Analyzing and Optimizing SQL at Scale: Single Origin and GPT-4

Analyzing and Optimizing SQL at Scale: Single Origin and GPT-4
With Single Origin, companies of any size can easily manage and optimize their SQL queries

In a prior post, we explored the capabilities of GPT-4 in detecting semantic equivalence between two SQL queries and identifying common patterns in a set of SQL queries. In light of the advancements in Language and Learning Models (LLMs), we wanted to update our comparison between Single Origin against GPT-4 for SQL analysis, with a focus on the scalability of each in analyzing a vast number of SQL queries and implementing optimization actions based on the results. Let’s dive in!

Analyzing and Optimizing SQL at Scale

Manually optimizing each query one by one can be a daunting and time-consuming task, especially when dealing with a high volume of SQL queries in an organization. This task involves inspecting each query, considering how it compares to all other queries, and assessing if it requires optimization. This process is unmanageable for small teams since the number of queries can easily run into thousands per day.

Fortunately, Single Origin provides an automated solution to analyze all queries and group similar ones together for efficient optimization. With just a click of a button and a few minutes, Single Origin can parse and group 1M queries based on their semantic similarity and suggest ways to consolidate and optimize the groups of queries.


Insights With Just a Few Clicks

When analyzing SQL queries, GPT-4 is undoubtedly a powerful tool. However, it comes at a steep cost. To analyze one million SQL queries using GPT-4, one might need to spend $10,000!

  • 1,000 tokens correspond to roughly 750 words; a typical SQL query contains approximately 30 lines with 5 words per line. 
  • This means that 1,000 tokens would cover approximately 6 SQL queries.
  • The cost of using GPT-4-32k is currently $0.06 per 1,000 tokens. To analyze 1,000 queries, one would need to spend $10. 
  • The cost would be a staggering $10,000 to analyze one million queries. This is a significant expense that may not be feasible for many organizations.

Fortunately, there is a more cost-effective solution available: Single Origin. Single Origin allows you to analyze up to one million queries on our smallest plan for just $300/month. This is due to our innovative approach to leveraging the semantics of SQL queries to optimize processing with a minimal memory footprint. 

Finally, if you use Single Origin, you do not need to build anything on GPT-4. There is no need for a human-in-the-loop to check for errors (including hallucinations, unexpected columns, and improper joins), prompt engineering, or build a UI to navigate the results. Even if GPT-4 can analyze SQL with 80-90% accuracy, more accuracy is needed at the scale of 1M queries. Single Origin is designed to understand and parse SQL because accuracy is critical for your mission-critical pipelines and metrics. 


In summary, while GPT-4 is an impressive tool, there are more scalable and cost-effective solutions for analyzing large volumes of SQL queries. Single Origin's innovative approach offers a more automated, practical, and affordable solution for analyzing and optimizing SQL queries at scale. It is a much faster and simpler solution, saving you valuable time and resources. 

With Single Origin, companies of any size can easily manage and optimize their SQL queries. If you’d like to get in touch with us to see how Single Origin can generate insights across all your SQL queries, contact Until next time!



Engineering @ Single Origin