Narrow Table

Understanding narrow tables

Narrow tables represent a fundamental database design pattern that contrasts with wide tables. In a narrow table structure, each row contains only essential fields, making it particularly efficient for:

• Sequential reads of specific columns
• Time-series data storage
• High-volume data ingestion
• Column-oriented storage systems

For example, a narrow table storing temperature sensor readings might contain just three columns: timestamp, sensor_id, and temperature_value.

Benefits of narrow table design

Storage efficiency

Narrow tables excel in columnar database systems because:

• Less memory is required for loading column data
• Better compression ratios are achievable
• More efficient use of CPU cache lines
• Reduced I/O overhead during queries

Query performance

Queries on narrow tables often perform better because:

• Fewer columns need to be processed
• Simpler query plans
• More efficient column pruning
• Better utilization of database page cache

Common use cases

Time-series data

Narrow tables are particularly well-suited for time-series applications:

Market data storage

In financial markets, narrow tables efficiently store tick-level data:

SELECT * FROM trades
WHERE symbol = 'AAPL' 
SAMPLE BY 1h;

This query demonstrates efficient sampling of a narrow table containing market trades data.

Design considerations

When implementing narrow tables, consider:

1. Denormalization trade-offs
2. Join complexity with related data
3. Impact on application-level data access
4. Storage system characteristics

The decision to use narrow tables should align with:

• Query patterns
• Data access requirements
• Storage engine capabilities
• Performance objectives

For example, if your application frequently needs to access related attributes, the overhead of joining multiple narrow tables might outweigh the benefits of the narrow structure.

Best practices

1. Identify core attributes:

   • Keep only essential columns
   • Move rarely accessed fields to separate tables
   • Consider query patterns when deciding column inclusion

2. Optimize for common queries:

   • Align column selection with frequent query patterns
   • Consider denormalization when beneficial
   • Balance storage efficiency with query performance

3. Monitor performance:

   • Track query execution times
   • Monitor storage utilization
   • Assess join overhead
   • Evaluate compression effectiveness

The effectiveness of narrow tables often depends on the underlying storage engine and its optimization capabilities for column-oriented access patterns.