What is InstaQuery™

InstaQuery by Kloudgen, leverages generative AI to optimize SQL queries with unparalleled efficiency. Achieving 100% accuracy in testing 1200 TPC benchmark queries in just 40 minutes, InstaQuery stands out for its unique approach.

"In the realm of LLMs, hallucinations are a common challenge. However, industry examples like QueryGPT from Uber demonstrate that guiding LLMs towards specialized tasks reduces hallucinations significantly. By introducing multiple in-between agents to sub-divide prompts, productivity and efficiency can be boosted significantly"

With InstaQuery, it leverages over 50 agents to identify anti-SQL patterns and tailor dynamic specialized LLM prompts for rewriting SQL queries with nearly 100% accuracy. While our solutions, is available as a Web App and REST API for 15 data platform, we offer Native App support for snowflake for enhanced security for both InstaQuery and InstaQuery API.

Specifically for Snowflake, our applications not only rewrite queries but also automatically detect long running or expensive queries. Then through static analysis, dynamic analysis and environment checks, we identify over 102 inefficiencies and provide comprehensive recommendations in under a minute.

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Static inefficiencies in query processing occur when queries are designed poorly from the start. These inefficiencies are caused by static factors such as data schema design and index structures. Key examples of static inefficiencies include:

  1. Non-optimal joins and aggregations**: Joining large tables without indexing or proper partitioning leads to excessive data scanning.
  2. Improper data types and storage structures**: Using inappropriate data types or poor compression settings can increase storage costs and slow down query performance.
  3. Lack of clustering**: Without proper clustering keys, queries that filter data on non-clustered columns can result in full-table scans, drastically increasing execution time and cost.

Dynamic inefficiencies arise from system load, execution conditions, and resource allocation during query runtime. Environmental inefficiencies refer to broader system setup factors that impact overall performance and costs. Together, dynamic and environmental inefficiencies contribute to unnecessary resource usage.

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Some examples of dynamic and Environmental inefficiencies

Benefits of InstaQuery:

  • Allows full automation and scalability by processing thousands of queries using API integration.
  • It increases developers’ productivity by over 500% by using Instaquery as part of their SDLC process and by deploying optimized queries and avoiding environmental inefficiencies to start with.
  • It automates Admin and support tasks and allows clients to repurpose those resource for revenue generating workloads
  • It can make migrations of legacy workloads to cloud much faster by ensuring that optimized workloads are being deployed.