FQL syntax

File Query Language (FQL) is a SQL-like string syntax for querying structured files. It mirrors the fluent API feature-for-feature, and every fluent query can be serialized to FQL by casting it to a string.

use FQL\Query;

$results = Query\Provider::fql(<<<SQL
    SELECT *
    FROM xml(./products.xml).SHOP.SHOPITEM
    WHERE PRICE <= 200
    ORDER BY PRICE DESC
    LIMIT 10
SQL)->execute()->fetchAll();

Comments

FQL supports comments inside query strings. Comments are ignored during parsing and can be used to annotate or temporarily disable parts of a query.

# Single-line comment (hash)
-- Single-line comment (double dash)

/* Multi-line
   comment */

SELECT id, name
FROM json(products.json).data.products
WHERE price > 100 -- filter cheap products
/* AND stock > 0 */
ORDER BY price DESC

# or -- starts a single-line comment (everything until end of line is ignored)
/* ... */ encloses a multi-line comment

FileQuery syntax

The FROM, JOIN, and INTO clauses use the FileQuery notation to reference files:

format(pathToFile[, params]).path.to.data

Part	Description
`format`	Format identifier: `xml`, `json`, `csv`, `yaml`, `neon`, `ods`, `xls`, `log`, `dir`. If omitted, format is detected from the file extension.
`pathToFile`	Relative or absolute path to the file (unquoted)
`params`	Optional: positional (`"value"`) or named (`key: "value"`). Cannot be mixed
`path.to.data`	Dot-separated path to the data root within the file

Examples:

xml(feed.xml).SHOP.ITEM
csv(data.csv, "windows-1250", ";").*
csv(data.csv, encoding: "windows-1250", delimiter: ";").*
json(data.json).data.users
log(access.log).*
log(access.log, "apache_common").*
log(access.log, format: "apache_common").*

Default parameter values:

Format	Parameter	Default
CSV	`encoding`	`utf-8`
CSV	`delimiter`	`,`
CSV	`useHeader`	`1`
XML	`encoding`	`utf-8`
LOG	`format`	`nginx_combined`

Field path syntax

Field references use dot notation to access nested data. Advanced path features include:

Syntax	Description	Example
`a.b.c`	Standard nested access	`brand.code`
`a.b.0.c`	Indexed access	`items.0.name`
`a.b[].c`	Iterated access (all array elements)	`items[].price`
`a.b.*.c`	Wildcard — expands all keys of an associative array	`attributes.*.value`
`key.with.dot`	Backtick-escaped key	`Prod. cena`.`s dph`

Backtick escaping lets you reference keys that contain dots, spaces, or special characters. This syntax works in SELECT, WHERE, ORDER BY, GROUP BY, ON, and function arguments.

SELECT `items`.`Item`.`Prod. cena`.`s dph` AS priceWithVat,
       items[].name AS allNames
FROM json(data.json).data

Scalar functions accept [] paths as arguments and operate on the flattened array. For example, LENGTH(products.product[]) returns the number of elements, and IMPLODE(products.product[].name, ', ') joins every nested name into a single string. Backtick-escaped segments work the same way inside function arguments.

Prior to FiQueLa 3.0.2, scalar functions wrapping a [] path returned null because the parser bypassed function detection whenever the field contained []. Upgrade to 3.0.2 or later to use array-iterator paths inside scalar functions.

SELECT

SELECT
    [DISTINCT]
    select_expr [AS alias] [, select_expr [AS alias]] ...
    [EXCLUDE excl_expr [, excl_expr] ...]

select_expr — column name, function call, or literal. Supports dot notation for nested fields. * selects all fields and can be combined with additional expressions (SELECT *, totalPrice).
AS alias — optional alias for the expression.
EXCLUDE — removes the listed fields from the output. Fields excluded this way can still be used in HAVING conditions.

Commas are mandatory between expressions in SELECT, GROUP BY, and ORDER BY clauses. For example, SELECT id, name, price is valid, but SELECT id name price throws an UnexpectedValueException. The fluent API (->select('id, name')) is unaffected by this requirement.

Aliased wildcards

When using source aliases (from FROM ... AS or JOIN ... AS), you can select all fields from a specific aliased source using alias.*:

SELECT p.*, o.total_price
FROM json(products.json).data.products AS p
LEFT JOIN xml(orders.xml).orders.order AS o
    ON p.id = o.product_id

If two aliased sources share field names and you use alias.* for both, a SelectException is thrown on ambiguous field conflicts. Examples:

SELECT id, name, brand.code AS brandCode
FROM json(products.json).data.products

SELECT DISTINCT category
FROM json(products.json).data.products

SELECT
    brand.code AS brandCode,
    COUNT(id) AS productCount
EXCLUDE productCount
FROM json(products.json).data.products
HAVING productCount > 1

Literal values

You can use quoted literal values directly in SELECT. Literal strings are automatically cast to their appropriate type:

SELECT
    "my label" AS label,
    "1" AS integerOne,
    "1.5" AS floatValue,
    "true" AS boolTrue,
    "null" AS nullValue
FROM json(products.json).data.products

Quoted value	Resulting type
`"1"`	`int`
`"1.5"`	`float`
`"true"` / `"false"`	`bool`
`"null"`	`null`
`"2025-05-14 12:00:00"`	`DateTimeImmutable`
`"any other text"`	`string`

You can also reference a previously aliased field in a later expression within the same SELECT:

SELECT
    EXPLODE(tags, "|") AS tagList,
    tagList[0] AS firstTag
FROM json(products.json).data.products

FROM

FROM file_reference [AS alias]

where file_reference is a FileQuery string. The path inside the FileQuery sets the data root:

FROM csv(./examples/data/products.csv).*
FROM xml(./feed.xml).SHOP.SHOPITEM
FROM json(./data.json).data.users

FROM aliasing

You can alias the FROM source using AS. Aliased fields are then accessible via alias.field dot notation, which is especially useful when joining multiple sources:

SELECT p.id, p.name, p.price
FROM json(products.json).data.products AS p
WHERE p.price > 100

The alias applies to the entire data source. You can combine it with alias.* in SELECT to select all fields from the aliased source.

WHERE and HAVING

[WHERE where_condition [AND|OR|XOR where_condition ...]]
[HAVING where_condition [AND|OR|XOR where_condition ...]]

Condition syntax:

field_expr comparison_operator value_expr

WHERE filters rows before aggregation. HAVING filters after aggregation. Examples:

WHERE price > 100 AND name LIKE "%widget%"
HAVING total_count > 5 OR total_revenue > 1000

FQL supports parentheses for grouping conditions:

WHERE price > 100
  AND (name LIKE "%widget%" OR stock > 0)

See Conditions for all operators.

GROUP BY

[GROUP BY group_expr [, group_expr] ...]

Dot notation is supported for nested fields:

GROUP BY brand.code
GROUP BY category.id, brand.code

ORDER BY

[ORDER BY order_expr [ASC|DESC] [, order_expr [ASC|DESC]] ...]

Dot notation is supported for sorting by nested fields:

ORDER BY price DESC
ORDER BY productCount DESC, totalPrice ASC
ORDER BY brand.code ASC

LIMIT and OFFSET

[LIMIT row_count [OFFSET offset]]
[LIMIT row_count, offset]

LIMIT 10
LIMIT 20 OFFSET 40
LIMIT 20, 40

EXPLAIN and EXPLAIN ANALYZE

Prepend EXPLAIN to return a query execution plan without processing data. Use EXPLAIN ANALYZE to execute the query and collect real row counts and timings.

EXPLAIN
SELECT id, name
FROM json(./products.json).data.products
WHERE price > 100
ORDER BY name DESC
LIMIT 10

EXPLAIN ANALYZE
SELECT id, name
FROM json(./products.json).data.products
WHERE price > 100
ORDER BY name DESC
LIMIT 10

Output columns: phase, rows_in, rows_out, filtered, time_ms, duration_pct, mem_peak_kb, note.

UNION and UNION ALL

select_statement
UNION [ALL]
select_statement
[UNION [ALL] select_statement ...]

UNION removes duplicate rows. UNION ALL keeps all rows. The number of selected columns must match across all queries.

SELECT name, price FROM json(products.json).data.products
WHERE price <= 100
UNION
SELECT name, price FROM json(products.json).data.products
WHERE price >= 400

SELECT name, price FROM xml(./feed1.xml).SHOP.ITEM
WHERE price > 100
UNION
SELECT name, price FROM xml(./feed2.xml).SHOP.ITEM
WHERE price > 200
UNION ALL
SELECT name, price FROM xml(./feed3.xml).SHOP.ITEM

INTO

Export query results to a file using the same FileQuery notation:

SELECT name, price
FROM csv(./products.csv).*
INTO json(./exports/products.json).root.items

The meaning of the .query path in INTO depends on the target format:

Format	`.query` meaning	Example
XML	`ROOT.ROW` — root element and row element	`.SHOP.ITEM`
JSON	Nested key path for the resulting array	`.root.items`
XLSX/ODS	`SheetName.StartCell`	`.Sheet1.B4`
CSV	Ignored	—
NDJSON	Ignored	—

Existing target files are not overwritten — an exception is thrown if the file already exists. Missing output directories are created automatically.

DESCRIBE

Use DESCRIBE to inspect the schema of a data source. Returns one row per column with type statistics.

DESCRIBE file_reference

file_reference follows the FileQuery syntax. DESCRIBE is a standalone statement — it cannot be combined with SELECT, WHERE, GROUP BY, ORDER BY, LIMIT, JOIN, UNION, or EXPLAIN. Examples:

DESCRIBE json(./examples/data/products.json).data.products

DESCRIBE csv(./examples/data/products.csv).*

Output columns

Column	Type	Description
`column`	string	Column name (dot notation for nested objects)
`path`	string[]	Array of path segments to the column. Useful for correctly evaluating nested fields whose keys contain dots or special characters.
`types`	array	Map of type name to occurrence count
`totalRows`	int	Number of non-empty rows for this column
`totalTypes`	int	Number of distinct types observed
`dominant`	string	Most frequent type
`suspicious`	bool	`true` if column has mixed non-empty types (except int+double)
`confidence`	float	Ratio of dominant type occurrences to total (0.0–1.0)
`completeness`	float	Ratio of non-empty rows to total rows (0.0–1.0)
`constant`	bool	`true` if all non-empty values are identical
`isEnum`	bool	`true` if column has 2–5 unique values
`isUnique`	bool	`true` if all non-empty values are unique

FQL vs fluent API

SELECT
  brand.code AS brandCode,
  GROUP_CONCAT(id, "/") AS products,
  SUM(price) AS totalPrice,
  COUNT(id) AS productCount
FROM json(products.json).data.products
WHERE
  price < 300
  OR price > 400
GROUP BY brand.code
ORDER BY productCount DESC

Get Started

Core Concepts

Querying

Functions Reference

Advanced

Comments

FileQuery syntax

Field path syntax

SELECT

Aliased wildcards

Literal values

FROM

FROM aliasing

WHERE and HAVING

GROUP BY

ORDER BY

LIMIT and OFFSET

EXPLAIN and EXPLAIN ANALYZE

UNION and UNION ALL

INTO

DESCRIBE

Output columns

FQL vs fluent API

Get Started

Core Concepts

Querying

Functions Reference

Advanced

Documentation Index

​Comments

​FileQuery syntax

​Field path syntax

​SELECT

​Aliased wildcards

​Literal values

​FROM

​FROM aliasing

​WHERE and HAVING

​GROUP BY

​ORDER BY

​LIMIT and OFFSET

​EXPLAIN and EXPLAIN ANALYZE

​UNION and UNION ALL

​INTO

​DESCRIBE

​Output columns

​FQL vs fluent API

Comments

FileQuery syntax

Field path syntax

SELECT

Aliased wildcards

Literal values

FROM

FROM aliasing

WHERE and HAVING

GROUP BY

ORDER BY

LIMIT and OFFSET

EXPLAIN and EXPLAIN ANALYZE

UNION and UNION ALL

INTO

DESCRIBE

Output columns

FQL vs fluent API