ORC File
ORC (Optimized Row Columnar) is a highly efficient columnar storage file format designed for big data processing. It provides advanced compression, predicate pushdown capabilities, and optimized reading patterns for large-scale data analysis.
How ORC files work
ORC files organize data into stripes, each containing index data, row data, and a footer. This structure enables efficient data access and processing:
Each stripe typically contains:
- Index entries for fast data location
- Column-based row groups with statistics
- Metadata about the stripe's contents
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Key features and benefits
Advanced compression
ORC supports multiple compression methods including:
- Dictionary encoding for string columns
- Run-length encoding for repeated values
- Bit packing for integers
Predicate pushdown
ORC's built-in indexing allows queries to skip irrelevant data blocks, significantly improving query performance through predicate pushdown.
Type support
The format supports complex data types including:
- Nested structures
- Arrays
- Maps
- Timestamps with various precisions
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Comparison with other formats
ORC vs Parquet
While both Apache Parquet and ORC are columnar formats, they have distinct characteristics:
Integration with data systems
ORC works particularly well with:
- Apache Hive
- Apache Spark
- Data lake systems
- Modern lakehouse architecture implementations
Use cases and applications
Time-series data storage
ORC's columnar nature makes it efficient for storing and querying time-series data, especially when:
- Analyzing specific columns over time ranges
- Performing temporal aggregations
- Managing high-cardinality datasets
Analytics workloads
The format excels in analytical scenarios requiring:
- Fast column access
- Efficient filtering
- Aggregation over large datasets
- Complex predicate evaluation
Performance considerations
Read optimization
- Stripe size configuration affects read performance
- Column pruning reduces I/O overhead
- Statistics help skip unnecessary data reads
Write patterns
- Larger stripe sizes improve compression
- Regular statistics collection enhances query planning
- Proper column ordering can improve compression ratios
Best practices
- Stripe Size: Configure based on typical query patterns
- Compression: Choose appropriate compression based on data characteristics
- Schema Design: Order columns to maximize compression efficiency
- Statistics: Maintain up-to-date statistics for query optimization