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, '$.name.last', 'STRING', 'null')
| jsoncolumn.name.last | sum(jsoncolumn.score) |
|---|---|
| "mouse" | "207" |
| "dwag" | "104" |
| "duck" | "203" |
Note that the JSON_MATCH function utilizes JsonIndex and can only be used if a JsonIndex is already present on the JSON column. As shown in the examples above, the second argument of JSON_MATCH operator takes a predicate. This predicate is evaluated against the JsonIndex and supports =, !=, IS NULL, or IS NOT NULL operators. Relational operators, such as >, <, >=, and <= are currently not supported. However, you can combine the use of JSON_MATCH and JSON_EXTRACT_SCALAR function (which supports >, <, >=, and <= operators) to get the necessary functinoality as shown below.
SELECT json_extract_scalar(jsoncolumn, '$.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.
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'')')