JSON Functions

To see how JSON data can be queried, assume that we have the following table:

Table myTable:
  id        INTEGER
  jsoncolumn    JSON 

Table data:
101,{"name":{"first":"daffy"\,"last":"duck"}\,"score":101\,"data":["a"\,"b"\,"c"\,"d"]}
102,{"name":{"first":"donald"\,"last":"duck"}\,"score":102\,"data":["a"\,"b"\,"e"\,"f"]}
103,{"name":{"first":"mickey"\,"last":"mouse"}\,"score":103\,"data":["a"\,"b"\,"g"\,"h"]}
104,{"name":{"first":"minnie"\,"last":"mouse"}\,"score":104\,"data":["a"\,"b"\,"i"\,"j"]}
105,{"name":{"first":"goofy"\,"last":"dwag"}\,"score":104\,"data":["a"\,"b"\,"i"\,"j"]}
106,{"person":{"name":"daffy duck"\,"companies":[{"name":"n1"\,"title":"t1"}\,{"name":"n2"\,"title":"t2"}]}}
107,{"person":{"name":"scrooge mcduck"\,"companies":[{"name":"n1"\,"title":"t1"}\,{"name":"n2"\,"title":"t2"}]}}

We also assume that "jsoncolumn" has a Json Index on it. Note that the last two rows in the table have different structure than the rest of the rows. In keeping with JSON specification, a JSON column can contain any valid JSON data and doesn't need to adhere to a predefined schema. To pull out the entire JSON document for each row, we can run the query below:

SELECT id, jsoncolumn 
  FROM myTable

id	jsoncolumn
"101"	"{"name":{"first":"daffy","last":"duck"},"score":101,"data":["a","b","c","d"]}"
102"	"{"name":{"first":"donald","last":"duck"},"score":102,"data":["a","b","e","f"]}
"103"	"{"name":{"first":"mickey","last":"mouse"},"score":103,"data":["a","b","g","h"]}
"104"	"{"name":{"first":"minnie","last":"mouse"},"score":104,"data":["a","b","i","j"]}"
"105"	"{"name":{"first":"goofy","last":"dwag"},"score":104,"data":["a","b","i","j"]}"
"106"	"{"person":{"name":"daffy duck","companies":[{"name":"n1","title":"t1"},{"name":"n2","title":"t2"}]}}"
"107"	"{"person":{"name":"scrooge mcduck","companies":[{"name":"n1","title":"t1"},{"name":"n2","title":"t2"}]}}"

To drill down and pull out specific keys within the JSON column, we simply append the JsonPath expression of those keys to the end of the column name.

SELECT id,
       json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
       json_extract_scalar(jsoncolumn, '$.name.first', 'STRING', 'null') first_name
       json_extract_scalar(jsoncolumn, '$.data[1]', 'STRING', 'null') value
  FROM myTable

id	last_name	first_name	value
101	duck	daffy	b
102	duck	donald	b
103	mouse	mickey	b
104	mouse	minnie	b
105	dwag	goofy	b
106	null	null	null
107	null	null	null

Note that the third column (value) is null for rows with id 106 and 107. This is because these rows have JSON documents that don't have a key with JsonPath $.data[1]. We can filter out these rows.

SELECT id,
       json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
       json_extract_scalar(jsoncolumn, '$.name.first', 'STRING', 'null') first_name,
       json_extract_scalar(jsoncolumn, '$.data[1]', 'STRING', 'null') value
  FROM myTable
 WHERE JSON_MATCH(jsoncolumn, '"$.data[1]" IS NOT NULL')

id	last_name	first_name	value
101	duck	daffy	b
102	duck	donald	b
103	mouse	mickey	b
104	mouse	minnie	b
105	dwag	goofy	b

Certain last names (duck and mouse for example) repeat in the data above. We can get a count of each last name by running a GROUP BY query on a JsonPath expression.

  SELECT json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
         count(*)
    FROM myTable
   WHERE JSON_MATCH(jsoncolumn, '"$.data[1]" IS NOT NULL')
GROUP BY json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null')
ORDER BY 2 DESC

jsoncolumn.name.last	count(*)
"mouse"	"2"
"duck"	"2"
"dwag"	"1"

Also there is numerical information (jsconcolumn.$.id) embeded within the JSON document. We can extract those numerical values from JSON data into SQL and sum them up using the query below.

  SELECT json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
         sum(json_extract_scalar(jsoncolumn, '$.id', 'INT', 0)) total
    FROM myTable
   WHERE JSON_MATCH(jsoncolumn, '"$.name.last" IS NOT NULL')
GROUP BY json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null')

jsoncolumn.name.last	sum(jsoncolumn.score)
"mouse"	"207"
"dwag"	"104"
"duck"	"203"

JSON_MATCH and JSON_EXTRACT_SCALAR

Note that the JSON_MATCH function utilizes JsonIndex and can only be used if a JsonIndex is already present on the JSON column. As shown in the examples above, the second argument of JSON_MATCH operator takes a predicate. This predicate is evaluated against the JsonIndex and supports =, !=, IS NULL, or IS NOT NULL operators. Relational operators, such as >, <, >=, and <= are currently not supported. However, you can combine the use of JSON_MATCH and JSON_EXTRACT_SCALAR function (which supports >, <, >=, and <= operators) to get the necessary functinoality as shown below.

  SELECT json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
         sum(json_extract_scalar(jsoncolumn, '$.id', 'INT', 0)) total
    FROM myTable
   WHERE JSON_MATCH(jsoncolumn, '"$.name.last" IS NOT NULL') AND json_extract_scalar(jsoncolumn, '$.id', 'INT', 0) > 102
GROUP BY json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null')

jsoncolumn.name.last	sum(jsoncolumn.score)
"mouse"	"207"
"dwag"	"104"

JSON_MATCH function also provides the ability to use wildcard * JsonPath expressions even though it doesn't support full JsonPath expressions.

  SELECT json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
         json_extract_scalar(jsoncolumn, '$.id', 'INT', 0) total
    FROM myTable
   WHERE JSON_MATCH(jsoncolumn, '"$.data[*]" = ''f''')
GROUP BY json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null')

last_name	total
"duck"	"102"

While, JSON_MATCH supports IS NULL and IS NOT NULL operators, these operators should only be applied to leaf-level path elements, i.e the predicate JSON_MATCH(jsoncolumn, '"$.data[*]" IS NOT NULL') is not valid since "$.data[*]" does not address a "leaf" element of the path; however, "$.data[0]" IS NOT NULL') is valid since "$.data[0]" unambigously identifies a leaf element of the path.

JSON_EXTRACT_SCALAR does not utilize JsonIndex and therefore performs slower than JSON_MATCH which utilizes JsonIndex. However, JSON_EXTRACT_SCALAR supports a wider range for of JsonPath expressions and operators. To make the best use of fast index access (JSON_MATCH) along with JsonPath expressions (JSON_EXTRACT_SCALAR) you can combine the use of these two functions in WHERE clause.

JSON_MATCH syntax

The second argument of the JSON_MATCH function is a boolean expression in string form. This section shows how to correctly write the second argument of JSON_MATCH. Let's assume we want to search a JSON array array data for values k and j. This can be done by the following predicate:

data[0] IN ('k', 'j')

To convert this predicate into string form for use in JSON_MATCH, we first turn the left side of the predicate into an identifier by enclosing it in double quotes:

"data[0]" IN ('k', 'j')

Next, the literals in the predicate also need to be enclosed by '. Any existing ' need to be escaped as well. This gives us:

"data[0]" IN (''k'', ''j'')

Finally, we need to create a string out of the entire expression above by enclosing it in ':

'"data[0]" IN (''k'', ''j'')'

Now we have the string representation of the original predicate and this can be used in JSON_MATCH function:

   WHERE JSON_MATCH(jsoncolumn, '"data[0]" IN (''k'', ''j'')')