Columnar Storage

Columnar storage is a data organization technique where data is stored and retrieved by columns rather than rows. In a traditional row-based storage system, data is stored row by row, with each row containing values for all the columns. This structure is suitable for transactional databases, where data is frequently updated and individual records need to be accessed quickly.

In contrast, columnar storage stores each column's values together in a separate data block. 

Columnar storage design has several advantages:

Improved Query Performance: Columnar databases are optimized for analytical queries that involve aggregations, filtering, and complex joins. When you only need specific columns for your query, columnar storage allows for more efficient data retrieval by minimizing the amount of data read from disk.

Compression: Columns typically contain similar types of data, which can be highly compressible. Columnar databases can apply compression techniques that reduce storage costs and improve query speed. This is especially beneficial for large datasets.

Analytics-Friendly: Analytical workloads often involve scanning large volumes of data, and columnar storage is well-suited for this type of operation. It minimizes I/O operations and allows for parallel processing, making it ideal for data warehousing environments.

Reduced Storage Costs: By using compression and storing similar data types together, columnar databases can significantly reduce storage costs, particularly in the cloud.

Firebolt's Approach to Columnar Storage

Firebolt is a modern, cloud-native data warehouse designed to harness the full potential of columnar storage. Firebolt stores data efficiently in columnar format to drive savings in storage costs. Additionally, Firebolt stores the data in a sorted, compressed, indexed format using Sparse indexes to provide extremely granular data pruning. While most data warehouses provide data pruning at partition or micro-partition level granularity, Firebolt provides range level data pruning. This reduces the amount of data scanned, leading to sub-second response times and efficient use of CPU and memory resources.