What is ORC?
Optimized Row Columnar (ORC) is a self-describing, type-aware columnar file format designed for Hadoop workloads. It provides efficient ways to store, read and process data, offering significant benefits over traditional row-based formats. ORC is widely used in Big Data processing and analytics environments.
History
ORC was created by Hortonworks, with major contributions from Cloudera and Facebook. It was introduced to tackle the limitations of both row formats (like CSV, JSON) and pure columnar formats (like RCFile). It has undergone various versions, enhancing its capabilities and performance.
Functionality and Features
- Compression: ORC reduces the size of the original data and offers a choice of compression techniques.
- Schema evolution: It supports adding new fields, ignoring missing fields, and changing fields' data types.
- Indexes: ORC includes light-weight indexes within the file to enable fast data skips.
Architecture
ORC divides data into stripes (similar to blocks in other file systems) that are independently compressed. Within these stripes, the data is divided into rows and columns, allowing for selective reads and highly efficient compression.
Benefits and Use Cases
ORC is well-suited for large streaming reads, but also for random access. It reduces storage requirements and improves performance, making it ideal for querying large datasets. ORC's columnar format is beneficial for analytic queries that involve aggregates.
Challenges and Limitations
While ORC offers many benefits, it may not be ideal for every case. For instance, ORC is not the best option for workloads involving frequent updates or small datasets, as its overhead may outweigh benefits. Also, converting existing data into ORC format can be time-consuming.
Comparisons
Compared to similar formats like Parquet and Avro, ORC is known for its strong compression and fast reads. However, Parquet is more universally adopted across various data processing frameworks. Avro, while being row-based, supports schema evolution better than both.
Integration with Data Lakehouse
In a data lakehouse, ORC works as a highly efficient storage format that supports both detailed queries and high-level analytical functions, providing a foundation for a single source of truth. Combined with a solution like Dremio, it enables faster analytics on a large scale.
Security Aspects
ORC files inherently do not have security protocols embedded in them. Therefore, the security of ORC files relies on the Hadoop file system security measures in place. It is crucial to ensure proper user authentication and access controls.
Performance
Utilizing ORC significantly improves performance in Hadoop based data processing environments. Its compressed, columnar architecture allows for faster reads and reduced disk I/O operations.
FAQs
What is ORC? ORC (Optimized Row Columnar) is a self-describing, type-aware columnar file format for Hadoop workloads, designed to offer efficient ways to store, read, and process data.
Why use ORC? ORC reduces the size of the original data, improves performance, allows schema evolution, and provides light-weight indexes for faster data skips.
What are the limitations of ORC? ORC may not be ideal for workloads involving frequent updates or small datasets, and converting existing data into ORC can be time-consuming.
How does ORC integrate with a data lakehouse? ORC acts as an efficient storage format in a data lakehouse, supporting detailed queries and high-level analytical functions, thereby forming a reliable basis for a single source of truth.
How does ORC compare to similar formats like Parquet and Avro? ORC is known for its strong compression and fast reads. However, Parquet is more universally adopted, and Avro supports schema evolution better than both.
Glossary
Columnar Format: A method of storing data that allows reading, storing, and processing data more efficiently.
Compression: The process of reducing the size of data to save space or speed up transmission.
Schema Evolution: The ability to modify the schema of a database to meet new business requirements.
Data Lakehouse: A hybrid data management platform that combines the features of both data warehouses and data lakes.
Hadoop: An open-source framework that allows for processing and storage of large data sets across clusters of computers.
