MapReduce 和 Apache Spark 的区别

地图简化 是一个框架，我们可以使用它编写函数，以可靠的方式在巨大的商品硬件集群上并行处理大量数据。它也是一种主要基于Java的分布式计算的处理方法和应用模型。 MapReduce 算法包含两个必要的任务，特别是 Map 和 Reduce。该映射获取一组记录并将其转换为其他所有数据集，其中各个因素被分解为键值对中存在的元组。此外，它有助于最小化任务，它将地图的输出作为输入并将这些统计元组组合成一个较小的元组集。正如标题 MapReduce 的顺序所暗示的那样，减少分配在地图作业之后继续执行。

Apache Spark是一个数据处理框架，它可以在非常庞大的信息集上快速运行处理任务，并且可以额外地在几台计算机上分配信息处理任务，无论是单独还是与不同的分配计算工具协同工作。这两个特征是海量信息和机器学习世界的关键，这需要通过海量信息存储来整理大量计算能量。 Spark 还通过易于使用的 API 将这些职责的一些编程负担从开发人员的肩上卸下，该 API 抽象了分布式计算和大型信息处理的大量繁重工作。

MapReduce 和 Spark 的区别

S.No.	MapReduce	Spark
1.	It is a framework that is open-source which is used for writing data into the Hadoop Distributed File System.	It is an open-source framework used for faster data processing.
2.	It is having a very slow speed as compared to Apache Spark.	It is much faster than MapReduce.
3.	It is unable to handle real-time processing.	It can deal with real-time processing.
4.	It is difficult to program as you required code for every process.	It is easy to program.
5.	It supports more security projects.	Its security is not as good as MapReduce and continuously working on its security issues.
6.	For performing the task, It is unable to cache in memory.	It can cache the memory data for processing its task.
7.	Its scalability is good as you can add up to n different nodes.	It is having low scalability as compared to MapReduce.
8.	It actually needs other queries to perform the task.	It has Spark SQL as its very own query language.