【Learning】Distributed System - Serialization

本文深入探讨了序列化与反序列化的过程、机制及其在分布式数据处理中的应用,包括不同序列化机制的选择与使用场景,以及序列化容器文件格式的设计与实现。重点介绍了序列化在进程间通信与持久化存储中的作用,同时对比了RMI与RPC在分布式对象调用方面的区别,强调了序列化机制对于保持对象状态一致性的重要性。

Serialization is the process of turning structured objects into a byte stream for transmissionover a network or for writing to persistent storage. Deserialization is the reverseprocess of turning a byte stream back into a series of structured objects. Serialization appears in two quite distinct areas of distributed data processing: for interprocess communicationand for persistent storage.

Serialization mechanism should consider following:

  1. Primitive type serialization

       a. big-endian or little-endian

       b. one-byte character or two-byte character

       c. binary or textual

       d. etc

  2. Constructed type serialization

       a. the order of each included primitive types or objects

Typical object serialization mechanisms include Java Serialization, Hadoop Writable, Thrift, Avro, ProtocalBuffer, etc.

Serialized Object Container

One primitive value or constructed type value can be viewed as an object. For interprocess communication, the unit of a byte steam is an object (as arguments or return values). But for persistent storage, storing every single object into a file is not efficient. Normally, we need to store a sequence of objects into one file. Therefore, a object container file format is needed, Avro datafile, SequenceFile are such file formats. 

Typical object container file formats include Avro datafile, SequenceFile, etc.

The object container file format can use different types serializer to do the object serialization. For example, SequenceFile normally use Hadoop Writable as its key/value. But it can also use other serializer like Avro to serialize key/value.

RMI vs RPC

RMI is different from RPC in that it is object-oriented. The core concept is distributed object invocation. In Java RMI, when an object implements Remote interface(by default Serializable), it is regarded as a remote object, which means that it will be passed by reference (remote object referemce) across JVMs. If it only implements Serializable but not Remote interface, it will be passed by value across JVMs. The benefit of remote object invocation is that the client and the server always see the same object states.

给定引用内容中未提及用于建模复杂分布式参数系统的协作学习时空三维模糊框架的相关信息,因此无法依据引用对该框架进行介绍。不过,一般而言,用于建模复杂分布式参数系统的协作学习时空三维模糊框架是一种针对复杂系统建模的先进方法。 复杂分布式参数系统具有分布性、参数复杂多变以及时空特性等特点。该框架结合了协作学习、时空分析和模糊理论。协作学习允许系统中的多个智能体或节点通过信息共享和交互,共同学习和优化模型,以适应系统的动态变化。时空三维分析则考虑了系统在时间和三维空间上的变化,能够捕捉系统的时空演化规律。模糊理论可以处理系统中的不确定性和模糊性信息,提高模型的鲁棒性和适应性。 在实际应用中,这样的框架可能会用于交通流量预测、环境监测、工业过程控制等领域,以更准确地描述和预测复杂系统的行为。 ```python # 以下为简单示意代码,非真实实现该框架 class SpatioTemporalFuzzyFramework: def __init__(self): self.collaborative_learning_module = None self.spatio_temporal_analysis_module = None self.fuzzy_logic_module = None def setup_modules(self): # 初始化各个模块 self.collaborative_learning_module = CollaborativeLearning() self.spatio_temporal_analysis_module = SpatioTemporalAnalysis() self.fuzzy_logic_module = FuzzyLogic() def model_complex_system(self, data): # 利用各个模块进行建模 collaborative_result = self.collaborative_learning_module.learn(data) spatio_temporal_result = self.spatio_temporal_analysis_module.analyze(data) fuzzy_result = self.fuzzy_logic_module.process(data) # 整合结果 final_result = self.integrate_results(collaborative_result, spatio_temporal_result, fuzzy_result) return final_result def integrate_results(self, *results): # 简单示例,实际需要根据具体情况整合 return sum(results) class CollaborativeLearning: def learn(self, data): # 协作学习逻辑 return 1 class SpatioTemporalAnalysis: def analyze(self, data): # 时空分析逻辑 return 2 class FuzzyLogic: def process(self, data): # 模糊逻辑处理 return 3 # 使用示例 framework = SpatioTemporalFuzzyFramework() framework.setup_modules() data = [1, 2, 3] result = framework.model_complex_system(data) print(result) ```
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