JSON se column types kaise infer hote hain?

Column mein har value vote karti hai: whole numbers INTEGER dete hain (32 bits ke baad BIGINT), koi bhi decimal DOUBLE PRECISION banata hai, true/false BOOLEAN dete hain, ISO-dated strings TIMESTAMP dete hain, aur koi bhi conflict TEXT par settle hota hai. Saari rows scan hoti hain, isliye late float ek integer column ko corrupt nahin kar sakta.

SQL dialects ke beech kya change hota hai?

Identifier quoting (double quotes, backticks, brackets), type spellings (BOOLEAN vs TINYINT(1) vs BIT, DOUBLE PRECISION vs DOUBLE vs FLOAT vs REAL), boolean literals (TRUE vs 1), aur wo N-prefix jo SQL Server Unicode text par chahta hai.

Alag-alag keys wale objects kaise handle hote hain?

Columns saari rows ki keys ka union hote hain, first-seen order mein; jis row mein koi key missing hai wahan NULL insert hota hai. Heterogeneous API data ko table mein land karne ka usually yahi tareeka chahiye hota hai.

Kya generated script production-ready hai?

Ye import-ready hai: safe quoting aur sensible types. Ek production table ko phir bhi primary key, NOT NULL constraints aur indexes chahiye, jo koi bhi inference data se guess nahin kar sakti — pehle type list review karein, phir harden karein.

JSON to SQL

JSON array ko inferred column types wali CREATE TABLE aur batched INSERT statements mein badlein — PostgreSQL, MySQL, SQLite ya SQL Server ke liye.

JSON to SQL poori tarah aapke browser mein chalta hai. Aapka data device par convert hota hai aur kabhi upload nahin hota.

CSV to SQL converter kholein

JSON to SQL ke baare mein

JSON to SQL objects ka array leta hai — ek API response, ek export, ek fixture file — aur wo do statements produce karta hai jo ise database mein le jaate hain: ek CREATE TABLE jiske column types har column ki saari values se inferred hote hain, aur INSERTs jo multi-row VALUES lists mein batch hote hain. Type inference properly hoti hai: 32 bits paar karne wale integers BIGINT ban jaate hain, ISO-formatted strings TIMESTAMP ban jaate hain, mixed columns safely TEXT tak degrade hote hain, aur har canonical type target dialect ki spelling mein render hota hai — BOOLEAN MySQL mein TINYINT(1) aur SQL Server mein BIT ban kar aata hai. Wo details jo hand-written scripts todti hain exactly wo yahan target hain: identifiers dialect ke hisaab se quote hote hain taaki order naam ka column kabhi keyword se collide na kare, values ke andar single quotes correctly doubled hote hain taaki O’Brien jaise naam survive kar sakein, missing keys NULL ban jaate hain, aur nested objects JSON text ki tarah serialize hote hain.

Features

Sirf first nahin, saari rows se inferred types wali CREATE TABLE
Configurable batch size ke saath multi-row INSERTs
PostgreSQL, MySQL, SQLite aur SQL Server dialects
Dialect-wise identifier quoting — reserved words safe hain
Value escaping: quotes doubled, NULLs explicit
Missing keys NULL ban jaate hain; nested values JSON text ban jaate hain

JSON to SQL kaise use karein

Objects ka JSON array paste karein
Table ka naam rakhein aur dialect chunein
Zarurat ho toh rows-per-INSERT batch size adjust karein
SQL copy karein — review ke liye types pehle list hote hain

Example

Input

[{"id":1,"name":"O'Brien","active":true}]

Output

CREATE TABLE "users" ("id" INTEGER, "name" TEXT, "active" BOOLEAN);
INSERT INTO "users" ("id", "name", "active") VALUES (1, 'O''Brien', TRUE);

O’’Brien ka doubled quote hi hai jo script ko valid rakhta hai.

Common errors aur troubleshooting

Ek numeric-lagta column TEXT nikla. — Us column mein koi value string hai — quotes wala "42", ek empty string, ya N/A. Inference har row scan karta hai aur kisi bhi mismatch par TEXT tak degrade hota hai; outliers clean karke reconvert karein.
Script order ya user naam ke column par fail hota hai. — Fail nahin hona chahiye — identifiers exactly isi liye dialect ke hisaab se quote hote hain. Fail ho toh check karein ki dialect database se match karta hai: backticks MySQL ke liye, brackets SQL Server ke liye.
Dates plain text ki tarah import hui. — Sirf ISO 8601 shapes (2024-01-15, optional time ke saath) TIMESTAMP infer hoti hain. 15/01/2024 jaise local formats ambiguous hain aur jaan-bujhkar TEXT rehte hain — pehle ISO mein normalize karein.

Aksar pooche jaane wale sawaal

JSON se column types kaise infer hote hain?: Column mein har value vote karti hai: whole numbers INTEGER dete hain (32 bits ke baad BIGINT), koi bhi decimal DOUBLE PRECISION banata hai, true/false BOOLEAN dete hain, ISO-dated strings TIMESTAMP dete hain, aur koi bhi conflict TEXT par settle hota hai. Saari rows scan hoti hain, isliye late float ek integer column ko corrupt nahin kar sakta.
SQL dialects ke beech kya change hota hai?: Identifier quoting (double quotes, backticks, brackets), type spellings (BOOLEAN vs TINYINT(1) vs BIT, DOUBLE PRECISION vs DOUBLE vs FLOAT vs REAL), boolean literals (TRUE vs 1), aur wo N-prefix jo SQL Server Unicode text par chahta hai.
Alag-alag keys wale objects kaise handle hote hain?: Columns saari rows ki keys ka union hote hain, first-seen order mein; jis row mein koi key missing hai wahan NULL insert hota hai. Heterogeneous API data ko table mein land karne ka usually yahi tareeka chahiye hota hai.
Kya generated script production-ready hai?: Ye import-ready hai: safe quoting aur sensible types. Ek production table ko phir bhi primary key, NOT NULL constraints aur indexes chahiye, jo koi bhi inference data se guess nahin kar sakti — pehle type list review karein, phir harden karein.

Related tools

CSV to SQL Converter — CSV ko MySQL, PostgreSQL, SQLite ya SQL Server ke INSERT statements mein badlein, browser mein.
JSON se CSV — Flat JSON objects ke array ko CSV mein convert karein.
JSON Formatter — JSON ko beautify, minify aur validate karein, error ki location ke saath.
JSON se TypeScript — Ek JSON sample se TypeScript interfaces generate karein.
JSON Flatten — Nested JSON ko dot-notation keys mein flatten karein ya wapas unflatten karein.
Sort JSON Keys — JSON object ki keys ko recursively A→Z ya Z→A sort karein