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Most people do not understand the knowledge points of this article "what are the similarities and differences between Hadoop and Spark", so the editor summarizes the following content, detailed content, clear steps, and has a certain reference value. I hope you can get something after reading this article. Let's take a look at this "what are the similarities and differences between Hadoop and Spark" article.

The level of problem solving is different.

First of all, Hadoop and Apache Spark are both big data frameworks, but they exist for different purposes. Hadoop is essentially more of a distributed data infrastructure: it distributes huge data sets to multiple nodes in a cluster of ordinary computers for storage, which means you don't need to purchase and maintain expensive server hardware. Hadoop also indexes and tracks the data, making big data's processing and analysis efficiency unprecedented. Spark is a tool specially used to deal with those distributed storage big data, it will not carry out distributed data storage.

The two can be combined and separated.

Hadoop not only provides a common HDFS distributed data storage function, but also provides a data processing function called MapReduce, so we can put aside Spark and use Hadoop's own MapReduce to complete data processing. Spark does not have to be attached to Hadoop to survive, but as mentioned above, after all, it does not provide a file management system, so it must be integrated with other distributed file systems to operate. Here we can choose Hadoop's HDFS or other cloud-based data system platforms, but Spark is still used on Hadoop by default. After all, everyone thinks that their combination is the best.

Spark data processing speed kills MapReduce in seconds.

Spark is much faster than MapReduce because of its different ways of processing data. MapReduce processes the data step by step: "read the data from the cluster, process it once, write the results to the cluster, read the updated data from the cluster, do the next processing, write the results to the cluster, and so on." Booz Allen Hamilton data scientist Kirk Borne parses In contrast, Spark completes all data analysis in memory in close to "real-time" time: "read data from the cluster, complete all necessary analytical processing, write the results back to the cluster, and finally complete". The batch processing speed of Spark is nearly 10 times faster than that of MapReduce, and the analysis speed of data in memory is nearly 100 times faster. If the data to be processed and the results are required, most of the cases are static. And if you also have patience to wait for the completion of batch processing, MapReduce processing is perfectly acceptable, but if you need convection data for analysis, such as data collected by sensors from factories, or if your application requires multiple data processing, then you should probably use Spark for processing, most machine learning algorithms require multiple data processing, in addition, Spark is usually used in the following application scenarios: real-time market activities, online product recommendations, network security analysis, machine diary monitoring and so on.

Disaster recovery

The two disaster recovery methods are very different, but both are very good. Because Hadoop writes each processed data to disk, it is inherently flexible to deal with system errors; Spark data objects are stored in flexible distributed datasets (RDD: Resilient Distributed Dataset) distributed in the data cluster, and these data objects can be placed either in memory or on disk, so RDD can also provide completed disaster recovery functions.

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