【死磕Sharding-jdbc】---数据源

public final class Main {    public static void main(final String[] args) throws SQLException {        // step1: 配置sharding数据源        DataSource dataSource = getShardingDataSource();        // step2：创建表        createTable(dataSource);        // step3：插入数据        insertData(dataSource);        printSimpleSelect(dataSource);        printGroupBy(dataSource);        printHintSimpleSelect(dataSource);        dropTable(dataSource);    }    ... ...}

private static ShardingDataSource getShardingDataSource() throws SQLException {    // 构造DataSourceRule，即key与数据源的KV对；    DataSourceRule dataSourceRule = new DataSourceRule(createDataSourceMap());    // 建立逻辑表是t_order，实际表是t_order_0，t_order_1的TableRule    TableRule orderTableRule = TableRule.builder("t_order").actualTables(Arrays.asList("t_order_0", "t_order_1")).dataSourceRule(dataSourceRule).build();    // 建立逻辑表是t_order_item，实际表是t_order_item_0，t_order_item_1的TableRule    TableRule orderItemTableRule = TableRule.builder("t_order_item").actualTables(Arrays.asList("t_order_item_0", "t_order_item_1")).dataSourceRule(dataSourceRule).build();    ShardingRule shardingRule = ShardingRule.builder()                .dataSourceRule(dataSourceRule)                .tableRules(Arrays.asList(orderTableRule, orderItemTableRule))                // 增加绑定表--绑定表代表一组表，这组表的逻辑表与实际表之间的映射关系是相同的。比如t_order与t_order_item就是这样一组绑定表关系,它们的分库与分表策略是完全相同的,那么可以使用它们的表规则将它们配置成绑定表，绑定表所有路由计算将会只使用主表的策略；                .bindingTableRules(Collections.singletonList(new BindingTableRule(Arrays.asList(orderTableRule, orderItemTableRule))))                // 指定数据库sharding策略--根据user_id字段的值取模                .databaseShardingStrategy(new DatabaseShardingStrategy("user_id", new ModuloDatabaseShardingAlgorithm()))                // 指定表sharding策略--根据order_id字段的值取模                .tableShardingStrategy(new TableShardingStrategy("order_id", new ModuloTableShardingAlgorithm())).build();    return new ShardingDataSource(shardingRule);}// 创建两个数据源，一个是ds_jdbc_0，一个是ds_jdbc_1，并绑定映射关系keyprivate static Map<String, DataSource> createDataSourceMap() {    Map<String, DataSource> result = new HashMap<>(2);    result.put("ds_jdbc_0", createDataSource("ds_jdbc_0"));    result.put("ds_jdbc_1", createDataSource("ds_jdbc_1"));    return result;}// 以dbcp组件创建一个数据源private static DataSource createDataSource(final String dataSourceName) {    BasicDataSource result = new BasicDataSource();    result.setDriverClassName(com.mysql.jdbc.Driver.class.getName());    result.setUrl(String.format("jdbc:mysql://localhost:3306/%s", dataSourceName));    result.setUsername("root");    // sharding-jdbc默认以密码为空的root用户访问，如果修改了root用户的密码，这里修改为真实的密码即可；    result.setPassword("");    return result;}

private Collection<String> doSharding(final Collection<ShardingValue<?>> shardingValues, final Collection<String> availableTargetNames) {    // shardingAlgorithm即sharding算法分为三种：NoneKey，SingleKey和MultipleKeys    if (shardingAlgorithm instanceof NoneKeyShardingAlgorithm) {        return Collections.singletonList(((NoneKeyShardingAlgorithm) shardingAlgorithm).doSharding(availableTargetNames, shardingValues.iterator().next()));    }    if (shardingAlgorithm instanceof SingleKeyShardingAlgorithm) {        // 得到SingleKeyShardingAlgorithm的具体实现，在ShardingStrategy的构造方法中赋值        SingleKeyShardingAlgorithm<?> singleKeyShardingAlgorithm = (SingleKeyShardingAlgorithm<?>) shardingAlgorithm;        // ShardingValue就是sharding的列和该列的值，在这里分别为order_id和1000        ShardingValue shardingValue = shardingValues.iterator().next();        // sharding列的类型分为三种：SINGLE，LIST和RANGE        switch (shardingValue.getType()) {            // 如果是where order_id=1000，那么type就是SINGLE            case SINGLE:                // doEqualSharding只返回一个值，为了doSharding()返回值的统一，用Collections.singletonList()包装成集合；                return Collections.singletonList(singleKeyShardingAlgorithm.doEqualSharding(availableTargetNames, shardingValue));            case LIST:                return singleKeyShardingAlgorithm.doInSharding(availableTargetNames, shardingValue);            case RANGE:                return singleKeyShardingAlgorithm.doBetweenSharding(availableTargetNames, shardingValue);            default:                throw new UnsupportedOperationException(shardingValue.getType().getClass().getName());        }    }    if (shardingAlgorithm instanceof MultipleKeysShardingAlgorithm) {        return ((MultipleKeysShardingAlgorithm) shardingAlgorithm).doSharding(availableTargetNames, shardingValues);    }    throw new UnsupportedOperationException(shardingAlgorithm.getClass().getName());}

public ShardingValueType getType() {    //     if (null != value) {        return ShardingValueType.SINGLE;    }    if (!values.isEmpty()) {        return ShardingValueType.LIST;    }    return ShardingValueType.RANGE;}

public final class ModuloTableShardingAlgorithm implements SingleKeyTableShardingAlgorithm<Integer> {    // 分析前提，假设预期分到两个表中[t_order_0,t_order_1]，且执行的SQL为SELECT o.* FROM t_order o where o.order_id=1001 AND o.user_id=10，那么分表列order_id的值为1001    @Override    public String doEqualSharding(final Collection<String> tableNames, final ShardingValue<Integer> shardingValue) {        // 遍历表名[t_order_0,t_order_1]        for (String each : tableNames) {            // 直到表名是以分表列order_id的值1001对2取模的值即1结尾，那么就是命中的表名，即t_order_1            if (each.endsWith(shardingValue.getValue() % tableNames.size() + "")) {                return each;            }        }        throw new UnsupportedOperationException();    }    @Override    public Collection<String> doInSharding(final Collection<String> tableNames, final ShardingValue<Integer> shardingValue) {        Collection<String> result = new LinkedHashSet<>(tableNames.size());        // 从这里可知，doInSharding()和doEqualSharding()的区别就是doInSharding()时分表列有多个值（shardingValue.getValues()），例如order_id的值为[1001,1002]，遍历这些值，然后每个值按照doEqualSharding()的逻辑计算表名        for (Integer value : shardingValue.getValues()) {            for (String tableName : tableNames) {                if (tableName.endsWith(value % tableNames.size() + "")) {                    result.add(tableName);                }            }        }        return result;    }    @Override    public Collection<String> doBetweenSharding(final Collection<String> tableNames, final ShardingValue<Integer> shardingValue) {        Collection<String> result = new LinkedHashSet<>(tableNames.size());        // 从这里可知，doBetweenSharding()和doInSharding()的区别就是doBetweenSharding()时分表列的多个值通过shardingValue.getValueRange()得到；而doInSharding()是通过shardingValue.getValues()得到；        Range<Integer> range = shardingValue.getValueRange();        for (Integer i = range.lowerEndpoint(); i <= range.upperEndpoint(); i++) {            for (String each : tableNames) {                if (each.endsWith(i % tableNames.size() + "")) {                    result.add(each);                }            }        }        return result;    }}

public ShardingValue<?> getShardingValue(final List<Object> parameters) {    List<Comparable<?>> conditionValues = getValues(parameters);    switch (operator) {        case EQUAL:            return new ShardingValue<Comparable<?>>(column.getTableName(), column.getName(), conditionValues.get(0));        case IN:            return new ShardingValue<>(column.getTableName(), column.getName(), conditionValues);        case BETWEEN:            return new ShardingValue<>(column.getTableName(), column.getName(), Range.range(conditionValues.get(0), BoundType.CLOSED, conditionValues.get(1), BoundType.CLOSED));        default:            throw new UnsupportedOperationException(operator.getExpression());    }}

private static ShardingDataSource getShardingDataSource() throws SQLException {    DataSourceRule dataSourceRule = new DataSourceRule(createDataSourceMap());    TableRule orderTableRule = TableRule            .builder("t_order")            .actualTables(Arrays.asList("t_order_0", "t_order_1", "t_order_2"))            .dataSourceRule(dataSourceRule)            .build();    TableRule orderItemTableRule = TableRule            .builder("t_order_item")            .actualTables(Arrays.asList("t_order_item_0", "t_order_item_1", "t_order_item_2"))            .dataSourceRule(dataSourceRule)            .build();    ... ...}private static Map<String, DataSource> createDataSourceMap() {    Map<String, DataSource> result = new HashMap<>(3);    result.put("ds_jdbc_0", createDataSource("ds_jdbc_0"));    result.put("ds_jdbc_1", createDataSource("ds_jdbc_1"));    result.put("ds_jdbc_2", createDataSource("ds_jdbc_2"));    return result;}