option<DateTime<Utc>>,
pub created_at_to: Option<DateTime<Utc>>,SQL：
WHERE price >= $1 AND price <= $2
AND created_at >= $3 AND created_at <= $4// 按类别查找产品
let query = ProductQuery {
category_id: Some(electronics_category_id),
..Default::default()
};
let products = repo.query(query).await?;// 获取第2页（每页20项）
let query = ProductQuery {
limit: Some(20),
offset: Some(20),
..Default::default()
};
let products = repo.query(query).await?;// 搜索价格实惠的电子产品
let query = ProductQuery {
category_id: Some(electronics_category_id),
price_from: Some(0),
price_to: Some(10000), // $100.00
name: Some("%phone%".into()),
limit: Some(50),
..Default::default()
};
let products = repo.query(query).await?;// 获取本月创建的产品
let now = Utc::now();
let month_start = now.with_day(1).unwrap().date_naive().and_hms_opt(0, 0, 0).unwrap();
let query = ProductQuery {
created_at_from: Some(month_start.and_utc()),
created_at_to: Some(now),
..Default::default()
};
let products = repo.query(query).await?;use axum::{extract::Query, Json};
#[derive(Deserialize)]
pub struct ProductQueryParams {
pub name: Option<String>,
pub category_id: Option<Uuid>,
pub min_price: Option<i64>,
pub max_price: Option<i64>,
pub page: Option<i64>,
pub per_page: Option<i64>,
}
async fn list_products(
Query(params): Query<ProductQueryParams>,
pool: Extension<PgPool>,
) -> Result<Json<Vec<ProductResponse>>, AppError> {
let page = params.page.unwrap_or(1);
let per_page = params.per_page.unwrap_or(20).min(100);
let query = ProductQuery {
name: params.name.map(|n| format!("%{}%", n)),
category_id: params.category_id,
price_from: params.min_price,
price_to: params.max_price,
limit: Some(per_page),
offset: Some((page - 1) * per_page),
..Default::default()
};
let products = pool.query(query).await?;
let responses: Vec<_> = products.into_iter().map(ProductResponse::from).collect();
Ok(Json(responses))
}当启用 soft_delete 时，查询自动排除已删除的记录：
#[derive(Entity)]
#[entity(table = "documents", soft_delete)]
pub struct Document {
#[id]
pub id: Uuid,
#[field(create, response)]
#[filter(like)]
pub title: String,
#[field(skip)]
pub deleted_at: Option<DateTime<Utc>>,
}生成的SQL：
SELECT * FROM documents
WHERE deleted_at IS NULL
AND ($1 IS NULL OR title ILIKE $1)
LIMIT $2 OFFSET $3包含已删除记录的附加方法：
async fn query_with_deleted(&self, query: DocumentQuery) -> Result<Vec<Document>, Self::Error>;对于复杂查询，使用 sql = "trait" 并实现自定义过滤：
#[derive(Entity)]
#[entity(table = "products", sql = "trait")]
pub struct Product { /* ... */ }
pub trait ProductQueryExt {
async fn search_fulltext(&self, term: &str, limit: i64) -> Result<Vec<Product>, sqlx::Error>;
async fn find_by_tags(&self, tags: &[String]) -> Result<Vec<Product>, sqlx::Error>;
}
#[async_trait]
impl ProductQueryExt for PgPool {
async fn search_fulltext(&self, term: &str, limit: i64) -> Result<Vec<Product>, sqlx::Error> {
let rows: Vec<ProductRow> = sqlx::query_as(
r#"
SELECT * FROM products
WHERE to_tsvector('english', name || ' ' || description)
@@ plainto_tsquery('english', $1)
ORDER BY ts_rank(to_tsvector('english', name || ' ' || description),
plainto_tsquery('english', $1)) DESC
LIMIT $2
"#
)
.bind(term)
.bind(limit)
.fetch_all(self)
.await?;
Ok(rows.into_iter().map(Product::from).collect())
}
async fn find_by_tags(&self, tags: &[String]) -> Result<Vec<Product>, sqlx::Error> {
let rows: Vec<ProductRow> = sqlx::query_as(
"SELECT * FROM products WHERE tags && $1"
)
.bind(tags)
.fetch_all(self)
.await?;
Ok(rows.into_iter().map(Product::from).collect())
}
}- 默认分页 — 始终应用合理的限制以防止大结果集
- 验证模式 — 清理LIKE模式以防止SQL问题
- 索引过滤列 — 为经常过滤的字段创建数据库索引
- 使用特定过滤器 — 尽可能优先使用精确匹配而非模式匹配
- 与排序结合 — 考虑向查询结构体添加排序字段
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