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. | |
01 Jan 2018 4 Citations | Thus the performance of Spark SQL has improved. |
01 Dec 2016 46 Citations | 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. |
01 Dec 2016 19 Citations | 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. |
28 Citations | 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. | |
26 Citations | 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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