15.1.21.9 Secondary Indexes and Generated Columns

InnoDB supports secondary indexes on virtual generated columns. Other index types are not supported. A secondary index defined on a virtual column is sometimes referred to as a “virtual index”.

A secondary index may be created on one or more virtual columns or on a combination of virtual columns and regular columns or stored generated columns. Secondary indexes that include virtual columns may be defined as UNIQUE.

When a secondary index is created on a virtual generated column, generated column values are materialized in the records of the index. If the index is a covering index (one that includes all the columns retrieved by a query), generated column values are retrieved from materialized values in the index structure instead of computed “on the fly”.

There are additional write costs to consider when using a secondary index on a virtual column due to computation performed when materializing virtual column values in secondary index records during INSERT and UPDATE operations. Even with additional write costs, secondary indexes on virtual columns may be preferable to generated stored columns, which are materialized in the clustered index, resulting in larger tables that require more disk space and memory. If a secondary index is not defined on a virtual column, there are additional costs for reads, as virtual column values must be computed each time the column's row is examined.

Values of an indexed virtual column are MVCC-logged to avoid unnecessary recomputation of generated column values during rollback or during a purge operation. The data length of logged values is limited by the index key limit of 767 bytes for COMPACT and REDUNDANT row formats, and 3072 bytes for DYNAMIC and COMPRESSED row formats.

Adding or dropping a secondary index on a virtual column is an in-place operation.

Indexing a Generated Column to Provide a JSON Column Index

As noted elsewhere, JSON columns cannot be indexed directly. To create an index that references such a column indirectly, you can define a generated column that extracts the information that should be indexed, then create an index on the generated column, as shown in this example:

mysql> CREATE TABLE jemp (
    ->     c JSON,
    ->     g INT GENERATED ALWAYS AS (c->"$.id"),
    ->     INDEX i (g)
    -> );
Query OK, 0 rows affected (0.28 sec)

mysql> INSERT INTO jemp (c) VALUES
     >   ('{"id": "1", "name": "Fred"}'), ('{"id": "2", "name": "Wilma"}'),
     >   ('{"id": "3", "name": "Barney"}'), ('{"id": "4", "name": "Betty"}');
Query OK, 4 rows affected (0.04 sec)
Records: 4  Duplicates: 0  Warnings: 0

mysql> SELECT c->>"$.name" AS name
     >     FROM jemp WHERE g > 2;
+--------+
| name   |
+--------+
| Barney |
| Betty  |
+--------+
2 rows in set (0.00 sec)

mysql> EXPLAIN SELECT c->>"$.name" AS name
     >    FROM jemp WHERE g > 2\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: jemp
   partitions: NULL
         type: range
possible_keys: i
          key: i
      key_len: 5
          ref: NULL
         rows: 2
     filtered: 100.00
        Extra: Using where
1 row in set, 1 warning (0.00 sec)

mysql> SHOW WARNINGS\G
*************************** 1. row ***************************
  Level: Note
   Code: 1003
Message: /* select#1 */ select json_unquote(json_extract(`test`.`jemp`.`c`,'$.name'))
AS `name` from `test`.`jemp` where (`test`.`jemp`.`g` > 2)
1 row in set (0.00 sec)

(We have wrapped the output from the last statement in this example to fit the viewing area.)

When you use EXPLAIN on a SELECT or other SQL statement containing one or more expressions that use the -> or ->> operator, these expressions are translated into their equivalents using JSON_EXTRACT() and (if needed) JSON_UNQUOTE() instead, as shown here in the output from SHOW WARNINGS immediately following this EXPLAIN statement:

mysql> EXPLAIN SELECT c->>"$.name"
     > FROM jemp WHERE g > 2 ORDER BY c->"$.name"\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: jemp
   partitions: NULL
         type: range
possible_keys: i
          key: i
      key_len: 5
          ref: NULL
         rows: 2
     filtered: 100.00
        Extra: Using where; Using filesort
1 row in set, 1 warning (0.00 sec)

mysql> SHOW WARNINGS\G
*************************** 1. row ***************************
  Level: Note
   Code: 1003
Message: /* select#1 */ select json_unquote(json_extract(`test`.`jemp`.`c`,'$.name')) AS
`c->>"$.name"` from `test`.`jemp` where (`test`.`jemp`.`g` > 2) order by
json_extract(`test`.`jemp`.`c`,'$.name')
1 row in set (0.00 sec)

See the descriptions of the -> and ->> operators, as well as those of the JSON_EXTRACT() and JSON_UNQUOTE() functions, for additional information and examples.

This technique also can be used to provide indexes that indirectly reference columns of other types that cannot be indexed directly, such as GEOMETRY columns.

It is also possible to create an index on a JSON column using the JSON_VALUE() function with an expression that can be used to optimize queries employing the expression. See the description of that function for more information and examples.

JSON columns and indirect indexing in NDB Cluster

It is also possible to use indirect indexing of JSON columns in MySQL NDB Cluster, subject to the following conditions:

1. NDB handles a JSON column value internally as a BLOB. This means that any NDB table having one or more JSON columns must have a primary key, else it cannot be recorded in the binary log.

2. The NDB storage engine does not support indexing of virtual columns. Since the default for generated columns is VIRTUAL, you must specify explicitly the generated column to which to apply the indirect index as STORED.

The CREATE TABLE statement used to create the table jempn shown here is a version of the jemp table shown previously, with modifications making it compatible with NDB:

CREATE TABLE jempn (
  a BIGINT NOT NULL AUTO_INCREMENT PRIMARY KEY,
  c JSON DEFAULT NULL,
  g INT GENERATED ALWAYS AS (c->"$.id") STORED,
  INDEX i (g)
) ENGINE=NDB;

We can populate this table using the following INSERT statement:

INSERT INTO jempn (c) VALUES
  ('{"id": "1", "name": "Fred"}'),
  ('{"id": "2", "name": "Wilma"}'),
  ('{"id": "3", "name": "Barney"}'),
  ('{"id": "4", "name": "Betty"}');

Now NDB can use index i, as shown here:

mysql> EXPLAIN SELECT c->>"$.name" AS name
    ->           FROM jempn WHERE g > 2\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: jempn
   partitions: p0,p1,p2,p3
         type: range
possible_keys: i
          key: i
      key_len: 5
          ref: NULL
         rows: 3
     filtered: 100.00
        Extra: Using pushed condition (`test`.`jempn`.`g` > 2)
1 row in set, 1 warning (0.01 sec)

mysql> SHOW WARNINGS\G
*************************** 1. row ***************************
  Level: Note
   Code: 1003
Message: /* select#1 */ select
json_unquote(json_extract(`test`.`jempn`.`c`,'$.name')) AS `name` from
`test`.`jempn` where (`test`.`jempn`.`g` > 2)   
1 row in set (0.00 sec)

You should keep in mind that a stored generated column, as well as any index on such a column, uses DataMemory.