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More 23c features #35

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73 changes: 73 additions & 0 deletions features/ceil-floor-round-for-datetime-types.adoc
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= CEIL, FLOOR, and ROUND for datetime data types
:database-version: 23.2
:database-category: sql

[[feature_summary]]

You can now pass `DATE`, `TIMESTAMP`, and `INTERVAL` values to the `CEIL` and `FLOOR` functions. These functions include an optional second argument to specify a rounding unit. You can also pass `INTERVAL` values to `ROUND` and `TRUNC` functions.

[source,sql]
[subs="verbatim"]
----
with vals as (
select
interval '+123-5' year(9) to month ymi,
interval '+0 12:34:56' day to second dsi,
to_date ( '12-APR-2023 12:34:56', 'DD-MON-YYYY HH24:MI:SS' ) dt,
to_timestamp ( '12-APR-2023 12:34:56', 'DD-MON-YYYY HH24:MI:SS' ) ts
)
select
-- CEIL rounds up values
ceil ( ymi ) as year_ceil,
ceil ( dsi ) as day_ceil,
ceil ( dt ) as dt_day_ceil,
ceil ( ts ) as ts_day_ceil,
-- FLOOR rounds down the datetime value to the units in the second paramter
floor ( ymi, 'year' ) as ymi_year_floor,
floor ( dsi, 'hh24' ) as dsi_hour_floor,
floor ( dt, 'hh24' ) as dt_hour_floor,
floor ( ts, 'hh24' ) as ts_hour_floor,
-- ROUND now supports INTERVALs
round ( dsi, 'mi' ) as dsi_minute_round
from vals;
----

.Result
[source,sql]
[subs="verbatim"]
----
SQL> with vals as (
2 select
3 interval '+123-5' year(9) to month ymi,
4 interval '+0 12:34:56' day to second dsi,
5 to_date ( '12-APR-2023 12:34:56', 'DD-MON-YYYY HH24:MI:SS' ) dt,
6 to_timestamp ( '12-APR-2023 12:34:56', 'DD-MON-YYYY HH24:MI:SS' ) ts
7 )
8 select
9 -- CEIL rounds up values
10 ceil ( ymi ) as year_ceil,
11 ceil ( dsi ) as day_ceil,
12 ceil ( dt ) as dt_day_ceil,
13 ceil ( ts ) as ts_day_ceil,
14 -- FLOOR rounds down the datetime value to the units in the second paramter
15 floor ( ymi, 'year' ) as ymi_year_floor,
16 floor ( dsi, 'hh24' ) as dsi_hour_floor,
17 floor ( dt, 'hh24' ) as dt_hour_floor,
18 floor ( ts, 'hh24' ) as ts_hour_floor,
19 -- ROUND now supports INTERVALs
20 round ( dsi, 'mi' ) as dsi_minute_round
21 from vals;

YEAR_CE DAY_CEIL DT_DAY_CEIL TS_DAY_CEIL YMI_YEA DSI_HOUR_FLOOR DT_HOUR_FLOOR TS_HOUR_FLOOR DSI_MINUTE_ROUND
------- ------------------- ----------------- ----------------- ------- ------------------- ----------------- ----------------- -------------------
+124-00 +01 00:00:00.000000 13-APR-2023 00:00 13-APR-2023 00:00 +123-00 +00 12:00:00.000000 12-APR-2023 12:00 12-APR-2023 12:00 +00 12:35:00.000000
----

== Benefits

These functions make it easy to find the upper and lower bounds for date and time values for a specified unit.

== Further information

* Availability: All Offerings
* https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/ROUND-and-TRUNC-Date-Functions.html#GUID-8E10AB76-21DA-490F-A389-023B648DDEF8[CEIL, FLOOR, ROUND, and TRUNC Date Functions Documentation]
130 changes: 130 additions & 0 deletions features/default-on-null-update.adoc
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= Default ON NULL for UPDATE statements
:database-version: 23.2
:database-category: sql

[[feature_summary]]

You can specify a default value when updating a column to `NULL`. The database assigns the default value to the column instead of `NULL`.

[source,sql]
[subs="verbatim"]
----
create table default_values (
id integer,
c1 number default extract ( second from systimestamp ),
c2 number default on null extract ( second from systimestamp ),
c3 number default on null for insert only extract ( second from systimestamp ),
c4 number default on null for insert and update extract ( second from systimestamp )
);

-- For the columns with DEFAULT ON NULL, the database replaces NULL with the timestamp value
insert into default_values
values ( 1, null, null, null, null );

select * from default_values;

-- The standard DEFAULT clause has no impact on UPDATEs, so C1 remains NULL
update default_values
set c1 = null;

-- DEFAULT ON NULL adds an implicit NOT NULL constraint to the column
-- The default only applies on insert, so this raises an ORA-01407 error
update default_values
set c2 = null;

-- DEFAULT ON NULL FOR INSERT ONLY is equivalent to DEFAULT ON NULL
-- So this also raises an ORA-01407 error
update default_values
set c3 = null;

-- C4 is DEFAULT ON NULL FOR INSERT AND UPDATE
-- So the database replaces NULL with its default value
update default_values
set c4 = null;

-- The value of C4 has changed; all other columns retain their INSERT value
select * from default_values;
----

.Result
[source,sql]
[subs="verbatim"]
----
SQL> create table default_values (
2 id integer,
3 c1 number default extract ( second from systimestamp ),
4 c2 number default on null extract ( second from systimestamp ),
5 c3 number default on null for insert only extract ( second from systimestamp ),
6 c4 number default on null for insert and update extract ( second from systimestamp )
7 );

Table created.

SQL>
SQL> -- For the columns with DEFAULT ON NULL, the database replaces NULL with the timestamp value
SQL> insert into default_values
2 values ( 1, null, null, null, null );

1 row created.

SQL>
SQL> select * from default_values;

ID C1 C2 C3 C4
---------- ---------- ---------- ---------- ----------
1 <null> 1.585189 1.585189 1.585189

SQL>
SQL> -- The standard DEFAULT clause has no impact on UPDATEs, so C1 remains NULL
SQL> update default_values
2 set c1 = null;

1 row updated.

SQL>
SQL> -- DEFAULT ON NULL adds an implicit NOT NULL constraint to the column
SQL> -- The default only applies on insert, so this raises an ORA-01407 error
SQL> update default_values
2 set c2 = null;
set c2 = null
*
ERROR at line 2:
ORA-01407: cannot update ("CHRIS"."DEFAULT_VALUES"."C2") to NULL


SQL>
SQL> -- DEFAULT ON NULL FOR INSERT ONLY is equivalent to DEFAULT ON NULL
SQL> -- So this also raises an ORA-01407 error
SQL> update default_values
2 set c3 = null;
set c3 = null
*
ERROR at line 2:
ORA-01407: cannot update ("CHRIS"."DEFAULT_VALUES"."C3") to NULL


SQL>
SQL> -- C4 is DEFAULT ON NULL FOR INSERT AND UPDATE
SQL> -- So the database replaces NULL with its default value
SQL> update default_values
2 set c4 = null;

1 row updated.

SQL>
SQL> -- The value of C4 has changed; all other columns retain their INSERT value
SQL> select * from default_values;

ID C1 C2 C3 C4
---------- ---------- ---------- ---------- ----------
1 <null> 1.585189 1.585189 1.608401
----

== Benefits

Providing a default value for `NULL` on `UPDATE` simplifies code to map `NULL` to a non `NULL` value when changing data.

== Further information

* Availability: All Offerings
* https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/CREATE-TABLE.html#GUID-F9CE0CC3-13AE-4744-A43C-EAC7A71AAAB6__CJABFBAA[CREATE TABLE Documentation]
124 changes: 124 additions & 0 deletions features/fuzzy-match-operator.adoc
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= FUZZY_MATCH operator
:database-version: 23.2
:database-category: sql

[[feature_summary]]

The `FUZZY_MATCH` operator enables you to compare strings for similarity using algorithms such as Jaro-Winkler and Levenshtein distance.

[source,sql]
[subs="verbatim"]
----
/* Compare the team names to "Champions City" with the different fuzzy matching algorithms */
with teams as (
select * from (
values ( 'Champions United' ),
( 'Runner-up City' ),
( 'Midtable Town' ),
( 'Underdogs United' ),
( 'Upstart FC' ),
( 'Relegated Athletic' )
) t ( team_name )
), match_value as (
select 'Champions City' match_team
)
select team_name, match_team,
fuzzy_match ( levenshtein, team_name, match_team ) as levenshtein,
fuzzy_match ( jaro_winkler, team_name, match_team ) as jaro_winkler,
fuzzy_match ( bigram, team_name, match_team ) as bigram,
fuzzy_match ( trigram, team_name, match_team ) as trigram,
fuzzy_match ( whole_word_match, team_name, match_team ) as wwm,
fuzzy_match ( longest_common_substring, team_name, match_team ) as lcs
from teams
cross join match_value;


/* Find the team names that match "Champions City" with Jaro-Winkler score > 90 */
with teams as (
select * from (
values ( 'Champions United' ),
( 'Runner-up City' ),
( 'Midtable Town' ),
( 'Underdogs United' ),
( 'Upstart FC' ),
( 'Relegated Athletic' )
) t ( team_name )
), match_value as (
select 'Champions City' match_team
)
select team_name, match_team
from teams
join match_value
on fuzzy_match ( jaro_winkler, team_name, match_team ) > 90;
----

.Result
[source,sql]
[subs="verbatim"]
----
SQL> /* Compare the team names to "Champions City" for the different fuzzy matching algorithms */
SQL> with teams as (
2 select * from (
3 values ( 'Champions United' ),
4 ( 'Runner-up City' ),
5 ( 'Midtable Town' ),
6 ( 'Underdogs United' ),
7 ( 'Upstart FC' ),
8 ( 'Relegated Athletic' )
9 ) t ( team_name )
10 ), match_value as (
11 select 'Champions City' match_team
12 )
13 select team_name, match_team,
14 fuzzy_match ( levenshtein, team_name, match_team ) as levenshtein,
15 fuzzy_match ( jaro_winkler, team_name, match_team ) as jaro_winkler,
16 fuzzy_match ( bigram, team_name, match_team ) as bigram,
17 fuzzy_match ( trigram, team_name, match_team ) as trigram,
18 fuzzy_match ( whole_word_match, team_name, match_team ) as wwm,
19 fuzzy_match ( longest_common_substring, team_name, match_team ) as lcs
20 from teams
21 cross join match_value;

TEAM_NAME MATCH_TEAM LEVENSHTEIN JARO_WINKLER BIGRAM TRIGRAM WWM LCS
------------------ -------------- ----------- ------------ ---------- ---------- ---------- ----------
Champions United Champions City 75 92 66 57 50 62
Runner-up City Champions City 36 57 30 25 50 35
Midtable Town Champions City 8 44 0 0 0 7
Underdogs United Champions City 32 49 13 0 0 12
Upstart FC Champions City 8 56 0 0 0 7
Relegated Athletic Champions City 12 41 0 0 0 5

6 rows selected.

SQL>
SQL> /* Find the team names that match "Champions City" with Jaro-Winkler score > 90 */
SQL> with teams as (
2 select * from (
3 values ( 'Champions United' ),
4 ( 'Runner-up City' ),
5 ( 'Midtable Town' ),
6 ( 'Underdogs United' ),
7 ( 'Upstart FC' ),
8 ( 'Relegated Athletic' )
9 ) t ( team_name )
10 ), match_value as (
11 select 'Champions City' match_team
12 )
13 select team_name, match_team
14 from teams
15 join match_value
16 on fuzzy_match ( jaro_winkler, team_name, match_team ) > 90;

TEAM_NAME MATCH_TEAM
------------------ --------------
Champions United Champions City
----

== Benefits

This operator makes it easier to find similar strings with SQL.

== Further information

* Availability: All Offerings
* https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/data-quality-operators.html#GUID-C13A179C-1F82-4522-98AA-E21C6504755E[FUZZY_MATCH Documentation]
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