Summary of Built-in Functions v7.4

WarehousePG supports built-in functions and operators including analytic functions and window functions that can be used in window expressions. For information about using built-in WarehousePG functions see, "Using Functions and Operators" in the WarehousePG Administrator Guide.

• WarehousePG Function Types
• Built-in Functions and Operators
• JSON Functions and Operators
• Window Functions
• Advanced Aggregate Functions
• Text Search Functions and Operators
• Range Functions and Operators

Parent topic: WarehousePG Reference Guide

WarehousePG Function Types

WarehousePG evaluates functions and operators used in SQL expressions. Some functions and operators are only allowed to run on the coordinator since they could lead to inconsistencies in WarehousePG segment instances. This table describes the WarehousePG Function Types.

In WarehousePG, data is divided up across segments — each segment is a distinct PostgreSQL database. To prevent inconsistent or unexpected results, do not run functions classified as VOLATILE at the segment level if they contain SQL commands or modify the database in any way. For example, functions such as setval() are not allowed to run on distributed data in WarehousePG because they can cause inconsistent data between segment instances.

To ensure data consistency, you can safely use VOLATILE and STABLE functions in statements that are evaluated on and run from the coordinator. For example, the following statements run on the coordinator (statements without a FROM clause):

SELECT setval('myseq', 201);
SELECT foo();

If a statement has a FROM clause containing a distributed table and the function in the FROM clause returns a set of rows, the statement can run on the segments:

SELECT * from foo();

WarehousePG does not support functions that return a table reference (rangeFuncs) or functions that use the refCursor datatype.

Built-in Functions and Operators

The following table lists the categories of built-in functions and operators supported by PostgreSQL. All functions and operators are supported in WarehousePG as in PostgreSQL with the exception of STABLE and VOLATILE functions, which are subject to the restrictions noted in WarehousePG Function Types. See the Functions and Operators section of the PostgreSQL documentation for more information about these built-in functions and operators.

JSON Functions and Operators

This section describes:

• functions and operators for processing and creating JSON data
• the SQL/JSON path language

Processing and Creating JSON Data

WarehousePG includes built-in functions and operators that create and manipulate JSON data:

• JSON Operators
• JSON Creation Functions
• JSON Aggregate Functions
• JSON Processing Functions

JSON Operators

This table describes the operators that are available for use with the json and jsonb data types.

Note There are parallel variants of these operators for both the json and jsonb data types. The field/element/path extraction operators return the same data type as their left-hand input (either json or jsonb), except for those specified as returning text, which coerce the value to text. The field/element/path extraction operators return NULL, rather than failing, if the JSON input does not have the right structure to match the request; for example if no such element exists. The field/element/path extraction operators that accept integer JSON array subscripts all support negative subscripting from the end of arrays.

These standard comparison operators are available for jsonb, but not for json. They follow the ordering rules for B-tree operations outlined at jsonb Indexing.

Note The != operator is converted to <> in the parser stage. It is not possible to implement != and <> operators that do different things.

Operators that require the jsonb data type as the left operand are described in the following table. Many of these operators can be indexed by jsonb operator classes. For a full description of jsonb containment and existence semantics, refer to jsonb Containment and Existence. jsonb Indexing describes how these operators can be used to effectively index jsonb data.

Note The || operator concatenates two JSON objects by generating an object containing the union of their keys, taking the second object's value when there are duplicate keys. All other cases produce a JSON array: first, any non-array input is converted into a single-element array, and then the two arrays are concatenated. It does not operate recursively; only the top-level array or object structure is merged.

Note The @? and @@ operators suppress the following errors: lacking object field or array element, unexpected JSON item type, and numeric errors. This behavior might be helpful while searching over JSON document collections of varying structure.

JSON Creation Functions

This table describes the functions that create json and jsonb data type values. (There are no equivalent functions for jsonb for row_to_json() and array_to_json(). However, the to_jsonb() function supplies much the same functionality as these functions would.)

Note array_to_json() and row_to_json() have the same behavior as to_json() except for offering a pretty-printing option. The behavior described for to_json() likewise applies to each individual value converted by the other JSON creation functions.

Note The hstore extension has a cast from hstore to json, so that hstore values converted via the JSON creation functions will be represented as JSON objects, not as primitive string values.

JSON Aggregate Functions

This table shows the functions that aggregate records to an array of JSON objects and pairs of values to a JSON object

JSON Processing Functions

This table shows the functions that are available for processing json and jsonb values.

Many of these processing functions and operators convert Unicode escapes in JSON strings to the appropriate single character. This is a not an issue if the input data type is jsonb, because the conversion was already done. However, for json data type input, this might result in an error being thrown as described in About JSON Data.

 key | value -----+------- a   | "foo" b   | "bar"

 key | value -----+------- a   | foo b   | bar

 json_object_keys ------------------ f1 f2

 a |   b       |      c ---+-----------+------------- 1 | {2,"a b"} | (4,"a b c")

 a | b ---+--- 1 | 2 3 | 4

   value ----------- 1 true [2,false]

   value ----------- foo bar

 a |    b    |    c    | d |       r ---+---------+---------+---+--------------- 1 | [1,2,3] | {1,2,3} |   | (123,"a b c")

 a |  b ---+----- 1 | foo 2 |

[ { "f1": 1, "f2": null }, 2, null, 3 ]

 jsonb_path_query ------------------ 2 3 4

Notes on JSON processing functions examples

1. The functions json[b]_populate_record(), json[b]_populate_recordset(), json[b]_to_record() and json[b]_to_recordset() operate on a JSON object, or array of objects, and extract the values associated with keys whose names match column names of the output row type. Object fields that do not correspond to any output column name are ignored, and output columns that do not match any object field will be filled with nulls. To convert a JSON value to the SQL type of an output column, the following rules are applied in sequence:

   • A JSON null value is converted to a SQL null in all cases.
   • If the output column is of type json or jsonb, the JSON value is just reproduced exactly.
   • If the output column is a composite (row) type, and the JSON value is a JSON object, the fields of the object are converted to columns of the output row type by recursive application of these rules.
   • Likewise, if the output column is an array type and the JSON value is a JSON array, the elements of the JSON array are converted to elements of the output array by recursive application of these rules.
   • Otherwise, if the JSON value is a string literal, the contents of the string are fed to the input conversion function for the column's data type.
   • Otherwise, the ordinary text representation of the JSON value is fed to the input conversion function for the column's data type.

   While the examples for these functions use constants, the typical use would be to reference a table in the FROM clause and use one of its json or jsonb columns as an argument to the function. Extracted key values can then be referenced in other parts of the query, like WHERE clauses and target lists. Extracting multiple values in this way can improve performance over extracting them separately with per-key operators.

2. All the items of the path parameter of jsonb_set() as well as jsonb_insert() except the last item must be present in the target. If create_missing is false, all items of the path parameter of jsonb_set() must be present. If these conditions are not met the target is returned unchanged.

   If the last path item is an object key, it will be created if it is absent and given the new value. If the last path item is an array index, if it is positive the item to set is found by counting from the left, and if negative by counting from the right - -1 designates the rightmost element, and so on. If the item is out of the range -array_length .. array_length -1, and create_missing is true, the new value is added at the beginning of the array if the item is negative, and at the end of the array if it is positive.

1. The json_typeof function's null return value should not be confused with a SQL NULL. While calling json_typeof('null'::json) will return null, calling json_typeof(NULL::json) will return a SQL NULL.

1. If the argument to json_strip_nulls() contains duplicate field names in any object, the result could be semantically somewhat different, depending on the order in which they occur. This is not an issue for jsonb_strip_nulls() since jsonb values never have duplicate object field names.

2. The jsonb_path_exists(), jsonb_path_match(), jsonb_path_query(), jsonb_path_query_array(), and jsonb_path_query_first() functions have optional vars and silent arguments.

   If the vars argument is specified, it provides an object containing named variables to be substituted into a jsonpath expression.

   If the silent argument is specified and has the true value, these functions suppress the same errors as the @? and @@ operators.

The SQL/JSON Path Language

SQL/JSON path expressions specify the items to be retrieved from the JSON data, similar to XPath expressions used for SQL access to XML. In WarehousePG, path expressions are implemented as the jsonpath data type and can use any elements described in jsonpath Type.

JSON query functions and operators pass the provided path expression to the path engine for evaluation. If the expression matches the queried JSON data, the corresponding SQL/JSON item is returned. Path expressions are written in the SQL/JSON path language and can also include arithmetic expressions and functions. Query functions treat the provided expression as a text string, so it must be enclosed in single quotes.

A path expression consists of a sequence of elements allowed by the jsonpath data type. The path expression is evaluated from left to right, but you can use parentheses to change the order of operations. If the evaluation is successful, a sequence of SQL/JSON items (SQL/JSON sequence) is produced, and the evaluation result is returned to the JSON query function that completes the specified computation.

To refer to the JSON data to be queried (the context item), use the $ sign in the path expression. It can be followed by one or more accessor operators, which go down the JSON structure level by level to retrieve the content of context item. Each operator that follows deals with the result of the previous evaluation step.

For example, suppose you have some JSON data from a GPS tracker that you would like to parse, such as:

{
  "track": {
    "segments": [
      {
        "location"[ 47.763, 13.4034 ],
        "start time": "2018-10-14 10:05:14",
        "HR": 73
      },
      {
        "location"[ 47.706, 13.2635 ],
        "start time": "2018-10-14 10:39:21",
        "HR": 135
      }
    ]
  }
}

To retrieve the available track segments, you need to use the .<key> accessor operator for all the preceding JSON objects:

'$.track.segments'

If the item to retrieve is an element of an array, you have to unnest this array using the [*] operator. For example, the following path will return location coordinates for all the available track segments:

'$.track.segments[*].location'

To return the coordinates of the first segment only, you can specify the corresponding subscript in the [] accessor operator. Note that the SQL/JSON arrays are 0-relative:

'$.track.segments[0].location'

The result of each path evaluation step can be processed by one or more jsonpath operators and methods listed in SQL/JSON Path Operators and Methods below. Each method name must be preceded by a dot. For example, you can get an array size:

'$.track.segments.size()'

For more examples of using jsonpath operators and methods within path expressions, see SQL/JSON Path Operators and Methods below.

When defining the path, you can also use one or more filter expressions that work similar to the WHERE clause in SQL. A filter expression begins with a question mark and provides a condition in parentheses:

? (condition)

Filter expressions must be specified right after the path evaluation step to which they are applied. The result of this step is filtered to include only those items that satisfy the provided condition. SQL/JSON defines three-valued logic, so the condition can be true, false, or unknown. The unknown value plays the same role as SQL NULL and can be tested for with the is unknown predicate. Further path evaluation steps use only those items for which filter expressions return true.

Functions and operators that can be used in filter expressions are listed in jsonpath Filter Expression Elements. The path evaluation result to be filtered is denoted by the @ variable. To refer to a JSON element stored at a lower nesting level, add one or more accessor operators after @.

Suppose you would like to retrieve all heart rate values higher than 130. You can achieve this using the following expression:

'$.track.segments[*].HR ? (@ > 130)'

To get the start time of segments with such values instead, you have to filter out irrelevant segments before returning the start time, so the filter expression is applied to the previous step, and the path used in the condition is different:

'$.track.segments[*] ? (@.HR > 130)."start time"'

You can use several filter expressions on the same nesting level, if required. For example, the following expression selects all segments that contain locations with relevant coordinates and high heart rate values:

'$.track.segments[*] ? (@.location[1] < 13.4) ? (@.HR > 130)."start time"'

Using filter expressions at different nesting levels is also allowed. The following example first filters all segments by location, and then returns high heart rate values for these segments, if available:

'$.track.segments[*] ? (@.location[1] < 13.4).HR ? (@ > 130)'

You can also nest filter expressions within each other:

'$.track ? (exists(@.segments[*] ? (@.HR > 130))).segments.size()'

This expression returns the size of the track if it contains any segments with high heart rate values, or an empty sequence otherwise.

Deviations from Standard

WarehousePG's implementation of SQL/JSON path language has the following deviations from the SQL/JSON standard:

• .datetime() item method is not implemented yet mainly because immutable jsonpath functions and operators cannot reference session timezone, which is used in some datetime operations. Datetime support will be added to jsonpath in future versions of WarehousePG.

• A path expression can be a Boolean predicate, although the SQL/JSON standard allows predicates only in filters. This is necessary for implementation of the @@ operator. For example, the following jsonpath expression is valid in WarehousePG:

  → WrapCopy

  '$.track.segments[*].HR < 70'

• There are minor differences in the interpretation of regular expression patterns used in like_regex filters as described in Regular Expressions.

Strict And Lax Modes

When you query JSON data, the path expression may not match the actual JSON data structure. An attempt to access a non-existent member of an object or element of an array results in a structural error. SQL/JSON path expressions have two modes of handling structural errors:

• lax (default) — the path engine implicitly adapts the queried data to the specified path. Any remaining structural errors are suppressed and converted to empty SQL/JSON sequences.

• strict — if a structural error occurs, an error is raised.

The lax mode facilitates matching of a JSON document structure and path expression if the JSON data does not conform to the expected schema. If an operand does not match the requirements of a particular operation, it can be automatically wrapped as an SQL/JSON array or unwrapped by converting its elements into an SQL/JSON sequence before performing this operation. Besides, comparison operators automatically unwrap their operands in the lax mode, so you can compare SQL/JSON arrays out-of-the-box. An array of size 1 is considered equal to its sole element. Automatic unwrapping is not performed only when:

• The path expression contains type() or size() methods that return the type and the number of elements in the array, respectively.

• The queried JSON data contain nested arrays. In this case, only the outermost array is unwrapped, while all the inner arrays remain unchanged. Thus, implicit unwrapping can only go one level down within each path evaluation step.

For example, when querying the GPS data listed above, you can abstract from the fact that it stores an array of segments when using the lax mode:

'lax $.track.segments.location'

In the strict mode, the specified path must exactly match the structure of the queried JSON document to return an SQL/JSON item, so using this path expression will cause an error. To get the same result as in the lax mode, you have to explicitly unwrap the segments array:

'strict $.track.segments[*].location'

The .** accessor can lead to surprising results when using the lax mode. For instance, the following query selects every HR value twice:

lax $.**.HR

This happens because the .** accessor selects both the segments array and each of its elements, while the .HR accessor automatically unwraps arrays when using the lax mode. To avoid surprising results, we recommend using the .** accessor only in the strict mode. The following query selects each HR value just once:

strict $.**.HR

Regular Expressions

SQL/JSON path expressions allow matching text to a regular expression with the like_regex filter. For example, the following SQL/JSON path query would case-insensitively match all strings in an array that starts with an English vowel:

'$[*] ? (@ like_regex "^[aeiou]" flag "i")'

The optional flag string may include one or more of the characters i for case-insensitive match, m to allow ^ and $ to match at newlines, s to allow . to match a newline, and q to quote the whole pattern (reducing the behavior to a simple substring match).

The SQL/JSON standard borrows its definition for regular expressions from the LIKE_REGEX operator, which in turn uses the XQuery standard. WarehousePG does not currently support the LIKE_REGEX operator. Therefore, the like_regex filter is implemented using the POSIX regular expression engine as described in POSIX Regular Expressions. This leads to various minor discrepancies from standard SQL/JSON behavior which are catalogued in Differences From XQuery (LIKE_REGEX). Note, however, that the flag-letter incompatibilities described there do not apply to SQL/JSON, as it translates the XQuery flag letters to match what the POSIX engine expects.

Keep in mind that the pattern argument of like_regex is a JSON path string literal, written according to the rules given in jsonpath Type. This means in particular that any backslashes you want to use in the regular expression must be doubled. For example, to match string values of the root document that contain only digits:

$.* ? (@ like_regex "^\\d+$")

SQL/JSON Path Operators and Methods

The following table describes the operators and methods available in jsonpath:

SQL/JSON Filter Expression Elements

The following table describes the available filter expressions elements for jsonpath:

Window Functions

The following are WarehousePG built-in window functions. All window functions are immutable. For more information about window functions, see "Window Expressions" in the WarehousePG Administrator Guide.

Advanced Aggregate Functions

The following built-in advanced analytic functions are WarehousePG extensions of the PostgreSQL database. Analytic functions are immutable.

Note The WarehousePG MADlib Extension for Analytics provides additional advanced functions to perform statistical analysis and machine learning with WarehousePG data. See MADlib Extension for Analytics.

CREATE TABLE intarr_tbl (a int, arr int[]); INSERT INTO intarr_tbl SELECT i, array[i, i] FROM generate_series(1, 5)i; INSERT INTO intarr_tbl SELECT 6, '{6, NULL}'::int[]; INSERT INTO intarr_tbl SELECT 8, '{NULL, 7}'::int[]; SELECT gp_array_agg(arr ORDER BY arr) FROM intarr_tbl; 

CREATE TABLE table1(a int4, b int4); INSERT INTO table1 VALUES (4,5), (2,1), (1,3), (3,null), (3,7); SELECT gp_array_agg(a ORDER BY b NULLS FIRST) FROM table1; 

SELECT department_id, MEDIAN(salary) FROM employees GROUP BY department_id; 

SELECT department_id, PERCENTILE_CONT (0.5) WITHIN GROUP (ORDER BY salary DESC) "Median_cont"; FROM employees GROUP BY department_id;

SELECT department_id, PERCENTILE_DISC (0.5) WITHIN GROUP (ORDER BY salary DESC) "Median_desc"; FROM employees GROUP BY department_id;

CREATE TABLE mymatrix (myvalue int[]); INSERT INTO mymatrix VALUES (array[[1,2],[3,4]]); INSERT INTO mymatrix VALUES (array[[0,1],[1,0]]); SELECT sum(myvalue) FROM mymatrix; sum --------------- {1,3},{4,4}

Text Search Functions and Operators

The following tables summarize the functions and operators that are provided for full text searching. See Using Full Text Search for a detailed explanation of WarehousePG's text search facility.

Note The tsquery containment operators consider only the lexemes listed in the two queries, ignoring the combining operators.

In addition to the operators shown in the table, the ordinary B-tree comparison operators (=, <, etc) are defined for types tsvector and tsquery. These are not very useful for text searching but allow, for example, unique indexes to be built on columns of these types.

Note All the text search functions that accept an optional regconfig argument will use the configuration specified by default_text_search_config when that argument is omitted.

The functions in the following table are listed separately because they are not usually used in everyday text searching operations. They are helpful for development and debugging of new text search configurations.

Range Functions and Operators

See Range Types for an overview of range types.

The following table shows the operators available for range types.

The simple comparison operators <, >, <=, and >= compare the lower bounds first, and only if those are equal, compare the upper bounds. These comparisons are not usually very useful for ranges, but are provided to allow B-tree indexes to be constructed on ranges.

The left-of/right-of/adjacent operators always return false when an empty range is involved; that is, an empty range is not considered to be either before or after any other range.

The union and difference operators will fail if the resulting range would need to contain two disjoint sub-ranges, as such a range cannot be represented.

The following table shows the functions available for use with range types.

The lower and upper functions return null if the range is empty or the requested bound is infinite. The lower_inc, upper_inc, lower_inf, and upper_inf functions all return false for an empty range.