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If your Snowpark queries are spilling to disk or your ML training jobs keep timing out, the fix might not be a bigger warehouse. It might be the wrong resource_constraints setting. David Grömling has written up a guide on how to match memory and CPU architecture to your actual workload. ML, ETL, data pipelines, interactive analytics. Including when Snowpark-optimized warehouses are overkill. Worth a read if you're burning credits without getting the performance you expected: https://lnkd.in/eMzYiseP #Snowflake #Snowpark #FinOps #CloudCost

Our GenRewrite work is now officially accepted to appear at ACM SIGMOD 2026. Slow query performance is one of the most common complaints in database systems. Part of this is on query optimizers to do better, but a significant portion is caused by poorly written queries - whether from inexperienced users or auto-generated by modern BI tools like Looker, where it's not uncommon for queries to span hundreds of lines. When the gap between what the user wrote and what the optimizer needs is too large, traditional query optimization falls on its face. Whether it's a DBA trying to hand-optimize queries or applying rewrite rules, the problem remains. The traditional approach has been rewrite rules - pattern-matching transformations that replace query fragments with optimized equivalents. But these are rigid. A rule-based rewriter can only optimize patterns it already knows. If your query doesn't match an existing rule, you're out of luck. With LLMs now capable of complex reasoning, the natural question is: can we do better? Can LLMs discover rewrite opportunities that existing rules miss? This is what GenRewrite explores. The challenge is that simply prompting an LLM to "rewrite this query faster" doesn't work well on complex queries. Most outputs either fail to parse or produce incorrect results. GenRewrite introduces Natural Language Rewrite Rules (NLR2s) - human-readable descriptions that guide the LLM toward effective rewrites and enable knowledge transfer across queries. The system learns over time: as it successfully rewrites queries, it accumulates NLR2s and applies them to similar queries based on performance bottleneck analysis. Some examples of NLR2s that GenRewrite discovered: * "Replace the correlated subquery with a precomputed aggregate in a separate CTE" * "Use common table expressions (CTEs) to precompute aggregates" * "Replace a correlated subquery that computes an aggregate with an inline view that pre-aggregates data" We also use counterexample-guided iterative correction to fix semantic and syntactic errors, improving correctness rates significantly - 70% of GenRewrite's rewrites are semantically equivalent to the original, compared to 52% for baseline LLM approaches. On TPC-DS, GenRewrite achieves 2x or higher speedup on 25 queries - 35% more queries than other LLM-based approaches and 160% more queries than LLM-enhanced rule-based systems. Some individual rewrites hit 9x to 24x speedups. On SQLStorm-generated queries that the LLM never saw during training, the gap widens further. Kudos to my PhD student Jie Liu for his great work on this! Full paper: https://lnkd.in/eHBdvw7c #LLM #GenAI #GenRewrite #QueryRewriting #SIGMOD2026