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18、【死磕Sharding-jdbc】—复杂路由实现

原文作者:阿飞Javaer
原文链接:https://www.jianshu.com/p/bef720fd070c


路由条件

ParsingSQLRouter.java中决定是简单路由还是复杂路由的条件如下;

private RoutingResult route(final List<Object> parameters, final SQLStatement sqlStatement) {
    Collection<String> tableNames = sqlStatement.getTables().getTableNames();
    RoutingEngine routingEngine;
    if (1 == tableNames.size()
            || shardingRule.isAllBindingTables(tableNames)
            || shardingRule.isAllInDefaultDataSource(tableNames)) {
        routingEngine = new SimpleRoutingEngine(shardingRule, parameters, tableNames.iterator().next(), sqlStatement);
    } else {
        // TODO config for cartesian set
        routingEngine = new ComplexRoutingEngine(shardingRule, parameters, tableNames, sqlStatement);
    }
    return routingEngine.route();
}
  • 是否只有一张表--tableNames.size()

说明:这个"一张表"并不是指SQL中只有一张表,而是有分库分表规则的表数量,例如下面这段构造ShardingRule的源码,tableRules()有两个表,所以tableNames.size()的值为2;如果(Arrays.asList(orderTableRule))即只有1个表,那么tableNames.size()的值为1;

ShardingRule.builder()
.dataSourceRule(dataSourceRule)
.tableRules(Arrays.asList(orderTableRule, userTableRule))
.databaseShardingStrategy(*** ***).tableShardingStrategy(*** ***) .build();
  • 是否都是绑定表--shardingRule.isAllBindingTables(tableNames)

说明:isAllBindingTables(tableNames)判断tableNames是否都属于绑定表,例如下面这段构造ShardingRule的源码,.bindingTableRules()里的参数就是绑定表集合,这里是t_order和t_order_item都是绑定表,那么:SELECT od.user_id, od.order_id, oi.item_id, od.status FROM t_order od join t_order_item oi on od.order_id=oi.order_id这个SQL只有t_order和t_order_item两个表且都是绑定表,那么shardingRule.isAllBindingTables(tableNames)为true;

ShardingRule.builder()
.dataSourceRule(dataSourceRule)
.tableRules(Arrays.asList(orderTableRule, orderItemTableRule, userTableRule))
.bindingTableRules(Collections.singletonList(new BindingTableRule(Arrays.asList(orderTableRule, orderItemTableRule))))
. *** ***;
  • 是否都在默认数据源中--shardingRule.isAllInDefaultDataSource(tableNames)

说明:sharding-jdbc判断逻辑源码如下,即只要在表规则集合中能够匹配到逻辑表,就认为不属于默认数据源中(默认数据源不分库分表),例如ShardingRule.builder().dataSourceRule(dataSourceRule).tableRules(Arrays.asList(orderTableRule, orderItemTableRule, userTableRule)),根据tableRules参数可知,主要SQL中有t_usert_ordert_order_item三个表的任意一个表,那么shardingRule.isAllInDefaultDataSource(tableNames)都为false;

public boolean isAllInDefaultDataSource(final Collection<String> logicTables) {
    for (String each : logicTables) {
        if (tryFindTableRule(each).isPresent()) {
            return false;
        }
    }
    return !logicTables.isEmpty();
}

public Optional<TableRule> tryFindTableRule(final String logicTableName) {
    for (TableRule each : tableRules) {
        if (each.getLogicTable().equalsIgnoreCase(logicTableName)) {
            return Optional.of(each);
        }
    }
    return Optional.absent();
}

构造复杂路由

综上分析,如果三个条件都不满足就走复杂路由ComplexRoutingEngine,构造这种场景:
t_order和t_order_item分库分表且绑定表关系,加入一个新的分库分表t_user;ShardingRule如下:

ShardingRule shardingRule = ShardingRule.builder()
        .dataSourceRule(dataSourceRule)
        .tableRules(Arrays.asList(orderTableRule, orderItemTableRule, userTableRule))
        .bindingTableRules(Collections.singletonList(new BindingTableRule(Arrays.asList(orderTableRule, orderItemTableRule))))
        .databaseShardingStrategy(new DatabaseShardingStrategy("user_id", new ModuloDatabaseShardingAlgorithm()))
        .tableShardingStrategy(new TableShardingStrategy("order_id", new ModuloTableShardingAlgorithm()))
        .build();

执行的SQL为:

SELECT od.user_id, od.order_id, oi.item_id, od.status 
FROM `t_user` tu 
join t_order od on tu.user_id=od.user_id 
join t_order_item oi on od.order_id=oi.order_id 
where tu.`status`='VALID' and tu.user_id=?

构造的这个场景:tableNames.size()=3(三张表t_user,t_order,t_order_item都有分库分表规则,所以值为3),shardingRule.isAllBindingTables(tableNames)为false(t_user表不属于绑定表范围);shardingRule.isAllInDefaultDataSource(tableNames)为false(三张表都不属于默认数据源中的表);所以这个SQL会走复杂路由的逻辑;

ComplexRoutingEngine

复杂路由引擎的核心逻辑就是拆分成多个简单路由,然后求笛卡尔积,复杂路由核心源码如下:

@RequiredArgsConstructor
@Slf4j
public final class ComplexRoutingEngine implements RoutingEngine {

    // 分库分表规则
    private final ShardingRule shardingRule;

    // SQL请求参数,猪油一个user_id的值为10
    private final List<Object> parameters;

    // 逻辑表集合:t_order,t_order_item,t_user,三个逻辑表
    private final Collection<String> logicTables;

    // SQL解析结果
    private final SQLStatement sqlStatement;

    // 复杂路由的核心逻辑
    @Override
    public RoutingResult route() {
        Collection<RoutingResult> result = new ArrayList<>(logicTables.size());
        Collection<String> bindingTableNames = new TreeSet<>(String.CASE_INSENSITIVE_ORDER);
        // 遍历逻辑表集合
        for (String each : logicTables) {
            Optional<TableRule> tableRule = shardingRule.tryFindTableRule(each);
            // 如果遍历的表配置了分库分表规则
            if (tableRule.isPresent()) {
                // 如果绑定关系表已经处理过,那么不需要再处理,例如t_order处理过,由于t_order_item与其是绑定关系,那么不需要再处理
                if (!bindingTableNames.contains(each)) {
                    // 根据当前遍历的逻辑表构造一个简单路由规则
                    result.add(new SimpleRoutingEngine(shardingRule, parameters, tableRule.get().getLogicTable(), sqlStatement).route());
                }

                // 根据当前逻辑表,查找其对应的所有绑定表,例如根据t_order就能够查询出t_order和t_order_item;假如配置了.bindingTableRules(***t_point, t_point_detail***),那么,根据t_point能查询出t_point和t_point_detail,其目的是N个绑定表只需要路由一个绑定表即可,因为绑定表之间的路由关系完全一致。
                Optional<BindingTableRule> bindingTableRule = shardingRule.findBindingTableRule(each);
                if (bindingTableRule.isPresent()) {
                    bindingTableNames.addAll(Lists.transform(bindingTableRule.get().getTableRules(), new Function<TableRule, String>() {

                        @Override
                        public String apply(final TableRule input) {
                            return input.getLogicTable();
                        }
                    }));
                }
            }
        }
        log.trace("mixed tables sharding result: {}", result);
        // 如果是复杂路由,但是路由结果为空,那么抛出异常
        if (result.isEmpty()) {
            throw new ShardingJdbcException("Cannot find table rule and default data source with logic tables: '%s'", logicTables);
        }
        // 如果结果的size为1,那么直接返回即可
        if (1 == result.size()) {
            return result.iterator().next();
        }
        // 对刚刚的路由结果集合计算笛卡尔积,就是最终复杂的路由结果
        return new CartesianRoutingEngine(result).route();
    }
}

由上面源码分析可知,会分别对t_user和t_order构造简单路由(t_order_item和t_order是绑定关系,二者取其一即可);

  • t_user只分库不分表(因为构造TableRule时逻辑表和实际表一致),且请求参数为user_id=10,所以t_user这个逻辑表的简单路由结果为:数据源ds_jdbc_0,实际表t_user;
  • t_order分库分表,且请求参数user_id被解析为t_user的条件(笛卡尔积路由引擎会处理),所以t_order的简单路由结果为:数据源ds_jdbc_0和ds_jdbc_1,实际表t_order_0和t_order_1;

debug的result如下:

201805231001

CartesianRoutingEngine

如上分析,求得简单路由结果集后,求笛卡尔积就是复杂路由的最终路由结果,笛卡尔积路由引擎CartesianRoutingEngine的核心源码如下:

@RequiredArgsConstructor
@Slf4j
public final class CartesianRoutingEngine implements RoutingEngine {

    private final Collection<RoutingResult> routingResults;

    @Override
    public CartesianRoutingResult route() {
        CartesianRoutingResult result = new CartesianRoutingResult();
        // getDataSourceLogicTablesMap()的分析参考下面的分析
        for (Entry<String, Set<String>> entry : getDataSourceLogicTablesMap().entrySet()) {
            // 根据数据源&逻辑表,得到实际表集合,即[["t_user"],["t_order_0","t_order_1"]]
            List<Set<String>> actualTableGroups = getActualTableGroups(entry.getKey(), entry.getValue());
            // 把逻辑表名封装,TableUnit的属性有:数据源名称,逻辑表名,实际表名(这三个属性才能确定最终访问的表)
            List<Set<TableUnit>> tableUnitGroups = toTableUnitGroups(entry.getKey(), actualTableGroups);
            // 计算所有实际表的笛卡尔积
            result.merge(entry.getKey(), getCartesianTableReferences(Sets.cartesianProduct(tableUnitGroups)));
        }
        log.trace("cartesian tables sharding result: {}", result);
        return result;
    }

    // 得到数据源-逻辑表集合组成的Map
    private Map<String, Set<String>> getDataSourceLogicTablesMap() {
        // 这里很关键,是得到数据源的交集(上面分析时t_user逻辑表路由到数据源ds_jdbc_0,而t_order表路由到数据源ds_jdbc_0和ds_jdbc_1,数据源交集就是ds_jdbc_0)
        Collection<String> intersectionDataSources = getIntersectionDataSources();
        Map<String, Set<String>> result = new HashMap<>(routingResults.size());
        for (RoutingResult each : routingResults) {
            for (Entry<String, Set<String>> entry : each.getTableUnits().getDataSourceLogicTablesMap(intersectionDataSources).entrySet()) {
                if (result.containsKey(entry.getKey())) {
                    result.get(entry.getKey()).addAll(entry.getValue());
                } else {
                    result.put(entry.getKey(), entry.getValue());
                }
            }
        }
        // 得到的最终结果为数据源-逻辑表集合组成的Map,这里就是{"ds_jdbc_0":["t_order", "t_user"]}
        return result;
    }
    ... ...
}

计算得到的笛卡尔积结果如下:

201805231002

sql.show结果如下,可以看到重写后的2条实际SQL:t_user&t_order_0,以及t_user&t_order_1(t_order_item与t_order是绑定表,保持一致即可):

[INFO ] 2018-05-08 11:13:02,044 --main-- [Sharding-JDBC-SQL] Logic SQL: SELECT od.user_id, od.order_id, oi.item_id, od.status FROM `t_user` tu join t_order od on tu.user_id=od.user_id join t_order_item oi on od.order_id=oi.order_id where tu.`status`='VALID' and tu.user_id=? 
... ...
[INFO ] 2018-05-08 11:13:02,059 --main-- [Sharding-JDBC-SQL] Actual SQL: ds_jdbc_0 ::: SELECT od.user_id, od.order_id, oi.item_id, od.status FROM t_user tu join t_order_0 od on tu.user_id=od.user_id join t_order_item_0 oi on od.order_id=oi.order_id where tu.`status`='VALID' and tu.user_id=? ::: [10] 
[INFO ] 2018-05-08 11:13:02,059 --main-- [Sharding-JDBC-SQL] Actual SQL: ds_jdbc_0 ::: SELECT od.user_id, od.order_id, oi.item_id, od.status FROM t_user tu join t_order_1 od on tu.user_id=od.user_id join t_order_item_1 oi on od.order_id=oi.order_id where tu.`status`='VALID' and tu.user_id=? ::: [10]

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