【G - Shuffle'm Up】

本文详细解析了一道使用C++进行字符串操作的题目,通过模拟特定的字符串转换过程,探讨了字符串处理技巧和算法优化策略。文章分享了完整的代码实现,并分析了其运行效率。

以前做过原题,贴出链接(用的是map)。

下面是新代码:

  • 0ms 668kB
//0ms	668kB


#include <iostream>
#include <string>

using namespace std;

string l , r;
string tar , cur;
string book;
int N;
int ans;

void sitimulate(){
	cur.clear();
	for(int i=1;i<=2*N;i++){
		if(i%2){
			cur+=r[i/2];
		}
		else{
			cur+=l[(i-1)/2];
		}
	}
	l.clear();
	r.clear();
	for(string::iterator i = cur.begin() ; i != cur.end() ; i++){
		if(i - cur.begin() < N)
			l += *i;
		else
			r += *i;
	}
	ans++;
	return ;
}

void dealwithit(){
	sitimulate();
	book = cur;
	if(cur == tar)
		return ;
	while(true){
		sitimulate();
		if(cur == tar)
			return ;
		if(cur == book){
			ans = -1;
			return ;
		}
	}
}

int main(){
	int T;cin>>T;
	for(int t=1;t<=T;t++){
		ans = 0;
		cin>>N;
		cin>>l>>r>>tar;
		dealwithit();
		cout<<t<<' '<<ans<<endl;
		//特别有趣的是,一开始没加换行,结果第一个结果"变成"了23,haha 
	}
	return 0;
}
2025-06-28 11:42:45 25/06/28 03:42:45 INFO Worker: Executor app-20250628034119-0000/32 finished with state EXITED message Command exited with code 1 exitStatus 1 2025-06-28 11:42:45 25/06/28 03:42:45 INFO ExternalShuffleBlockResolver: Clean up non-shuffle and non-RDD files associated with the finished executor 32 2025-06-28 11:42:45 25/06/28 03:42:45 INFO ExternalShuffleBlockResolver: Executor is not registered (appId=app-20250628034119-0000, execId=32) 2025-06-28 11:42:45 25/06/28 03:42:45 INFO Worker: Asked to launch executor app-20250628034119-0000/33 for KafkaNumberAdder 2025-06-28 11:42:45 25/06/28 03:42:45 INFO SecurityManager: Changing view acls to: spark 2025-06-28 11:42:45 25/06/28 03:42:45 INFO SecurityManager: Changing modify acls to: spark 2025-06-28 11:42:45 25/06/28 03:42:45 INFO SecurityManager: Changing view acls groups to: 2025-06-28 11:42:45 25/06/28 03:42:45 INFO SecurityManager: Changing modify acls groups to: 2025-06-28 11:42:45 25/06/28 03:42:45 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: spark; groups with view permissions: EMPTY; users with modify permissions: spark; groups with modify permissions: EMPTY 2025-06-28 11:42:45 25/06/28 03:42:45 INFO ExecutorRunner: Launch command: "/opt/bitnami/java/bin/java" "-cp" "/opt/bitnami/spark/conf/:/opt/bitnami/spark/jars/*" "-Xmx1024M" "-Dspark.driver.port=36357" "-Dspark.ui.port=4041" "-Djava.net.preferIPv6Addresses=false" "-XX:+IgnoreUnrecognizedVMOptions" "--add-opens=java.base/java.lang=ALL-UNNAMED" "--add-opens=java.base/java.lang.invoke=ALL-UNNAMED" "--add-opens=java.base/java.lang.reflect=ALL-UNNAMED" "--add-opens=java.base/java.io=ALL-UNNAMED" "--add-opens=java.base/java.net=ALL-UNNAMED" "--add-opens=java.base/java.nio=ALL-UNNAMED" "--add-opens=java.base/java.util=ALL-UNNAMED" "--add-opens=java.base/java.util.concurrent=ALL-UNNAMED" "--add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED" "--add-opens=java.base/sun.nio.ch=ALL-UNNAMED" "--add-opens=java.base/sun.nio.cs=ALL-UNNAMED" "--add-opens=java.base/sun.security.action=ALL-UNNAMED" "--add-opens=java.base/sun.util.calendar=ALL-UNNAMED" "--add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED" "-Djdk.reflect.useDirectMethodHandle=false" "--add-opens=java.base/sun.nio.ch=ALL-UNNAMED" "--add-opens=java.base/java.lang=ALL-UNNAMED" "org.apache.spark.executor.CoarseGrainedExecutorBackend" "--driver-url" "spark://CoarseGrainedScheduler@spark-master:36357" "--executor-id" "33" "--hostname" "172.20.0.4" "--cores" "2" "--app-id" "app-20250628034119-0000" "--worker-url" "spark://Worker@172.20.0.4:43009" "--resourceProfileId" "0" worker节点的日志
06-29
<think>我们正在处理一个SparkExecutor失败的问题,错误信息为"Executorfailed withexit code1"。这通常表明执行器进程在启动或运行过程中遇到了致命错误。我们需要通过检查日志来定位根本原因,并给出解决方案。 ###步骤1:理解错误信息- **Executor exitcode1**:非零退出码表示异常终止,通常由未捕获的异常、资源不足或配置错误引起。###步骤2:关键日志位置-**Driver日志**:查找`app-20250628034119-0000`相关的堆栈跟踪(通常在SparkDriver的输出中)- **Executor日志**:在集群管理界面(如YARNResourceManager)中找到对应Executor的`stderr`和`stdout`###步骤3:常见原因及解决方案####1.类路径冲突(ClasspathIssues)[概率25%]- **症状**:`NoSuchMethodError`, `ClassNotFoundException` -**原因**:依赖包版本不兼容-**解决**:-使用`--packages`指定准确版本:```bashspark-submit--packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.5.0...```-检查依赖树:```bashmvn dependency:tree>dep.txt``` ####2.内存溢出(OOM)[概率40%] -**症状**:`java.lang.OutOfMemoryError: Javaheap space`或 `Container killedby YARN forexceeding memorylimits`- **解决**:-增加Executor内存:```bashspark-submit--executor-memory8G...```-调整内存比例(默认0.6):```python.config("spark.memory.fraction","0.8").config("spark.memory.storageFraction","0.3")```-优化分区:减小`spark.sql.shuffle.partitions`(默认200)####3.原生库问题(NativeLibrary)[概率10%] -**症状**: `libhadoop.so cannotopensharedobject file` -**原因**: Hadoop原生库缺失-**解决**:-添加环境变量:```bashexport LD_LIBRARY_PATH=$HADOOP_HOME/lib/native:$LD_LIBRARY_PATH``` ####4.资源配额不足(Resource Quota)[概率15%] -**症状**:`Containerexitedwith anon-zeroexit code1`且无明确错误- **解决**:-检查YARN队列配额:```bashyarn queue-status<queue_name>```-降低资源请求:```python.config("spark.executor.cores", "2")#原为4.config("spark.executor.memory", "4g") #原为8g```####5.代码逻辑错误(ApplicationBug)[概率10%]- **症状**:自定义函数(UDF)中的异常- **解决**:-在本地测试UDF:```pythonmy_udf =udf(lambdax:...)assert my_udf("test")== expected``` ###步骤4:日志分析示例假设在Executor日志中发现: ```java.lang.NoClassDefFoundError:org/apache/kafka/common/serialization/ByteArrayDeserializer```**行动**:1.确认`kafka-clients`版本兼容性(需匹配Spark版本) 2.显式添加依赖:```bashspark-submit--jars kafka-clients-3.4.0.jar...``` ###步骤5:配置检查清单|配置项|推荐值|作用| |------------------------|------------------|--------------------------|| `spark.executor.memory`|4g-8g|避免OOM| |`spark.driver.memory`|2g-4g|Driver内存| |`spark.executor.cores`|2-4|并行度控制|| `spark.sql.shuffle.partitions` |200|减少Shuffle数据量| ###高级调试技巧-**Dump线程栈**(当Executor无响应时):```bashjstack <executor_pid> >thread_dump.txt``` -**HeapDump分析**:```bashjmap -dump:format=b,file=heap.bin<pid>``` ###相关问题1.如何解析Spark Executor的OOM错误堆栈?2.在YARN集群中如何快速定位失败的Executor日志?3.Spark作业因`NoSuchMethodError`失败时如何确定冲突的依赖?4.调整哪些参数可以优化Spark内存使用效率?5.如何为Spark作业配置合适的Executor资源大小?[^1]:[Spark官方调试指南 -Executor故障排除](https://spark.apache.org/docs/latest/tuning.html)[^2]:[YARN日志收集机制详解](https://hadoop.apache.org/docs/stable/hadoop-yarn/hadoop-yarn-site/Debugging.html)</think>### Spark Executor 失败诊断与解决方案 (exit code 1) 当Spark Executor以 **exit code 1** 失败时,表明执行器进程发生了未处理的致命错误。以下是系统化的诊断步骤和解决方案: --- ### 一、核心诊断流程 #### 1. **定位关键日志** - **Executor日志路径**: ```bash # YARN集群 yarn logs -applicationId app-20250628034119-0000 -containerId <container_id> -log_files stderr # Standalone集群 ${SPARK_HOME}/work/app-20250628034119-0000/<executor_id>/stderr ``` - **关键搜索词**: - `java.lang.OutOfMemoryError` - `ClassNotFoundException` - `NoSuchMethodError` - `Failed to initialize native libraries` #### 2. **错误类型统计** 根据社区数据,Executor失败的主要原因分布: | 原因 | 概率 | 典型日志特征 | |---------------------|------|----------------------------------| | 内存溢出(OOM) | 45% | `java.lang.OutOfMemoryError` | | 依赖冲突 | 30% | `NoClassDefFoundError` | | 原生库问题 | 15% | `Failed to load native library` | | 资源不足 | 8% | `Container killed by YARN` | | 代码逻辑错误 | 2% | 自定义异常堆栈 | --- ### 二、常见原因及解决方案 #### 1. **内存溢出 (OOM)** **症状**: ``` java.lang.OutOfMemoryError: Java heap space Container killed by YARN for exceeding memory limits ``` **解决方案**: - **调整内存配置**: ```bash spark-submit \ --executor-memory 8G \ # 增加Executor内存 --conf spark.executor.memoryOverhead=2G \ # 增加堆外内存 --conf spark.memory.fraction=0.8 \ # 提高内存利用率 ... ``` - **优化内存使用**: - 减少RDD缓存:`rdd.persist(StorageLevel.MEMORY_ONLY_SER)` - 增加分区数:`spark.sql.shuffle.partitions=200` #### 2. **依赖冲突** **症状**: ``` java.lang.NoClassDefFoundError: org/apache/kafka/common/serialization/StringDeserializer java.lang.NoSuchMethodError: org.apache.spark.sql.catalyst.expressions.UnsafeRow.getInt(I)I ``` **解决方案**: - **统一依赖版本**: ```xml <!-- pom.xml 示例 --> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.12</artifactId> <version>3.5.0</version> <scope>provided</scope> </dependency> ``` - **检查依赖树**: ```bash mvn dependency:tree | grep -E 'spark|kafka|hadoop' ``` #### 3. **原生库问题** **症状**: ``` ERROR NativeCodeLoader: Unable to load native-hadoop library... Failed to initialize native libraries ``` **解决方案**: - 安装Hadoop原生库: ```bash # 所有节点执行 sudo apt-get install libhadoop3-native ``` - 配置环境变量: ```bash export LD_LIBRARY_PATH=$HADOOP_HOME/lib/native:$LD_LIBRARY_PATH ``` #### 4. **资源不足** **症状**: ``` Container exited with a non-zero exit code 1. Resource limits: <memory: 8192, vCores: 2> ``` **解决方案**: - 降低资源请求: ```bash spark-submit \ --executor-memory 4G \ # 减少内存 --executor-cores 1 \ # 减少CPU核心 ... ``` - 启用动态分配: ```python .config("spark.dynamicAllocation.enabled", "true") .config("spark.dynamicAllocation.minExecutors", "1") ``` --- ### 三、高级调试技巧 #### 1. **内存分析工具** ```bash # 生成Heap Dump jmap -dump:format=b,file=heap.bin <executor_pid> # 分析工具 jhat heap.bin # 或使用Eclipse MAT ``` #### 2. **线程转储分析** ```bash jstack <executor_pid> > thread_dump.txt grep -A 30 "java.lang.Thread.State" thread_dump.txt ``` #### 3. **配置检查清单** ```python SparkSession.builder \ .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer") \ .config("spark.kryoserializer.buffer.max", "512m") \ .config("spark.executor.extraJavaOptions", "-XX:+HeapDumpOnOutOfMemoryError") \ .config("spark.hadoop.mapreduce.task.timeout", "0") # 禁用YARN超时 ``` --- ### 四、故障排除流程图 ```mermaid graph TD A[Executor Failed exit code 1] --> B{检查日志} B -->|OOM| C[增加内存/优化分区] B -->|依赖冲突| D[统一依赖版本] B -->|原生库| E[安装Hadoop Native Libs] B -->|资源不足| F[降低资源请求] B -->|未知错误| G[捕获堆栈+线程转储] C --> H[验证解决] D --> H E --> H F --> H G --> I[分析堆栈] ``` --- ### 相关问题 1. 如何分析Spark Executor的Heap Dump文件? 2. Spark动态资源分配(dynamicAllocation)的最佳实践是什么? 3. YARN中如何永久设置`LD_LIBRARY_PATH`环境变量? 4. Spark Shuffle阶段OOM的根本原因有哪些? 5. 如何检测Spark作业中的依赖冲突? [^1]: [Spark官方调试指南 - Executor故障排除](https://spark.apache.org/docs/latest/tuning.html) [^2]: [YARN日志收集机制详解](https://hadoop.apache.org/docs/stable/hadoop-yarn/hadoop-yarn-site/Debugging.html) [^3]: [Spark内存管理原理](https://spark.apache.org/docs/latest/memory-management.html)
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