The Columnar Storage Architecture of Apache Doris is a fundamental aspect of its efficiency and performance in handling analytical workloads. Let’s delve into the key components and principles of Doris’s columnar storage architecture:
1. Storage Layout:
In Apache Doris, data is organized and stored column-wise rather than row-wise, a design known as columnar storage. This approach contrasts with traditional row-oriented databases and offers several advantages for analytical processing:
- Column-based Organization: Each column of a table is stored separately on disk. This allows for efficient access to specific columns during query execution, reducing I/O overhead.
- Compression Efficiency: Columnar storage lends itself well to compression techniques because data within each column often exhibits similar characteristics. Doris utilizes various compression algorithms to minimize storage footprint and improve query performance.
2. Compression Techniques:
Doris employs several compression techniques to optimize storage efficiency while maintaining query performance:
- Dictionary Encoding: This technique replaces frequently occurring values with short dictionary codes, reducing storage requirements for repetitive data patterns.
- Run-Length Encoding (RLE): Run-length encoding replaces repeated consecutive values with a count and a single value, effectively reducing the storage space required for long sequences of identical values.
- Delta Encoding: Delta encoding stores the differences between consecutive values, which is particularly effective for columns with sorted or monotonic data.
3. Encoding Schemes:
Different encoding schemes are employed based on the data types and characteristics of each column:
- Integer Encoding: Integers are often encoded using dictionary encoding or delta encoding to reduce storage overhead.
- Floating-point Encoding: Floating-point numbers may utilize delta encoding to represent the differences between successive values efficiently.
- String Encoding: Strings can be encoded using dictionary encoding or other techniques depending on their distribution and frequency.
4. Block-Based Storage:
Data in Doris is organized into blocks, with each block containing a segment of each column. This block-based storage enables efficient data retrieval and processing by allowing Doris to operate on chunks of data in memory rather than accessing individual records from disk.
5. Data Compression and Dictionary Encoding:
Doris applies compression and dictionary encoding at both the column level and the block level:
- Column-level Compression: Each column is compressed independently, optimizing storage for the unique characteristics of that column’s data.
- Block-level Compression: Within each column, data is divided into blocks, and compression techniques are applied to each block individually. This allows for efficient storage and retrieval of data blocks during query execution.
6. Data Organization and Partitioning:
Data in Doris is partitioned and distributed across nodes in the cluster based on partition keys. This allows for parallel processing of queries and efficient resource utilization. Partitioning also facilitates data pruning, where Doris can skip reading entire partitions that are irrelevant to the query, further improving query performance.
Conclusion:
The columnar storage architecture of Apache Doris plays a pivotal role in its ability to efficiently handle analytical workloads. By organizing data column-wise, employing compression techniques, and optimizing data access patterns, Doris achieves high performance, scalability, and storage efficiency, making it a powerful solution for real-time analytics and data warehousing.