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Are there any performance considerations when choosing a language API SQL vs Python vs Scala in the context of spark? 

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We see Spark SQL as an evolution of both SQL-on-Spark and of Spark itself, offering richer APIs and optimizations while keeping the benefits of the Spark programming model.
Open accessJournal ArticleDOI
Xin Lian, Tianyu Zhang 
01 Jan 2018
4 Citations
Thus the performance of Spark SQL has improved.
Our results show that Spark-GPU improves the performance of machine learning workloads by up to 16.13x and the performance of SQL queries by up to 4.83x.
The final results revealed that our Spark (PySpark) based solution improved the performance (in terms of processing time) approximately fourfold when compared with the previous work developed in Python.
On the one hand, GeoSpark SQL provides a convenient SQL interface; on the other hand, GeoSpark SQL achieves both efficient storage management and high-performance parallel computing through integrating Hive and Spark.
Not only does Spark provide excellent scalability and performance, Spark SQL and the DataFrame API make it easy to interact with Kudu.
We argue that the functional object-oriented language Scala is in many ways a better choice.
On other hand, Spark–Scala is preferred to be used more than other tools when size of processing data is too large.
Our experiment shows much faster processing with Scala API than Python API.

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