{"id":13481535,"url":"https://github.com/wangzaixiang/scala-sql","last_synced_at":"2025-03-27T12:31:03.986Z","repository":{"id":3424807,"uuid":"4476296","full_name":"wangzaixiang/scala-sql","owner":"wangzaixiang","description":"scala SQL api","archived":false,"fork":false,"pushed_at":"2023-05-31T09:16:02.000Z","size":1541,"stargazers_count":89,"open_issues_count":0,"forks_count":20,"subscribers_count":10,"default_branch":"master","last_synced_at":"2024-06-19T03:06:14.261Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Scala","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":"mccraveiro/mongoose-migration","license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/wangzaixiang.png","metadata":{"files":{"readme":"Readme-ZH_CN.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2012-05-29T01:09:15.000Z","updated_at":"2024-01-20T11:43:52.000Z","dependencies_parsed_at":"2023-01-13T12:30:25.820Z","dependency_job_id":null,"html_url":"https://github.com/wangzaixiang/scala-sql","commit_stats":null,"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wangzaixiang%2Fscala-sql","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wangzaixiang%2Fscala-sql/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wangzaixiang%2Fscala-sql/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wangzaixiang%2Fscala-sql/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wangzaixiang","download_url":"https://codeload.github.com/wangzaixiang/scala-sql/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":213388283,"owners_count":15579711,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-07-31T17:00:52.592Z","updated_at":"2024-07-31T17:06:17.702Z","avatar_url":"https://github.com/wangzaixiang.png","language":"Scala","readme":"utility scala-sql\n=================\n\nscala-sql 2.0 是一个轻量级的 scala jdbc 库，它是一个简单的JDBC的封装，以提供类型安全的、简洁的scala API。\n- 没有新概念。你会用JDBC，就会发现scala-sql很自然。只需要1-2小时，你就会完全熟悉。\n- scala风格，强类型支持、case class支持（不可变风格）。\n- 可以扩展的数据类型支持。\n- 通过macro提供强大的类型支持。\n- 编译时期的SQL语法检查。\n- 提供 Row 类型，无需定义case class也可以读取数据。\n- 提供强类型的 batch API。\n\n\n# 基本用法\nscala-sql 为 `java.sql.Connection` \u0026 `java.sql.DataSource` 提供了如下增强的方法:\n- executeUpdate\n  ```scala\n    dataSource executeUpdate sql\"\"\"update table set name = ${name} and age = ${age} where id = ${id}\"\"\"\n  ```\n  你可以理解上面的代码等同于如下的Java代码:\n  ```java\n    DataSource dataSource = ...;\n    Connection conn = null;\n    PreparedStatement ps = null;\n    try {\n      conn = dataSource.getConnection();\n      ps = conn.prepareStatement(\"update table set name = ? and age = ? where id = ?\")\n      ps.setString(1, name);\n      ps.setInt(2, age);\n      ps.setInt(3, id);\n    \n      ps.executeUpdate\n    }\n    finally {\n      try {\n          if(ps != null) ps.close();\n      }\n      catch(SQLException ex){}\n      try {\n          if(conn != null) conn.close();\n      }\n      catch(SQLException ex) {}\n    }\n  }\n    ```\n  这个例子给出了 scala-sql 的最基本用法，他就是一个JDBC的简单封装，而没有引入更多的概念。\n  \n  在这个例子中，展示了scala-sql的最重要的一个特性：sql插值。他有如下特点：\n  - `${expr}` 不是字符串拼接，而是作为参数传递，因此，使用插值，没有SQL注入风险。\n  - `${expr}` 是强类型检查的。诸如 `java.sql.Connection` 或者 `java.swing.JFrame` 这样的值，在编译期间就会报错。\n  - scala-sql 支持基本的数据类型，包括：`boolean`, `byte`, `short`, `int`, `float`,\n  `double`, `string`, `java.math.BigDecimal`, `java.sql.Date`, `java.sql.Time`, `java.sql.Timestamp` 等.\n  - 支持 scala友好的数据类型 `scala.BigDecimal`, \n  - 支持 `scala.Option[T]` 这里T是上述合法的类型。\n  - 可以扩展支持新的类型 T，只需要提供一个隐士值 `JdbcValueAccessor[T]` 就可以像上述的基本类型一样的作为 `${expr}`传递给sql，以及使用在下面\n  需要映射的`case class`中。\n  - 如果你使用 SQL\"\" 字符串插值，还可以享受到在编译期间的SQL语法检查功能。 这个检查功能是通过连接到编译时期的一个数据库进行验证的，可以检查\n  包括语法、字段名在列的一系列错误。\n    \n- rows\n  ```scala\n    case class User(name: String, age: Int)\n  \n    val users: List[User] = dataSource.rows[User](sql\"select * from users where name like ${name}\")\n  ```\n  scala-sql 提供了一个简单的 ORM 机制，在这个例子中，我们只需要定义一个 `case class`，就可以完成从ResultSet到 case class的映射工作。而且，\n  有别于其他的框架，scala-sql是通过 Macro，在编译时期就自动生成了从 ResultSet 到 Case Class 的转换代码，不会使用到反射方式，性能非常高。\n\n  `rows[T](sql)` 这里的 T 可以是： \n  - Case Class. \n  - `Row` 可以理解为 Row 是一个离线的 ResultSet 行，它提供了和ResultSet一样的API，如 getInt(index) 、getString(name)等。\n  - 基础类型. 如果我们的SQL语句只查询单个字段，那么可以直接使用 `rows[Int](sql\"statement\"\")` 这种形式。\n  \n- foreach\n  ```scala\n  dataSource.foreach(sql\"select * from users where name like ${name}\" { u: User =\u003e\n    ...\n  }\n  ```\n  与 rows 相似。foreach 在迭代中执行代码，而不是返回一个 List[T]。\n- [batch 处理](docs/batch.md)\n  scala-sql提供了一种友好的方式来处理batch insert/update.\n  ```scala\n  case class User(name:String, age:Int, email: String)\n\n  def main(args: Array[String]): Unit = {\n\n    val conn = SampleDB.conn\n\n    // 代码块接收 User 作为参数，返回一个字符串插值。目前，仅支持在代码块的最后一个表达式是字符串插值。但前面代码可以自由，例如，进行必要的计算。\n    // 返回的 batch 对象，后续可以使用 addBatch(user: User) 来处理单行的插入，并以成批的方式进行提交。\n    // 也可以设置 autoCommitCount（批次提交记录数） 或者手动 commit 提交一批数据。\n    val batch = conn.createBatch[User] { u =\u003e\n      val name = u.name.toUpperCase()\n      sql\"insert into users(name, age, email) values(${name}, ${u.age}, ${u.email})\"\n    }\n    \n    val users = User(\"u1\", 10, \"u1\") :: User(\"u2\", 20, \"u2\") :: Nil\n\n    users.foreach { u =\u003e\n      batch.addBatch(u)\n    }\n\n    batch.close()\n\n    // print the rows for test\n    conn.rows[User](\"select * from users\").foreach(println)\n\n  }\n  ```\n  scala-sql还提供 `conn.createMySQLBatch` 方式，支持mysql的特定语法：`insert into table set col1=?, col2 =?` 并在编译期，转化为`insert into table (col1, col2) values(?,?)`的形式，使其也具备批量提交的能力。\n- generateKey\n- withStatement\n  ```scala\n  dataSource.withStatement { stmt: Statement =\u003e ...\n  }\n  ```\n- withPreparedStatement\n- withConnection\n  ```scala\n  dataSource.withConnection { conn: Connection =\u003e ...\n  }\n  ```\n- withTransaction\n  ```scala\n  dataSource.withTransaction { conn: Conntion =\u003e ...\n  }\n  ```\n\n# 编译期语法检查\nscala-sql 可以在编译时对源代码中的sql\"statement\"进行语法检查，诸如SQL语法错误，或者错误的表名、字段名拼写错误等，可以自动检查出来\n1. 在当前目录下编辑 scala-sql.properties 文件。 \n2. 设置 default.url, default.user, default.password, default.driver 信息，使之指向一个用于进行类型检查的数据库。\n3. 使用 SQL\"\" 插值。\n4. 如果我们的项目中会访问多个数据库，我们可以在最外层的类上加上 `@db(name=\"some\")` 注释, 在配置文件中定义：`some.url, some.user, some.password, some.driver` \n\n# JdbcValue[T]， JdbcValueAccessor[T]\nscala-sql defines type class `JdbcValueAccessor[T]`, any type which has an implicit context bound of `JdbcValueAccessor`\ncan be passed into query, and passed out from ResultSet. \nThis include:\n- primary SQL types, such as `byte`, `short`, `int`, `string`, `date`, `time`, `timestamp`, `BigDecimal`\n- scala types: such as `scala.BigDecimal`\n- optional types. Now you can pass a `Option[BigDecimal]` into statement which will auto support the `null`\n- customize your type via define a implicit value `JdbcValueAccessor[T]`\n\n# ResultSetMapper[T]\nscala-sql define type class `ResultSetMapper[T]`, any type which has an implicit context of `ResultSetMapper`\ncan be mapped to a ResulSet, thus, can be used in the `rows[T]`, `row[T]`, `foreach[T]` operations.\n\ninstead of writing the ResultSetMapper yourself, scala-sql provide a Macro which automate generate the\nmapper for Case Class. \n\nSo, does it support all `Case Class` ? of couse not, eg. you Case class `case class User(name: String, url: URL)` is not supported because the url field is not compatible with SQL. the scala-sql Macro provide a stronger type check mechanism for ensure the `Case Class` is able to mapping from ResultSet. \n\nsbt 依赖:\n=====\n```sbt\nlibraryDependencies +=  \"com.github.wangzaixiang\" %% \"scala-sql\" % \"2.0.7\"\n```\n","funding_links":[],"categories":["Database","Table of Contents"],"sub_categories":["Database"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwangzaixiang%2Fscala-sql","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwangzaixiang%2Fscala-sql","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwangzaixiang%2Fscala-sql/lists"}