As data architectures evolve to handle increasingly complex and semi-structured data, choosing the right data type in BigQuery becomes critical. Two popular options for nested or hierarchical data are the STRUCT and JSON data types. While both serve similar purposes, they differ significantly in performance, flexibility, and use cases. 

In this post, we’ll explore the performance implications of using BigQuery’s JSON data type compared to STRUCT. We will provide a benchmark-style example to help guide your architectural decisions. 

🧱 STRUCT vs. JSON: The Basics 

• STRUCT is a native BigQuery data type that allows you to define a fixed schema with nested fields. 

• JSON is a flexible, schema-less data type that stores data as raw strings in JSON format. 

⚙️ Performance Implications of JSON 

1. Query Execution Time 

JSON fields require parsing at query time using functions like JSON_EXTRACT or JSON_VALUE. This adds overhead compared to STRUCT, where fields are accessed directly. 

2. Column Pruning Limitations 

BigQuery cannot prune individual fields inside a JSON column. This means even if you query one field, the entire JSON blob is read and parsed. 

3. Storage and Compression 

JSON is stored as UTF-8 strings, which are typically larger and less compressible than STRUCTs. This can increase both storage and query costs. 

4. Partitioning and Clustering 

You cannot partition or cluster on fields inside a JSON column, limiting your ability to optimize performance for large datasets. 

📊 Benchmark Example 

Here’s a simulated benchmark comparing query performance between JSON and STRUCT for a dataset with 10,000 rows: 

Query Type	Data Type	Query Example	Avg Execution Time
Simple filter	STRUCT	WHERE attributes.score > 50	1.2 seconds
Simple filter	JSON	WHERE CAST(JSON_EXTRACT(data, ‘$.attributes.score’) AS FLOAT64) > 50	2.8 seconds
Field projection	STRUCT	Select nested fields directly	1.0 seconds
Field projection	JSON	Use JSON_EXTRACT for each field	2.5 seconds

✅ When to Use JSON 

Despite the performance trade-offs, JSON is a strong choice when: 

• The schema is dynamic or evolving. 

• You need to ingest raw data quickly without transformation. 

• You’re dealing with logs, events, or API payloads. 

• You prioritize flexibility over performance. 

🧠 Best Practices 

• Extract frequently used fields into native columns during ETL/ELT. 

• Use materialized views to cache parsed JSON fields. 

• Avoid deeply nested or large JSON blobs unless necessary. 

• Consider a hybrid approach: use STRUCT for stable fields and JSON for dynamic ones. 

📝 Summary 

Criteria	STRUCT	JSON
Schema Flexibility	Low	High
Query Performance	High	Moderate to Low
Storage Efficiency	High	Lower (due to string storage)
Partitioning/Clustering	Supported	Not supported
Use Case Fit	Stable, known schema	Dynamic, semi-structured data

Choosing between JSON and STRUCT in BigQuery is a trade-off between performance and flexibility. For high-performance analytics on well-defined data, STRUCT is ideal. For ingesting and exploring dynamic or semi-structured data, JSON offers unmatched adaptability.