java orm框架easy-query分庫分表之分表

高併發三駕馬車:分庫分表、MQ、緩存。今天給大家帶來的就是分庫分表的乾貨解決方案,哪怕你不用我的框架也可以從中聽到不一樣的結局方案和實現。

一款支持自動分表分庫的orm框架easy-query 幫助您解脫跨庫帶來的複雜業務代碼,並且提供多種結局方案和自定義路由來實現比中間件更高性能的數據庫訪問。

GITHUB github地址
GITEE gitee地址

目前市面上有的分庫分表JAVA組件有很多：中間件代理有:sharding-sphere(proxy),mycat 客戶端JDBC:sharding-sphere（jdbc）等等，中間件因爲代理了一層會導致所有的sql執行都要經過中間件，性能會大大折扣,但是因爲中間部署可以提供更加省的連接池，客戶端無需代理，僅需對sql進行分析即可實現，但是越靠近客戶的模式可以優化的性能越高，所以本次帶來的框架可以提供前所未有的分片規則自由和前所未有的便捷高性能。

本文 demo地址 https://github.com/xuejmnet/easy-sharding-test

怎麼樣的orm算是支持分表分庫

首先orm是否支持分表分庫不僅僅是看框架是否支持動態修改表名，讓數據正確存入對應的表或者修改對應的數據，這些說實話都是最最簡單的實現，真正需要支持分庫分表那麼需要orm實現複雜的跨表聚合查詢，這纔是分表分庫的精髓，很顯然目前的orm很少有支持的。接下來我將給大家演示基於springboot3.x的分表分庫演示,取模分片和時間分片。本章我們主要以使用爲主後面下一章我們來講解優化方案,包括原理解析,後續有更多的關於分表分庫的經驗是博主多年下來的實戰經驗分享給大家保證大家的happy coding。

初始化項目

進入 https://start.spring.io/ 官網直接下載

安裝依賴


		<!-- https://mvnrepository.com/artifact/com.alibaba/druid -->
		<dependency>
			<groupId>com.alibaba</groupId>
			<artifactId>druid</artifactId>
			<version>1.2.15</version>
		</dependency>
		<!-- mysql驅動 -->
		<dependency>
			<groupId>mysql</groupId>
			<artifactId>mysql-connector-java</artifactId>
			<version>8.0.17</version>
		</dependency>
		<dependency>
			<groupId>com.easy-query</groupId>
			<artifactId>sql-springboot-starter</artifactId>
			<version>0.9.7</version>
		</dependency>
		<dependency>
			<groupId>org.projectlombok</groupId>
			<artifactId>lombok</artifactId>
			<version>1.18.18</version>
		</dependency>
		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-starter-web</artifactId>
		</dependency>

application.yml配置

server:
  port: 8080

spring:

  datasource:
    type: com.alibaba.druid.pool.DruidDataSource
    driver-class-name: com.mysql.cj.jdbc.Driver
    url: jdbc:mysql://127.0.0.1:3306/easy-sharding-test?serverTimezone=GMT%2B8&characterEncoding=utf-8&useSSL=false&allowMultiQueries=true&rewriteBatchedStatements=true
    username: root
    password: root

logging:
  level:
    com.easy.query.core: debug

easy-query:
  enable: true
  name-conversion: underlined
  database: mysql

取模

常見的分片方式之一就是取模分片,取模分片可以讓以分片鍵爲條件的處理完美路由到對應的表,性能上來說非常非常高，但是侷限性也是很大的因爲無意義的id路由會導致僅支持這一個id條件而不支持其他條件的路由，只能全分片表掃描來獲取對應的數據，但是他的實現和理解也是最容易的，當然後續還有基因分片一種可以部分解決僅支持id帶來的問題不過也並不是非常的完美。

簡單的取模分片

我們本次測試案例採用order表對其進行5表拆分:order_00,order_01,order_02,order_03,order_04,採用訂單id取模進行分表
數據庫腳本

CREATE DATABASE IF NOT EXISTS `easy-sharding-test` CHARACTER SET 'utf8mb4';
USE `easy-sharding-test`;
create table order_00
(
    id varchar(32) not null comment '主鍵ID'primary key,
    uid varchar(50) not null comment '用戶id',
    order_no int null comment '訂單號'
)comment '訂單表';
create table order_01
(
    id varchar(32) not null comment '主鍵ID'primary key,
    uid varchar(50) not null comment '用戶id',
    order_no int null comment '訂單號'
)comment '訂單表';
create table order_02
(
    id varchar(32) not null comment '主鍵ID'primary key,
    uid varchar(50) not null comment '用戶id',
    order_no int null comment '訂單號'
)comment '訂單表';
create table order_03
(
    id varchar(32) not null comment '主鍵ID'primary key,
    uid varchar(50) not null comment '用戶id',
    order_no int null comment '訂單號'
)comment '訂單表';
create table order_04
(
    id varchar(32) not null comment '主鍵ID'primary key,
    uid varchar(50) not null comment '用戶id',
    order_no int null comment '訂單號'
)comment '訂單表';

//定義了一個對象並且設置表名和分片初始化器`shardingInitializer`,設置id爲主鍵，並且設置id爲分表建
@Data
@Table(value = "order",shardingInitializer = OrderShardingInitializer.class)
public class OrderEntity {
    @Column(primaryKey = true)
    @ShardingTableKey
    private String id;
    private String uid;
    private Integer orderNo;
}
//編寫訂單取模初始化器,只需要實現兩個方法，當然你也可以自己實現對應的`EntityShardingInitializer`這邊是繼承`easy-query`框架提供的分片取模初始化器
@Component
public class OrderShardingInitializer extends AbstractShardingModInitializer<OrderEntity> {
     /**
     * 設置模幾我們模5就設置5
     * @return
     */
    @Override
    protected int mod() {
        return 5;
    }

    /**
     * 編寫模5後的尾巴長度默認我們設置2就是左補0
     * @return
     */
    @Override
    protected int tailLength() {
        return 2;
    }
}
//編寫分片規則`AbstractModTableRule`由框架提供取模分片路由規則,如果需要自己實現可以繼承`AbstractTableRouteRule`這個抽象類
@Component
public class OrderTableRouteRule extends AbstractModTableRule<OrderEntity> {
    @Override
    protected int mod() {
        return 5;
    }

    @Override
    protected int tailLength() {
        return 2;
    }
}

初始化工作做好了開始編寫代碼

新增初始化


@RestController
@RequestMapping("/order")
@RequiredArgsConstructor(onConstructor_ = @Autowired)
public class OrderController {

    private final EasyQuery easyQuery;

    @GetMapping("/init")
    public Object init() {
        ArrayList<OrderEntity> orderEntities = new ArrayList<>(100);
        List<String> users = Arrays.asList("xiaoming", "xiaohong", "xiaolan");

        for (int i = 0; i < 100; i++) {
            OrderEntity orderEntity = new OrderEntity();
            orderEntity.setId(String.valueOf(i));
            int i1 = i % 3;
            String uid = users.get(i1);
            orderEntity.setUid(uid);
            orderEntity.setOrderNo(i);
            orderEntities.add(orderEntity);
        }
        long l = easyQuery.insertable(orderEntities).executeRows();
        return "成功插入:"+l;
    }
}

查詢單條

按分片鍵查詢

可以完美的路由到對應的數據庫表和操作單表擁有一樣的性能

    @GetMapping("/first")
    public Object first(@RequestParam("id") String id) {
        OrderEntity orderEntity = easyQuery.queryable(OrderEntity.class)
                .whereById(id).firstOrNull();
        return orderEntity;
    }
http://localhost:8080/order/first?id=20
{"id":"20","uid":"xiaolan","orderNo":20}


http-nio-8080-exec-1, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_03` t WHERE t.`id` = ? LIMIT 1
==> http-nio-8080-exec-1, name:ds0, Parameters: 20(String)
<== Total: 1

日誌稍微解釋一下

http-nio-8080-exec-1表示當前語句執行的線程,默認多個分片聚合後需要再線程池中查詢數據後聚合返回。
name:ds0 表示數據源叫做ds0，如果不分庫那麼這個數據源可以忽略,也可以自己指定配置文件中或者設置defaultDataSourceName

全程無需您去計算路由到哪裏,並且規則和業務代碼已經脫離解耦

不按分片鍵查詢

當我們的查詢爲非分片鍵查詢那麼會導致路由需要進行全分片掃描然後來獲取對應的數據進行判斷哪個時我們要的


    @GetMapping("/firstByUid")
    public Object firstByUid(@RequestParam("uid") String uid) {
        OrderEntity orderEntity = easyQuery.queryable(OrderEntity.class)
                .where(o->o.eq(OrderEntity::getUid,uid)).firstOrNull();
        return orderEntity;
    }

http://localhost:8080/order/firstByUid?uid=xiaoming
{"id":"18","uid":"xiaoming","orderNo":18}

//這邊把日誌精簡了一下可以看到他是開啓了5個線程進行分片查詢
==> SHARDING_EXECUTOR_1, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_00` t WHERE t.`uid` = ? LIMIT 1
==> SHARDING_EXECUTOR_5, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_03` t WHERE t.`uid` = ? LIMIT 1
==> SHARDING_EXECUTOR_4, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_04` t WHERE t.`uid` = ? LIMIT 1
==> SHARDING_EXECUTOR_3, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_02` t WHERE t.`uid` = ? LIMIT 1
==> SHARDING_EXECUTOR_2, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_01` t WHERE t.`uid` = ? LIMIT 1
==> SHARDING_EXECUTOR_3, name:ds0, Parameters: xiaoming(String)
==> SHARDING_EXECUTOR_4, name:ds0, Parameters: xiaoming(String)
==> SHARDING_EXECUTOR_5, name:ds0, Parameters: xiaoming(String)
==> SHARDING_EXECUTOR_1, name:ds0, Parameters: xiaoming(String)
==> SHARDING_EXECUTOR_2, name:ds0, Parameters: xiaoming(String)
<== Total: 1

因爲uid不是分片鍵所以在分片查詢的時候需要遍歷所有的表然後返回對應的數據，可能有同學會問就這?當然這只是簡單演示後續下一篇我會給出具體的優化方案來進行處理。

分頁查詢

分片後的分頁查詢是分片下的一個難點,這邊框架自帶功能，分片後分頁之所以難是因爲如果是自行實現業務代碼會變得非常複雜，有一種非常簡易的方式就是把分頁重寫pageIndex永遠爲1，然後全部取到內存後在進行stream過濾，但是帶來的另一個問題就是pageIndex不能便宜過大不然內存會完全存不下導致內存爆炸，並且如果翻頁到最後幾頁那將是災難性的，給程序帶來極其不穩定,但是easy-query提供了和sharding-sphere一樣的分片聚合方式並且因爲靠近業務的關係所以可以有效的優化深度分頁pageIndex過大


    @GetMapping("/page")
    public Object page(@RequestParam("pageIndex") Integer pageIndex,@RequestParam("pageSize") Integer pageSize) {
        EasyPageResult<OrderEntity> pageResult = easyQuery.queryable(OrderEntity.class)
                .orderByAsc(o -> o.column(OrderEntity::getOrderNo))
                .toPageResult(pageIndex, pageSize);
        return pageResult;
    }


http://localhost:8080/order/page?pageIndex=1&pageSize=10

{"total":100,"data":[{"id":"0","uid":"xiaoming","orderNo":0},{"id":"1","uid":"xiaohong","orderNo":1},{"id":"2","uid":"xiaolan","orderNo":2},{"id":"3","uid":"xiaoming","orderNo":3},{"id":"4","uid":"xiaohong","orderNo":4},{"id":"5","uid":"xiaolan","orderNo":5},{"id":"6","uid":"xiaoming","orderNo":6},{"id":"7","uid":"xiaohong","orderNo":7},{"id":"8","uid":"xiaolan","orderNo":8},{"id":"9","uid":"xiaoming","orderNo":9}]}
==> SHARDING_EXECUTOR_3, name:ds0, Preparing: SELECT COUNT(1) FROM `order_02` t
==> SHARDING_EXECUTOR_2, name:ds0, Preparing: SELECT COUNT(1) FROM `order_03` t
==> SHARDING_EXECUTOR_5, name:ds0, Preparing: SELECT COUNT(1) FROM `order_04` t
==> SHARDING_EXECUTOR_1, name:ds0, Preparing: SELECT COUNT(1) FROM `order_01` t
==> SHARDING_EXECUTOR_4, name:ds0, Preparing: SELECT COUNT(1) FROM `order_00` t
<== Total: 1
==> SHARDING_EXECUTOR_5, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_04` t ORDER BY t.`order_no` ASC LIMIT 10
==> SHARDING_EXECUTOR_2, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_03` t ORDER BY t.`order_no` ASC LIMIT 10
==> SHARDING_EXECUTOR_4, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_00` t ORDER BY t.`order_no` ASC LIMIT 10
==> SHARDING_EXECUTOR_1, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_01` t ORDER BY t.`order_no` ASC LIMIT 10
==> SHARDING_EXECUTOR_3, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_02` t ORDER BY t.`order_no` ASC LIMIT 10
<== Total: 10

這邊可以看到一行代碼實現分頁，下面是第二頁

http://localhost:8080/order/page?pageIndex=2&pageSize=10
{"total":100,"data":[{"id":"10","uid":"xiaohong","orderNo":10},{"id":"11","uid":"xiaolan","orderNo":11},{"id":"12","uid":"xiaoming","orderNo":12},{"id":"13","uid":"xiaohong","orderNo":13},{"id":"14","uid":"xiaolan","orderNo":14},{"id":"15","uid":"xiaoming","orderNo":15},{"id":"16","uid":"xiaohong","orderNo":16},{"id":"17","uid":"xiaolan","orderNo":17},{"id":"18","uid":"xiaoming","orderNo":18},{"id":"19","uid":"xiaohong","orderNo":19}]}

==> SHARDING_EXECUTOR_9, name:ds0, Preparing: SELECT COUNT(1) FROM `order_02` t
==> SHARDING_EXECUTOR_8, name:ds0, Preparing: SELECT COUNT(1) FROM `order_01` t
==> SHARDING_EXECUTOR_10, name:ds0, Preparing: SELECT COUNT(1) FROM `order_04` t
==> SHARDING_EXECUTOR_7, name:ds0, Preparing: SELECT COUNT(1) FROM `order_03` t
==> SHARDING_EXECUTOR_6, name:ds0, Preparing: SELECT COUNT(1) FROM `order_00` t
<== Total: 1
==> SHARDING_EXECUTOR_9, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_01` t ORDER BY t.`order_no` ASC LIMIT 20
==> SHARDING_EXECUTOR_8, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_03` t ORDER BY t.`order_no` ASC LIMIT 20
==> SHARDING_EXECUTOR_10, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_04` t ORDER BY t.`order_no` ASC LIMIT 20
==> SHARDING_EXECUTOR_6, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_02` t ORDER BY t.`order_no` ASC LIMIT 20
==> SHARDING_EXECUTOR_7, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no` FROM `order_00` t ORDER BY t.`order_no` ASC LIMIT 20
<== Total: 10

按時間分表

這邊我們簡單還是以order訂單爲例,按月進行分片假設我們從2022年1月到2023年5月一共17個月表名爲t_order_202201、t_order_202202、t_order_202203...t_order_202304、t_order_202305

數據庫腳本

create table t_order_202201
(
    id varchar(32) not null comment '主鍵ID'primary key,
    uid varchar(50) not null comment '用戶id',
    order_no int not null comment '訂單號',
    create_time datetime not null comment '創建時間'
)comment '訂單表';
create table t_order_202202
(
    id varchar(32) not null comment '主鍵ID'primary key,
    uid varchar(50) not null comment '用戶id',
    order_no int not null comment '訂單號',
    create_time datetime not null comment '創建時間'
)comment '訂單表';
....
create table t_order_202304
(
    id varchar(32) not null comment '主鍵ID'primary key,
    uid varchar(50) not null comment '用戶id',
    order_no int not null comment '訂單號',
    create_time datetime not null comment '創建時間'
)comment '訂單表';
create table t_order_202305
(
    id varchar(32) not null comment '主鍵ID'primary key,
    uid varchar(50) not null comment '用戶id',
    order_no int not null comment '訂單號',
    create_time datetime not null comment '創建時間'
)comment '訂單表';


@Data
@Table(value = "t_order",shardingInitializer = OrderByMonthShardingInitializer.class)
public class OrderByMonthEntity {

    @Column(primaryKey = true)
    private String id;
    private String uid;
    private Integer orderNo;
    /**
     * 分片鍵改爲時間
     */
    @ShardingTableKey
    private LocalDateTime createTime;
}

//路由規則可以直接繼承AbstractShardingMonthInitializer也可以自己實現
@Component
public class OrderByMonthShardingInitializer extends AbstractShardingMonthInitializer<OrderByMonthEntity> {
   /**
     * 開始時間不可以使用LocalDateTime.now()因爲會導致每次啓動開始時間都不一樣
     * @return
     */
    @Override
    protected LocalDateTime getBeginTime() {
        return LocalDateTime.of(2022,1,1,0,0);
    }

    /**
     * 如果不設置那麼就是當前時間,用於程序啓動後自動計算應該有的表包括最後時間
     * @return
     */
    @Override
    protected LocalDateTime getEndTime() {
        return LocalDateTime.of(2023,5,31,0,0);
    }

    @Override
    public void configure0(ShardingEntityBuilder<OrderByMonthEntity> builder) {
        //後續用來實現優化分表
    }
}
//按月分片路由規則也可以自己實現因爲框架已經封裝好了所以可以用框架自帶的
@Component
public class OrderByMonthTableRouteRule extends AbstractMonthTableRule<OrderByMonthEntity> {
    @Override
    protected LocalDateTime convertLocalDateTime(Object shardingValue) {
        return (LocalDateTime)shardingValue;
    }
}

初始化


@RestController
@RequestMapping("/orderMonth")
@RequiredArgsConstructor(onConstructor_ = @Autowired)
public class OrderMonthController {

    private final EasyQuery easyQuery;

    @GetMapping("/init")
    public Object init() {
        ArrayList<OrderByMonthEntity> orderEntities = new ArrayList<>(100);
        List<String> users = Arrays.asList("xiaoming", "xiaohong", "xiaolan");
        LocalDateTime beginTime=LocalDateTime.of(2022,1,1,0,0);
        LocalDateTime endTime=LocalDateTime.of(2023,5,31,0,0);
        int i=0;
        while(!beginTime.isAfter(endTime)){

            OrderByMonthEntity orderEntity = new OrderByMonthEntity();
            orderEntity.setId(String.valueOf(i));
            int i1 = i % 3;
            String uid = users.get(i1);
            orderEntity.setUid(uid);
            orderEntity.setOrderNo(i);
            orderEntity.setCreateTime(beginTime);
            orderEntities.add(orderEntity);
            beginTime=beginTime.plusDays(1);
            i++;
        }
        long l = easyQuery.insertable(orderEntities).executeRows();
        return "成功插入:"+l;
    }
}

http://localhost:8080/orderMonth/init
成功插入:516

獲取第一條數據

    @GetMapping("/first")
    public Object first(@RequestParam("id") String id) {
        OrderEntity orderEntity = easyQuery.queryable(OrderEntity.class)
                .whereById(id).firstOrNull();
        return orderEntity;
    }

http://localhost:8080/orderMonth/first?id=11
{"id":"11","uid":"xiaolan","orderNo":11,"createTime":"2022-01-12T00:00:00"}
//以每5組一個次併發執行聚合

==> SHARDING_EXECUTOR_1, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202205` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_1, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_2, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202207` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_2, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_3, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202303` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_3, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_4, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202212` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_4, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_5, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202302` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_5, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_1, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202304` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_5, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202206` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_2, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202305` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_1, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_2, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_4, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202209` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_3, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202204` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_5, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_3, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_4, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_2, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202208` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_5, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202201` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_3, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202210` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_5, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_4, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202202` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_3, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_2, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_4, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_1, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202211` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_1, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_2, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202203` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_5, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202301` t WHERE t.`id` = ? LIMIT 1
==> SHARDING_EXECUTOR_2, name:ds0, Parameters: 11(String)
==> SHARDING_EXECUTOR_5, name:ds0, Parameters: 11(String)
<== Total: 1

獲取範圍內的數據

    @GetMapping("/range")
    public Object first() {
        List<OrderByMonthEntity> list = easyQuery.queryable(OrderByMonthEntity.class)
                .where(o -> o.rangeClosed(OrderByMonthEntity::getCreateTime, LocalDateTime.of(2022, 3, 1, 0, 0), LocalDateTime.of(2022, 9, 1, 0, 0)))
                .toList();
        return list;
    }
http://localhost:8080/orderMonth/range
[{"id":"181","uid":"xiaohong","orderNo":181,"createTime":"2022-07-01T00:00:00"},{"id":"182","uid":"xiaolan","orderNo":182,"createTime":"2022-07-02T00:00:00"},{"id":"183","uid":"xiaoming","orderNo":183,"createTime":"2022-07-03T00:00:00"},...........,{"id":"239","uid":"xiaolan","orderNo":239,"createTime":"2022-08-28T00:00:00"},{"id":"240","uid":"xiaoming","orderNo":240,"createTime":"2022-08-29T00:00:00"},{"id":"241","uid":"xiaohong","orderNo":241,"createTime":"2022-08-30T00:00:00"},{"id":"242","uid":"xiaolan","orderNo":242,"createTime":"2022-08-31T00:00:00"}]

//可以精準定位到對應的分片路由上獲取數據
==> SHARDING_EXECUTOR_1, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202207` t WHERE t.`create_time` >= ? AND t.`create_time` <= ?
==> SHARDING_EXECUTOR_5, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202209` t WHERE t.`create_time` >= ? AND t.`create_time` <= ?
==> SHARDING_EXECUTOR_2, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202206` t WHERE t.`create_time` >= ? AND t.`create_time` <= ?
==> SHARDING_EXECUTOR_4, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202203` t WHERE t.`create_time` >= ? AND t.`create_time` <= ?
==> SHARDING_EXECUTOR_3, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202205` t WHERE t.`create_time` >= ? AND t.`create_time` <= ?
==> SHARDING_EXECUTOR_4, name:ds0, Parameters: 2022-03-01T00:00(LocalDateTime),2022-09-01T00:00(LocalDateTime)
==> SHARDING_EXECUTOR_3, name:ds0, Parameters: 2022-03-01T00:00(LocalDateTime),2022-09-01T00:00(LocalDateTime)
==> SHARDING_EXECUTOR_2, name:ds0, Parameters: 2022-03-01T00:00(LocalDateTime),2022-09-01T00:00(LocalDateTime)
==> SHARDING_EXECUTOR_5, name:ds0, Parameters: 2022-03-01T00:00(LocalDateTime),2022-09-01T00:00(LocalDateTime)
==> SHARDING_EXECUTOR_1, name:ds0, Parameters: 2022-03-01T00:00(LocalDateTime),2022-09-01T00:00(LocalDateTime)
==> SHARDING_EXECUTOR_4, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202208` t WHERE t.`create_time` >= ? AND t.`create_time` <= ?
==> SHARDING_EXECUTOR_2, name:ds0, Preparing: SELECT t.`id`,t.`uid`,t.`order_no`,t.`create_time` FROM `t_order_202204` t WHERE t.`create_time` >= ? AND t.`create_time` <= ?
==> SHARDING_EXECUTOR_4, name:ds0, Parameters: 2022-03-01T00:00(LocalDateTime),2022-09-01T00:00(LocalDateTime)
==> SHARDING_EXECUTOR_2, name:ds0, Parameters: 2022-03-01T00:00(LocalDateTime),2022-09-01T00:00(LocalDateTime)
<== Total: 185

最後

目前爲止你已經看到了easy-query對於分片的便捷性，但是本章只是開胃小菜,相信瞭解分庫分表的小夥伴肯定會說就這？不是和sharding-jdbc一樣嗎爲什麼要用你的呢。我想說第一篇只是給大家瞭解一下如何使用,後續的文章纔是分表分庫的精髓相信我你一定沒看過

demo地址 https://github.com/xuejmnet/easy-sharding-test

GITHUB github地址
GITEE gitee地址

javaer你還在手寫分表分庫?來看看這個框架怎麼做的乾貨滿滿

java orm框架easy-query分庫分表之分表

怎麼樣的orm算是支持分表分庫

初始化項目

安裝依賴

取模

簡單的取模分片

新增初始化

查詢單條

按分片鍵查詢

不按分片鍵查詢

分頁查詢

按時間分表

初始化

獲取第一條數據

獲取範圍內的數據

最後

這應該是java最好用的orm之一了

流暢的orm讓我發現我牴觸的是mybatis而不是java

放棄老舊的Mybatis,強類型替換字符串,這是一款你不應該錯過的ORM

解放生產力orm併發更新下應該這麼處理求求你別再用UpdateById了

數據安全之數據庫字段加解密檢索和前端返回脫敏？看看我這個最強解決方案

https://yachay.unat.edu.pe/blog/index.php?comment_area=format_blog&comment_component=blog&comment_co

linux以太網驅動總結

javaer你還在手寫分表分庫?來看看這個框架怎麼做的 乾貨滿滿

java orm框架easy-query分庫分表之分表

怎麼樣的orm算是支持分表分庫

初始化項目

安裝依賴

取模

簡單的取模分片

新增初始化

查詢單條

按分片鍵查詢

不按分片鍵查詢

分頁查詢

按時間分表

初始化

獲取第一條數據

獲取範圍內的數據

最後

javaer你還在手寫分表分庫?來看看這個框架怎麼做的乾貨滿滿