Are Wide Tables Fast or Slow?

Source: Dev.to

Introduction

Wide tables are a common component of Business Intelligence (BI) systems. They are typically created early in a project by joining multiple related tables into a single, denormalized table. This results in a dataset that does not conform to normal forms and contains a large amount of redundant data. Because wide tables must be pre‑created, they are also less flexible to use.

Why Wide Tables Are Considered Fast

• Querying a wide table avoids the overhead of real‑time multi‑table joins.
• Join operations in SQL have historically been difficult to write and can exhibit poor performance.
• Detailed analysis of SQL joins is available HERE.

Drawbacks of Wide Tables

• Data Redundancy: Extra data is read during computation, increasing I/O time.
  Example: An Orders table joined with OrderDetails (5 rows per order) repeats order data five times. Adding dimension tables (e.g., Customer, Employee, Region) further inflates the dataset.
• I/O Overhead: Queries that aggregate over the wide table (e.g., sum of order amounts by customer region) must scan a much larger volume of data.
• Normalization Violations: The result set does not conform to normal forms, leading to potential data errors.
• Inflexibility: Wide tables are “stiff” – any schema change requires rebuilding the table.

Despite these issues, wide tables often appear faster in practice because relational database joins can be significantly slower, even when the wide table incurs higher I/O costs.

Can Joins Be Optimized?

Yes, but traditional SQL lacks the mechanisms to perform targeted optimizations for different join patterns. The generic definition of a join (based on Cartesian product) makes it difficult to apply specialized optimizations. Vendors have pushed SQL optimization to its limits, yet many BI workloads still rely on wide tables to achieve acceptable performance.

SPL as a Solution

SPL (Structured Process Language) is an open‑source computing engine designed for structured data processing, independent of any specific database. SPL offers:

• Higher Join Performance: SPL’s join handling outperforms both traditional SQL joins and wide‑table‑based joins.
• Targeted Optimizations: SPL distinguishes between two common BI join types:
  • Foreign‑key joins – optimized with dimension table preload and numberization methods.
  • Primary‑key joins – optimized with an order‑based merge method.

By accelerating joins, SPL eliminates the need for wide tables, reducing data volume and improving overall BI performance.

SPL Join Optimization Techniques

Foreign‑Key Joins

• Dimension Table Preload: Loads dimension tables into memory once, avoiding repeated lookups.
• Numberization: Converts join keys to compact numeric representations to speed up matching.

Primary‑Key Joins

• Order‑Based Merge: Leverages sorted primary keys to merge tables in linear time, minimizing complexity.

Performance Comparison

A benchmark was conducted using the TPC‑H 100 GB dataset, focusing on a typical BI scenario:

1. Two‑table join: Fact table ↔ one dimension table.
2. Wide‑table aggregation: Same analysis performed on a pre‑built wide table.

Results (time measured in seconds) are summarized in the detailed test report HERE.

Key findings

• SQL Wide Table vs. SQL Join: The wide table was faster, confirming the earlier analysis.
• SPL Real‑Time Join vs. SQL: SPL’s join was 3–9× faster than joins in two SQL databases.
• SPL vs. ClickHouse Wide Table: While ClickHouse’s wide‑table performance was higher, SPL’s real‑time join still outperformed it by a large margin.

Considering wide‑table drawbacks (redundancy, errors, inflexibility), SPL’s real‑time join offers a clear advantage: it avoids wide‑table defects while delivering superior performance.

Conclusion

Wide tables can appear fast because they sidestep costly SQL joins, but they introduce significant redundancy and rigidity. SPL provides a robust alternative by delivering high‑performance, real‑time joins with specialized optimizations for common BI patterns. As a result, the reliance on costly wide tables diminishes, leading to cleaner data models and faster analytics.

SPL is open‑source; the source code is available on GitHub.