--- title: "json_type" slug: "dev-database-reference-sql-functions-json-json-type" updated: 2026-10-30T18:19:06Z published: 2026-10-30T18:19:06Z canonical: "docs.vastdata.com/dev-database-reference-sql-functions-json-json-type" --- > ## Documentation Index > Fetch the complete documentation index at: https://docs.vastdata.com/llms.txt > Use this file to discover all available pages before exploring further. # json_type *Applies to VAST Cluster 5.5.* *[Functions](/develop/docs/dev-database-reference-sql-functions-index) › [Parsing and JSON functions](/develop/docs/dev-database-reference-sql-functions-json) › Check and inspect.* Returns the type of a JSON document, or of the value at a path in it, as a name such as `OBJECT`, `ARRAY` or `VARCHAR`. ## Syntax ``` json_type(json) json_type(json, path) json_type(json, paths) ``` | Argument | Type | Description | | --- | --- | --- | | `json` | `VARCHAR` or `JSON` | The JSON document. | | `path` | `VARCHAR` | A [JSON path](/develop/docs/dev-database-reference-sql-functions-json), JSON pointer or top-level key. Without it, `json_type` returns the type of the whole document. | | `paths` | `VARCHAR[]` | A constant list of paths. | **Returns:** `VARCHAR`. A list of paths, or a path with a wildcard, returns `VARCHAR[]`. ## Set up the examples The examples connect with the ADBC driver, as in [VAST Query Engine (ADBC)](/develop/docs/dev-database-integrations-adbc), and work in a schema called `docs_sql_fn_json_json_type`. Set `QE_ENDPOINT`, `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` and `VASTDB_BUCKET` ([SQL reference](/develop/docs/dev-database-reference-sql-index#example-connect-and-use-a-transaction)), and run the blocks in order in one Python session. Below each block is the output you should see. This block connects, defines the `run()` and `plan()` helpers, and creates a table of four events with a JSON `payload` column: two objects, an array and a NULL. `DOC` is a sample document for the examples that don't read the table: ``` import os import re import adbc_driver_manager import adbc_driver_manager.dbapi as dbapi import adbc_driver_vastdb BUCKET = os.environ["VASTDB_BUCKET"] SCHEMA = f'"{BUCKET}/docs_sql_fn_json_json_type"' conn = dbapi.connect( driver=adbc_driver_vastdb.get_driver_path(), db_kwargs={ "vast.db.endpoint": os.environ["QE_ENDPOINT"], "vast.db.access_key": os.environ["AWS_ACCESS_KEY_ID"], "vast.db.secret_key": os.environ["AWS_SECRET_ACCESS_KEY"], }, autocommit=True, ) def server_message(e): """The server's message in a driver error, without the request ID or the echoed statement.""" text = re.sub(r"\[req=0x[0-9a-f]+\]: ", "", str(e)) found = (re.search(r'message: "(.*?)", request_id', text, re.S) or re.search(r"description: (.*)\)", text, re.S)) message = found.group(1) if found else text.strip().splitlines()[-1] message = re.sub(r"^Plan query: '.*?' failed with: ", "", message, flags=re.S) message = re.split(r"\\n|\n", message)[0].replace('\\"', '"') return message.replace(BUCKET, "") def run(sql): """Run one statement. Print the rows it returns, or the error.""" try: with conn.cursor() as cur: cur.execute(sql) rows = cur.fetchall() if cur.description else [] except (adbc_driver_manager.Error, OSError) as e: print(f"{type(e).__name__}: {server_message(e)}") else: print(rows) def plan(sql): """Print the plan without its ~N Rows lines: the table's row count, which can still be 0 just after an insert.""" with conn.cursor() as cur: cur.execute("EXPLAIN " + sql) steps = cur.fetchall()[0][1] print("\n".join(line for line in steps.splitlines() if "Rows" not in line)) TABLE = f"{SCHEMA}.events" run(f"CREATE SCHEMA IF NOT EXISTS {SCHEMA}") run(f"CREATE TABLE IF NOT EXISTS {TABLE} (id BIGINT, payload VARCHAR)") run(f"INSERT INTO {TABLE} VALUES " "(1, '{\"user\": \"ada\", \"n\": 3, \"tags\": [\"a\", \"b\"], \"geo\": {\"city\": \"Oslo\", \"lat\": 59.9}}'), " "(2, '{\"user\": \"alan\", \"n\": \"many\", \"tags\": [], \"geo\": null}'), " "(3, '[10, 20, [30, 40]]'), " "(4, NULL)") DOC = """'{"user": "ada", "n": 3, "tags": ["a", "b", "c"], "geo": {"city": "Oslo", "lat": 59.9}, "z": null}'""" ``` ``` [] [] [(4,)] ``` ## What it does `json_type` parses the document, follows the path if you give one, and names the type of what it finds: | JSON value | Result | | --- | --- | | An object, `{...}` | `'OBJECT'` | | An array, `[...]` | `'ARRAY'` | | A string, `"ada"` (also one that looks like a date) | `'VARCHAR'` | | An integer of 0 or more, `3` | `'UBIGINT'` | | A negative integer, `-1`, and `-0` | `'BIGINT'` | | A number with a fraction or an exponent, `1.5`, `1e2`; an integer too big for `UBIGINT` | `'DOUBLE'` | | `true` | `'BOOLEAN'` | | `null` | `'NULL'` (text, not SQL NULL) | ``` run(f"SELECT json_type({DOC}), json_type({DOC}, '$.geo'), json_type({DOC}, '$.tags'), json_type({DOC}, '$.user'), " f"json_type({DOC}, '$.z')") run("SELECT json_type('{\"a\": 3}', '$.a'), json_type('{\"a\": -1}', '$.a'), json_type('{\"a\": -0}', '$.a'), " "json_type('{\"a\": 1.5}', '$.a'), json_type('{\"a\": 1e2}', '$.a')") run("SELECT json_type('{\"a\": 18446744073709551616}', '$.a'), json_type('{\"a\": true}', '$.a'), " "json_type('{\"a\": \"2026-10-16\"}', '$.a')") ``` ``` [('OBJECT', 'OBJECT', 'ARRAY', 'VARCHAR', 'NULL')] [('UBIGINT', 'BIGINT', 'BIGINT', 'DOUBLE', 'DOUBLE')] [('DOUBLE', 'BOOLEAN', 'VARCHAR')] ``` ## NULLs and edge cases | Input | Result | | --- | --- | | A NULL document or a NULL path | `NULL` | | A path that isn't in the document (`'$.missing'`) | `NULL` | | A wildcard path (`'$.tags[*]'`) | one type per element: `['VARCHAR', 'VARCHAR', 'VARCHAR']` | | A list of paths | one type per path, `NULL` for a missing one | | A bare scalar document: `'"s"'`, `'42'`, `'true'`, `'null'` | `'VARCHAR'`, `'UBIGINT'`, `'BOOLEAN'`, `'NULL'` | | `'not json'` | Error: `Malformed JSON at byte 0 of input: invalid literal. Input: not json` | | An integer path, `json_type(json, 0)` | Error: `No function matches the given name and argument types`; write `'$[0]'` | The same cases, as the cluster returns them: ``` run(f"SELECT json_type(NULL), json_type({DOC}, NULL), json_type({DOC}, '$.missing'), json_type({DOC}, '$.tags[*]')") run(f"SELECT json_type({DOC}, ['$.geo', '$.n', '$.missing'])") run("SELECT json_type('\"s\"'), json_type('42'), json_type('true'), json_type('null')") run("SELECT json_type('not json')") run(f"SELECT json_type({DOC}, 0)") ``` ``` [(None, None, None, ['VARCHAR', 'VARCHAR', 'VARCHAR'])] [(['OBJECT', 'UBIGINT', None],)] [('VARCHAR', 'UBIGINT', 'BOOLEAN', 'NULL')] OSError: Malformed JSON at byte 0 of input: invalid literal. Input: not json InternalError: Binder Error: No function matches the given name and argument types 'json_type(STRING_LITERAL, INTEGER_LITERAL)'. You might need to add explicit type casts. ``` ## Examples The type of each document and of two fields. In row 2, `n` is a string and `geo` is JSON `null`: ``` run(f"SELECT id, json_type(payload), json_type(payload, '$.n'), json_type(payload, '$.geo') " f"FROM {TABLE} ORDER BY id LIMIT 10") ``` ``` [(1, 'OBJECT', 'UBIGINT', 'OBJECT'), (2, 'OBJECT', 'VARCHAR', 'NULL'), (3, 'ARRAY', None, None), (4, None, None, None)] ``` Keep the documents that are arrays: ``` run(f"SELECT id FROM {TABLE} WHERE json_type(payload) = 'ARRAY' ORDER BY id LIMIT 10") ``` ``` [(3,)] ``` Check a field's type before you cast it. `TRY_CAST` gives the same NULL for row 2's `"many"`, without telling you why: ``` run(f"SELECT id, CASE WHEN json_type(payload, '$.n') IN ('UBIGINT', 'BIGINT') " f"THEN (payload ->> '$.n')::BIGINT END FROM {TABLE} ORDER BY id LIMIT 10") ``` ``` [(1, 3), (2, None), (3, None), (4, None)] ``` ## How it runs in VAST The Query Engine runs on the cluster's CNodes, and a query on a table starts with a scan in VAST DataBase (`VAST_SCAN`). VAST DataBase stores a JSON document as text and can't look inside it, so `json_type` is always computed by the Query Engine: - **In the `SELECT` list,** the scan reads the whole `payload` column and the Query Engine parses each document it returns. - **In `WHERE`,** the scan sends `payload` for every row, and a `FILTER` step in the Query Engine parses each document and keeps the rows that match. No rows are skipped in storage. - **Every document it reads must be valid JSON.** The first invalid one stops the query with `Malformed JSON ...` (see [invalid JSON in a column](/develop/docs/dev-database-reference-sql-functions-json)). The plan of the `WHERE` example shows the `FILTER` step above the scan: ``` plan(f"SELECT id FROM {TABLE} WHERE json_type(payload) = 'ARRAY'") ``` ``` ┌───────────────────────────┐ │ PROJECTION │ │ ──────────────────── │ │ #1 │ │ │ └─────────────┬─────────────┘ ┌─────────────┴─────────────┐ │ FILTER │ │ ──────────────────── │ │ (json_type(payload) = │ │ 'ARRAY') │ │ │ └─────────────┬─────────────┘ ┌─────────────┴─────────────┐ │ VAST_SCAN │ │ ──────────────────── │ │ Function: VAST_SCAN │ │ │ │ Projections: │ │ payload │ │ id │ │ │ └───────────────────────────┘ ``` ## Make it faster - **Store the type if you filter on it often.** A `VARCHAR` column such as `payload_kind`, set when you write the row, is evaluated in VAST DataBase; `json_type(payload) = 'ARRAY'` parses every row in the Query Engine. - **Narrow the rows with a pushed-down condition.** A condition such as `id = 1` is evaluated in VAST DataBase, so a query that fails on the whole table because of one invalid document can still run on the rows it keeps. ## Limitations and errors - **Number types follow the JSON text, not the value.** `1e2` is `'DOUBLE'` though it's a whole number, and `-0` is `'BIGINT'`. Check for all three number types (`UBIGINT`, `BIGINT`, `DOUBLE`) when you only need to know it's a number. - **Paths are text.** There's no integer form; write an array position as `'$[0]'`. - **Invalid JSON stops the query.** Use [json_valid](/develop/docs/dev-database-reference-sql-functions-json-json-valid) to find invalid documents first. - **A list of paths must be constant.** A list from a column fails with `List of paths must be constant`. - **The document must be text.** A number fails with `No function matches the given name and argument types`, for example `'json_type(INTEGER_LITERAL)'`; a `BLOB` fails with `Malformed JSON ...`. A `DATE`, `BOOLEAN` or list is not rejected (it returns `'VARCHAR'`, `'BOOLEAN'` and `'ARRAY'`); pass text. ``` run("SELECT json_type(123)") run("SELECT json_type('\\xAA'::BLOB)") ``` ``` InternalError: Binder Error: No function matches the given name and argument types 'json_type(INTEGER_LITERAL)'. You might need to add explicit type casts. OSError: Malformed JSON at byte 0 of input: input length is 0. Input: ``` ## Similar functions | Function | `'{"a": 1}'` | `'[1, "a"]'` | | --- | --- | --- | | `json_type` | `'OBJECT'` | `'ARRAY'` | | [json_keys](/develop/docs/dev-database-reference-sql-functions-json-json-keys) | `['a']` | `[]` | | [json_array_length](/develop/docs/dev-database-reference-sql-functions-json-json-array-length) | `0` | `2` | | [json_structure](/develop/docs/dev-database-reference-sql-functions-json-json-structure) | `'{"a":"UBIGINT"}'` | `'["JSON"]'` (mixed types) | | `typeof` | `'VARCHAR'`: the SQL type of the argument, not of the JSON in it | `'VARCHAR'` | ``` run("SELECT json_type('{\"a\": 1}'), json_keys('{\"a\": 1}'), json_array_length('{\"a\": 1}'), " "json_structure('{\"a\": 1}'), typeof('{\"a\": 1}'::VARCHAR)") run("SELECT json_type('[1, \"a\"]'), json_keys('[1, \"a\"]'), json_array_length('[1, \"a\"]'), " "json_structure('[1, \"a\"]'), typeof('[1, \"a\"]'::VARCHAR)") ``` ``` [('OBJECT', ['a'], 0, '{"a":"UBIGINT"}', 'VARCHAR')] [('ARRAY', [], 2, '["JSON"]', 'VARCHAR')] ``` Clean up: ``` run(f"DROP TABLE IF EXISTS {TABLE}") run(f"DROP SCHEMA IF EXISTS {SCHEMA}") conn.close() ``` ``` [] [] ``` ## See also - [Parsing and JSON functions](/develop/docs/dev-database-reference-sql-functions-json): all the JSON functions, and [JSON paths](/develop/docs/dev-database-reference-sql-functions-json). - [Where filters run](/develop/docs/dev-database-reference-sql-select#where-filters-run) and [pushdowns](/develop/docs/dev-database-reference-pushdowns). - [EXPLAIN](/develop/docs/dev-database-reference-sql-explain): how to read a plan.