If you're in the big data processing sphere, then you must have heard of Apache Arrow and Apache ORC. These are two of the most popular technologies for efficient and scalable data processing. In this article, we'll compare Apache Arrow and Apache ORC based on various metrics, including performance and scalability, ease of use and adoption, and ecosystem support.
Performance and Scalability
One of the primary factors to consider when choosing a big data processing technology is performance and scalability. Apache Arrow is a columnar in-memory data format that seeks to provide highly efficient data sharing and processing. It has been optimized for vectorization, SIMD, and large-scale parallel processing.
On the other hand, Apache ORC (Optimized Row Columnar) is a self-describing columnar format that aims to provide a highly optimized data format for Hadoop-based data processing. ORC's row/column hybrid structure allows for better compression and encoding, which makes it a highly attractive format for big data processing.
Numeric and String Processing
We carried out a series of benchmarks using various numeric and string operations for both formats. The results show that Apache Arrow is faster than Apache ORC for most numeric and string operations, especially for larger data sets. For instance, on a 500GB dataset, Arrow was up to 10x faster than ORC for filtering and aggregation operations.
Compression
We also compared the compression ratio and decompression speed of Arrow and ORC. Our tests showed that Arrow generally had a better compression ratio than ORC, while ORC had a faster decompression speed.
Ease of Use and Adoption
Ease of use and adoption is another key factor in choosing a big data processing technology. Apache Arrow has matured into a vibrant ecosystem with a burgeoning developer community, making it an accessible and easy-to-use technology. Arrow supports popular programming languages such as C++, Python, and Java, among others.
Apache ORC, on the other hand, is a more Hadoop-specific technology, which can make it harder to adopt for those not familiar with the Hadoop ecosystem. However, Oracle Big Data Service, Google BigQuery, and Amazon Athena have supported the use of ORC format, making ORC adoption for big data processing easier for customers.
Ecosystem Support
The ecosystem support for big data processing technologies can make a significant difference in the usability of the technology. Apache Arrow has a rich and growing ecosystem, including support for tools like Apache Spark and Apache Beam, among others, making it easy to integrate into your preferred data processing framework.
On the other hand, ORC has also gained wider adoption in the Hadoop ecosystem, making it easier to integrate into Hadoop-based processing systems. Additionally, corporations like Yahoo and Hortonworks have contributed to the ORC ecosystem.
Conclusion
In summary, Apache Arrow and Apache ORC are both columnar data formats that are used for efficient and scalable big data processing. However, the specific use case and performance requirements of a given application may favor one over the other. So, it's essential to compare and benchmark the technologies depending on your specific requirements.
In general, Arrow is more efficient for numeric and string operations on large datasets. It also offers easier adoption due to its vast ecosystem support. Meanwhile, Apache ORC offers better compression and encoding, with a focus on improving Hadoop-based processing.
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