--- url: https://findutils.com/guides/json-to-sql title: "Convert JSON to SQL INSERT Statements Without Losing Types" description: "Convert a JSON array to SQL for PostgreSQL, MySQL or SQLite. Learn how types are inferred, why the CSV route damages booleans and null, and what nesting cannot do." category: converters content_type: guide guide_type: subtopic cluster: data-conversion locale: en read_time: 6 status: published author: "olgunozoktas" published_at: 2026-09-02T09:30:00Z excerpt: "Turn an API response or a fixture into CREATE TABLE and INSERT statements. Review type inference, NULL handling, dialect rules, and how nested objects are stored." tag_ids: ["json", "sql", "data-conversion", "postgresql", "database"] tags: ["JSON", "SQL", "Data Conversion", "PostgreSQL", "Database"] primary_keyword: "json to sql" secondary_keywords: ["convert json to sql", "json to insert statements", "json to create table", "json array to sql", "json to postgresql", "ndjson to sql"] tool_tag: "json-to-sql" related_tool: "json-to-sql" related_tools: ["json-to-sql", "csv-to-sql", "json-to-csv"] og_image: "/images/content/guides/json-to-sql-type-preservation.webp" image_alt: "A nested JSON structure passes through a type-preserving conversion gate into three database outputs." updated_at: "2026-09-08T09:09:24Z" --- Convert JSON to SQL by reading each value's real type instead of its text. FindUtils [JSON to SQL](/convert/json-to-sql/) turns an array of objects into `CREATE TABLE` plus `INSERT` statements for PostgreSQL, MySQL or SQLite, in your browser. It does not upload the JSON and it does not connect to a database. This guide explains how column types are decided, why the popular JSON-to-CSV-to-SQL workaround damages data, and where the tool deliberately stops. ## Why Not Just Go Through CSV? The two-hop route — [JSON to CSV](/convert/json-to-csv/), then [CSV to SQL](/convert/csv-to-sql/) — is the reason this tool exists, so it is worth being precise about what it costs. CSV has exactly one type: text. JSON has four that matter here. Everything that is not a string has to be flattened on the way through, and there is no way to tell afterwards what it used to be. | JSON value | After the CSV hop | Direct | |---|---|---| | `true` | `'true'` — a string | `TRUE` | | `null` | `'null'` or `''` | `NULL` | | `{"a":1}` | `'[object Object]'` | `'{"a":1}'` | | `1.5` | usually survives | `1.5` | The `[object Object]` case is the one people notice, because it is visibly broken. The boolean and null cases are worse: they look fine, import cleanly, and quietly give you a text column where you expected a boolean. For a flat array of strings and numbers the CSV route is perfectly good. The moment a value is a boolean, a null, or nested, it is the wrong tool. ## How to Convert JSON to SQL Online ### Step 1: Open the JSON to SQL Tool Go to [JSON to SQL](/convert/json-to-sql/). Use a synthetic API payload for the first conversion, then review the inferred types. ### Step 2: Paste the JSON An array of objects is the normal input. A single object is treated as one row, and NDJSON — one object per line — is accepted too; it is tried automatically if the whole paste is not valid JSON on its own. ### Step 3: Name the Table The table name is the only field you have to fill in. It is quoted for the dialect you pick, so a name with unusual characters is still safe. ### Step 4: Pick the Dialect PostgreSQL, MySQL or SQLite. The choice changes identifier quoting, the type names in the `CREATE TABLE`, and how booleans are written. ### Step 5: Review the Inferred Types The line under the output names every column and its type. Read it before you run anything — this is where a surprise shows up. ### Step 6: Copy or Download Copy the SQL, or download it as a `.sql` file. ## How Types Are Inferred Each column is decided across every row, not from the first one. - Whole numbers give an integer column. - A decimal anywhere widens that column to numeric. - `true` and `false` give a boolean column. - Anything else, including a mix of a boolean and a number, gives text. - **A null never decides anything.** A column of nulls and integers is an integer column. That last rule is the one that saves you. If a null narrowed the type, one missing field in one row would turn a numeric column into text. Booleans depend on the dialect. SQLite has no boolean literal, so it gets `1` and `0`; PostgreSQL and MySQL get `TRUE` and `FALSE`. ## Rows With Different Keys The columns are the union of every key across every row, in the order the keys are first seen. A row that lacks one of them gets `NULL` for that column. ```json [{"a": 1}, {"b": 2}] ``` becomes a table with both `a` and `b`, and two rows: `(1, NULL)` and `(NULL, 2)`. Nothing is clipped, and nothing is invented. One thing SQL cannot express: a key that is missing and a key explicitly set to `null` both become `NULL`. There is no third state to map them to. ## Nested Objects and Arrays A nested object or array is stored as its JSON text in one column. It is never `[object Object]`. The tool does not split nested data into related tables, and that is deliberate. Deciding that an array of objects should become a second table with a foreign key is schema design — it depends on how you intend to query the data, which the JSON does not say. A converter that guessed would be wrong often enough to be worse than useless. If you want the nesting flattened into columns instead, run the JSON through a flattener first and convert the result. ## Common Mistakes ### Mistake 1: Trusting the CSV Round Trip Covered above, and worth repeating because it fails silently. Check the type line. ### Mistake 2: Expecting Keys, Indexes or Constraints The `CREATE TABLE` names columns and types and nothing else. A primary key depends on which field is actually unique, which the JSON does not state. ### Mistake 3: Assuming the First Row Sets the Schema It does not. Every row is read before the types are fixed, which is why a decimal in row 900 still widens the column. ### Mistake 4: Running One Enormous Statement Turn on multi-row INSERTs for a large array. They are chunked at 500 rows so no single statement becomes unmanageable. ### Mistake 5: Pasting an Array of Non-Objects `[1, 2, 3]` has no keys, so it has no columns. The tool says so rather than inventing a column name. ## Review the Generated Schema The generated SQL gives you a useful starting table. It cannot choose rules that do not exist in the JSON. Check these items before you run the statements: 1. Confirm that the table name matches your target schema. 2. Review each inferred column type. 3. Decide whether a text column contains nested JSON. 4. Add a primary key only after you confirm uniqueness. 5. Add `NOT NULL` only after you inspect missing keys and null values. 6. Add indexes for the queries that your application will run. Run a small sample first when the destination already contains data. A database error is easier to correct before a large import starts. ## Tools Used in This Guide - [JSON to SQL](/convert/json-to-sql/) — this tool. - [CSV to SQL](/convert/csv-to-sql/) — the same emitter, starting from a spreadsheet. - [SQL to CSV](/convert/sql-to-csv/) — read rows back out of a dump. - [JSON to CSV](/convert/json-to-csv/) — when a flat table really is what you want. - [SQL Formatter](/developers/sql-formatter/) — pretty-print the result. ## FAQ **Is my JSON uploaded?** No. Parsing and generation both happen in your browser. **How are types decided?** From the real JSON values across every row. Nulls and missing keys are ignored while deciding. **What happens to a nested object?** It is stored as JSON text in one column, never `[object Object]`. **Can I paste a single object?** Yes, and NDJSON works too. **Which dialects are supported?** PostgreSQL, MySQL and SQLite. **Does it create indexes or foreign keys?** No. Those depend on how you query the data, which the JSON does not describe. ## Next Steps Once the table exists, [SQL to CSV](/convert/sql-to-csv/) reads the rows back out of any dump you take of it, and [SQL Formatter](/developers/sql-formatter/) makes a long INSERT block readable in a pull request.