--- title: 10 Simple Steps to Solve SQL Problems [2026] | daily.dev description: A structured methodology for approaching SQL problems: identify the requested result first, inspect table grain before writing conditions, build queries... canonical: https://daily.dev/posts/10-simple-steps-to-solve-sql-problems-2026--uicxpyy02 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: 10 Simple Steps to Solve SQL Problems [2026] | daily.dev og:description: A structured methodology for approaching SQL problems: identify the requested result first, inspect table grain before writing conditions, build queries... og:url: https://daily.dev/posts/10-simple-steps-to-solve-sql-problems-2026--uicxpyy02 og:image: https://api.daily.dev/og/posts/uiCXPYY02.png og:image:alt: 10 Simple Steps to Solve SQL Problems [2026] og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # 10 Simple Steps to Solve SQL Problems [2026] **[Planet MySQL](https://daily.dev/sources/planet-mysql)** · 10 min read · 0 upvotes · 0 comments ## Summary A structured methodology for approaching SQL problems: identify the requested result first, inspect table grain before writing conditions, build queries incrementally using CTEs like paid_orders and customer_totals, choose between WHERE and HAVING based on whether you're filtering rows or aggregates, test boundary cases (nulls, ties, empty results), and only run EXPLAIN after correctness is confirmed. A worked Python/SQLite example demonstrates filtering paid orders, grouping by customer and region, and applying a revenue threshold, with a short FAQ closing out the piece. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### When should I use WHERE versus HAVING in a SQL query? Use WHERE when the condition tests a row before grouping, and use HAVING when the condition tests an aggregate produced by the same SELECT, such as SUM(total) >= 100. If revenue is already exposed as a column in a prior CTE like customer_totals, an outer WHERE also works instead of HAVING. _Developers debugging query logic can find worked SQL breakdowns like this on daily.dev._ ### Why would a JOIN cause my SUM aggregate to return an inflated total? A one-to-many join, such as joining orders to order_items, can repeat each order once per matching item, inflating a subsequent SUM. The fix is to aggregate order_items first or join at a grain that matches the intended calculation, and to count rows before and after the join to catch the duplication. _Anyone chasing inflated aggregate bugs after a join can track SQL debugging patterns on daily.dev._ ### Should I run EXPLAIN before or after confirming my SQL query returns correct results? Run EXPLAIN only after the query already returns the intended rows and values on representative data, not before. PostgreSQL's EXPLAIN and EXPLAIN ANALYZE and SQLite's index documentation are plan-inspection tools that show where a correct query may slow down as data grows, but they don't verify correctness. _Developers tuning query performance after correctness checks can follow SQL practices on daily.dev._ --- Tags: [#python](https://daily.dev/tags/python), [#sql](https://daily.dev/tags/sql), [#sqlite](https://daily.dev/tags/sqlite) [View this post on daily.dev](https://daily.dev/posts/10-simple-steps-to-solve-sql-problems-2026--uicxpyy02) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"10 Simple Steps to Solve SQL Problems [2026]","url":"https://daily.dev/posts/10-simple-steps-to-solve-sql-problems-2026--uicxpyy02","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/10-simple-steps-to-solve-sql-problems-2026--uicxpyy02"},"datePublished":"2026-09-03T09:51:17.864Z","dateModified":"2026-09-03T10:09:01.927Z","description":"A structured methodology for approaching SQL problems: identify the requested result first, inspect table grain before writing conditions, build queries...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/c1ce425ea1143663f228e5f19614c18a?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/c1ce425ea1143663f228e5f19614c18a?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Planet MySQL","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Planet MySQL","logo":"https://media.daily.dev/image/upload/s--UKAtmXTq--/f_auto,q_auto/v1780213421/logos/planet-mysql?_a=BAMAMiWQ0","url":"https://daily.dev/sources/planet-mysql"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/10-simple-steps-to-solve-sql-problems-2026--uicxpyy02","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"python,sql,sqlite","timeRequired":"PT10M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Planet MySQL","item":"https://daily.dev/sources/planet-mysql"},{"@type":"ListItem","position":3,"name":"10 Simple Steps to Solve SQL Problems [2026]"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/10-simple-steps-to-solve-sql-problems-2026--uicxpyy02#faq","mainEntity":[{"@type":"Question","name":"When should I use WHERE versus HAVING in a SQL query?","acceptedAnswer":{"@type":"Answer","text":"Use WHERE when the condition tests a row before grouping, and use HAVING when the condition tests an aggregate produced by the same SELECT, such as SUM(total) >= 100. 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