JSON to PostgreSQL
Convert any JSON object or array into a PostgreSQL CREATE TABLE schema and INSERT statements instantly. Intelligently maps JSON types to PostgreSQL data types, uses JSONB for nested objects and arrays, generates SERIAL PRIMARY KEY, and handles nullable fields correctly. Runs entirely in your browser — no data transmitted.
Convert any JSON object or array into a PostgreSQL CREATE TABLE schema and INSERT statements instantly. Intelligently maps JSON types to PostgreSQL data types, uses JSONB for nested objects and arrays, generates SERIAL PRIMARY KEY, and handles nullable fields correctly. Runs entirely in your browser — no data transmitted.
This tool is designed to provide a seamless experience for developers by handling complex operations directly in your browser with maximum speed and security.
A JSON to PostgreSQL converter is a tool that reads a JSON object or array and generates two things: a CREATE TABLE statement that defines a PostgreSQL table schema matching your JSON structure, and INSERT statements that populate that table with your JSON data. The goal is to go from a raw JSON blob to executable SQL you can run against a real PostgreSQL database in one step.
The non-trivial part of this conversion is type mapping. JSON has six primitive types: string, number, boolean, null, object, and array. PostgreSQL has over fifty built-in data types, and choosing the right one for each JSON field is what separates a useful converter from one that generates schemas you immediately have to rewrite.
This is how the mapping works in practice. JSON strings become TEXT in PostgreSQL — not VARCHAR(n), because TEXT in PostgreSQL is equivalent in performance and storage, imposes no artificial length limit, and avoids the common mistake of setting VARCHAR(255) on a field that eventually receives a 300-character value. JSON numbers are mapped to either INTEGER or NUMERIC depending on whether the value in your sample data contains a decimal point. Booleans map to BOOLEAN. Null fields are marked as nullable in the schema. Nested JSON objects and JSON arrays — the types that have no direct relational equivalent — are stored as JSONB, PostgreSQL's binary JSON type, which supports indexing, querying, and efficient storage of structured data that does not fit cleanly into a flat relational model.
The JSONB choice for nested structures is deliberate and opinionated. The alternative is to recursively decompose nested objects into separate tables with foreign key relationships — a normalisation approach that is architecturally correct but requires you to know the full data model upfront. JSONB gives you a practical middle ground: the nested data lands in a single column, you can query it with PostgreSQL's -> and ->> operators and the @> containment operator, and you can add GIN indexes later when query performance requires it. For rapid prototyping and migration work, JSONB is almost always the faster path to a working schema.
This tool takes a JSON object or a JSON array of objects and generates two SQL artifacts: a CREATE TABLE statement defining the relational schema, and one or more INSERT statements populating the table with the values from your JSON data. The schema generation logic inspects every key in your JSON and infers the PostgreSQL data type for each one based on the value in your input. String values become TEXT columns. Integer values become INTEGER columns. Decimal number values become NUMERIC columns. Boolean values become BOOLEAN columns. Nested objects and arrays — anything that cannot be represented as a flat scalar value — become JSONB columns, preserving the nested structure in a queryable binary format. Fields with null values in your sample are defined as nullable; fields with non-null values are given a NOT NULL constraint by default, which you can relax if your production data sometimes omits those fields. Every generated table gets an id SERIAL PRIMARY KEY column prepended to the schema. SERIAL is PostgreSQL's auto-incrementing integer type — equivalent to GENERATED ALWAYS AS IDENTITY in modern PostgreSQL, but more universally recognised in existing codebases and documentation. If your JSON already contains an id field, the generator respects it and maps it accordingly rather than creating a duplicate. The INSERT statements produced by the tool use positional value lists matching the column order in the CREATE TABLE statement. String and JSONB values are single-quoted and escaped correctly to prevent syntax errors when your data contains apostrophes or special characters. Boolean values are output as TRUE and FALSE rather than quoted strings. Numeric values are output unquoted. The result is SQL you can execute directly against a PostgreSQL instance without editing. The entire operation runs in your browser. No JSON data leaves your machine. For developers working with API response samples, schema exploration, or migration planning, this means you can safely paste real payloads without routing them through a third-party server.
1. Paste your JSON into the input editor. This can be a single JSON object representing one record, or a JSON array of objects representing multiple rows. If you paste an array, the tool generates INSERT statements for every element in the array using a single unified schema derived from the full set of keys across all objects.
2. Click the Schema toggle if you want to preview how the tool is interpreting your JSON structure before generating SQL. This shows you the inferred column names and PostgreSQL types — useful for spotting type inference issues before running the conversion.
3. Click Convert to PostgreSQL Schema. The output panel shows both the CREATE TABLE statement and the INSERT statements in sequence, ready to copy and run.
4. Review the CREATE TABLE output carefully. Check that column types match your expectations — particularly for fields that might be integers in your sample but could receive decimal values in production. Change INTEGER to NUMERIC for any such fields before executing the schema.
5. Check nullable columns. Fields that appear with non-null values in your sample JSON are generated with NOT NULL constraints. If those fields can be absent in other records, remove the NOT NULL constraint before deploying to production.
6. For JSONB columns — any nested objects or arrays in your JSON — decide whether you want to keep them as JSONB or decompose them into separate related tables. JSONB is the right choice for flexible or variable nested structures. Normalised related tables are the right choice for nested structures with a fixed, well-understood shape that you will query by specific subfields frequently.
7. Copy the full SQL output and execute it in your PostgreSQL client — psql, pgAdmin, Supabase SQL editor, or any other tool that accepts PostgreSQL SQL. The schema and inserts are designed to run as a single block in sequence.
Most backend development workflows involve JSON at some point. REST APIs return JSON. Configuration files are JSON. Third-party webhooks deliver JSON payloads. When you need to persist any of this data in PostgreSQL — whether for analytics, long-term storage, or building a relational layer on top of an API — you need a schema. And writing that schema by hand from a JSON payload is the kind of work that should take thirty seconds, not thirty minutes. The mechanical part of schema design from JSON is straightforward but error-prone when done manually. You look at each field, decide on a PostgreSQL type, decide on nullability, decide how to handle nested structures, write the CREATE TABLE statement, write the INSERT statements, realise you forgot to escape a single quote in a string value, fix the syntax error, run it again. This tool compresses that entire cycle into a paste-and-click operation. Where this becomes genuinely valuable is during the early stages of a project, when you are still figuring out what your data model looks like. You have an API response. You want to understand its structure, persist a sample of it, and start writing queries against it — before you have committed to a full normalised schema design. JSONB columns let you do that without prematurely locking in a relational structure you will need to migrate away from later. I have used this pattern consistently when integrating third-party APIs whose response shapes are not fully documented: land the whole payload in a JSONB column, query it with ->> to extract the fields you actually need, and promote specific fields to proper typed columns when their shape stabilises. The INSERT statement generation is equally practical. Manually constructing INSERT statements from a JSON payload with ten fields and thirty rows is tedious and introduces escaping mistakes at almost every pass. Having the tool generate syntactically correct, properly escaped INSERT statements from your JSON removes that entire class of error. For seeding a development database with realistic sample data from an API response, this is the fastest path from payload to queryable rows. PostgreSQL's JSONB support is mature enough that using it for nested structures is not a workaround — it is a legitimate schema design choice. GIN indexes on JSONB columns support containment queries that are fast on tables with millions of rows. The -> and ->> operators are well-documented and widely understood. Choosing JSONB over aggressive normalisation early in a project buys you flexibility when the data model is still evolving, and migrating specific JSONB fields to typed columns later is a mechanical operation.
Intelligent type mapping — strings to TEXT
integers to INTEGER
decimals to NUMERIC
booleans to BOOLEAN
nested structures to JSONB
Automatic SERIAL PRIMARY KEY generation for every table
Correct NULL handling — non-null sample values generate NOT NULL constraints
Proper SQL escaping of string values including apostrophes and special characters
JSONB output for nested objects and arrays preserving full query capability
Runs entirely in your browser with zero data transmitted to any server
Handles both single JSON objects and JSON arrays of multiple records
Free with no account
no rate limits
no install required
Seeding a PostgreSQL development database with real API response data
Generating a starter schema when integrating a new third-party API
Rapid prototyping of a relational data model from a JSON payload
Migrating data from a document store like MongoDB or Firestore into PostgreSQL
Building a queryable archive of webhook payloads in a PostgreSQL table
Creating INSERT fixtures for database integration tests from JSON test data
Exploring and querying an API response shape before committing to a normalised schema
Converting JSON configuration exports into structured PostgreSQL lookup tables
Example Input
{
"id": 1,
"name": "Priya Singh",
"email": "priya@techcorp.io",
"role": "Principal Engineer",
"yearsExperience": 15,
"salary": 185000.00,
"isActive": true,
"joinedAt": "2019-03-12",
"skills": ["PostgreSQL", "Rust", "Go", "System Design"],
"profile": {
"bio": "Principal Engineer with 15 years in distributed systems and data infrastructure.",
"timezone": "Asia/Kolkata",
"githubHandle": "priyasingh-eng"
}
}Example Output
CREATE TABLE users (
id SERIAL PRIMARY KEY,
name TEXT NOT NULL,
email TEXT NOT NULL,
role TEXT NOT NULL,
"yearsExperience" INTEGER NOT NULL,
salary NUMERIC NOT NULL,
"isActive" BOOLEAN NOT NULL,
"joinedAt" TEXT NOT NULL,
skills JSONB,
profile JSONB
);
INSERT INTO users (name, email, role, "yearsExperience", salary, "isActive", "joinedAt", skills, profile)
VALUES (
'Priya Singh',
'priya@techcorp.io',
'Principal Engineer',
15,
185000.00,
TRUE,
'2019-03-12',
'["PostgreSQL","Rust","Go","System Design"]',
'{"bio":"Principal Engineer with 15 years in distributed systems and data infrastructure.","timezone":"Asia/Kolkata","githubHandle":"priyasingh-eng"}'
);ERROR: syntax error at or near "'" — insert fails on a string containing an apostrophe
Fix: Apostrophes in SQL string literals must be escaped as two single quotes — O'Brien becomes O''Brien. The tool escapes these automatically, but if you have manually edited the generated INSERT statement, check any string value containing an apostrophe and ensure it is doubled, not backslash-escaped. PostgreSQL does not accept backslash-escaped apostrophes in standard_conforming_strings mode, which is the default in all modern PostgreSQL versions.
ERROR: column does not exist — query against a camelCase column fails
Fix: PostgreSQL folds unquoted identifiers to lowercase. A column defined as "yearsExperience" (with double quotes in the CREATE TABLE statement) must be referenced with double quotes in every subsequent query: SELECT "yearsExperience" FROM users. If you omit the quotes, PostgreSQL looks for a column named yearsexperience (all lowercase) and does not find it. The permanent fix is to rename the column to snake_case (years_experience) when you first generate the schema, so you never need quoting in queries.
ERROR: invalid input syntax for type integer — insert fails on a numeric field
Fix: Your JSON sample contained an integer value for this field, so the generator mapped it to INTEGER. Your actual data contains a decimal value. Alter the column type to NUMERIC before running inserts: ALTER TABLE your_table ALTER COLUMN your_column TYPE NUMERIC. For new tables, change INTEGER to NUMERIC in the generated CREATE TABLE statement before executing it.
ERROR: null value in column violates not-null constraint
Fix: The tool generated a NOT NULL constraint based on your sample JSON, but a record in your actual data has a null or missing value for this field. Either remove the NOT NULL constraint from the column definition, or ensure your INSERT statement provides a value for that column. If the field is legitimately optional, dropping NOT NULL is the right fix.
ERROR: invalid input syntax for type jsonb
Fix: A JSONB column is receiving a value that is not valid JSON. This typically happens when a string value in your INSERT statement contains unescaped double quotes or is not properly single-quoted as a JSON string literal. Ensure the JSONB value in the INSERT is a single-quoted, valid JSON string. The generator produces correct output, but manual edits to the generated SQL can introduce escaping errors.
Using the generated schema directly in production without reviewing nullability — the tool infers NOT NULL from your sample data, but your sample may not represent the full range of values your production data will contain. Always audit nullable fields before deploying the schema. A field that is always present in your test JSON might be absent in edge-case production records, and a NOT NULL constraint on that column will cause inserts to fail silently or with a confusing error.
Keeping JSONB for nested structures that you actually need to query by specific subfields at scale — JSONB is correct for flexible or variable data, but if you have a nested profile object and you are going to run WHERE profile->>'timezone' = 'Asia/Kolkata' millions of times, you will get better performance by promoting timezone to a proper TEXT column with a standard B-tree index. JSONB with a GIN index handles containment queries well, but equality queries on extracted fields are faster on typed columns.
Assuming INTEGER is safe for all numeric JSON fields — if a field contains 42 in your sample but could contain 42.5 in production, the INTEGER column will truncate the decimal part silently on insert. Use NUMERIC for any monetary, measurement, or statistical field where decimal precision matters.
Not reviewing camelCase column names — JSON conventionally uses camelCase keys (yearsExperience, isActive, joinedAt), but PostgreSQL conventionally uses snake_case (years_experience, is_active, joined_at). The generator preserves your JSON key names as column names, quoting them with double quotes to make them valid identifiers. This works correctly but means every query referencing those columns must use double-quoted identifiers. Renaming to snake_case in the generated schema before running it will save you friction in every query you write against that table.
Running INSERT statements against a table that already has rows without checking for primary key conflicts — if your JSON contains an id field that was preserved as the primary key, inserting the same record twice will throw a duplicate key error. Use INSERT ... ON CONFLICT DO NOTHING or INSERT ... ON CONFLICT DO UPDATE if you need idempotent inserts.
JSON Cheatsheet
Interactive reference guide for valid JSON syntax, data types, parsing/stringifying methods, schema validation rules, and language integrations.
Regex Cheatsheet
Interactive guide to Regex anchors, character classes, quantifiers, lookarounds, capturing groups, and search flags.
HTTP Headers Cheatsheet
Complete guide to standard and security HTTP headers including Authorization, CORS control, caching policies, and CSP directives.
SQL Cheatsheet
Complete guide to SQL statements including SELECT queries, WHERE filters, aggregate functions, JOIN types, and DDL commands.
Does it support JSONB for nested objects and arrays?
Yes. Any JSON field whose value is a nested object or an array is mapped to a JSONB column in the generated schema. JSONB is PostgreSQL's binary JSON storage type — it is more efficient than the older JSON type, supports GIN indexing, and enables powerful querying using the -> operator (returns a JSON sub-object), the ->> operator (returns a text value), and the @> containment operator. For a nested object like profile: {bio, timezone, githubHandle}, the generator produces a JSONB column that you can query with WHERE profile->>'timezone' = 'Asia/Kolkata' without any additional configuration.
Can it generate a primary key automatically?
Yes. The generator adds an id SERIAL PRIMARY KEY column to every CREATE TABLE statement it produces. SERIAL is a shorthand for an INTEGER column with a sequence-backed default that auto-increments with every insert. If your JSON already contains an id field, the generator maps it as the primary key field and does not add a duplicate. In modern PostgreSQL (version 10+), GENERATED ALWAYS AS IDENTITY is the preferred syntax over SERIAL, but SERIAL is accepted in all PostgreSQL versions and is more widely recognised in documentation and tutorials.
What PostgreSQL type does it use for JSON string fields?
String fields are mapped to TEXT, not VARCHAR(n). This is a deliberate choice. TEXT and VARCHAR in PostgreSQL have identical performance and storage characteristics — there is no efficiency difference between them. TEXT simply has no length limit, which avoids the common error of setting VARCHAR(255) on a field that eventually receives a value longer than 255 characters and causes a data-too-long error on insert. If your use case requires an enforced length limit — for example, a country_code field that should always be exactly two characters — add the constraint manually after reviewing the generated schema.
How does it handle JSON arrays inside objects?
JSON arrays are stored as JSONB columns in the generated schema. This includes both arrays of primitives (like a skills array containing strings) and arrays of nested objects. JSONB stores the full array structure and allows you to query it with the @> containment operator — for example, WHERE skills @> '["PostgreSQL"]' matches any row where the skills JSONB array contains the string PostgreSQL. You can also use jsonb_array_elements() to unnest the array into rows for aggregation queries. If you frequently need to query individual array elements, consider a separate normalised table with a foreign key — but JSONB is the right starting point for most cases.
Is the generated SQL safe to run directly in PostgreSQL?
Yes, with one caveat. The generated CREATE TABLE and INSERT statements are syntactically correct PostgreSQL SQL, with string values properly single-quoted and apostrophes escaped. The caveat is that the tool infers schema decisions from your sample JSON — it cannot know your full production data shape. Before deploying to production, always review the generated schema for: column types that may be too narrow for your real data range, NOT NULL constraints on fields that might be absent in some records, JSONB columns that might benefit from decomposition into typed columns, and camelCase column names that you may prefer to rename to snake_case.
Can I convert a JSON array of multiple records at once?
Yes. Paste a JSON array of objects and the tool generates a single CREATE TABLE statement derived from the union of all keys across all objects in the array, followed by an INSERT statement for each element. If different objects in the array have different sets of keys, columns are derived from the complete set of keys seen across all records — fields absent from a particular record produce a null value in that record's INSERT statement.
Why does the tool use SERIAL instead of GENERATED ALWAYS AS IDENTITY?
SERIAL is the legacy PostgreSQL syntax for auto-incrementing primary keys and is supported in all PostgreSQL versions from very early releases. GENERATED ALWAYS AS IDENTITY is the SQL-standard equivalent introduced in PostgreSQL 10 and is preferred for new schemas. The generator uses SERIAL for maximum compatibility — it works correctly in PostgreSQL 9.x through the current version 16. If you are using PostgreSQL 10 or later and prefer the modern syntax, replace id SERIAL PRIMARY KEY with id INTEGER GENERATED ALWAYS AS IDENTITY PRIMARY KEY in the generated CREATE TABLE statement.
Is my JSON data sent to any server?
No. The entire conversion runs in JavaScript in your browser. Your JSON is never transmitted to any external server, logged, or stored anywhere outside the browser tab. You can verify this by opening your browser's network inspector before pasting any data — you will see zero outbound requests carrying your JSON. This makes the tool safe to use with real API payloads, production database records, or any sensitive data you would not route through a third-party service.
Data Conversion Best Practices for Developers – Complete Guide 2026
Data Conversion Best Practices for Developers in 2026. In-depth guide covering JSON, XML, CSV, Markdown conversions, data integrity, security risks, performance optimization, and expert strategies from a Principal Software Engineer with 15+ years experience.
What is JSON? How to Format, Validate & Use It (Complete Guide 2026)
What is JSON? How to Format, Validate & Use It (Complete Guide 2026). In-depth explanation of JSON syntax, real-world use cases, formatting best practices, common mistakes, advantages, disadvantages, and expert tips from a Principal Software Engineer with 15+ years experience.
How to validate JSON online (step-by-step guide)
Invalid JSON can break your application. Follow this guide to quickly validate and fix your JSON data.
Recent Activity
No recent activity