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To see how JSON data can be queried, assume that we have the following table:
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:
id | jsoncolumn |
---|---|
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.
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 $.data[1]. We can filter out these rows.
Certain last names (duck and mouse for example) repeat in the data above. We can get a count of each last name by running a GROUP BY query on a JsonPath expression.
Also there is numerical information (jsconcolumn.$.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.
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.
JSON_MATCH
function also provides the ability to use wildcard *
JsonPath expressions even though it doesn't support full JsonPath expressions.
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:
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:
Next, the literals in the predicate also need to be enclosed by '. Any existing ' need to be escaped as well. This gives us:
Finally, we need to create a string out of the entire expression above by enclosing it in ':
Now we have the string representation of the original predicate and this can be used in JSON_MATCH function:
id | last_name | first_name | value |
---|---|---|---|
id | last_name | first_name | value |
---|---|---|---|
jsoncolumn.name.last | count(*) |
---|---|
jsoncolumn.name.last | sum(jsoncolumn.score) |
---|---|
jsoncolumn.name.last | sum(jsoncolumn.score) |
---|---|
last_name | total |
---|---|