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This article mainly explains "Hadoop and spark performance comparison", interested friends may wish to take a look. The method introduced in this paper is simple, fast and practical. Let Xiaobian take you to learn "Hadoop and spark performance comparison"!

Performance comparison between Hadoop and spark

Spark runs 100 times faster than Hadoop in memory and 10 times faster on disk. Spark is known to sort 100 terabytes of data three times faster than Hadoop MapReduce on one-tenth the number of machines. Spark is also faster in machine learning applications such as Naive Bayes and k-means.

Spark outperforms Hadoop in terms of processing speed for the following reasons:

Every time you run MapReduce tasks, Spark is not limited by input and output. As it turns out, apps are much faster.

Spark's DAG can be optimized between steps. Hadoop doesn't have any periodic connections between MapReduce steps, which means no performance tuning occurs at that level.

However, if Spark runs on YARN with other shared services, performance may degrade and cause RAM overhead memory leaks. For this reason, Hadoop is considered to be a more efficient system if users have requests for batch processing.

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