5/15/2023 0 Comments Spark scala![]() ![]() Because each step requires a disk read, and write, MapReduce jobs are slower due to the latency of disk I/O. With each step, MapReduce reads data from the cluster, performs operations, and writes the results back to HDFS. However, a challenge to MapReduce is the sequential multi-step process it takes to run a job. Developers can write massively parallelized operators, without having to worry about work distribution, and fault tolerance. Hadoop MapReduce is a programming model for processing big data sets with a parallel, distributed algorithm. It has received contribution by more than 1,000 developers from over 200 organizations since 2009. In 2017, Spark had 365,000 meetup members, which represents a 5x growth over two years. Today, Spark has become one of the most active projects in the Hadoop ecosystem, with many organizations adopting Spark alongside Hadoop to process big data. Spark can run standalone, on Apache Mesos, or most frequently on Apache Hadoop. In June, 2013, Spark entered incubation status at the Apache Software Foundation (ASF), and established as an Apache Top-Level Project in February, 2014. The first paper entitled, “Spark: Cluster Computing with Working Sets” was published in June 2010, and Spark was open sourced under a BSD license. The goal of Spark was to create a new framework, optimized for fast iterative processing like machine learning, and interactive data analysis, while retaining the scalability, and fault tolerance of Hadoop MapReduce. Python enjoys a larger community supportĪs Python is the go-to programming language these days, it has built huge community support compared to Scala, whose adaptability is quite small compared to Python.Apache Spark started in 2009 as a research project at UC Berkley’s AMPLab, a collaboration involving students, researchers, and faculty, focused on data-intensive application domains. ![]() Due to its nature, the former is more suitable for projects dealing with high volumes of data. Scala is static-typed, while Python is a dynamically typed language. Engineers starting out might find it easier to write in Python than Scala. Compared to that, Python is much easier to grasp. Though Scala has been making a name recently, it is not very easy to learn. Spark is native in Scala, hence making writing Spark jobs in Scala the native way. If faster performance is a requirement, Scala is a good bet. Scala is faster than Python due to its static type language. Both of them have their own pros and cons, and proper evaluation of needs must be done before choosing one over the other. Companies like Netflix and Airbnb, which deal with huge amounts of data, use Scala and write many pipelines. Usually, Python is suitable for smaller projects, and Scala works best for large-scale ones. If one has to choose between Scala and Python for Apache Spark, the choice should be completely based on the project they are working on. A developer can query more than 175,000 releases of Scala libraries.
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