{"slug": "sqlazy-deterministic-sql-generation", "title": "SQLazy–Deterministic SQL Generation", "summary": "SQLazy launches a deterministic SQL generation tool that lets users write complex queries step-by-step in natural language, then compiles them into auditable, production-ready SQL. The compiler guarantees no AI hallucinations, and the workflow supports debugging and multi-database portability. The tool is available as a web app and a desktop IDE.", "body_md": "**Write complex SQL step-by-step in natural language — then compile it into auditable, production-ready SQL.**\n\nAI-assisted. Compiler-guaranteed. No black boxes.\n\nMIT License for examples • SQLazy application is proprietary\n\nWriting analytical SQL is hard.\n\nReviewing AI-generated SQL is even harder.\n\nModern AI can produce SQL that *runs* — but you never know whether you can trust it.\n\nWhen queries become deeply nested with window functions and subqueries, they become:\n\n- hard to review\n- hard to debug\n- hard to maintain\n- hard to port across databases\n\nSQLazy turns SQL development into a **step-by-step workflow** you can actually understand and audit.\n\nInstead of generating one giant SQL statement, SQLazy lets you:\n\n- Describe each step in semi-natural language\n- Verify the logic step-by-step\n- Compile the steps into native SQL\n\nThe final SQL is generated by a **compiler**, not an LLM.\n\nThat means:\n\n- No AI hallucinations in the final SQL\n- Fully auditable logic\n- Production-ready output\n\nAI helps you **write the steps**, not the final SQL.\n\nYou review the logic first.\n\nThen the compiler gives you the trustworthy the SQL.\n\nResult: **AI productivity + compiler reliability.**\n\nEven the most complex analytics queries become simple building blocks:\n\n- calculate\n- filter\n- summarize\n- group\n- join\n- rank\n- segment\n\n...\n\nNo more nested SQL nightmares.\n\nDebugging complex SQL is painful.\n\nYou usually can't inspect intermediate results.\n\nSQLazy provides **step-by-step execution** so you can see:\n\n- intermediate tables\n- intermediate calculations\n- intermediate filters\n\nExactly like debugging code.\n\nWrite your logic once.\n\nGenerate SQL for multiple databases.\n\nNo more rewriting queries for different SQL dialects.\n\nLLMs help you:\n\n- rewrite messy natural language into SQLazy steps\n- decompose complex requirements into workflows\n\nBut **you stay in control** of the logic.\n\nInstead of writing one large SQL statement, the logic is expressed as a step-by-step workflow.\n\nEach step represents a single transformation.\n\n| Variable | Anchor | Statement |\n|---|---|---|\n| t1 | stock | filter CODE = 100046 |\n| t2 | sort DT asc | |\n| t3 | segment CL down as NoRisingDays | |\n| t4 | summarize DT count as ContinuousDays group NoRisingDays | |\n| summarize ContinuousDays max as max_ContinuousDays |\n\nThis workflow is easy to read and easy to review.\n\nEach step does only **one simple thing**.\n\n```\nWITH t2 AS (\n  SELECT\n    CODE,\n    DT,\n    CL\n  FROM\n    stock\n  WHERE\n    CODE = 100046\n)\nSELECT\n  MAX(ContinuousDays) AS max_ContinuousDays\nFROM\n  (\n    SELECT\n      NoRisingDays,\n      COUNT(DT) AS ContinuousDays\n    FROM\n      (\n        SELECT\n          CODE,\n          DT,\n          CL,\n          SUM(\n            CASE\n              WHEN CL < col__3 THEN 1\n              ELSE 0\n            END\n          ) OVER (\n            ORDER BY\n              DT ASC\n          ) + 1 AS NoRisingDays\n        FROM\n          (\n            SELECT\n              t2. *,\n              LAG(CL) OVER (\n                ORDER BY\n                  DT ASC\n              ) AS col__3\n            FROM\n              t2\n          ) sub__4\n      ) t3\n    GROUP BY\n      NoRisingDays\n  ) t4\n```\n\nThis SQL is:\n\n- deeply nested\n- hard to review\n- hard to debug\n- hard to modify\n\nBut the SQLazy workflow is easy to read, review, and audit.\n\n🌐 Try the Web App: [https://sqlazy.com](https://sqlazy.com)\n\n- online playground\n- free usage (limited compute)\n- perfect for quick experiments & sharing\n\nBest for daily work and large datasets.\n\nFeatures:\n\n- unlimited local debugging\n- bring your own LLM key\n- customizable prompts\n- full performance\n\n| Feature | Web App (sqlazy.com) | Desktop IDE |\n|---|---|---|\n| Natural language → SQL | ✅ Full SQL generation capabilities | ✅ Full SQL generation + native SPL generation |\n| AI assistance & planning | ✅ Usage-limited (hosted LLM) | ✅ Bring your own LLM key, no usage limits, customizable prompts |\n| Step-by-step debugging | ✅ Limited dataset size | ✅ Powered by esProc engine, supports large datasets |\n| Data stays in your environment | ❌ Runs in the cloud | ✅ Supports private/on-prem deployment (commercial license) |\n| Interface & language support | English UI and terminology | Full English + Chinese UI and terminology |\n| Database dialect support | Same core compiler | Same core compiler |\n| Availability | Free | Free for personal & team online use; commercial license for private/on-prem deployment |\n| Version control | Always up-to-date in the cloud | Free edition auto-updates; commercial edition supports version locking |\n\nWe collected real SQL problems from the Internet and solved them step-by-step with SQLazy. These examples show how complex analytical queries can be expressed as clear, auditable workflows.\n\nExplore the examples by real analytics scenarios:\n\nDetect continuous patterns in time-series data – one of the hardest problems to solve with standard SQL.\n\n- Find the longest consecutive rising streak for a single stock\n- Get the exact start and end dates of the longest rising period\n- Filter all stocks with rising streaks longer than N days\n- Extract every rising streak period that exceeds 3 days\n\n👉 [https://github.com/SPLWare/sqlazy/tree/master/examples/consecutive-trends](https://github.com/SPLWare/sqlazy/tree/master/examples/consecutive-trends)\n\nSplit event streams into logical sessions and generate sequence numbers based on time gaps or state changes.\n\n- Number events and reset counters when time gaps exceed 1 hour\n- Create conditional running totals that reset when a condition is met\n\n👉 [https://github.com/SPLWare/sqlazy/tree/master/examples/event-sequencing](https://github.com/SPLWare/sqlazy/tree/master/examples/event-sequencing)\n\nDynamic grouping based on data values and running status – eliminates complex `SUM(CASE WHEN ...) OVER (...)`\n\npatterns.\n\n- Calculate totals differently for single-customer vs multi-customer groups\n- Group records automatically when a running status flag changes\n\n👉 [https://github.com/SPLWare/sqlazy/tree/master/examples/conditional-grouping](https://github.com/SPLWare/sqlazy/tree/master/examples/conditional-grouping)\n\nRolling window calculations and interval aggregations with automatic missing value backfilling.\n\n- Build minute-by-minute price bars (OHLC) with gap filling\n- Calculate 5-minute rolling totals over time-series data\n\n👉 [https://github.com/SPLWare/sqlazy/tree/master/examples/time-window-analytics](https://github.com/SPLWare/sqlazy/tree/master/examples/time-window-analytics)\n\nPrepare messy datasets for reporting and analytics with simple, readable steps.\n\n- Hide repeated values in reports (replace duplicates with NULL)\n- Generate status flags based on recent consecutive row values\n\n👉 [https://github.com/SPLWare/sqlazy/tree/master/examples/data-cleaning](https://github.com/SPLWare/sqlazy/tree/master/examples/data-cleaning)\n\nAdd, modify or inject rows into grouped results without complex union operations.\n\n- Insert a header row before each group of records\n- Split total amounts across multiple rows while preserving the grand total\n\n👉 [https://github.com/SPLWare/sqlazy/tree/master/examples/group-enhancement](https://github.com/SPLWare/sqlazy/tree/master/examples/group-enhancement)\n\nReshape data for BI tools and reports – no more dynamic SQL or stored procedures.\n\n- Turn unique row values into dynamic columns (automatic pivot tables)\n- Split invoice totals evenly across line items while maintaining sum integrity\n\n👉 [https://github.com/SPLWare/sqlazy/tree/master/examples/dynamic-reporting](https://github.com/SPLWare/sqlazy/tree/master/examples/dynamic-reporting)\n\nReal-world trading and finance SQL problems that analysts deal with daily.\n\n- Calculate the longest consecutive rising streak for any stock\n- Identify all stocks that have risen for more than 3 consecutive days\n- Generate OHLC price bars from raw tick data\n- Extract all rising streak periods for market analysis\n- Compute rolling averages and totals for technical indicators\n\n👉 [https://github.com/SPLWare/sqlazy/tree/master/examples/financial-analysis](https://github.com/SPLWare/sqlazy/tree/master/examples/financial-analysis)\n\nSessionization and event tracking patterns for user behavior analysis.\n\n- Split user activity into sessions based on inactivity timeouts\n- Add summary header rows before each user's event group\n- Backfill missing time intervals in user activity data\n- Track session-based counters that reset on new sessions\n\n👉 [https://github.com/SPLWare/sqlazy/tree/master/examples/behavioral-analytics](https://github.com/SPLWare/sqlazy/tree/master/examples/behavioral-analytics)\n\nComplex summarization patterns that require stateful calculations across rows.\n\n- Dynamic conditional grouping with custom aggregation logic\n- Stateful running totals with reset triggers\n- Calculations that depend on values from previous consecutive rows\n- Nested ordered aggregation within hierarchical groups\n\n👉 [https://github.com/SPLWare/sqlazy/tree/master/examples/advanced-aggregation](https://github.com/SPLWare/sqlazy/tree/master/examples/advanced-aggregation)\n\n- Step-by-step natural language → SQL\n- Step debugging\n- Cross-database SQL generation\n- MySQL / PostgreSQL / Oracle support\n\n- Snowflake / BigQuery support\n- Recursive queries\n- Fully automatic workflow generation from plain English\n\n- Very old database versions (e.g. MySQL 5.5)\n\nTypical AI SQL tools generate the **final SQL directly**.\n\nSQLazy takes a different approach:\n\n| Typical AI SQL tools | SQLazy |\n|---|---|\n| Generate final SQL | Generate step-by-step workflow |\n| Hard to review | Easy to review |\n| Hard to debug | Step debugging |\n| May hallucinate SQL | Final SQL generated by compiler |\n\nAI helps you **write the logic**.\nThe compiler produces the **trusted SQL**.\n\nBasic SQL knowledge is helpful, but SQLazy reduces the need to:\n\n- write deeply nested queries\n- memorize complex window functions\n- deal with dialect differences\n\nYou focus on **logic**, not syntax.\n\nYes.\n\nSQLazy provides **step-by-step debugging** in both the Web App and Desktop IDE.\n\nYou can execute each step and inspect intermediate results.\n\nThis makes it useful not only for development, but also for:\n\n- learning how analytical SQL works\n- understanding window functions and grouping logic\n- experimenting with different parameters and transformations\n\nMany users use SQLazy as an **interactive SQL learning environment**.\n\nSQLazy is a **development tool**, not a runtime engine.\n\nThe output of SQLazy is plain native SQL.\n\nYou simply:\n\n- Copy the generated SQL\n- Paste it into your database, BI tool, or data platform\n- Run it\n\nNo integration or deployment is required.\n\nNo runtime engine is required.\n\nSQLazy uses the **esProc SPL engine internally for step debugging only**,\n\nso it can simulate execution and support multiple SQL dialects.\n\nFrom the user's perspective, SQLazy only produces **portable SQL text**.\n\nSQLazy focuses primarily on **readability and correctness** of generated SQL.\n\nThe compiler performs basic logical optimizations, such as:\n\n- merging steps when possible\n- avoiding unnecessary CTE layers\n- combining aggregation and filtering into\n`GROUP BY + HAVING`\n\nAdvanced performance optimization is expected to be handled by the **database query optimizer**.\n\nSQLazy is a **query development and design tool**.\n\nFor debugging:\n\n- The Desktop IDE supports data sampling for large datasets\n- The Web App works with uploaded sample data\n\nLarge-scale execution performance depends on the target database.\n\nSQLazy already supports complex analytical scenarios, including:\n\n- multi-table joins\n- window functions\n- advanced aggregations\n- time-series and sessionization logic\n\nRecursive queries are not yet supported but are on the roadmap.\n\nFor extremely complex scenarios, the Desktop IDE can also generate **SPL code** as an alternative.\n\nYes.\n\nThe Desktop IDE can run fully offline and does not upload user data.\n\nThe free edition requires an online license check at startup.\n\nThe commercial edition works completely offline.\n\nYes. These features are currently in development.\n\nPlanned capabilities include:\n\n- user-defined functions and macros\n- branching and loops\n- support for complex stored-procedure-style workflows\n\nThese features are planned for upcoming releases.\n\nThis repository contains **documentation and example workflows only**.\n\nThe SQLazy application (Web App and Desktop IDE) is proprietary software and is **not included** in this repository.\n\nAll files inside the `/examples`\n\ndirectory are released under the MIT License.\n\nSee the [LICENSE](/SPLWare/SQLazy/blob/master/LICENSE) file for details.\n\n- Issues:\n[https://github.com/SPLWare/sqlazy/issues](https://github.com/SPLWare/sqlazy/issues) - Forum:\n[https://c.esproc.com/](https://c.esproc.com/) - Enterprise licensing:\n[contact@scudata.com](mailto:contact@scudata.com)\n\nSQLazy is built on top of the esProc SPL engine:\n\n[https://github.com/SPLWare/esProc](https://github.com/SPLWare/esProc)", "url": "https://wpnews.pro/news/sqlazy-deterministic-sql-generation", "canonical_source": "https://github.com/SPLWare/SQLazy", "published_at": "2026-06-17 01:53:43+00:00", "updated_at": "2026-06-17 02:23:08.458648+00:00", "lang": "en", "topics": ["ai-tools", "developer-tools", "natural-language-processing", "large-language-models"], "entities": ["SQLazy", "esProc"], "alternates": {"html": "https://wpnews.pro/news/sqlazy-deterministic-sql-generation", "markdown": "https://wpnews.pro/news/sqlazy-deterministic-sql-generation.md", "text": "https://wpnews.pro/news/sqlazy-deterministic-sql-generation.txt", "jsonld": "https://wpnews.pro/news/sqlazy-deterministic-sql-generation.jsonld"}}