我有一张桌子需要处理各种角色。字符包括Ø，®等。
我已将我的表设置为utf-8作为默认排序规则，所有列都使用表默认值，但是当我尝试插入这些字符时，我收到错误：字符串值不正确：'\ xEF \ xBF \ xBD'列' buyerName'在第1行
我的连接字符串定义为
string mySqlConn = "server="+server+";user="+username+";database="+database+";port="+port+";password="+password+";charset=utf8;";
我不知道为什么我仍然会看到错误。我是否错过了.net连接器或我的MySQL设置？
- 编辑 -
我的（新）C＃插入语句如下所示：
MySqlCommand insert = new MySqlCommand( "INSERT INTO fulfilled_Shipments_Data " +
"(amazonOrderId,merchantOrderId,shipmentId,shipmentItemId,"+
"amazonOrderItemId,merchantOrderItemId,purchaseDate,"+ ...
VALUES (@amazonOrderId,@merchantOrderId,@shipmentId,@shipmentItemId,"+
"@amazonOrderItemId,@merchantOrderItemId,@purchaseDate,"+
"paymentsDate,shipmentDate,reportingDate,buyerEmail,buyerName,"+ ...
insert.Parameters.AddWithValue("@amazonorderId",lines[0]);
insert.Parameters.AddWithValue("@merchantOrderId",lines[1]);
insert.Parameters.AddWithValue("@shipmentId",lines[2]);
insert.Parameters.AddWithValue("@shipmentItemId",lines[3]);
insert.Parameters.AddWithValue("@amazonOrderItemId",lines[4]);
insert.Parameters.AddWithValue("@merchantOrderItemId",lines[5]);
insert.Parameters.AddWithValue("@purchaseDate",lines[6]);
insert.Parameters.AddWithValue("@paymentsDate",lines[7]);
insert.ExecuteNonQuery();
假设这是使用参数化语句的正确方法，它仍然会给出错误
"Incorrect string value: '\xEF\xBF\xBD' for column 'buyerName' at row 1"
还有其他想法吗？
答案 0 :(得分：18)
\xEF\xBF\xBD是unicode字符U+FFFD的UTF-8编码。这是一个特殊的角色，也被称为“替换角色”。来自the wikipedia page about the special unicode characters的引用：
替换字符 （通常是带有白色问号的黑色菱形）是在Specials表中的代码点U + FFFD的Unicode标准中找到的符号。当系统无法将数据流解码为正确的符号时，它用于指示问题。当字体不包含字符时最常见，但是当数据无效且与任何字符都不匹配时也会看到它：
因此，您的数据源似乎包含损坏的数据。您也可能尝试使用错误的编码读取数据。这些线来自哪里？
如果您无法修复数据，并且您的输入确实包含无效字符，则可以删除替换字符：
lines[n] = lines[n].Replace("\xFFFD", "");
答案 1 :(得分：3)
永远，永远，永远创建这样的SQL语句。这对SQL注入是开放的。
我正在添加这个作为答案，因为这是一个根本性的错误，你可能需要重写你的大部分程序。
这不是你如何为SQL语句提供参数，并且不值得任何人回答你的问题，因为你应该使用参数化查询，这可能也会解决你的问题。
答案 2 :(得分：2)
Mattmanser是对的，永远不会通过在查询中直接连接参数来编写SQL查询。参数化查询的一个示例是：
string lastname = "Doe";
double height = 6.1;
DateTime date = new DateTime(1978,4,18);
var connection = new MySqlConnection(connStr);
try
{
connection.Open();
var command = new MySqlCommand(
"SELECT * FROM tblPerson WHERE LastName = @Name AND Height > @Height AND BirthDate < @BirthDate", connection);
command.Parameters.AddWithValue("@Name", lastname);
command.Parameters.AddWithValue("@Height", height);
command.Parameters.AddWithValue("@Name", birthDate);
MySqlDataReader reader = command.ExecuteReader();
...
}
finally
{
connection.Close();
}
答案 3 :(得分：0)
对于那些使用PHP有类似问题的人，请尝试使用函数scala> x.show()
15/08/25 06:55:33 INFO MemoryStore: ensureFreeSpace(283428) called with curMem=2248728, maxMem=278302556
15/08/25 06:55:33 INFO MemoryStore: Block broadcast_48 stored as values in memory (estimated size 276.8 KB, free 263.0 MB)
15/08/25 06:55:33 INFO MemoryStore: ensureFreeSpace(22390) called with curMem=2532156, maxMem=278302556
15/08/25 06:55:33 INFO MemoryStore: Block broadcast_48_piece0 stored as bytes in memory (estimated size 21.9 KB, free 263.0 MB)
15/08/25 06:55:33 INFO BlockManagerInfo: Added broadcast_48_piece0 in memory on 192.168.72.167:60712 (size: 21.9 KB, free: 265.2 MB)
15/08/25 06:55:33 INFO BlockManagerMaster: Updated info of block broadcast_48_piece0
15/08/25 06:55:33 INFO SparkContext: Created broadcast 48 from hadoopFile at AvroRelation.scala:75
15/08/25 06:55:34 INFO FileInputFormat: Total input paths to process : 1
15/08/25 06:55:34 INFO SparkContext: Starting job: RangePartitioner at Exchange.scala:88
15/08/25 06:55:34 INFO DAGScheduler: Got job 32 (RangePartitioner at Exchange.scala:88) with 2 output partitions (allowLocal=false)
15/08/25 06:55:34 INFO DAGScheduler: Final stage: Stage 52(RangePartitioner at Exchange.scala:88)
15/08/25 06:55:34 INFO DAGScheduler: Parents of final stage: List()
15/08/25 06:55:34 INFO DAGScheduler: Missing parents: List()
15/08/25 06:55:34 INFO DAGScheduler: Submitting Stage 52 (MapPartitionsRDD[100] at RangePartitioner at Exchange.scala:88), which has no missing parents
15/08/25 06:55:34 INFO MemoryStore: ensureFreeSpace(4040) called with curMem=2554546, maxMem=278302556
15/08/25 06:55:34 INFO MemoryStore: Block broadcast_49 stored as values in memory (estimated size 3.9 KB, free 263.0 MB)
15/08/25 06:55:34 INFO MemoryStore: ensureFreeSpace(2243) called with curMem=2558586, maxMem=278302556
15/08/25 06:55:34 INFO MemoryStore: Block broadcast_49_piece0 stored as bytes in memory (estimated size 2.2 KB, free 263.0 MB)
15/08/25 06:55:34 INFO BlockManagerInfo: Added broadcast_49_piece0 in memory on 192.168.72.167:60712 (size: 2.2 KB, free: 265.2 MB)
15/08/25 06:55:34 INFO BlockManagerMaster: Updated info of block broadcast_49_piece0
15/08/25 06:55:34 INFO SparkContext: Created broadcast 49 from broadcast at DAGScheduler.scala:839
15/08/25 06:55:34 INFO DAGScheduler: Submitting 2 missing tasks from Stage 52 (MapPartitionsRDD[100] at RangePartitioner at Exchange.scala:88)
15/08/25 06:55:34 INFO YarnScheduler: Adding task set 52.0 with 2 tasks
15/08/25 06:55:34 INFO TaskSetManager: Starting task 0.0 in stage 52.0 (TID 840, quickstart.cloudera, NODE_LOCAL, 1425 bytes)
15/08/25 06:55:34 INFO BlockManagerInfo: Added broadcast_49_piece0 in memory on quickstart.cloudera:61000 (size: 2.2 KB, free: 530.1 MB)
15/08/25 06:55:34 INFO BlockManagerInfo: Added broadcast_48_piece0 in memory on quickstart.cloudera:61000 (size: 21.9 KB, free: 530.1 MB)
15/08/25 06:55:34 INFO TaskSetManager: Starting task 1.0 in stage 52.0 (TID 841, quickstart.cloudera, NODE_LOCAL, 1425 bytes)
15/08/25 06:55:34 INFO TaskSetManager: Finished task 0.0 in stage 52.0 (TID 840) in 95 ms on quickstart.cloudera (1/2)
15/08/25 06:55:34 INFO TaskSetManager: Finished task 1.0 in stage 52.0 (TID 841) in 22 ms on quickstart.cloudera (2/2)
15/08/25 06:55:34 INFO DAGScheduler: Stage 52 (RangePartitioner at Exchange.scala:88) finished in 0.117 s
15/08/25 06:55:34 INFO YarnScheduler: Removed TaskSet 52.0, whose tasks have all completed, from pool
15/08/25 06:55:34 INFO DAGScheduler: Job 32 finished: RangePartitioner at Exchange.scala:88, took 0.132854 s
org.apache.spark.SparkException: Task not serializable
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:166)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158)
at org.apache.spark.SparkContext.clean(SparkContext.scala:1628)
at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:635)
at org.apache.spark.sql.execution.Project.execute(basicOperators.scala:40)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:96)
at org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:103)
at org.apache.spark.sql.DataFrame.collect(DataFrame.scala:815)
at org.apache.spark.sql.DataFrame.head(DataFrame.scala:758)
at org.apache.spark.sql.DataFrame.take(DataFrame.scala:809)
at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:178)
at org.apache.spark.sql.DataFrame.show(DataFrame.scala:314)
at org.apache.spark.sql.DataFrame.show(DataFrame.scala:320)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:48)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:53)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:55)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:57)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:59)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:61)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:63)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:65)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:67)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:69)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:71)
at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:73)
at $iwC$$iwC$$iwC$$iwC.<init>(<console>:75)
at $iwC$$iwC$$iwC.<init>(<console>:77)
at $iwC$$iwC.<init>(<console>:79)
at $iwC.<init>(<console>:81)
at <init>(<console>:83)
at .<init>(<console>:87)
at .<clinit>(<console>)
at .<init>(<console>:7)
at .<clinit>(<console>)
at $print(<console>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1338)
at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:856)
at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:901)
at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:813)
at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:656)
at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:664)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:669)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:996)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:944)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:944)
at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:944)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1058)
at org.apache.spark.repl.Main$.main(Main.scala:31)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:569)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:166)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:189)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:110)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.reflect.InvocationTargetException
at sun.reflect.GeneratedMethodAccessor28.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.serializer.SerializationDebugger$ObjectStreamClassMethods$.getObjFieldValues$extension(SerializationDebugger.scala:240)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visitSerializable(SerializationDebugger.scala:150)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visit(SerializationDebugger.scala:99)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visitSerializable(SerializationDebugger.scala:158)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visit(SerializationDebugger.scala:99)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visitSerializable(SerializationDebugger.scala:158)
at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visit(SerializationDebugger.scala:99)
at org.apache.spark.serializer.SerializationDebugger$.find(SerializationDebugger.scala:58)
at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:39)
at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47)
at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:80)
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:164)
... 66 more
Caused by: java.lang.ArrayIndexOutOfBoundsException
。它只是有效！