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<title>The JSON1 Extension</title>
<tcl>hd_keywords json1 {the json1 extension} {JSON SQL functions}</tcl>


The <b>json1</b> extension is a [loadable extension] that
implements fifteen [application-defined SQL functions] and
two [table-valued functions] that are useful for
managing [ | JSON] content stored in an SQLite database.
There are thirteen scalar functions:

set tabcnt 0
proc tabentry {fx desc lnk} {
  global tabcnt
  incr tabcnt
  hd_puts "<li value='$tabcnt'>\n"
  set fx [string trim $fx]
  set hlink "<a href='#$lnk'>"
  regsub -all {^json\(} $fx "${hlink}json</a>(" fx
  regsub -all {(json_[a-z_]+)} $fx "$hlink\\1</a>" fx
  regsub -all {(value[1-9]?|path|label[1-9]?)} $fx "<i>\\1</i>" fx
  regsub -all {\((json)} $fx "(<i>\\1</i>" fx
  hd_puts "$fx\n"
  # hd_puts "[string trim $desc]\n"
  hd_puts "</li>\n\n"
set tsctr 100

tabentry {json(json)} {
  Validate and minify a JSON string
} jmini

tabentry {json_array(value1,value2,...)} {
  Return a JSON array holding the function arguments.
} jarray

tabentry {json_array_length(json)<br>json_array_length(json,path)} {
  Return the number of elements in the JSON array identified by
  the arguments.
} jarraylen

tabentry {json_extract(json,path,...)} {
  Extract values or subcomponents from a JSON string.
} jex

tabentry {json_insert(json,path,value,...)} {
  Insert values into a JSON string without overwriting existing values.
} jins

tabentry {json_object(label1,value1,...)} {
  Construct and return a new JSON object based on the arguments.
} jobj

tabentry {json_patch(json1,json2)} {
  Apply the [|RFC-7396] MergePatch
} jpatch

tabentry {json_remove(json,path,...)} {
  Remove the specified values from a JSON string.
} jrm

tabentry {json_replace(json,path,value,...)} {
  Update existing values within a JSON string.
} jrepl

tabentry {json_set(json,path,value,...)} {
  Insert or replace values in a JSON string.  Overwrite existing elements
  or create new entries in the JSON string for elements that do not 
  previously exist.
} jset

tabentry {json_type(json)<br>json_type(json,path)} {
  Return the type of a JSON string or subcomponent.
} jtype

tabentry {json_valid(json)} {
  Return true (1) if the input text is a valid JSON string
} jvalid

tabentry {json_quote(value)} {
  Convert an SQL value (a number or a string) into its corresponding JSON
} jvalid

<p>There are two aggregate SQL functions:

tabentry {json_group_array(value)} {
  Return a JSON array composed of all <i>value</i> elements 
  in the aggregation.
} jgrouparray

tabentry {json_group_object(name,value)} {
  Return a JSON object composed of all <i>name</i> and <i>value</i> pairs
  in the aggregation.
} jgroupobject

<p>The two [table-valued functions] are:

tabentry {json_each(json)<br>json_each(json,path)} {
  Return one row describing each element in an array or object
  at the top-level or at "path" within the input JSON.
} jeach

tabentry {json_tree(json)<br>json_tree(json,path)} {
  Walk the JSON recursively starting at the top-level or at the
  specified "path" and return one row for each element.
} jtree

hd_puts {
.jans {color: #050;}
.jex {color: #025;}
proc jexample {args} {
  hd_puts "<ul>\n"
  foreach {sql res} $args {
    ### uncomment to generate test cases from the examples
    # global tsctr
    # puts "do_execsql_test json102-$tsctr \173"
    # incr tsctr 10
    # puts "  SELECT [string trim $sql];"
    # set x \173[string trim $res ']\175
    # puts "\175 \173$x\175"
    hd_putsnl "<li><span class='jex'>[string trim $sql]</span>"
    hd_putsnl "<span class='jans'>&rarr; [string trim $res]</span></li>\n"
  hd_puts "</ul>\n"

<tcl>hd_fragment howtocompile</tcl>
<h1>Compiling the JSON1 Extension</h1>

The [loadable extensions] documentation describes
how to [compile loadable extensions] as shared libraries.  The
techniques described there work fine for the json1 module.

The json1 source code is included with the SQLite [amalgamation], though
it is disabled by default.  Add the [-DSQLITE_ENABLE_JSON1] compile-time
option to enable the json1 extension that is built into the [amalgamation].
The standard makefiles include -DSQLITE_ENABLE_JSON1 when building the
[command-line shell] and some of the test utilities so this extension is
normally available in the [command-line shell].

<h1>Interface Overview</h1>

The json1 extension (currently) stores JSON as ordinary text.

Backwards compatibility constraints mean that SQLite is only able to
store values that are NULL, integers, floating-point numbers, text,
and BLOBs.  It is not possible to add a sixth "JSON" type.

The json1 extension does not (currently) support a binary encoding
of JSON.  Experiments have been unable to find a binary encoding
that is significantly smaller or faster than a plain text encoding.
(The present implementation parses JSON text at over 300 MB/s.)
All json1 functions currently throw an error if any of their
arguments are BLOBs because BLOBs are reserved
for a future enhancement in which BLOBs will store the binary encoding
for JSON.

The "1" at the end of the name for the json1 extension is deliberate.
The designers anticipate that there will be future incompatible JSON
extensions building upon the lessons learned from json1.
Once sufficient experience is gained, some kind of
JSON extension might be folded into the SQLite core.  For now,
JSON support remains an extension.

<h2>JSON arguments</h2>

For functions that accept JSON as their first argument, that argument
can be a JSON object, array, number, string, or null.  SQLite numeric
values and NULL values are interpreted as JSON numbers and nulls, respectively.
SQLite text values can be understood as JSON objects, arrays, or strings.
If an SQLite text value that is not a well-formed JSON object, array, or
string is passed into json1 function, that function will usually throw
an error.  (Exceptions to this rule are [json_valid()] and

For the purposes of determining validity, leading and trailing whitespace
on JSON inputs is ignored.  Interior whitespace is also ignored, in accordance
with the JSON spec.  These routines accept exactly the 
[ | rfc-7159 JSON syntax]
&mdash; no more and no less.

<h2>PATH arguments</h2>

For functions that accept PATH arguments, that PATH must be well-formed or
else the function will throw an error.
A well-formed PATH is a text value that begins with exactly one
'$' character followed by zero or more instances
of ".<i>objectlabel</i>" or "&#91<i>arrayindex</i>&#93".

<h2>VALUE arguments</h2>

For functions that accept "<i>value</i>" arguments (also shown as
"<i>value1</i>" and "<i>value2</i>"),
those arguments is usually understood
to be a literal strings that are quoted and becomes JSON string values
in the result.  Even if the input <i>value</i> strings look like 
well-formed JSON, they are still interpreted as literal strings in the

However, if a <i>value</i> argument come directly from the result of another
json1 function, then the argument is understood to be actual JSON and
the complete JSON is inserted rather than a quoted string.

For example, in the following call to json_object(), the <i>value</i>
argument looks like a well-formed JSON array.  However, because it is just
ordinary SQL text, it is interpreted as a literal string and added to the
result as a quoted string:

jexample {json_object('ex','[52,3.14159]')} {'{"ex":"[52,3.14159]"}'}

But if the <i>value</i> argument in the outer json_object() call is the
result of another json1 function like [json()] or [json_array()], then
the value is understood to be actual JSON and is inserted as such:

jexample \
  {json_object('ex',json('[52,3.14159]'))} {'{"ex":[52,3.14159]}'}  \
  {json_object('ex',json_array(52,3.14159))} {'{"ex":[52,3.14159]}'}

To be clear: "<i>json</i>" arguments are always interpreted as JSON
regardless of where the value for that argument comes from.  But
"<i>value</i>" arguments are only interpreted as JSON if those arguments
come directly from another json1 function.


The json1 extension uses the [sqlite3_value_subtype()] and
[sqlite3_result_subtype()] interfaces that were introduced with
SQLite version 3.9.0 ([dateof:3.9.0])
The json1 extension will not work
in earlier versions of SQLite.

The current implementation of this JSON library uses a recursive descent
parser.  In order to avoid using excess stack space, any JSON input that has
more than 2000 levels of nesting is considered invalid.   Limits on nesting
depth are allowed for compatible implementations of JSON by
[|RFC-7159 section 9].

<h1>Function Details</h1>

<p>The following sections provide additional detail on the operation of
the various functions that are part of the json1 extension.

<tcl>hd_fragment jmini {json SQL function} {json}</tcl>
<h2>The json() function</h2>

<p>The json(X) function verifies that its argument X is a valid
JSON string and returns a minified version of that JSON string
(with all unnecessary whitespace removed).  If X is not a well-formed
JSON string, then this routine throws an error.

<p>If the argument X to json(X) contains JSON objects with duplicate
labels, then it is undefined whether or not the duplicates are
preserved.  The current implementation preserves duplicates.
However, future enhancements
to this routine may choose to silently remove duplicates.


jexample \
  {json(' { "this" : "is", "a": [ "test" ] } ')} \

<tcl>hd_fragment jarray {json_array SQL function} {json_array}</tcl>
<h2>The json_array() function</h2>

<p>The json_array() SQL function accepts zero or more arguments and
returns a well-formed JSON array that is composed from those arguments.
If any argument to json_array() is a BLOB then an error is thrown.

<p>An argument with SQL type TEXT it is normally converted into a quoted 
JSON string.  However, if the argument is the output from another json1
function, then it is stored as JSON.  This allows calls to json_array()
and [json_object()] to be nested.  The [json()] function can also
be used to force strings to be recognized as JSON.


jexample \
  {json_array(1,2,'3',4)} {'[1,2,"3",4]'} \
  {json_array('[1,2]')} {'["[1,2]"]'} \
  {json_array(json_array(1,2))} {'[[1,2]]'} \
  {json_array(1,null,'3','[4,5]','{"six":7.7}')} \
      {'[1,null,"3","[4,5]","{\"six\":7.7}"]'} \
  {json_array(1,null,'3',json('[4,5]'),json('{"six":7.7}'))} \

<tcl>hd_fragment jarraylen {json_array_length SQL function} \
<h2>The json_array_length() function</h2>

<p>The json_array_length(X) function returns the number of elements
in the JSON array X, or 0 if X is some kind of JSON value other
than an array.  The json_array_length(X,P) locates the array at path P
within X and returns the length of that array, or 0 if path P locates
an element or X other than a JSON array, and NULL if path P does not
locate any element of X.  Errors are thrown if either X is not 
well-formed JSON or if P is not a well-formed path.


jexample \
  {json_array_length('[1,2,3,4]')} {4} \
  {json_array_length('[1,2,3,4]', '$')} {4} \
  {json_array_length('[1,2,3,4]', '$[2]')} {0} \
  {json_array_length('{"one":[1,2,3]}')} {0} \
  {json_array_length('{"one":[1,2,3]}', '$.one')} {3} \
  {json_array_length('{"one":[1,2,3]}', '$.two')} {NULL}

<tcl>hd_fragment jex {json_extract SQL function} {json_extract}</tcl>
<h2>The json_extract() function</h2>

<p>The json_extract(X,P1,P2,...) extracts and returns one or more 
values from the
well-formed JSON at X.  If only a single path P1 is provided, then the
SQL datatype of the result is NULL for a JSON null, INTEGER or REAL
for a JSON numeric value, an INTEGER zero for a JSON false value,
an INTEGER one for a JSON true value, the dequoted text for a 
JSON string value, and a text representation for JSON object and array values.
If there are multiple path arguments (P1, P2, and so forth) then this
routine returns SQLite text which is a well-formed JSON array holding
the various values.


jexample \
  {json_extract('{"a":2,"c":[4,5,{"f":7}]}', '$')} \
      {'{"a":2,"c":[4,5,{"f":7}]}'} \
  {json_extract('{"a":2,"c":[4,5,{"f":7}]}', '$.c')} \
      {'[4,5,{"f":7}]'} \
  {json_extract('{"a":2,"c":[4,5,{"f":7}]}', '$.c[2]')} \
      {'{"f":7}'} \
  {json_extract('{"a":2,"c":[4,5,{"f":7}]}', '$.c[2].f')} {7} \
  {json_extract('{"a":2,"c":[4,5],"f":7}','$.c','$.a')} {'[[4,5],2]'} \
  {json_extract('{"a":2,"c":[4,5,{"f":7}]}', '$.x')} NULL \
  {json_extract('{"a":2,"c":[4,5,{"f":7}]}', '$.x', '$.a')} {'[null,2]'}

<tcl>hd_fragment jins {json_insert SQL function} {json_insert}</tcl>
<tcl>hd_fragment jrepl {json_replace SQL function} {json_replace}</tcl>
<tcl>hd_fragment jset {json_set SQL function} {json_set}</tcl>
<h2>The json_insert(), json_replace, and json_set() functions</h2>

<p>The json_insert(), json_replace, and json_set() functions all take
a single JSON value as their first argument followed by zero or more
pairs of path and value arguments, and return a new JSON string formed
by updating the input JSON by the path/value pairs.  The functions
differ only in how they deal with creating new values and overwriting
preexisting values.

<table border=1 cellpadding=3 cellspacing=0>
<th>Function<th>Overwrite if already exists?<th>Create if does not exist?
<td>json_insert()<td align='center'>No<td align='center'>Yes
<td>json_replace()<td align='center'>Yes<td align='center'>No
<td>json_set()<td align='center'>Yes<td align='center'>Yes

<p>The json_insert(), json_replace(), and json_set() functions always
take an odd number of arguments.  The first argument is always the original
JSON to be edited.  Subsequent arguments occur in pairs with the first
element of each pair being a path and the second element being the value
to insert or replace or set on that path.

<p>Edits occurs sequentially from left to right.  Changes caused by
prior edits can affect the path search for subsequent edits.

<p>If the value of a path/value pair is an SQLite TEXT value, then it
is normally inserted as a quoted JSON string, even if the string looks
like valid JSON.  However, if the value is the result of another
json1 function (such as [json()] or [json_array()] or [json_object()])
then it is interpreted as JSON and is inserted as JSON retaining all
of its substructure.

<p>These routines throw an error if the first JSON argument is not
well-formed or if any PATH argument is not well-formed or if any
argument is a BLOB.


jexample \
  {json_insert('{"a":2,"c":4}', '$.a', 99)} {'{"a":2,"c":4}'} \
  {json_insert('{"a":2,"c":4}', '$.e', 99)} {'{"a":2,"c":4,"e":99}'} \
  {json_replace('{"a":2,"c":4}', '$.a', 99)} {'{"a":99,"c":4}'} \
  {json_replace('{"a":2,"c":4}', '$.e', 99)} {'{"a":2,"c":4}'} \
  {json_set('{"a":2,"c":4}', '$.a', 99)} {'{"a":99,"c":4}'} \
  {json_set('{"a":2,"c":4}', '$.e', 99)} {'{"a":2,"c":4,"e":99}'} \
  {json_set('{"a":2,"c":4}', '$.c', '[97,96]')} \
            {'{"a":2,"c":"[97,96]"}'} \
  {json_set('{"a":2,"c":4}', '$.c', json('[97,96]'))} \
            {'{"a":2,"c":[97,96]}'} \
  {json_set('{"a":2,"c":4}', '$.c', json_array(97,96))} \

<tcl>hd_fragment jobj {json_object SQL function} {json_object}</tcl>
<h2>The json_object() function</h2>

<p>The json_object() SQL function accepts zero or more pairs of arguments
and returns a well-formed JSON object that is composed from those arguments.
The first argument of each pair is the label and the second argument of
each pair is the value.
If any argument to json_object() is a BLOB then an error is thrown.

<p>The json_object() function currently allows duplicate labels without
complaint, though this might change in a future enhancement.

<p>An argument with SQL type TEXT it is normally converted into a quoted 
JSON string even if the input text is well-formed JSON.  
However, if the argument is the direct result from another json1
function, then it is treated as JSON and all of its JSON type information
and substructure is preserved.  This allows calls to json_object()
and [json_array()] to be nested.  The [json()] function can also
be used to force strings to be recognized as JSON.


jexample \
  {json_object('a',2,'c',4)} {'{"a":2,"c":4}'} \
  {json_object('a',2,'c','{e:5}')} {'{"a":2,"c":"{e:5}"}'} \
  {json_object('a',2,'c',json_object('e',5))} {'{"a":2,"c":{"e":5}}'}

<tcl>hd_fragment jpatch {json_patch SQL function} {json_patch}</tcl>
<h2>The json_patch() function</h2>

<p>The json_patch(T,P) SQL function runs the
[|RFC-7396] MergePatch algorithm
to apply patch P against input T.  The patched copy of T is returned.

<p>MergePatch can add, modify, or delete elements of a JSON Object,
and so for JSON Objects, the json_patch() routine is a generalized
replacement for [json_set()] and [json_remove()].  However, MergePatch
treats JSON Array objects as atomic.  MergePatch cannot append to an
Array nor modify individual elements of an Array.  It can only insert,
replace, or delete the whole Array as a single unit.  Hence, json_patch()
is not as useful when dealing with JSON that includes Arrays,
especially Arrays with lots of substructure.


jexample \
 {json_patch('{"a":1,"b":2}','{"c":3,"d":4}')} \
         {'{"a":1,"b":2,"c":3,"d":4}'} \
 {json_patch('{"a":[1,2],"b":2}','{"a":9}')} \
         {'{"a":9,"b":2}'} \
 {json_patch('{"a":[1,2],"b":2}','{"a":null}')} \
         {'{"b":2}'} \
 {json_patch('{"a":1,"b":2}','{"a":9,"b":null,"c":8}')} \
         {'{"a":9,"c":8}'} \
 {json_patch('{"a":{"x":1,"y":2},"b":3}','{"a":{"y":9},"c":8}')} \

<tcl>hd_fragment jrm {json_remove SQL function} {json_remove}</tcl>
<h2>The json_remove() function</h2>

<p>The json_remove(X,P,...) function takes a single JSON value as its
first argument followed by zero or more path arguments.
The json_remove(X,P,...) function returns
a new JSON value that is the X with all the elements 
identified by path arguments removed.  Paths that select elements
not found in X are silently ignored.

<p>Removals occurs sequentially from left to right.  Changes caused by
prior removals can affect the path search for subsequent arguments.

<p>If the json_remove(X) function is called with no path arguments,
then it returns the input X reformatted, with excess whitespace

<p>The json_remove() function throws an error if the first argument
is not well-formed JSON or if any later argument is not a well-formed
path, or if any argument is a BLOB.


jexample \
  {json_remove('[0,1,2,3,4]','$[2]')} {'[0,1,3,4]'} \
  {json_remove('[0,1,2,3,4]','$[2]','$[0]')} {'[1,3,4]'} \
  {json_remove('[0,1,2,3,4]','$[0]','$[2]')} {'[1,2,4]'} \
  {json_remove('{"x":25,"y":42}')} {'{"x":25,"y":42}'} \
  {json_remove('{"x":25,"y":42}','$.z')} {'{"x":25,"y":42}'} \
  {json_remove('{"x":25,"y":42}','$.y')} {'{"x":25}'} \
  {json_remove('{"x":25,"y":42}','$')} NULL

<tcl>hd_fragment jtype {json_type SQL function} {json_type}</tcl>
<h2>The json_type() function</h2>

<p>The json_type(X) function returns the "type" of the outermost element
of X.  The json_type(X,P) function returns the "type" of the element
in X that is selected by path P.  The "type" returned by json_type() is
on of the following an SQL text values:
'null', 'true', 'false', 'integer', 'real', 'text', 'array', or 'object'.
If the path P in json_type(X,P) selects an element that does not exist
in X, then this function returns NULL.

<p>The json_type() function throws an error if any of its arguments are
not well-formed or is a BLOB.


jexample \
  {json_type('{"a":[2,3.5,true,false,null,"x"]}')} 'object' \
  {json_type('{"a":[2,3.5,true,false,null,"x"]}','$')} 'object' \
  {json_type('{"a":[2,3.5,true,false,null,"x"]}','$.a')} 'array' \
  {json_type('{"a":[2,3.5,true,false,null,"x"]}','$.a[0]')} 'integer' \
  {json_type('{"a":[2,3.5,true,false,null,"x"]}','$.a[1]')} 'real' \
  {json_type('{"a":[2,3.5,true,false,null,"x"]}','$.a[2]')} 'true' \
  {json_type('{"a":[2,3.5,true,false,null,"x"]}','$.a[3]')} 'false' \
  {json_type('{"a":[2,3.5,true,false,null,"x"]}','$.a[4]')} 'null' \
  {json_type('{"a":[2,3.5,true,false,null,"x"]}','$.a[5]')} 'text' \
  {json_type('{"a":[2,3.5,true,false,null,"x"]}','$.a[6]')} NULL

<tcl>hd_fragment jvalid {json_valid SQL function} {json_valid}</tcl>
<h2>The json_valid() function</h2>

<p>The json_valid(X) function return 1 if the argument X is well-formed
JSON and return 0 if the argument X is not well-formed JSON.


jexample \
  {json_valid('{"x":35}')} 1 \
  "json_valid('\173\"x\":35')" 0

<tcl>hd_fragment jquote {json_quote SQL function} {json_quote}</tcl>
<h2>The json_quote() function</h2>

<p>The json_quote(X) function converts the SQL value X (a number or a
string) into its corresponding JSON representation. 


jexample \
  {json_quote(3.14159)} 3.14159 \
  "json_quote('verdant')" \"verdant\"

hd_fragment jgrouparray {json_group_array SQL function} \
hd_fragment jgroupobject {json_group_object SQL function} \
<h2>The json_group_array() and json_group_object()
aggregate SQL functions</h2>

<p>The json_group_array(X) function is an 
[Aggregate Functions|aggregate SQL function] that returns a JSON array
comprised of all X values in the aggregation.
Similarly, the json_group_object(NAME,VALUE) function returns a JSON object
comprised of all NAME/VALUE pairs in the aggregation.

<tcl>hd_fragment jeach {json_each table-valued function} {json_each}</tcl>
<tcl>hd_fragment jtree {json_tree table-valued function} {json_tree}</tcl>
<h2>The json_each() and json_tree() table-valued functions</h2>

<p>The json_each(X) and json_tree(X) [table-valued functions] walk the
JSON value provided as their first argument and return one row for each
element.  The json_each(X) function only walks the immediate children
of the top-level array or object or 
or just the top-level element itself if the top-level
element is a primitive value.
The json_tree(X) function recursively walks through the
JSON substructure starting with the top-level element.  

<p>The json_each(X,P) and json_tree(X,P) functions work just like
their one-argument counterparts except that they treat the element
identified by path P as the top-level element.

<p>The schema for the table returned by json_each() and json_tree() is
as follows:

CREATE TABLE json_tree(
    key ANY,             -- key for current element relative to its parent
    value ANY,           -- value for the current element
    type TEXT,           -- 'object','array','string','integer', etc.
    atom ANY,            -- value for primitive types, null for array & object
    id INTEGER,          -- integer ID for this element
    parent INTEGER,      -- integer ID for the parent of this element
    fullkey TEXT,        -- full path describing the current element
    path TEXT,           -- path to the container of the current row
    json JSON HIDDEN,    -- 1st input parameter: the raw JSON
    root TEXT HIDDEN     -- 2nd input parameter: the PATH at which to start

The "key" column is the integer array index for elements of a JSON array 
and the text label for elements of a JSON object.  The key column is
NULL in all other cases.

The "atom" column is the SQL value corresponding to primitive elements - 
elements other than JSON arrays and objects.  The "atom" column is NULL
for a JSON array or object.  The "value" column is the same as the
"atom" column for primitive JSON elements but takes on the text JSON value
for arrays and objects.

The "type" column is an SQL text value taken from ('null', 'true', 'false',
'integer', 'real', 'text', 'array', 'object') according to the type of
the current JSON element.

The "id" column is an integer that identifies a specific JSON element
within the complete JSON string.  The "id" integer is an internal housekeeping
number, the computation of which might change in future releases.  The
only guarantee is that the "id" column will be different for every row.

The "parent" column is always NULL for json_each().
For json_tree(),
the "parent" column is the "id" integer for the parent of the current
element, or NULL for the top-level JSON element or the element identified
by the root path in the second argument.

The "fullkey" column is a text path that uniquely identifies the current
row element within the original JSON string.  The complete key to the
true top-level element is returned even if an alternative starting point
is provided by the "root" argument.

The "path" column is the path to the array or object container the holds 
the current row, or the path to the current row in the case where the 
iteration starts on a primitive type and thus only provides a single
row of output.

<h3>Examples using json_each() and json_tree()</h3>

<p>Suppose the table "CREATE TABLE user(name,phone)" stores zero or
more phone numbers as a JSON array object in the field.
To find all users who have any phone number with a 704 area code:

  FROM user, json_each(
 WHERE json_each.value LIKE '704-%';

<p>Now suppose the field contains plain text if the user
has only a single phone number and a JSON array if the user has multiple
phone numbers.  The same question is posed: "Which users have a phone number
in the 704 area code?"  But now the json_each() function can only be called
for those users that have two or more phone numbers since json_each()
requires well-formed JSON as its first argument:

SELECT name FROM user WHERE phone LIKE '704-%'
  FROM user, json_each(
 WHERE json_valid(
   AND json_each.value LIKE '704-%';

<p>Consider a different database with "CREATE TABLE big(json JSON)".
To see a complete line-by-line decomposition of the data:

SELECT big.rowid, fullkey, value
  FROM big, json_tree(big.json)
 WHERE json_tree.type NOT IN ('object','array');

<p>In the previous, the "type NOT IN ('object','array')" term of the
WHERE clause suppresses containers and only lets through leaf elements.
The same effect could be achieved this way:

SELECT big.rowid, fullkey, atom
  FROM big, json_tree(big.json)

<p>Suppose each entry in the BIG table is a JSON object 
with a '$.id' field that is a unique identifier
and a '$.partlist' field that can be a deeply nested object.
You want to find the id of every entry that contains one
or more references to uuid '6fa5181e-5721-11e5-a04e-57f3d7b32808' anywhere
in its '$.partlist'.

SELECT DISTINCT json_extract(big.json,'$.id')
  FROM big, json_tree(big.json, '$.partlist')
 WHERE json_tree.key='uuid'
   AND json_tree.value='6fa5181e-5721-11e5-a04e-57f3d7b32808';