Apache Spark is a popular open-source big data processing engine used by many organizations to analyze and process large datasets. Apache Spark 3, released in 2020, offers several new features and improvements over its predecessor, Apache Spark 2, which was released in 2016. Here's a detailed comparison of some of the key differences between these two versions:
- Performance: Spark 3 includes several performance improvements, such as the Adaptive Query Execution feature that automatically optimizes query execution based on data characteristics and hardware resources, and improvements to the Apache Arrow integration that can improve data transfer performance. These enhancements make Spark 3 faster and more efficient than Spark 2.
- Python API: Spark 3 includes a new Pandas UDF API that allows users to apply custom Python functions to Spark DataFrames. This makes it easier to work with Spark data in Python, which is a popular language for data analysis and machine learning. In contrast, Spark 2 had limited support for applying custom Python functions to dataframes.
- SQL engine: Spark 3 includes several enhancements to the SQL engine, such as improved support for ANSI SQL:2011 syntax, better support for window functions, and support for table-valued functions. These enhancements make it easier to work with SQL-based data pipelines and improve compatibility with existing SQL-based tools and systems. In contrast, Spark 2 had limited support for ANSI SQL:2011 syntax and window functions.
- Machine learning library: Spark 3 includes several enhancements to the machine learning library, such as support for new deep learning algorithms like TensorFlow and Keras, improved performance, and better integration with other Spark components. These enhancements make it easier to build and deploy machine learning models with Spark. In contrast, Spark 2 had limited support for deep learning algorithms and TensorFlow/Keras integration.
Overall, Apache Spark 3 offers several new features and improvements that make it a more powerful and efficient data processing engine than Spark 2. If you're currently using Spark 2, it may be worth considering upgrading to Spark 3 to take advantage of these new features and improvements. By doing so, you can improve the performance, flexibility, and scalability of your big data processing workflows.
