JsQuery – json query language with GIN indexing support

Introduction

JsQuery – is a language to query jsonb data type, introduced in PostgreSQL release 9.4.

It's primary goal is to provide an additional functionality to jsonb (currently missing in PostgreSQL), such as a simple and effective way to search in nested objects and arrays, more comparison operators with indexes support. We hope, that jsquery will be eventually a part of PostgreSQL.

Jsquery is released as jsquery data type (similar to tsquery) and @@ match operator for jsonb.

Authors

Teodor Sigaev [email protected], Postgres Professional, Moscow, Russia
Alexander Korotkov [email protected], Postgres Professional, Moscow, Russia
Oleg Bartunov [email protected], Postgres Professional, Moscow, Russia

Availability

JsQuery is realized as an extension and not available in default PostgreSQL installation. It is available from github under the same license as PostgreSQL and supports PostgreSQL 9.4+.

Regards

Development was sponsored by Wargaming.net.

Installation

JsQuery is PostgreSQL extension which requires PostgreSQL 9.4 or higher. Before build and install you should ensure following:

PostgreSQL version is 9.4 or higher.
You have development package of PostgreSQL installed or you built PostgreSQL from source.
You have flex and bison installed on your system. JsQuery was tested on flex 2.5.37-2.5.39, bison 2.7.12.
Your PATH variable is configured so that pg_config command available, or set PG_CONFIG variable.

Typical installation procedure may look like this:

$ git clone https://github.com/postgrespro/jsquery.git
$ cd jsquery
$ make USE_PGXS=1
$ sudo make USE_PGXS=1 install
$ make USE_PGXS=1 installcheck
$ psql DB -c "CREATE EXTENSION jsquery;"

JSON query language

JsQuery extension contains jsquery datatype which represents whole JSON query as a single value (like tsquery does for fulltext search). The query is an expression on JSON-document values.

Simple expression is specified as path binary_operator value or path unary_operator. See following examples.

x = "abc" – value of key "x" is equal to "abc";
$ @> [4, 5, "zzz"] – the JSON document is an array containing values 4, 5 and "zzz";
"abc xyz" >= 10 – value of key "abc xyz" is greater than or equal to 10;
volume IS NUMERIC – type of key "volume" is numeric.
$ = true – the whole JSON document is just a true.
similar_ids.@# > 5 – similar_ids is an array or object of length greater than 5;
similar_product_ids.# = "0684824396" – array "similar_product_ids" contains string "0684824396".
*.color = "red" – there is object somewhere which key "color" has value "red".
foo = * – key "foo" exists in object.

Path selects set of JSON values to be checked using given operators. In the simplest case path is just an key name. In general path is key names and placeholders combined by dot signs. Path can use following placeholders:

# – any index of array;
#N – N-th index of array;
% – any key of object;
* – any sequence of array indexes and object keys;
@# – length of array or object, could be only used as last component of path;
$ – the whole JSON document as single value, could be only the whole path.

Expression is true when operator is true against at least one value selected by path.

Key names could be given either with or without double quotes. Key names without double quotes shouldn't contain spaces, start with number or concur with jsquery keyword.

The supported binary operators are:

Equality operator: =;
Numeric comparison operators: >, >=, <, <=;
Search in the list of scalar values using IN operator;
Array comparison operators: && (overlap), @> (contains), <@ (contained in).

The supported unary operators are:

Check for existence operator: = *;
Check for type operators: IS ARRAY, IS NUMERIC, IS OBJECT, IS STRING and IS BOOLEAN.

Expressions could be complex. Complex expression is a set of expressions combined by logical operators (AND, OR, NOT) and grouped using braces.

Examples of complex expressions are given below.

a = 1 AND (b = 2 OR c = 3) AND NOT d = 1
x.% = true OR x.# = true

Prefix expressions are expressions given in the form path (subexpression). In this case path selects JSON values to be checked using given subexpression. Check results are aggregated in the same way as in simple expressions.

#(a = 1 AND b = 2) – exists element of array which a key is 1 and b key is 2
%($ >= 10 AND $ <= 20) – exists object key which values is between 10 and 20

Path also could contain following special placeholders with "every" semantics:

#: – every indexes of array;
%: – every key of object;
*: – every sequence of array indexes and object keys.

Consider following example.

%.#:($ >= 0 AND $ <= 1)

This example could be read as following: there is at least one key which value is array of numerics between 0 and 1.

We can rewrite this example in the following form with extra braces.

%(#:($ >= 0 AND $ <= 1))

The first placeholder % checks that expression in braces is true for at least one value in object. The second placeholder #: checks value to be array and all its elements satisfy expressions in braces.

We can rewrite this example without #: placeholder as follows.

%(NOT #(NOT ($ >= 0 AND $ <= 1)) AND $ IS ARRAY)

In this example we transform assertion that every element of array satisfy some condition to assertion that there is no one element which doesn't satisfy the same condition.

Some examples of using paths are given below.

numbers.#: IS NUMERIC – every element of "numbers" array is numeric.
*:($ IS OBJECT OR $ IS BOOLEAN) – JSON is a structure of nested objects with booleans as leaf values.
#:.%:($ >= 0 AND $ <= 1) – each element of array is object containing only numeric values between 0 and 1.
documents.#:.% = * – "documents" is array of objects containing at least one key.
%.#: ($ IS STRING) – JSON object contains at least one array of strings.
#.% = true – at least one array element is objects which contains at least one "true" value.

Usage of path operators and braces need some explanation. When same path operators are used multiple times they may refer different values while you can refer same value multiple time by using braces and $ operator. See following examples.

# < 10 AND # > 20 – exists element less than 10 and exists another element greater than 20.
#($ < 10 AND $ > 20) – exists element which both less than 10 and greater than 20 (impossible).
#($ >= 10 AND $ <= 20) – exists element between 10 and 20.
# >= 10 AND # <= 20 – exists element great or equal to 10 and exists another element less or equal to 20. Query can be satisfied by array with no elements between 10 and 20, for instance [0,30].

Same rules apply when you search inside objects and branchy structures.

Type checking operators and "every" placeholders are useful for document schema validation. JsQuery matchig operator @@ is immutable and can be used in CHECK constraint. See following example.

CREATE TABLE js (
    id serial,
    data jsonb,
    CHECK (data @@ '
        name IS STRING AND
        similar_ids.#: IS NUMERIC AND
        points.#:(x IS NUMERIC AND y IS NUMERIC)'::jsquery));

In this example check constraint validates that in "data" jsonb column: value of "name" key is string, value of "similar_ids" key is array of numerics, value of "points" key is array of objects which contain numeric values in "x" and "y" keys.

See our pgconf.eu presentation for more examples.

GIN indexes

JsQuery extension contains two operator classes (opclasses) for GIN which provide different kinds of query optimization.

jsonb_path_value_ops
jsonb_value_path_ops

In each of two GIN opclasses jsonb documents are decomposed into entries. Each entry is associated with particular value and it's path. Difference between opclasses is in the entry representation, comparison and usage for search optimization.

For example, jsonb document {"a": [{"b": "xyz", "c": true}, 10], "d": {"e": [7, false]}} would be decomposed into following entries:

"a".#."b"."xyz"
"a".#."c".true
"a".#.10
"d"."e".#.7
"d"."e".#.false

Since JsQuery doesn't support search in particular array index, we consider all array elements to be equivalent. Thus, each array element is marked with same # sign in the path.

Major problem in the entries representation is its size. In the given example key "a" is presented three times. In the large branchy documents with long keys size of naive entries representation becomes unreasonable. Both opclasses address this issue but in a slightly different way.

jsonb_path_value_ops

jsonb_path_value_ops represents entry as pair of path hash and value. Following pseudocode illustrates it.

(hash(path_item_1.path_item_2. ... .path_item_n); value)

In comparison of entries path hash is the higher part of entry and value is its lower part. This determines the features of this opclass. Since path is hashed and it is higher part of entry we need to know the full path to the value in order to use it for search. However, once path is specified we can use both exact and range searches very efficiently.

jsonb_value_path_ops

jsonb_value_path_ops represents entry as pair of value and bloom filter of path.

(value; bloom(path_item_1) | bloom(path_item_2) | ... | bloom(path_item_n))

In comparison of entries value is the higher part of entry and bloom filter of path is its lower part. This determines the features of this opclass. Since value is the higher part of entry we can perform only exact value search efficiently. Range value search is possible as well but we would have to filter all the the different paths where matching values occur. Bloom filter over path items allows index usage for conditions containing % and * in their paths.

Query optimization

JsQuery opclasses perform complex query optimization. Thus it's valuable for developer or administrator to see the result of such optimization. Unfortunately, opclasses aren't allowed to do any custom output to the EXPLAIN. That's why JsQuery provides following functions which allows to see how particular opclass optimizes given query.

gin_debug_query_path_value(jsquery) – for jsonb_path_value_ops
gin_debug_query_value_path(jsquery) – for jsonb_value_path_ops

Result of these functions is a textual representation of query tree which leafs are GIN search entries. Following examples show different results of query optimization by different opclasses.

# SELECT gin_debug_query_path_value('x = 1 AND (*.y = 1 OR y = 2)');
 gin_debug_query_path_value
----------------------------
 x = 1 , entry 0           +

# SELECT gin_debug_query_value_path('x = 1 AND (*.y = 1 OR y = 2)');
 gin_debug_query_value_path
----------------------------
 AND                       +
   x = 1 , entry 0         +
   OR                      +
     *.y = 1 , entry 1     +
     y = 2 , entry 2       +

Unfortunately, jsonb have no statistics yet. That's why JsQuery optimizer has to do imperative decision while selecting conditions to be evaluated using index. This decision is made by assumtion that some condition types are less selective than others. Optimizer divides conditions into following selectivity class (listed by descending of selectivity).

Equality (x = c)
Range (c1 < x < c2)
Inequality (x > c)
Is (x is type)
Any (x = *)

Optimizer evades index evaluation of less selective conditions when possible. For example, in the x = 1 AND y > 0 query x = 1 is assumed to be more selective than y > 0. That's why index isn't used for evaluation of y > 0.

# SELECT gin_debug_query_path_value('x = 1 AND y > 0');
 gin_debug_query_path_value
----------------------------
 x = 1 , entry 0           +

With lack of statistics decisions made by optimizer can be inaccurate. That's why JsQuery supports hints. Comments /*-- index */ and /*-- noindex */ placed in the conditions forces optimizer to use and not use index correspondingly.

SELECT gin_debug_query_path_value('x = 1 AND y /*-- index */ > 0');
 gin_debug_query_path_value
----------------------------
 AND                       +
   x = 1 , entry 0         +
   y > 0 , entry 1         +

SELECT gin_debug_query_path_value('x /*-- noindex */ = 1 AND y > 0');
 gin_debug_query_path_value
 ----------------------------
  y > 0 , entry 0           +

Contribution

Please, notice, that JsQuery is still under development and while it's stable and tested, it may contains some bugs. Don't hesitate to raise issues at github with your bug reports.

If you're lacking of some functionality in JsQuery and feeling power to implement it then you're welcome to make pull requests.

postgrespro/jsquery

postgrespro

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