Native implementation supports a vectorized ORC reader and has been the default ORC implementation since Spark 2.3. Since Spark 3.1.0, SPARK-33480 removes this difference by supporting CHAR/VARCHAR from Spark-side. hive implementation is designed to follow Hive’s behavior and uses Hive SerDe.įor example, historically, native implementation handles CHAR/VARCHAR with Spark’s native String while hive implementation handles it via Hive CHAR/VARCHAR.native implementation is designed to follow Spark’s data source behavior like Parquet.Two implementations share most functionalities with different design goals. Spark supports two ORC implementations ( native and hive) which is controlled by. You agree that we have no liability for any damages.Apache ORC is a columnar format which has more advanced features like native zstd compression, bloom filter and columnar encryption. User assumes all risk of use, damage, or injury. The information is "AS IS", "WITH ALL FAULTS". PrintĪrticles on are general information, and are not intended to substitute for professional advice. Birmingham, United Kingdom: Packt Publishing, 2016. ![]() ![]() Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library. Birmingham, United Kingdom: Packt Publishing, 2018. ![]() Indiana, United States: Sams Publishing, 2017. California, United States: O'Reilly Media, 2018. Spark: The Definitive Guide: Big Data Processing Made Simple. Parquet: Comparison ChartĬhambers, Bill and Matei Zaharia. Parquet, on the other hand, stores data in pages and each page contains header information, information about definition levels and repetition levels, and the actual data. The footer is where the key statistics for each column within a stripe such as count, min, max, and sum are cached. Each stripe has index, row data and footer. However, ORC files are organized into stripes of data, which are the basic building blocks for data and are independent of each other. – Working with ORC files is just as simple as working with Parquet files. In fact, Parquet is the default file format for writing and reading data in Apache Spark. One key difference between the two is that ORC is better optimized for Hive, whereas Parquet works really well with Apache Spark. While Parquet has a much broader range of support for the majority of the projects in the Hadoop ecosystem, ORC only supports Hive and Pig. – Both ORC and Parquet are popular column-oriented big data file formats that share almost a similar design in that both share data in columns. ![]() Parquet is now an Apache incubator project. Parquet, on the other hand, was inspired from the nested data storage format outlined in the Google Dremel paper and developed by Cloudera, in collaboration with Twitter. It is a successor to the traditional Record Columnar File (RCFile) format and provides a more efficient way to store relational data than the RCFile, reducing the size of the data by up to 75 percent. – ORC was inspired from the row columnar format which was developed by Facebook to support columnar reads, predictive pushdown and lazy reads. Difference between ORC and Parquet Origin In fact, it is the default file format for writing and reading data in Spark. Parquet works really well with Apache Spark. Parquet is also a better file format in reducing storage costs and speeding up the reading step when it comes to large sets of data. In fact, it is particularly designed keeping nested data structures in mind. It is more efficient at doing data IO style operations and it is very flexible when it comes to supporting a complex nested data structure. It provides significant advantages in performance and storage requirements with respect to traditional storage solutions. Like ORC, Parquet provides columnar compressions saving you a great deal of storage space while allowing you to read individual columns instead of reading complete files. Parquet is very popular among the big data practitioners because it provides a plethora of storage optimizations, particularly in analytics workloads. Parquet is yet another open-source column-oriented file format in the Hadoop ecosystem backed by Cloudera, in collaboration with Twitter.
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