Code Exception Safe Functions

本文探讨了在C++中实现异常安全代码的关键要点。通过具体的代码示例,文章展示了如何正确处理资源分配与释放,避免对象状态不一致的问题,并强调了减少try块使用的重要性。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

The hard part about writing exception safe code isn't the throwing or catching of exceptions; it's everything in between. As a thrown exception wends its way from the throw expression to the catch clause, every partially executed function on that path must "clean up" any important resources that it controls before its activation record is popped off the execution stack. Generally (but not always), all that is required to write an exception safe function is a moment's reflection and some common sense.

For example, consider the implementation of String assignment from Assignment and Initialization Are Different [12, 41]:

String &String::operator =( const char *str ) {
    if( !str ) str = "";
    char *tmp = strcpy( new char[ strlen(str)+1 ], str );
    delete [] s_;
    s_ = tmp;
    return *this;
}

The implementation of this function may look superfluously ornate, since we could have coded it with fewer lines and no temporary:

String &String::operator =( const char *str ) {
    delete [] s_;                                        
    if( !str ) str = "";
    s_ = strcpy( new char[ strlen(str)+1 ], str );
    return *this;
}

However, while the array delete comes with a social guarantee not to throw an exception (see Exception Safety Axioms [38, 131]), the array new a couple of lines later makes no such promise. If we delete the old buffer before we know whether allocation of the new buffer will succeed, we'll leave the String object in a bad state. Herb Sutter summarizes the situation well in his Exceptional C++, which I'll paraphrase as this: First do anything that could cause an exception "off to the side" without changing important state, and then use operations that can't throw an exception to finish up. That's what we did in the first implementation of String::operator = above. Let's look at another example from Commands and Hollywood [19, 67]:

void Button::setAction( const Action *newAction ) {
    Action *temp = newAction->clone(); // off to the side...
    delete action_; // then change state!
    action_ = temp;
}

Because it's a virtual function, we really know nothing about the exception-related behavior of the call to clone, so we assume the worst. If the clone operation succeeds, we continue with an exception safe deletion and pointer assignment. Otherwise, a thrown exception from clone will cause premature exit from Button::setAction with no harm done. Newer C++ programmers have a tendency to "clean up" code like this in such a way as to make it exception unsafe:

void Button::setAction( const Action *newAction ) {              
    delete action_; // change state!                             
    action_ = newAction->clone(); // then maybe throw?           
}                                                                

Performing the deletion (which is assumed to be exception safe) before the clone (which makes no such promise) will leave the Button object in an inconsistent state if clone throws an exception.

Notice that properly written exception safe code employs relatively few try blocks. A novice attempt to write exception safe code is often littered with unnecessary and often damaging trys and catches:

void Button::setAction( const Action *newAction ) {              
    delete action_;                                              
    try {                                                        
        action_ = newAction->clone();                            
    }                                                            
    catch( ... ) {                                               
        action_ = 0;                                             
        throw;                                                   
    }                                                            
}                                                                

This version with its fussy try block and catch clause is exception safe in the sense that the Button object is left in a consistent (but likely different) state if clone throws an exception. However, our previous version was shorter, simpler, and more exception safe because it left the Button object not merely consistent but unchanged.

It's a good idea to minimize the use of try blocks, where possible, and employ them primarily in locations where you really want to examine the type of a passing exception in order to do something with it. In practice, these locations are often at module boundaries between your code and third-party libraries and between your code and the operating system.

<think>嗯,用户遇到了Spark PySpark任务中的Python异常问题,显示为PythonException。我需要根据提供的引用信息和常见问题来帮他分析可能的原因和解决方法。 首先,引用[1]提到PythonException的跟踪日志,可能涉及代码执行失败。我需要考虑用户代码是否有语法错误或逻辑问题。比如,在executor上运行的Python函数是否有未处理的异常,或者依赖库是否缺失。 然后,引用[2]指出java.lang.NoClassDefFoundError,这说明Spark的类路径中缺少Jackson库。这可能是因为依赖包未正确打包或提交任务时未包含相关JAR包。用户需要检查是否在spark-submit中通过--jars或--packages参数添加了必要的依赖。 引用[3]中的本地模式测试配置可能与分布式环境不同,用户可能在本地测试没问题,但在集群运行时出现问题。需要确认依赖是否在集群的所有节点上存在,特别是当使用Hive或Spark SQL时,可能需要额外的配置。 引用[4]提到升级Hive集群到Spark后出现的SASL认证问题,这可能与网络或安全配置有关,但当前用户的问题更偏向于依赖和代码错误。不过,这也提醒我要检查集群配置是否一致,比如Spark版本兼容性。 综合来看,可能的原因包括:Python代码错误、依赖缺失(Python或Java)、资源不足、环境不一致。解决方法需要逐步排查,例如检查日志定位错误位置,确保依赖正确打包,配置资源参数,以及验证环境配置。</think>### Spark任务中PySpark的PythonException异常分析与解决步骤 当在Spark任务中遇到`PythonException`错误时,需从代码、依赖、环境三个层面逐步排查。以下是具体分析及解决方案: --- #### 一、错误原因分析 1. **Python代码问题** - **未捕获的异常**:用户自定义的Python函数(如UDF、map/reduce操作)中未处理异常,导致Executor崩溃[^1]。 - **依赖缺失**:Executor节点缺少Python第三方库(如`pandas`、`numpy`),但Driver节点已安装。 - **语法错误**:代码中存在语法错误(如缩进错误、变量未定义),在分布式执行时触发异常。 2. **依赖冲突** - **Java依赖缺失**:如引用[2]中`java.lang.NoClassDefFoundError`表明缺少`jackson-core`库,常见于Spark版本与Hive/Jackson组件的兼容性问题。 - **Python环境不一致**:Driver与Executor的Python版本或库版本不一致(例如`pyarrow`版本冲突)。 3. **资源配置问题** - **内存不足**:Executor内存不足导致任务失败,可能伴随`MemoryError`。 - **网络问题**:Executor与Driver通信失败,尤其在启用SASL认证时需检查安全配置(如引用[4]中的SASL报错)。 --- #### 二、解决方案 1. **检查Python代码** - **添加异常捕获**:在关键代码段(如UDF)中使用`try-except`捕获异常: ```python from pyspark.sql.functions import udf @udf("string") def safe_transform(x): try: return x.upper() except Exception as e: return str(e) ``` - **本地验证代码**:通过`spark-submit --master local`在本地运行,复现问题[^3]。 2. **确保依赖一致性** - **打包Python环境**:使用`venv`或`conda-pack`将Python虚拟环境打包为ZIP,通过`--archives`提交: ```bash spark-submit --archives environment.zip#environment ... ``` - **添加Java依赖**:通过`--jars`或`--packages`引入缺失的JAR包(如Jackson): ```bash spark-submit --packages com.fasterxml.jackson.core:jackson-core:2.13.0 ... ``` 3. **调整资源配置** - **增加Executor内存**:在`spark-submit`中配置: ```bash --conf spark.executor.memory=8g ``` - **检查集群配置**:确认Hive/Spark版本兼容性,更新`hive-site.xml`中的Spark引擎参数[^4]。 --- #### 三、验证步骤 1. **查看完整日志** - 通过Spark UI找到失败的Stage,查看Executor日志中的`stderr`部分。 2. **简化任务复现** - 注释部分代码,逐步执行以定位问题模块。 3. **依赖检查命令** - 在Executor节点手动执行`pip list | grep <package>`验证库是否存在。 ---
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值