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From Leonard Xu <xbjt...@gmail.com>
Subject Re: 关于RetractStream流写mysql出现java.sql.SQLException: No value specified for parameter 1问题
Date Thu, 23 Apr 2020 08:23:34 GMT
Hi,
我本地复现了下,用1.10.0发现的你的sql是ok的,结果也符合预期☺️,如下[1]:
看到你建了JIRA,我们在issue里继续跟进吧

祝好,
Leonard Xu

[1]
mysql> select * from order_state_cnt;
+------------+--------------+------+
| order_date | product_code | cnt  |
+------------+--------------+------+
| 2020-04-01 | product1     |    3 |
| 2020-04-01 | product2     |    5 |
| 2020-04-01 | product1     |    5 |
| 2020-04-01 | product2     |    9 |
+------------+--------------+------+
4 rows in set (0.00 sec)

mysql> select * from order_state_cnt;
+------------+--------------+------+
| order_date | product_code | cnt  |
+------------+--------------+------+
| 2020-04-01 | product1     |    3 |
| 2020-04-01 | product2     |    5 |
| 2020-04-01 | product1     |    5 |
| 2020-04-01 | product2     |    9 |
| 2020-04-01 | product1     |    2 |
| 2020-04-01 | product2     |    4 |
+------------+--------------+------+
6 rows in set (0.00 sec)



> 在 2020年4月23日,10:48,1101300123 <hdxg1101300123@163.com> 写道:
> 
> 
> 
> 我给你一些数据和代码吧!和我真实场景错误一样
> 订单主表:orders
> 13点两条记录;order_state是状态 0取消 1待支付
> {"order_no":"order1","order_state":1,"pay_time":"","create_time":"2020-04-01 13:00:00","update_time":"2020-04-01
13:00:00"}
> {"order_no":"order2","order_state":1,"pay_time":"","create_time":"2020-04-01 13:00:00","update_time":"2020-04-01
13:00:00"}
> 
> 
> 13:15
> 来了一条新的记录 取消订单
> {"order_no":"order1","order_state":0,"pay_time":"","create_time":"2020-04-01 13:00:00","update_time":"2020-04-01
13:15:00"}
> 
> 
> 订单明细表:order_detail
> 4条记录
> {"order_no":"order1","product_code":"product1","quantity":3,"create_time":"2020-04-01
13:00:00","update_time":"2020-04-01 13:00:00"}
> {"order_no":"order1","product_code":"product2","quantity":5,"create_time":"2020-04-01
13:00:00","update_time":"2020-04-01 13:00:00"}
> {"order_no":"order2","product_code":"product1","quantity":2,"create_time":"2020-04-01
13:00:00","update_time":"2020-04-01 13:00:00"}
> {"order_no":"order2","product_code":"product2","quantity":4,"create_time":"2020-04-01
13:00:00","update_time":"2020-04-01 13:00:00"}
> 
> 
> 需求的要求是当订单创建后我们就要统计该订单对应的商品数量,而当订单状态变为取消时我们要减掉该订单对应的商品数量。
> 
> 
> 代码
> package Learn.kafkasql;
> 
> import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
> import org.apache.flink.table.api.EnvironmentSettings;
> import org.apache.flink.table.api.Table;
> import org.apache.flink.table.api.java.StreamTableEnvironment;
> import org.apache.flink.types.Row;
> 
> public class SqlCount {
> public static void main(String[] args) throws Exception {
>        StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();
> env.setParallelism(1);
> EnvironmentSettings settings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
> StreamTableEnvironment tenv = StreamTableEnvironment.create(env,settings);
> 
> tenv.sqlUpdate("CREATE TABLE orders " +
> "            (" +
> "               order_no     string," +
> "               order_state  int," +
> "               pay_time     string," +
> "               create_time  string," +
> "               update_time  string" +
> "             ) " +
> "       WITH (" +
> "               'connector.type' = 'kafka',       " +
> "               'connector.version' = 'universal', " +//--kafka版本
> "               'connector.topic' = 'tp_orders'," +//--kafkatopic
> "               'connector.properties.zookeeper.connect' = '192.168.179.120:2181', "
+
> "               'connector.properties.bootstrap.servers' = '192.168.179.120:9092'," +
> "               'connector.properties.group.id' = 'testGroup'," +
> "               'connector.startup-mode' = 'latest-offset'," +
> "               'format.type' = 'json'  " +//--数据为json格式
> "             )");
> tenv.sqlUpdate("CREATE TABLE order_detail " +
> "            (" +
> "               order_no     string," +
> "               product_code string," +
> "               quantity     int," +
> "               create_time  string," +
> "               update_time  string" +
> "             ) " +
> "       WITH (" +
> "               'connector.type' = 'kafka', " +
> "               'connector.version' = 'universal',  " +//--kafka版本
> "               'connector.topic' = 'tp_order_detail'," +//--kafkatopic
> "               'connector.properties.zookeeper.connect' = '192.168.179.120:2181', "
+
> "               'connector.properties.bootstrap.servers' = '192.168.179.120:9092'," +
> "               'connector.properties.group.id' = 'testGroup'," +
> "               'connector.startup-mode' = 'latest-offset'," +
> "               'format.type' = 'json'  " +//--数据为json格式
> "             )");
> 
> tenv.sqlUpdate("CREATE TABLE product_sale" +
> "             (" +
> "              order_date string," +
> "              product_code string," +
> "              cnt int" +
> "              ) " +
> "         WITH (" +
> "           'connector.type' = 'jdbc', " +
> "           'connector.url' = 'jdbc:mysql://192.168.179.120:3306/flink?serverTimezone=UTC&useSSL=true',
" +
> "           'connector.table' = 'order_state_cnt', " +
> "           'connector.driver' = 'com.mysql.jdbc.Driver', " +
> "           'connector.username' = 'root'," +
> "           'connector.password' = '123456'," +
> "           'connector.write.flush.max-rows' = '1'," +//--默认每5000条数据写入一次,测试调小一点
> "           'connector.write.flush.interval' = '2s'," +//--写入时间间隔
> "           'connector.write.max-retries' = '3'" +
> "         )");
> tenv.sqlUpdate("insert into product_sale " +
> "select create_date,product_code,sum(quantity)" +
> "from (select t1.order_no," +
> "             t1.create_date," +
> "             t2.product_code," +
> "             t2.quantity" +
> "       from (select order_no," +
> "                    order_state," +
> "                    substring(create_time,1,10) create_date," +
> "                    update_time ," +
> "                    row_number() over(partition by order_no order by update_time desc)
as rn" +
> "              from orders" +
> "              )t1" +
> "       left join order_detail t2" +
> "            on t1.order_no=t2.order_no" +
> "      where t1.rn=1" +//--取最新的订单状态数据
> "      and t1.order_state<>0" +//--不包含取消订单
> "   )t3" +
> " group by create_date,product_code");
> 
> Table table = tenv.sqlQuery("select create_date,product_code,sum(quantity)" +
> "from (select t1.order_no," +
> "             t1.create_date," +
> "             t2.product_code," +
> "             t2.quantity" +
> "       from (select order_no," +
> "                    order_state," +
> "                    substring(create_time,1,10) create_date," +
> "                    update_time ," +
> "                    row_number() over(partition by order_no order by update_time desc)
as rn" +
> "              from orders" +
> "              )t1" +
> "       left join order_detail t2" +
> "            on t1.order_no=t2.order_no" +
> "      where t1.rn=1" +
> "      and t1.order_state<>0" +
> "   )t3" +
> " group by create_date,product_code");
> tenv.toRetractStream(table, Row.class).print();
> tenv.execute("count");
> }
> }
> mysql 建表语句
> CREATE TABLE `order_state_cnt` (
>  `order_date` varchar(12) ,
>  `product_code` varchar(12) ,
>  `cnt` int 
> ) ENGINE=InnoDB DEFAULT CHARSET=utf8
> 
> 
> 使用的是kafka命令行一条条发送数据的方式
> 
> 
> 主要是deleteStatement.executeBatch();这个方法报错
> @Override
> public void executeBatch() throws SQLException {
> if (keyToRows.size() > 0) {
> for (Map.Entry<Row, Tuple2<Boolean, Row>> entry : keyToRows.entrySet()) {
> Row pk = entry.getKey();
> Tuple2<Boolean, Row> tuple = entry.getValue();
> if (tuple.f0) {
> processOneRowInBatch(pk, tuple.f1);
> } else {
> setRecordToStatement(deleteStatement, pkTypes, pk);
> deleteStatement.addBatch();
> }
> }
> internalExecuteBatch();
> deleteStatement.executeBatch();
> keyToRows.clear();
> }
> }
> 在2020年4月23日 00:21,Leonard Xu<xbjtdcq@gmail.com> 写道:
> 赞详细的分析!
> 
> 没能复现你说的问题,最后一步的分析应该有点小问题,我看下了jdbc
mysql的实现
> com/mysql/jdbc/PreparedStatement.java#executeBatchInternal() 1289行
> 是会判断batchedArgs数组的大小后会直接返回的,应该不会执行,你可以进一步调试确认下
> ```
> if (this.batchedArgs == null || this.batchedArgs.size() == 0) {
> return new long[0];
> }
> ```
> 
> 祝好,
> Leonard Xu
> 
> 在 2020年4月22日,21:58,1101300123 <hdxg1101300123@163.com> 写道:
> 
> 
> 
> 我在SQL关联后把结果写入mysql出现          No value specified for parameter
1错误?
> 我的版本是1.10.0,代码如下
> JDBCUpsertTableSink build = JDBCUpsertTableSink.builder()  
> .setTableSchema(results.getSchema())
> .setOptions(JDBCOptions.builder()
> .setDBUrl("。。。。MultiQueries=true&useUnicode=true&characterEncoding=UTF-8")
> .setDriverName("com.mysql.jdbc.Driver")
> .setUsername("jczx_cjch")
> .setPassword("jczx_cjch2")
> .setTableName("xkf_join_result")
> .build())
> .setFlushIntervalMills(1000)
> .setFlushMaxSize(100)
> .setMaxRetryTimes(3)
> .build();
> 
> 
> DataStream<Tuple2<Boolean, Row>> retract = bsTableEnv.toRetractStream(results,
Row.class);
> retract.print();
> build.emitDataStream(retract);
> 
> 
> 
> 
> 就会出现如下错误
> java.sql.SQLException: No value specified for parameter 1
> at com.mysql.jdbc.SQLError.createSQLException(SQLError.java:965)
> at com.mysql.jdbc.SQLError.createSQLException(SQLError.java:898)
> at com.mysql.jdbc.SQLError.createSQLException(SQLError.java:887)
> at com.mysql.jdbc.SQLError.createSQLException(SQLError.java:861)
> at com.mysql.jdbc.PreparedStatement.checkAllParametersSet(PreparedStatement.java:2211)
> at com.mysql.jdbc.PreparedStatement.fillSendPacket(PreparedStatement.java:2191)
> at com.mysql.jdbc.PreparedStatement.fillSendPacket(PreparedStatement.java:2121)
> at com.mysql.jdbc.PreparedStatement.execute(PreparedStatement.java:1162)
> at org.apache.flink.api.java.io.jdbc.writer.UpsertWriter.executeBatch(UpsertWriter.java:118)
> at org.apache.flink.api.java.io.jdbc.JDBCUpsertOutputFormat.flush(JDBCUpsertOutputFormat.java:159)
> at org.apache.flink.api.java.io.jdbc.JDBCUpsertSinkFunction.snapshotState(JDBCUpsertSinkFunction.java:56)
> at org.apache.flink.streaming.util.functions.StreamingFunctionUtils.trySnapshotFunctionState(StreamingFunctionUtils.java:118)
> at org.apache.flink.streaming.util.functions.StreamingFunctionUtils.snapshotFunctionState(StreamingFunctionUtils.java:99)
> at org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator.snapshotState(AbstractUdfStreamOperator.java:90)
> at org.apache.flink.streaming.api.operators.AbstractStreamOperator.snapshotState(AbstractStreamOperator.java:402)
> at org.apache.flink.streaming.runtime.tasks.StreamTask$CheckpointingOperation.checkpointStreamOperator(StreamTask.java:1420)
> at org.apache.flink.streaming.runtime.tasks.StreamTask$CheckpointingOperation.executeCheckpointing(StreamTask.java:1354)
> 
> 
> 
> 
> 我的输出数据是(true,2020-04-22 21:34:00,2020-04-22 21:34:15,20200422213541465568468)是这样的
> 我查看源码发现
> 先调用JDBCUpsertOutputFormat类的writeRecord方法给UpsertWriter类的成员变量map中添加元素
> @Override
> public synchronized void writeRecord(Tuple2<Boolean, Row> tuple2) throws IOException
{
> checkFlushException();
> try {
> jdbcWriter.addRecord(tuple2);
> batchCount++;
> if (batchCount >= flushMaxSize) {
> flush();
> }
> } catch (Exception e) {
> throw new RuntimeException("Writing records to JDBC failed.", e);
> }
> }
> 之后调用flush()方法,调用UpsertWriter类执行executeBatch方法
> public synchronized void flush() throws Exception {
> checkFlushException();
> for (int i = 1; i <= maxRetryTimes; i++) {
> try {
> jdbcWriter.executeBatch();
> batchCount = 0;
> break;
> } catch (SQLException e) {
> LOG.error("JDBC executeBatch error, retry times = {}", i, e);
> if (i >= maxRetryTimes) {
> throw e;
> }
> Thread.sleep(1000 * i);
> }
> }
> }
> 
> 
> 然后会调用UpsertWriter类 实现JDBCWriter类在executeBatch方法中先判断map是否为空,然后循环map;之后判断2元组第一个元素的true调用内部类处理元素,否则删除数据;但是我每次的数据只有一条就是map的大小是1,且2元组的第一个元素值是true,循环结束执行
deleteStatement.executeBatch();方法就会出错,因为删除的语句站位符还没有填充;
> 
> 
> @Override
> public void executeBatch() throws SQLException {
> if (keyToRows.size() > 0) {
> for (Map.Entry<Row, Tuple2<Boolean, Row>> entry : keyToRows.entrySet()) {
> Row pk = entry.getKey();
> Tuple2<Boolean, Row> tuple = entry.getValue();
> if (tuple.f0) {
> processOneRowInBatch(pk, tuple.f1);
> } else {
> setRecordToStatement(deleteStatement, pkTypes, pk);
> deleteStatement.addBatch();
> }
> }
> internalExecuteBatch();
> deleteStatement.executeBatch();
> keyToRows.clear();
> }
> }


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