1. 介绍

当if else过多时，可以通过策略模式来进行重构。先定义一个接口，各else处理分支实现这个接口，并定义 < 条件 , 处理类 > 的映射关系，然后根据条件找到响应的处理类执行即可。

当有新的分支时，只需要新增一个实现类，增加一个映射关系即可。

2. 策略模式结构

策略接口：提供了一个函数式接口，提供了分支逻辑实现的多种方法：匿名类、普通类等

context类：通过组合策略接口，并执行统一方法，实现面向接口的动态方法。

先来一个官方的伪代码：

策略接口和实现类

// 一个函数式的策略接口
interface Strategy is
    method execute(a, b)

// 实现类：实现了+、-、* 算法。
class ConcreteStrategyAdd implements Strategy is
    method execute(a, b) is
        return a + b

class ConcreteStrategySubtract implements Strategy is
    method execute(a, b) is
        return a - b

class ConcreteStrategyMultiply implements Strategy is
    method execute(a, b) is
        return a * b

上下文类的逻辑

class Context is
    // 引用策略接口：面向策略
    private strategy: Strategy


    //传入策略实现类，或者维护一个map
    method setStrategy(Strategy strategy) is
        this.strategy = strategy

    //根据传入的实现类，执行不同的逻辑
    method executeStrategy(int a, int b) is
        return strategy.execute(a, b)

client

class ExampleApplication is
    method main() is
       。。。
        创建上下文对象

        if (action == addition) then
            context.setStrategy(new ConcreteStrategyAdd())

        if (action == subtraction) then
            context.setStrategy(new ConcreteStrategySubtract())

        if (action == multiplication) then
            context.setStrategy(new ConcreteStrategyMultiply())

        result = context.executeStrategy(First number, Second number)

3. 策略模式更一般的用法：工厂类的查表

现在我们知道通过策略模式可以去掉 if-else 分支判断。

更一般的框架，可以根据策略工厂类的逻辑，借助“查表法”（根据 type查表、或字符匹配）来替代根据 type 分支判断。

60 | 策略模式（上）：如何避免冗长的if-else/switch分支判断代码？

4. 策略模式使用

4.1. 场景一: 表迁移

有一个需求：项目版本升级（不同版本之间数据表结构不同），需要迁移数据库到新的版本，要求出一个框架，适配项目中所有的数据库的迁移。

首先可以使用策略模式，一个表对应一个实现类，通过**类名子串（“t1,t2,t3”）**标识要迁移的表有哪些，然后遍历实例化这些实现类，并放到一个list中，最后遍历这个list，完成某个库的迁移；

其次数据迁移涉及到库的连接和库的关闭，所以在创建迁移逻辑之前和迁移之后要维护好这些逻辑，可以统一放到一个context类中。

大致逻辑：

1.策略接口和实现

 迁移时的sql逻辑接口
 @FunctionalInterface
 public interface JdbcDataMigrateFunction {
        /**
         * 所有迁移和被迁移的表都在一个mysql节点下
         */
        void dataMigrate(Connection dbConn, JdbcPros pros);
    }

@Log4j2
public class FlinkJobAndInstanceFunction implements JdbcDataMigrateFunction {
...
}

2.context的逻辑

//构造分支的运行逻辑：1. 根据字符串去创建分支逻辑类；2. 分支运行逻辑构建：连接数据库，遍历执行分支逻辑，关闭连接。
public class JdbcTableBuilder {

    private JdbcPros jdbcPros;
    private JdbcDataMigrateFunction[] jdbcDataMigrateFunctions;
    private Connection datalakeDbConn;

    public JdbcTableBuilder(String propertiesPath) {
        jdbcPros = getJdbcPros(propertiesPath);
        String datalakeMigrationTables = jdbcPros.getDatalakeMigrationTables();
        String[] datalakeTableNames = datalakeMigrationTables.split(",");
        jdbcDataMigrateFunctions = new JdbcDataMigrateFunction[datalakeTableNames.length];
        for (int i = 0; i < datalakeTableNames.length; i++) {
            jdbcDataMigrateFunctions[i] = createJdbcDataMigrateFunction(datalakeTableNames[i]);
        }
    }

    JdbcDataMigrateFunction createJdbcDataMigrateFunction(String dataflowTableName) {
        switch (dataflowTableName) {
            case "table1":
                return new FlinkJobFunction();
            // todo:添加其他表的迁移逻辑
//            case ""
//                ;
            default:
                throw new RuntimeException("No logical instance of the table migration could be found");
        }
    }


    //CONTEXT的逻辑
    public void runDataMigrate() {
        open(jdbcPros);
        //run
        for (JdbcDataMigrateFunction function : jdbcDataMigrateFunctions) {
            function.dataMigrate(datalakeDbConn, dodpDbConn);
        }
        //close
        closeConn();
    }


    /**
     * 1、jdbc链接
     */
    public void open(JdbcPros pros) {
        try {
            DriverManager.getDrivers();
            Class.forName(pros.getDrivername());

            dodpDbConn = DriverManager.getConnection(
                    pros.getDodpDbURL(),
                    pros.getDodpDbUsername(),
                    pros.getDodpDbPassword());
        } catch (SQLException | ClassNotFoundException e) {
            e.printStackTrace();
        }
    }


    /**
     * 关闭Connection
     */
    public void closeConn() {
        if (dodpDbConn != null) {
            try {
                dodpDbConn.close();
            } catch (SQLException se) {
            } finally {
                dodpDbConn = null;
            }
        }
    }

4.2. flink connector类型转换

/** Base class for all converters that convert between JDBC object and Flink internal object. */

public class JdbcRowConverter
        extends AbstractRowConverter<
                ResultSet, JsonArray, FieldNamedPreparedStatement, LogicalType> {

    private static final long serialVersionUID = 1L;

    //context逻辑：根据flink sql中字段类型来构建类型转换规则
    public JdbcRowConverter(RowType rowType) {
        super(rowType);
        for (int i = 0; i < rowType.getFieldCount(); i++) {
            toInternalConverters.add(
                    wrapIntoNullableInternalConverter(
                            createInternalConverter(rowType.getTypeAt(i))));
            toExternalConverters.add(
                    wrapIntoNullableExternalConverter(
                            createExternalConverter(fieldTypes[i]), fieldTypes[i]));
        }
    }

    @Override
    protected ISerializationConverter<FieldNamedPreparedStatement>
            wrapIntoNullableExternalConverter(
                    ISerializationConverter<FieldNamedPreparedStatement> serializationConverter,
                    LogicalType type) {
        final int sqlType =
                JdbcTypeUtil.typeInformationToSqlType(
                        TypeConversions.fromDataTypeToLegacyInfo(
                                TypeConversions.fromLogicalToDataType(type)));
        return (val, index, statement) -> {
            if (val == null
                    || val.isNullAt(index)
                    || LogicalTypeRoot.NULL.equals(type.getTypeRoot())) {
                statement.setNull(index, sqlType);
            } else {
                serializationConverter.serialize(val, index, statement);
            }
        };
    }

    //内部转换：将数据转换为flink能进行处理的数据
    @Override
    public RowData toInternal(ResultSet resultSet) throws Exception {
        GenericRowData genericRowData = new GenericRowData(rowType.getFieldCount());
        for (int pos = 0; pos < rowType.getFieldCount(); pos++) {
            Object field = resultSet.getObject(pos + 1);
            genericRowData.setField(pos, toInternalConverters.get(pos).deserialize(field));
        }
        return genericRowData;
    }

    @Override
    public RowData toInternalLookup(JsonArray jsonArray) throws Exception {
        GenericRowData genericRowData = new GenericRowData(rowType.getFieldCount());
        for (int pos = 0; pos < rowType.getFieldCount(); pos++) {
            Object field = jsonArray.getValue(pos);
            genericRowData.setField(pos, toInternalConverters.get(pos).deserialize(field));
        }
        return genericRowData;
    }

    //外部数据转换
    @Override
    public FieldNamedPreparedStatement toExternal(
            RowData rowData, FieldNamedPreparedStatement statement) throws Exception {
        for (int index = 0; index < rowData.getArity(); index++) {
            toExternalConverters.get(index).serialize(rowData, index, statement);
        }
        return statement;
    }

    //具体策略的实现类：由flink sql字段名匹配类型转换规则实例
    @Override
    protected IDeserializationConverter createInternalConverter(LogicalType type) {
        switch (type.getTypeRoot()) {
            case NULL:
                return val -> null;
            case BOOLEAN:
            case FLOAT:
            case DOUBLE:
            case INTERVAL_YEAR_MONTH:
            case INTERVAL_DAY_TIME:
            case INTEGER:
            case BIGINT:
                return val -> val;
            case TINYINT:
                return val -> ((Integer) val).byteValue();
            case SMALLINT:
                // Converter for small type that casts value to int and then return short value,
                // since
                // JDBC 1.0 use int type for small values.
                return val -> val instanceof Integer ? ((Integer) val).shortValue() : val;
            case DECIMAL:
                final int precision = ((DecimalType) type).getPrecision();
                final int scale = ((DecimalType) type).getScale();
                // using decimal(20, 0) to support db type bigint unsigned, user should define
                // decimal(20, 0) in SQL,
                // but other precision like decimal(30, 0) can work too from lenient consideration.
                return val ->
                        val instanceof BigInteger
                                ? DecimalData.fromBigDecimal(
                                        new BigDecimal((BigInteger) val, 0), precision, scale)
                                : DecimalData.fromBigDecimal((BigDecimal) val, precision, scale);
            case DATE:
                return val ->
                        (int) ((Date.valueOf(String.valueOf(val))).toLocalDate().toEpochDay());
            case TIME_WITHOUT_TIME_ZONE:
                return val ->
                        (int)
                                ((Time.valueOf(String.valueOf(val))).toLocalTime().toNanoOfDay()
                                        / 1_000_000L);
            case TIMESTAMP_WITH_TIME_ZONE:
            case TIMESTAMP_WITHOUT_TIME_ZONE:
                return val -> TimestampData.fromTimestamp((Timestamp) val);
            case CHAR:
            case VARCHAR:
                return val -> StringData.fromString(val.toString());
            case BINARY:
            case VARBINARY:
                return val -> (byte[]) val;
            case ARRAY:
            case ROW:
            case MAP:
            case MULTISET:
            case RAW:
            default:
                throw new UnsupportedOperationException("Unsupported type:" + type);
        }
    }

    @Override
    protected ISerializationConverter<FieldNamedPreparedStatement> createExternalConverter(
            LogicalType type) {
        switch (type.getTypeRoot()) {
            case BOOLEAN:
                return (val, index, statement) ->
                        statement.setBoolean(index, val.getBoolean(index));
            case TINYINT:
                return (val, index, statement) -> statement.setByte(index, val.getByte(index));
            case SMALLINT:
                return (val, index, statement) -> statement.setShort(index, val.getShort(index));
            case INTEGER:
            case INTERVAL_YEAR_MONTH:
                return (val, index, statement) -> statement.setInt(index, val.getInt(index));
            case BIGINT:
            case INTERVAL_DAY_TIME:
                return (val, index, statement) -> statement.setLong(index, val.getLong(index));
            case FLOAT:
                return (val, index, statement) -> statement.setFloat(index, val.getFloat(index));
            case DOUBLE:
                return (val, index, statement) -> statement.setDouble(index, val.getDouble(index));
            case CHAR:
            case VARCHAR:
                // value is BinaryString
                return (val, index, statement) ->
                        statement.setString(index, val.getString(index).toString());
            case BINARY:
            case VARBINARY:
                return (val, index, statement) -> statement.setBytes(index, val.getBinary(index));
            case DATE:
                return (val, index, statement) ->
                        statement.setDate(
                                index, Date.valueOf(LocalDate.ofEpochDay(val.getInt(index))));
            case TIME_WITHOUT_TIME_ZONE:
                return (val, index, statement) ->
                        statement.setTime(
                                index,
                                Time.valueOf(
                                        LocalTime.ofNanoOfDay(val.getInt(index) * 1_000_000L)));
            case TIMESTAMP_WITH_TIME_ZONE:
            case TIMESTAMP_WITHOUT_TIME_ZONE:
                final int timestampPrecision = ((TimestampType) type).getPrecision();
                return (val, index, statement) ->
                        statement.setTimestamp(
                                index, val.getTimestamp(index, timestampPrecision).toTimestamp());
            case DECIMAL:
                final int decimalPrecision = ((DecimalType) type).getPrecision();
                final int decimalScale = ((DecimalType) type).getScale();
                return (val, index, statement) ->
                        statement.setBigDecimal(
                                index,
                                val.getDecimal(index, decimalPrecision, decimalScale)
                                        .toBigDecimal());
            case ARRAY:
            case MAP:
            case MULTISET:
            case ROW:
            case RAW:
            default:
                throw new UnsupportedOperationException("Unsupported type:" + type);
        }
    }
}