[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"categories":3,"comparison-postgresql-vs-mongodb":85,"comparison-postgresql-vs-mongodb-related":200},[4,15,25,35,45,55,65,75],{"id":5,"documentId":6,"name":7,"slug":8,"shortName":9,"description":10,"icon":11,"createdAt":12,"updatedAt":12,"publishedAt":13,"seoTitle":14,"seoDescription":14},43,"d2n69w6u5jw2n9uxksqsks74","AI Chatbots & Assistants","ai-chatbots","AI Chatbots","Conversational AI assistants for reasoning, writing and everyday tasks.","Bot","2026-09-07T09:42:12.650Z","2026-09-07T09:42:12.677Z",null,{"id":16,"documentId":17,"name":18,"slug":19,"shortName":20,"description":21,"icon":22,"createdAt":23,"updatedAt":23,"publishedAt":24,"seoTitle":14,"seoDescription":14},45,"q3aub1u192qczyq8cyurhbau","AI Coding Tools","ai-coding","AI Coding","AI pair programmers, code completion and agentic coding tools.","Code","2026-09-07T09:42:12.712Z","2026-09-07T09:42:12.726Z",{"id":26,"documentId":27,"name":28,"slug":29,"shortName":30,"description":31,"icon":32,"createdAt":33,"updatedAt":33,"publishedAt":34,"seoTitle":14,"seoDescription":14},49,"kyxgrju9qcbsahxr6c3gy1rk","AI Dev Frameworks","ai-frameworks","AI Frameworks","Frameworks and tooling for building LLM-powered applications.","Blocks","2026-09-07T09:42:12.793Z","2026-09-07T09:42:12.817Z",{"id":36,"documentId":37,"name":38,"slug":39,"shortName":40,"description":41,"icon":42,"createdAt":43,"updatedAt":43,"publishedAt":44,"seoTitle":14,"seoDescription":14},47,"et5dhnhik9xxbypugwx4pfh5","AI Image & Video","ai-image-video","AI Image\u002FVideo","Generative models for images, art and video production.","Image","2026-09-07T09:42:12.751Z","2026-09-07T09:42:12.767Z",{"id":46,"documentId":47,"name":48,"slug":49,"shortName":50,"description":51,"icon":52,"createdAt":53,"updatedAt":53,"publishedAt":54,"seoTitle":14,"seoDescription":14},37,"cnjfk8r7abaq5mkotekzo8fz","Backend & Databases","backend-databases","Backend","Databases, APIs and data-layer architecture for server-side systems.","Database","2026-09-07T09:42:12.415Z","2026-09-07T09:42:12.474Z",{"id":56,"documentId":57,"name":58,"slug":59,"shortName":60,"description":61,"icon":62,"createdAt":63,"updatedAt":63,"publishedAt":64,"seoTitle":14,"seoDescription":14},39,"s4ujx5e37x7cla49kfbz9xnq","DevOps & Cloud","devops-cloud","DevOps","Containers, infrastructure and cloud platforms for shipping and running software.","Cloud","2026-09-07T09:42:12.508Z","2026-09-07T09:42:12.541Z",{"id":66,"documentId":67,"name":68,"slug":69,"shortName":70,"description":71,"icon":72,"createdAt":73,"updatedAt":73,"publishedAt":74,"seoTitle":14,"seoDescription":14},35,"zy2v9u5ac9aeyfl4q9mnsm1g","Frontend Frameworks","frontend","Frontend","UI frameworks, meta-frameworks, styling and state management for the browser.","LayoutTemplate","2026-09-07T09:42:12.327Z","2026-09-07T09:42:12.375Z",{"id":76,"documentId":77,"name":78,"slug":79,"shortName":80,"description":81,"icon":82,"createdAt":83,"updatedAt":83,"publishedAt":84,"seoTitle":14,"seoDescription":14},41,"kqqemwew4iwvgu0896gur8la","Programming Languages","languages","Languages","Systems and general-purpose languages compared for performance and ergonomics.","Terminal","2026-09-07T09:42:12.577Z","2026-09-07T09:42:12.595Z",{"id":5,"documentId":86,"title":87,"slug":88,"dek":89,"updated":90,"featured":91,"verdict":92,"bestForA":93,"bestForB":94,"createdAt":95,"updatedAt":95,"publishedAt":96,"seoTitle":14,"seoDescription":14,"category":97,"tags":98,"features":108,"a":144,"b":172},"th9v03l74qmaqgg4mytz77kz","PostgreSQL vs MongoDB","postgresql-vs-mongodb","Relational rigor versus document flexibility for your primary datastore.","2026-08-05",true,"PostgreSQL is the safer default for most applications — it handles both relational and document-style data well via JSONB. Reach for MongoDB when your data is naturally document-shaped, your schema will change constantly, or you need effortless horizontal sharding from day one.","Applications that value data integrity and complex relational queries.","Rapidly evolving products with nested, document-shaped data.","2026-09-07T09:42:14.175Z","2026-09-07T09:42:14.216Z",{"id":46,"documentId":47,"name":48,"slug":49,"shortName":50,"description":51,"icon":52,"createdAt":53,"updatedAt":53,"publishedAt":54,"seoTitle":14,"seoDescription":14},[99,102,105],{"id":100,"value":101},647,"database",{"id":103,"value":104},648,"sql",{"id":106,"value":107},649,"nosql",[109,114,119,124,129,134,139],{"id":110,"label":111,"a":112,"b":113},258,"Data model","Relational (+ JSONB)","Document (BSON)",{"id":115,"label":116,"a":117,"b":118},259,"Transactions","Full ACID","ACID (multi-doc, heavier)",{"id":120,"label":121,"a":122,"b":123},260,"Horizontal scaling","Via extensions","Native sharding",{"id":125,"label":126,"a":127,"b":128},261,"Query language","SQL","MQL (JSON-like)",{"id":130,"label":131,"a":132,"b":133},262,"Schema","Enforced (flexible w\u002F JSONB)","Schemaless",{"id":135,"label":136,"a":137,"b":138},263,"Vector search","pgvector extension","Atlas Vector Search",{"id":140,"label":141,"a":142,"b":143},264,"Best for","Structured, relational data","Rapidly evolving, nested data",{"id":145,"name":146,"tagline":147,"score":148,"logo":14,"pros":149,"cons":162},85,"PostgreSQL","The world’s most advanced open-source relational database",9.3,[150,153,156,159],{"id":151,"value":152},650,"Strong ACID guarantees and mature transactions",{"id":154,"value":155},651,"Rich types, full-text search, and JSONB for flexible fields",{"id":157,"value":158},652,"Extensions like PostGIS and pgvector cover niche needs",{"id":160,"value":161},653,"Excellent tooling, ORMs and hosting options",[163,166,169],{"id":164,"value":165},654,"Schema changes need more planning than schemaless stores",{"id":167,"value":168},655,"Horizontal write-scaling needs extra tooling (Citus, etc.)",{"id":170,"value":171},656,"Vertical scaling has real ceilings for huge datasets",{"id":173,"name":174,"tagline":175,"score":176,"logo":14,"pros":177,"cons":190},86,"MongoDB","A document database built for developer velocity",8.3,[178,181,184,187],{"id":179,"value":180},657,"Flexible schema is great for fast-moving prototypes",{"id":182,"value":183},658,"Native horizontal sharding for huge datasets",{"id":185,"value":186},659,"Document model maps naturally to nested JSON objects",{"id":188,"value":189},660,"Atlas offers a very smooth managed experience",[191,194,197],{"id":192,"value":193},661,"Easy to end up with inconsistent document shapes over time",{"id":195,"value":196},662,"Multi-document transactions are heavier than relational joins",{"id":198,"value":199},663,"Complex relational queries are more awkward than SQL joins",[201],{"updated":202,"id":16,"documentId":203,"title":204,"slug":205,"dek":206,"featured":207,"verdict":208,"bestForA":209,"bestForB":210,"createdAt":211,"updatedAt":211,"publishedAt":212,"seoTitle":14,"seoDescription":14,"category":213,"tags":214,"features":221,"a":251,"b":279},"2026-05-22","u1h7z0qfvs6zzvozgui505ep","GraphQL vs REST","graphql-vs-rest","Two dominant API paradigms for client-server communication.",false,"REST is still the pragmatic default for public APIs and simple services thanks to caching and universal familiarity. GraphQL earns its complexity when you have many client types (web, mobile, TV) with very different data needs pulling from a shared backend.","Products with multiple client apps that need flexible, precise data.","Public APIs, simple services, and teams that want easy caching.","2026-09-07T09:42:14.462Z","2026-09-07T09:42:14.497Z",{"id":46,"documentId":47,"name":48,"slug":49,"shortName":50,"description":51,"icon":52,"createdAt":53,"updatedAt":53,"publishedAt":54,"seoTitle":14,"seoDescription":14},[215,218],{"id":216,"value":217},680,"api",{"id":219,"value":220},681,"architecture",[222,227,232,237,242,247],{"id":223,"label":224,"a":225,"b":226},271,"Endpoints","Single endpoint","Multiple resource endpoints",{"id":228,"label":229,"a":230,"b":231},272,"Over-fetching","Rare (client picks fields)","Common",{"id":233,"label":234,"a":235,"b":236},273,"Caching","App-level (client cache)","Native HTTP caching",{"id":238,"label":239,"a":240,"b":241},274,"Typing","Strongly typed schema","Depends on tooling (OpenAPI)",{"id":243,"label":244,"a":245,"b":246},275,"Learning curve","Moderate","Low",{"id":248,"label":141,"a":249,"b":250},276,"Complex, multi-client products","Simple or public APIs",{"id":252,"name":253,"tagline":254,"score":255,"logo":14,"pros":256,"cons":269},89,"GraphQL","A query language for your API",8.1,[257,260,263,266],{"id":258,"value":259},682,"Clients fetch exactly the fields they need, nothing more",{"id":261,"value":262},683,"One endpoint, strongly typed schema, great introspection",{"id":264,"value":265},684,"Great for aggregating many backends into one graph",{"id":267,"value":268},685,"Reduces the classic over-fetching\u002Funder-fetching problem",[270,273,276],{"id":271,"value":272},686,"Caching is harder than REST’s native HTTP caching",{"id":274,"value":275},687,"N+1 query issues need care (DataLoader, etc.)",{"id":277,"value":278},688,"Adds operational complexity (schema, resolvers, gateway)",{"id":280,"name":281,"tagline":282,"score":283,"logo":14,"pros":284,"cons":297},90,"REST","The tried-and-true resource-oriented API style",8.4,[285,288,291,294],{"id":286,"value":287},689,"Simple mental model — resources and HTTP verbs",{"id":289,"value":290},690,"Native HTTP caching, CDNs and browser tooling just work",{"id":292,"value":293},691,"Easier to secure, rate-limit and monitor per-endpoint",{"id":295,"value":296},692,"Universally understood, minimal tooling required",[298,301,304],{"id":299,"value":300},693,"Over- or under-fetching is common without extra endpoints",{"id":302,"value":303},694,"Versioning multiple client needs gets messy over time",{"id":305,"value":306},695,"No built-in introspection or strong typing by default"]