wenyuan65
wenyuan65

理解Sharding jdbc原理，看这一篇就够了

相比于Spring基于AbstractRoutingDataSource实现的分库分表功能，Sharding jdbc在单库单表扩展到多库多表时，兼容性方面表现的更好一点。例如，spring实现的分库分表sql写法如下：

select id, name, price, publish, intro 
from book${tableIndex}
where id = #{id,jdbcType=INTEGER}

sql中的表名book需要加一个分表的后缀tableIndex，也就是需要在sql注入的参数中指定插入哪个表。相比，Sharding jdbc在这一块封装的更好一点。其sql中，根本不需要指定tableIndex，而是根据分库分表策略自动路由。

select id, name, price, publish, intro
from book 
where id = #{id,jdbcType=INTEGER}

Sharding jdbc的这种特性，在水平扩展的时候无疑更具有吸引力。试想一下，一个项目开发一段时间后，单库单表数据量急剧上升，需要分库分表解决数据库的访问压力。而现有sql配置都是基于单库单表实现的，如果基于spring的AbstractRoutingDataSource实现，需要修改每一个相关表的sql，修改涉及较多地方，出错概率较大。而基于Sharding jdbc实现时，sql无需修改，只需要在spring中添加Sharding jdbc的相关配置即可，减少了修改面，大大简化分库分表的实现难度。

那么，Sharding jdbc是如何实现这种分库分表的逻辑呢？下面我们用一段简单、易懂的代码描述Sharding jdbc的原理。

通常我们在写一段访问数据库的数据时，逻辑是这样的：

    ClassPathXmlApplicationContext ctx = new ClassPathXmlApplicationContext("application.xml");
    DataSource dataSource = ctx.getBean("dataSource", DataSource.class);
    Connection connection = dataSource.getConnection();
		
    String sql = "select id, name, price, publish, intro from book where id = 111";
    PreparedStatement ps = connection.prepareStatement(sql);
    ResultSet rs = ps.executeQuery();
    // handle ResultSet...

Sharding jdbc是基于JDBC协议实现的，当我们获得dataSource时，这个dataSource是Sharding jdbc自己定义的一个SpringShardingDataSource类型的数据源，该数据源在返回getConnection()及prepareStatement()时，分别返回ShardingConnection和ShardingPreparedStatement的实例对象。然后在executeQuery()时，ShardingPreparedStatement做了这样的一件事：

  1. 根据逻辑sql，经过分库分表策略逻辑计算，获得分库分表的路由结果SQLRouteResult；
  2. SQLRouteResult中包含真实的数据源以及转换后的真正sql，利用真实的数据源去执行获得ResultSet；
  3. 将ResultSet列表封装成一个可以顺序读的ResultSet对象IteratorReducerResultSet。
class ShardingPreparedStatement implements PreparedStatement {

	@Override
	public ResultSet executeQuery() throws SQLException {
		List routeResults = routeSql(logicSql);
		
		List resultSets = new ArrayList<>(routeResults.size());
		for (SQLRouteResult routeResult : routeResults) {
			PreparedStatement ps = routeResult.getDataSource().getConnection.prepareStatement(routeResult.getParsedSql());
			ResultSet rs = ps.executeQuery();
			resultSets.add(rs);
		}
		
		return new IteratorReducerResultSet(resultSets);
	}
        .....

}

其中，分库分表策略的sql路由过程，我们将Sharding jdbc中的相关代码全部抽出来，放到一起来观看这个过程的实现：

    // 环境准备
    @SuppressWarnings("resource")
    ClassPathXmlApplicationContext ctx = new ClassPathXmlApplicationContext("application.xml");
    SpringShardingDataSource dataSource = ctx.getBean(SpringShardingDataSource.class);
    Field field = SpringShardingDataSource.class.getSuperclass().getDeclaredField("shardingContext");
    field.setAccessible(true);
    ShardingContext sctx = (ShardingContext)field.get(dataSource);
    ShardingRule shardingRule = sctx.getShardingRule();
		
    String logicSql = "select id, name, price, publish, intro from book where id = ?";
    List parameters = new ArrayList<>();
    parameters.add(2000);
        
    // sql解析
    MySqlStatementParser parser = new MySqlStatementParser(logicSql);
    MySQLSelectVisitor visitor = new MySQLSelectVisitor();
    SQLStatement statement = parser.parseStatement();
    visitor.getParseContext().setShardingRule(shardingRule);
    statement.accept(visitor);
		
    SQLParsedResult parsedResult = visitor.getParseContext().getParsedResult();
    if (visitor.getParseContext().isHasOrCondition()) {
        new OrParser(statement, visitor).fillConditionContext(parsedResult);
    } 
    visitor.getParseContext().mergeCurrentConditionContext();
    System.out.println("Parsed SQL result: " + parsedResult);
    System.out.println("Parsed SQL: " + visitor.getSQLBuilder());
    parsedResult.getRouteContext().setSqlBuilder(visitor.getSQLBuilder());
    parsedResult.getRouteContext().setSqlStatementType(SQLStatementType.SELECT);
        
    // 分库分表路由
    SQLRouteResult result = new SQLRouteResult(parsedResult.getRouteContext().getSqlStatementType(), parsedResult.getMergeContext(), parsedResult.getGeneratedKeyContext());
    for (ConditionContext each : parsedResult.getConditionContexts()) {
        Collection tables = parsedResult.getRouteContext().getTables();
        final Set logicTables = new HashSet<>();
        tables.forEach(a -> logicTables.add(a.getName()));
        	
        SingleTableRouter router = new SingleTableRouter(shardingRule, 
            logicTables.iterator().next(), 
            each, 
            parsedResult.getRouteContext().getSqlStatementType());
        	
        RoutingResult routingResult = router.route();
            
        // sql改写 --> routingResult.getSQLExecutionUnits() 
        // 		---> SingleRoutingTableFactor.replaceSQL(sqlBuilder).buildSQL()
        // 结果合并
        result.getExecutionUnits().addAll(routingResult.getSQLExecutionUnits(parsedResult.getRouteContext().getSqlBuilder()));
    }
//        amendSQLAccordingToRouteResult(parsedResult, parameters, result);
    for (SQLExecutionUnit each : result.getExecutionUnits()) {
        System.out.println(each.getDataSource() + " " + each.getSql() + " " + parameters);
    } 
  
     
   
  1. 准备环境。由于Sharding jdbc分库分表中ShardingRule这个类是贯穿整个路由过程，我们在Spring中写好Sharding jdbc的配置，利用反射获取一个这个对象。（Sharding jdbc版本以及配置，在文章最后列出，方便debug这个过程）
    2.  
   
  2. sql解析。Sharding jdbc使用阿里的Druid库解析sql。在这个过程中，Sharding jdbc实现了一个自己的sql解析内容缓存容器SqlBuilder。当语法分析中解析到一个表名的时候，在SqlBuilder中缓存一个sql相关的逻辑表名的token。并且，Sharding jdbc会将sql按照语义解析为多个segment。例如，"select id, name, price, publish, intro from book where id = ？"将解析为，"select id, name, price, publish, intro | from | book | where | id = ?"。
    3.  
   
  3. 分库分表路由。根据ShardingRule中指定的分库分表列的参数值，以及分库分表策略，实行分库分表，得到一个RoutingResult 。RoutingResult 中包含一个真实数据源，以及逻辑表名和实际表名。
    4.  
   
  4. sql改写。在SqlBuilder中，查找sql中解析的segment，将和逻辑表名一致的segment替换成实际表名。（segment中可以标注该地方是不是表名）
    5.  
  
 
  
以上代码执行结果如下：
 
  
Parsed SQL result: SQLParsedResult(routeContext=RouteContext(tables=[Table(name=book, alias=Optional.absent())], sqlStatementType=null, sqlBuilder=null), generatedKeyContext=GeneratedKeyContext(columns=[], columnNameToIndexMap={}, valueTable={}, rowIndex=0, columnIndex=0, autoGeneratedKeys=0, columnIndexes=null, columnNames=null), conditionContexts=[ConditionContext(conditions={})], mergeContext=MergeContext(orderByColumns=[], groupByColumns=[], aggregationColumns=[], limit=null))
Parsed SQL: SELECT id, name, price, publish, intro FROM [Token(book)] WHERE id = ?
dataSource1 SELECT id, name, price, publish, intro FROM book_00 WHERE id = ? [2000]
dataSource2 SELECT id, name, price, publish, intro FROM book_02 WHERE id = ? [2000]
dataSource1 SELECT id, name, price, publish, intro FROM book_02 WHERE id = ? [2000]
dataSource2 SELECT id, name, price, publish, intro FROM book_01 WHERE id = ? [2000]
dataSource0 SELECT id, name, price, publish, intro FROM book_00 WHERE id = ? [2000]
dataSource0 SELECT id, name, price, publish, intro FROM book_01 WHERE id = ? [2000]
dataSource2 SELECT id, name, price, publish, intro FROM book_00 WHERE id = ? [2000]
dataSource1 SELECT id, name, price, publish, intro FROM book_01 WHERE id = ? [2000]
dataSource0 SELECT id, name, price, publish, intro FROM book_02 WHERE id = ? [2000]
 
  
实际上，我们可以用更通俗易懂的代码表示sql改写的这个过程：
 
  
    String logicSql = "select id, name, price, publish, intro from book where id = 111";
    MySqlStatementParser parser = new MySqlStatementParser(logicSql);
    SQLStatement statement = parser.parseStatement();
    MySQLSimpleVisitor visitor = new MySQLSimpleVisitor();
    statement.accept(visitor);
		
    String logicTable = "book";
    String realTable = "book_00";
    String token = "\\$\\{" + logicTable + "\\}";
		
    String sqlBuilder = visitor.getAppender().toString();
    String sql = sqlBuilder.replaceAll(token, realTable);
		
    System.out.println(sqlBuilder);
    System.out.println(sql);
 
  
MySQLSimpleVisitor代码如下：
 
  
public class MySQLSimpleVisitor extends MySqlOutputVisitor {

    public MySQLSimpleVisitor() {
        super(new StringBuilder());
    }

    @Override
    public boolean visit(SQLExprTableSource x) {
        StringBuilder sb = new StringBuilder();
        sb.append("${");
        sb.append(x.getExpr().toString()).append('}');
        print(sb.toString());
		
        if (x.getAlias() != null) {
            print(' ');
            print(x.getAlias());
        }

        for (int i = 0; i < x.getHintsSize(); ++i) {
            print(' ');
            x.getHints().get(i).accept(this);
        }

        return false;
    }
	
}
 
  
结果如下：
 
  
SELECT id, name, price, publish, intro
FROM ${book}
WHERE id = 111
SELECT id, name, price, publish, intro
FROM book_00
WHERE id = 111
 
  
以上，大致将Sharding jdbc的原理及实现过程介绍了一下，如果想要了解正真的实现过程和细节，还需要对照代码仔细推敲。
 
  
本文的实现环境：
 
  
        
            com.dangdang
            sharding-jdbc-core
            1.4.2
        
        
            com.dangdang
            sharding-jdbc-config-spring
            1.4.0
        
 
  
application.xml
 
  



	

	
		
		
		
		
	

	
		
		
		
		
	

	
		
		
		
		
	
	
	
	
	
	
        
            
                
            
        
    

 
  
MemberSingleKeyTableShardingAlgorithm.java
 
  
public class MemberSingleKeyTableShardingAlgorithm implements SingleKeyTableShardingAlgorithm {

	public String doEqualSharding(Collection availableTargetNames, ShardingValue shardingValue) {
		String routeDBSuffix = getRouteDBSuffix(shardingValue.getValue());
        for (String each : availableTargetNames) {
            if (each.endsWith(routeDBSuffix)) {
                return each;
            }
        }
        throw new IllegalArgumentException();
	}

	public Collection doInSharding(Collection availableTargetNames, ShardingValue shardingValue) {
		Collection result = new LinkedHashSet(availableTargetNames.size());
        for (int value : shardingValue.getValues()) {
        	String routeDBSuffix = getRouteDBSuffix(value);
            for (String tableName : availableTargetNames) {
                if (tableName.endsWith(routeDBSuffix)) {
                    result.add(tableName);
                }
            }
        }
        return result;
	}

	public Collection doBetweenSharding(Collection availableTargetNames,
			ShardingValue shardingValue) {
		Collection result = new LinkedHashSet(availableTargetNames.size());
		Range range = (Range) shardingValue.getValueRange();
		for (int i = range.lowerEndpoint(); i <= range.upperEndpoint(); i++) {
			String routeDBSuffix = getRouteDBSuffix(i);
			for (String each : availableTargetNames) {
				if (each.endsWith(routeDBSuffix)) {
					result.add(each);
				}
			}
		}
		return result;
	}
	
	public String getRouteDBSuffix(Integer shardingCode) {
		int modValue = shardingCode % 3;
		return "0" + modValue;
	}

}
 
  
 
 
  

                            
                        
                    
                    
                    

                    
                    
                    

                
                

                    
                

            
        
    
    

        
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