Five Things you might not know or may have forgotten about the Oracle Database

Five Things You Might Not Know About The
Oracle Database
Maria Colgan
Distinguished Product Manager
Oracle Database
Or may
have forgotten

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Five things you may not know or have forgotten about the Oracle Database
3.
Parameterized
Views
2.
Online
Operations
5.
Power of
Immutability
4.
PL/SQL
Packages
1.
Benefits of
Invisibility
Copyright © 2023, Oracle and/or its affiliates

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Benefits of Invisibility

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Managing Indexes
One of the most common approaches for query tuning on OLTP systems is to add an index
The more complex the app, the more indexes you are likely to have
But over time, how valuable are those indexes?
After all, each additional index increases the overhead of each DML operation
However, we are all relucent to drop an index just in case it causes a performance regression!

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Determine which indexes are useful via DBA_INDEX_USAGE view
SELECT i.index_name, u.total_access_count tot_access, u.total_exec_count exec_cnt,
u.bucket_0_access_count B0, u.bucket_1_access_count B1, u.bucket_2_10_access_count B2_10,
u.bucket_11_100_access_count B11_100, u.bucket_101_1000_access_count B101_1K,
u.bucket_1000_plus_access_count B1K, u.last_used
FROM dba_index_usage u, dba_indexes i
WHERE i.owner='MARIA'
AND i.index_name = u.name (+)
ORDER BY u.total_access_count;
Use an outer join with DBA_INDEXES to capture
indexes that are never used
Note: Index usage info is flushed from memory to disk every 15 minutes
Query LAST_FLUSH_TIME in V$INDEX_USAGE_INFO to determine the last time it was updated

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INDEX_NAME TOT_ACCESS EXEC_CNT B0 B1 B2_10 B11_100 B101_1K B1K LAST_USED
---------------- ---------- ---------- ------- ------- ------- -------- ---------- ---------- ---------
PROD_CUST_SALES 1 1 0 0 0 0 0 1 06-JAN-23
INDX_LOC 2 2 0 1 1 0 0 0 12-JAN-23
INDX_DEPTNO 19 19 0 18 1 0 0 0 26-JAN-23
PROD_SUP_INDX 27 27 25 0 0 0 2 0 26-JAN-23
EMPNO_PK_IND 82 82 48 32 0 2 0 0 26-JAN-23
CHAN_SOLD
PROD_SUB_IDX
The histogram indicates how useful an
index is by showing how many accesses
fall in each bucket of rows returned

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INDEX_NAME TOT_ACCESS EXEC_CNT B0 B1 B2_10 B11_100 B101_1K B1K LAST_USED
---------------- ---------- ---------- ------- ------- ------- -------- ---------- ---------- ---------
PROD_CUST_SALES 1 1 0 0 0 0 0 1 06-JUL-22
INDX_LOC 2 2 0 1 1 0 0 0 06-JUL-22
INDX_DEPTNO 19 19 0 18 1 0 0 0 06-JUL-22
PROD_SUP_INDX 27 27 25 0 0 0 2 0 06-JUL-22
EMPNO_PK_IND 82 82 48 32 0 2 0 0 06-JUL-22
CHAN_SOLD
PROD_SUB_IDX
These two indexes have never been used and are candidates to be dropped

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Leverage Invisible Indexes before dropping indexes for good
-- Mark indexes you are looking to remove as invisible, if no one complains after some time; delete them
ALTER INDEX prod_sub_idx INVISIBLE;
-- New indexes can be marked invisible until you have an opportunity to prove they improve performance
CREATE INDEX my_idx ON t(x, object_id) INVISIBLE;
-- Test newly created invisible indexes by setting OPTIMIZER_USE_INVISBLE_INDEXES to TRUE
ALTER SESSION SET optimizer_use_invisible_indexes = TRUE;

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Managing shared schema objects across apps
One of the difficulties with the relational model today is:
• Different applications may need different columns from the same table
• However, all apps are impacted by any DDL change on the table
• Dropping a column will break queries still referring to it
• Adding a column breaks “SELECT *” queries
• Dropping a column will delete the data from all the rows, even those that are already archived
CUSTOMERS
FIRST_NAME
LAST_NAME
INITIALS
:
APP A
APP B

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Sharing schema objects across apps
-- Code for App A
SELECT *
FROM customers;
FIRST_NAME LAST_NAME IN ADDRESS …
---------- ---------- -- --------
Dan Jones DJ New York
Joseph Smith JS Los Angeles
-- Code for App B
SELECT first_name, last_name, initials
FROM customers;
FIRST_NAME LAST_NAME IN
---------- ---------- --
Dan Jones DJ
Joseph Smith JS
CUSTOMERS
FIRST_NAME
LAST_NAME
INITIALS
:
-- App A and B share
a single Customers table

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Sharing schema objects across apps
-- Code for App A
SELECT *
FROM customers;
FIRST_NAME LAST_NAME ADDRESS …
---------- ---------- --------
Dan Jones New York
Joseph Smith Los Angeles
-- Code for App B
FROM customers;
*
ERROR at line 1:
ORA-00904: "INITIALS": invalid identifier
-- App A no longer needs the initials
columns and decides to drop the column
ALTER TABLE customers
DROP COLUMN initials;
CUSTOMERS
FIRST_NAME
LAST_NAME
:

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Invisible Columns
-- Code for App A
SELECT *
FROM customers;
FIRST_NAME LAST_NAME ADDRESS …
---------- ---------- --------
Dan Jones New York
Joseph Smith Los Angeles
-- Code for App B
FROM customers;
FIRST_NAME LAST_NAME IN
---------- ---------- --
Dan Jones DJ
Joseph Smith JS
-- But if App A simple marks the column
invisible, other Apps that query it directly
will still work
ALTER TABLE customers
MODIFY initials INVISIBLE;
CUSTOMERS
FIRST_NAME
LAST_NAME
:

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Managing Statistics
• By default, optimizer statistics are published as soon as they are gathered
• But what if you want to experiment with different statistic-gathering options?
• How do you protect yourself from the possibility of unpredictable changes in execution plans

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Using Invisible or Pending statistics allows you to test without worry
-- Check the current statistics publishing setting for the database
SELECT dbms_stats.get_prefs('PUBLISH', 'MARIA', 'SALES') FROM dual;
DBMS_STATS.GET_PREFS('PUBLISH’)
-------------------------------------------
TRUE
-- Check the current column stats for the SALES table
SELECT column_name, histogram FROM user_tab_col_statistics WHERE table_Name ='SALES';
COLUMN_NAME HISTOGRAM
------------------------------ ---------------
PROD_ID NONE
CUST_ID NONE
TIME_ID NONE
CHANNEL_ID NONE
PROMO_ID NONE
QUANTITY_SOLD NONE
AMOUNT_SOLD NONE
None of the columns have a histogram

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-- Set new statistics gathered for MARIA.SALES to be invisible to the optimizer by default
BEGIN
dbms_stats.set_table_prefs('MARIA', 'SALES', 'PUBLISH', 'FALSE');
END;
/
-- Gather a new set of statistics for MARIA.SALES that included new histograms types introduced in 12c
BEGIN
dbms_stats.gather_table_stats('MARIA', 'SALES' method_opt=>'FOR ALL COLUMNS SIZE 254');
END;
/

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-- Confirm the pending statistics are what we are expecting
SELECT report FROM table(dbms_stats.diff_table_stats_in_pending('MARIA','SALES', systimestamp,0));
The returned REPORT is a CLOB
You must use the set long and longchunksize command to define the width
of a long so the report can be displayed properly

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SELECT report FROM table(dbms_stats.diff_table_stats_in_pending('MARIA','SALES', systimestamp,0));
DBMS_STAT.DIFF_TABLE_STATS functions are table functions,
you must use the key word TABLE when you are selecting from them,
otherwise you will receive an error saying the object does not exist

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SELECT report FROM table(dbms_stats.diff_table_stats_in_pending('MARIA','SALES', sysdate,0));
REPORT
--------------------------------------------------------------------------------
:
COLUMN STATISTICS DIFFERENCE:
.............................
COLUMN_NAME SRC NDV DENSITY HIST NULLS LEN MIN MAX SAMPSIZE
..................................................................................
AMOUNT_SOLD A 583 .001715265 NO 0 4 C105 C2525 960
B 583 .001715265 NO 0 4 C105 C2525 960
CUST_ID A 630 .001587301 NO 0 5 C202 C3135 960
B 630 .001576 YES 0 5 C202 C3135 960
PROD_ID A 766 .001305483 NO 0 5 C106 C3056 960
B 766 .001298 YES 0 5 C106 C3056 960
PROMO_ID A 116 .008620689 NO 0 4 C102 C2646 960
B 116 .008620689 NO 0 4 C102 C2646 960
QUANTITY_SOLD A 44 .022727272 NO 0 3 C102 C130 960
B 44 .022727272 NO 0 3 C102 C130 960
TIME_ID A 620 .001612903 NO 0 8 77C60 78640 960
B 620 .001608 YES 0 8 77C60 78640 960
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Source A is the current
published stats for the SALES
table, while source B is the
pending stats

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--Set the initialization parameter optimizer_use_pending_statistics to TRUE to test out the new statistics
ALTER SESSION SET optimizer_use_pending_statistics =TRUE;
-- After testing the pending statistics with histograms, we can go ahead and publish them
BEGIN
dbms_stats.publish_pending_stats('MARIA','SALES');
END;
/

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--Set the initialization parameter OPTIMIZER_USE_PENDING_STATISTICS to TRUE to test new statistics
ALTER SESSION SET optimizer_use_pending_statistics =TRUE;
-- After testing the pending statistics with histograms, we can go ahead and publish them
BEGIN
dbms_stats.publish_pending_stats('MARIA','SALES');
END;
/
-- Confirm the histograms that we gather as pending statistics are now visible
SELECT column_name, histogram FROM user_tab_col_statistics WHERE table_Name ='SALES';
COLUMN_NAME HISTOGRAM
------------------------------ ---------------
PROD_ID HYBRID
CUST_ID HYBRID
TIME_ID HYBRID
CHANNEL_ID NONE
PROMO_ID NONE
QUANTITY_SOLD NONE
AMOUNT_SOLD NONE

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Useful Online Operations

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Why Online Operations are so important
DBAs often have a long list of maintenance activities & data model changes they need to do
• Goal to improve overall system performance
In the past, these tasks have required exclusive locks
• Forcing DBAs to wait until application downtime to complete them
With online operations, these tasks can occur, when necessary, without delay
Online operations
• Wait for active transactions to end before beginning
• Do not block any new DML statements (with the only exception being parallel DML)

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Adding a column to a table happens online
A fast online operation that allocates no space and generates no redo or undo
-- Adding a column with or without a default value is a metadata-only operation since 11g
SELECT bytes/1024/1024 MB, blocks FROM user_segments WHERE segment_name='SALES';
MB BLOCKS
176 22528
ALTER TABLE sales ADD tax_code varchar2(20) DEFAULT 'xxx-xxx' NOT NULL;
Table SALES altered.
SELECT bytes/1024/1024 MB, blocks FROM user_segments WHERE segment_name='SALES';
MB BLOCKS
176 22528

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09:37:38 SQL> CREATE TABLE sales3 AS SELECT * FROM sales;
Table SALES3 created.
09:37:41 SQL> SELECT column_name, num_distinct, num_nulls, histogram
FROM user_tab_col_statistics
WHERE table_name='SALES3';
COLUMN_NAME NUM_DISTINCT NUM_NULLS HISTOGRAM
COMMENTS 0 10075747 NONE
SHIPMODE 7 0 NONE
REVENUE 4296902 0 NONE
PRICE 2416797 0 NONE
AMOUNT_SOLD 50 0 NONE
DATE_ID 2395 0 NONE
SUPPLIER_ID 1924463 0 NONE
PRODUCT_ID 1370318 0 NONE
CUST_ID 2021661 0 NONE
ORDER_ID 2116177 0 NONE
Online statistics gathering
Online statistics automatically gathers
base statistics as part of any CTAS or
IAS operation into an empty table or
partition
Histograms are not created as they
require an additional pass of the data
and add an overhead to the initial op

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09:47:40 SQL> ALTER SESSION SET "_optimizer_gather_stats_on_load_hist"=TRUE;
09:47:40 SQL> CREATE TABLE sales3 AS SELECT * FROM sales;
Table SALES3 created.
09:47:51 SQL> SELECT column_name, num_distinct, num_nulls, histogram
FROM user_tab_col_statistics
WHERE table_name='SALES3';
COLUMN_NAME NUM_DISTINCT NUM_NULLS HISTOGRAM
COMMENTS 0 10075747 NONE
SHIPMODE 7 0 FREQUENCY
REVENUE 4296902 0 HYBRID
PRICE 2416797 0 HYBRID
AMOUNT_SOLD 50 0 FREQUENCY
DATE_ID 2395 0 HYBRID
SUPPLIER_ID 1924463 0 HYBRID
PRODUCT_ID 1370318 0 HYBRID
CUST_ID 2021661 0 HYBRID
ORDER_ID 2116177 0 HYBRID
Online statistics gathering with histograms
Be careful utilizing underscore
parameters. Only use them
within a session, and if you fully
understand the behavior, they
control

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-- Ability to convert a non-partitioned table to a partitioned table online
ALTER TABLE sales MODIFY
PARTITION BY RANGE(date_id)
INTERVAL(NUMTOYMINTERVAL(1,'MONTH'))
( PARTITION p0 VALUES LESS THAN (TO_DATE('01-JAN-2018', 'DD-MON-YYYY')),
PARTITION p1 VALUES LESS THAN (TO_DATE('01-FEB-2018', 'DD-MON-YYYY')),
PARTITION p2 VALUES LESS THAN (TO_DATE('01-MAR-2018', 'DD-MON-YYYY’))
)
ONLINE
UPDATE INDEXES
( idx_dt_rev local,
idx_ord_sold GLOBAL PARTITION BY RANGE (order_id)
( PARTITION ip0 VALUES LESS THAN (MAXVALUE))
);
Online partitioning of a non-partitioned table
SALES
SALES
The use of the keyword ONLINE enables concurrent
DML operations while the conversion is ongoing

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-- Ability to convert a non-partitioned table to a partitioned table online
ALTER TABLE sales MODIFY
PARTITION BY RANGE(date_id)
INTERVAL(NUMTOYMINTERVAL(1,'MONTH'))
( PARTITION p0 VALUES LESS THAN (TO_DATE('01-JAN-2004', 'DD-MON-YYYY')),
PARTITION p1 VALUES LESS THAN (TO_DATE('01-FEB-2004', 'DD-MON-YYYY')),
PARTITION p2 VALUES LESS THAN (TO_DATE('01-MAR-2004', 'DD-MON-YYYY’))
)
ONLINE
UPDATE INDEXES
( idx_dt_rev local,
idx_ord_sold GLOBAL PARTITION BY RANGE (order_id)
( PARTITION ip0 VALUES LESS THAN (MAXVALUE))
);
Online partitioning of a non-partitioned table
SALES
SALES
If you don’t specify what
should happen to the
indexes, Oracle will create
local partitioned index if
they contain the partition
key or are bitmap indexes
Otherwise, indexes become
global non-partitioned
indexes

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Parameterized Views

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Database Views
Database views have been used for decades to help simplify both ad-hoc queries and reporting
But the problem with defining views is they are either too specific or not specific enough

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Database Views
Database views have been used for decades to help simplify both ad-hoc queries and reporting
But the problem with defining views is they are either too specific or not specific enough
In this example, we need a view to help quickly find details about orders that are waiting to ship
CREATE OR REPLACE VIEW orders_waiting
AS
SELECT i.*
FROM orders o, order_items i
WHERE o.order_status > 6
AND o.order_total >= 100
AND o.order_id = i.order_id’;
Extremely specific, efficient view
includes no unnecessary data but not reusable
CREATE OR REPLACE VIEW order_details
AS
SELECT o.order_status, o.order_total,
o.cust_id, o.order_date, o.rep,
o.order_mode, o.promotion_id, i.*
WHERE o.order_id = i.order_id;
Nonspecific view that includes
lots of unnecessary data , so it can be reusable

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SQL Macros allow you to create portable, parameterized code
Easy to create portable code by factoring out common SQL expressions and statements into reusable,
parameterized constructs with SQL Macros
A Macro acts like a pre-processor for SQL with no expensive context switching
• Enabling faster performance
Two types of SQL Macros:
• TABLE expressions typically found in FROM-clause that act as a parameterized views
• SCALAR expressions to encapsulate calculations and business logic typically found in SELECT list,
WHERE/HAVING, GROUP BY/ORDER BY clauses

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CREATE OR REPLACE FUNCTION orders_waiting_to_ship(order_value integer)
RETURN clob sql_macro AS
stmt clob;
BEGIN
stmt := '
SELECT i.*
WHERE o.order_status > 6
AND o.order_total >= order_value
AND o.order_id = i.order_id';
RETURN stmt;
END orders_waiting_to_ship;
/
Parameterized View – AKA SQL Macro

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SELECT *
FROM orders_waiting_to_ship(100)
ORDER BY order_id;
ORDER_ID PRODUCT_ID PRICE DESCRIPTION
___________ _____________ ________ ______________
70707 7 17 Lip gloss
70707 8 28 Mascara
70707 9 39 Blusher
70707 10 30 lipstick
80808 8 28 Mascara
80808 8 28 Mascara
80808 8 28 Mascara
80808 8 28 Mascara
Parameterized View – AKA SQL Macro

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Useful PL/SQL Packages

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DBMS_LOCK.SLEEP()
From time to time, all code needs to pause or sleep for a short time
SQL> DECLARE
v_start timestamp;
v_end timestamp;
BEGIN
v_start := SYSTIMESTAMP;
-- Sleep for 10 seconds
DBMS_LOCK.SLEEP(10);
v_end := SYSTIMESTAMP;
DBMS_OUTPUT.PUT_LINE('This procedure started at ' ||v_start);
DBMS_OUTPUT.PUT_LINE('This procedure ended at ' ||v_end);
END;
/
This procedure started at 10-SEP-22 12.39.40.587041 AM
This procedure ended at 10-SEP-22 12.39.50.637738 AM
PL/SQL procedure successfully completed.
Elapsed: 00:00:10.02
The problem is DBMS_LOCK includes
other, more sensitive methods
Therefore, not granted to public
Requires DBA intervention to get a
accessible sleep function in PL/SQL

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DBMS_SESSION.SLEEP()
From time to time, all code needs to pause or sleep for a short time
SQL> DECLARE
v_start timestamp;
v_end timestamp;
BEGIN
v_start := SYSTIMESTAMP;
-- Sleep for 10 seconds
DBMS_SESSION.SLEEP(10);
v_end := SYSTIMESTAMP;
DBMS_OUTPUT.PUT_LINE('This procedure started at ' ||v_start);
DBMS_OUTPUT.PUT_LINE('This procedure ended at ' ||v_end);
END;
/
This procedure started at 10-SEP-22 12.39.40.587041 AM
This procedure ended at 10-SEP-22 12.39.50.637738 AM
PL/SQL procedure successfully completed.
Elapsed: 00:00:10.02
Sleep function moved to DBMS_SESSION
DBMS_SESSION is granted to public
Compatible with DBMS_LOCK.SLEEP,
simply do a search/replace

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How do you determine which view is the right view to use?
Views help to hide complexity from developers
But they can also cause problems
They make it incredibly easy to write apparently simple
SQL statements that result in a highly complex SQL
statement being sent to the database
The DBMS_UTILITY.EXPAND_SQL_TEXT procedure
expands references to views, turning them into
subqueries in the original statement
SELECT * FROM sales_v;

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DBMS_UTILITY.EXPAND_SQL_TEXT
set serveroutput on
DECLARE
l_clob CLOB;
BEGIN
dbms_utility.Expand_sql_text(input_sql_text => 'SELECT * FROM sales_v', -
output_sql_text => l_clob);
dbms_output.Put_line(l_clob);
END;
/

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SELECT "A1"."order_id" "ORDER_ID",
"A1"."time_id" "TIME_ID",
"A1"."cust_id" "CUST_ID",
"A1"."prod_id" "PROD_ID"
FROM (SELECT "A3"."order_id" "ORDER_ID",
"A3"."time_id" "TIME_ID",
"A3"."cust_id" "CUST_ID",
"A3"."prod_id" "PROD_ID"
FROM "SH"."sales" "A3",
"SH"."products" "A2"
WHERE "A3"."prod_id" = "A2"."prod_id") "A1"
View definition has alias A1

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How to determine why production is different to test
Trying to figure out why things behave differently in production than they do in test can be time-
consuming and painful
The DBMS_COMPARISON package allows you to compare objects, schemas, or data between two
different databases or schemas
Note: for a table comparison you do need a unique index on both tables

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-- Begin by creating the comparison
BEGIN
dbms_comparison.create_comparison(
comparison_name => 'COMP_SALES',
schema_name => 'SH',
object_name => 'SALES',
dblink_name => 'orcl2_test'
);
END;
/

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DBMS_COMPARISON
Execute the COMPARE function to perform the compare operation
DECLARE
scan_info DBMS_COMPARISON.COMPARISON_TYPE;
BEGIN
IF NOT DBMS_COMPARISON.COMPARE
( comparison_name => 'COMP_SALES'
, scan_info => scan_info
, perform_row_dif => TRUE
) THEN
DBMS_OUTPUT.PUT_LINE('Scan ID:'||scan_info.scan_id);
END IF;
END;
/
Scan ID: 1
The scan_id allows you to find out what the differences are between the systems
It will return a BOOLEAN
indicating if the object is
consistent or not

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DBMS_COMPARISON
SELECT c.column_name, r.index_value
case when r.local_rowid is null then ‘No’ else 'Yes’ end LOC,
Case when r.remote_rowid is null then 'No’ else 'Yes’ end REM
FROM user_comparison_columns c, user_comparison_row_dif r, user_comparison_scan s
WHERE c.comparison_name = 'COMP_SALES‘
AND r.scan_id = s.scan_id
AND r.status = 'DIF’
AND c.index_column = 'Y’
AND c.comparison_name = r.comparison_name
AND s.scan_id = 1
ORDER BY r.index_value;
COLUMN_NAME INDEX_VALUE LOC REM
----------- ----------- --- ----
TAX_CODE 0.05 No Yes
The test environment has a different tax code

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Immutability

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Protecting data from corrupt or compromised users
All information stored in a database is important
And this data is often most vulnerable to users with valid credentials
Over the years, Oracle has offered several techniques to help protect that data:
• Read-only tablespaces
• Read-only users
• Read-only views
• Auditing
• Schema-only accounts
But up until now, there hasn’t been a way to allow a table to continue to accept new data but prevent
anyone, including SYSDBAs, from changing any existing of the data

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Oracle Immutable Tables
Oracle Immutable Tables prevent illicit modifications by database users
• Allows relational data, JSON, or LOB documents
New data can be added, but existing data cannot be changed or deleted by
anyone using the database
• Even Database Administrators
The table definition can not be changed, and it’s not possible to convert
immutable table to updatable or vice-versa
• It also not possible to modify table metadata in the database dictionary
Side benefit: immutability also prevents accidental changes due to human error
Available in Database 19.11, 21.3

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Prevent illicit changes by Insiders using Immutable Tables
-- Oracle Immutable Tables prevent illicit modifications by insiders using SQL but allow new data to be added
CREATE IMMUTABLE TABLE trade_ledger (id number, stock_name varchar2(40), value number)
NO DROP UNTIL 40 DAYS IDLE
NO DELETE UNTIL 100 DAYS AFTER INSERT;
-- Anyone with the appropriate privileges can insert into an immutable table
INSERT INTO trade_ledger VALUES( 1000, ‘ORCL’, 500);
1 row inserted.
-- However, it’s not possible to delete from the table until the specified amount of time has elapsed
DELETE FROM trade_ledger WHERE id=1000;
SQL Error: ORA-05715: operation not allowed on the blockchain or immutable table

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Five things you may not know or have forgotten about the Oracle Database
3.
Parameterized
Views
2.
Online
Operations
5.
Power of
Immutability
4.
PL/SQL
Packages
1.
Benefits of
Invisibility

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More Information Visit:
SQLMaria.com
@SQLMaria
youtube.com/c/MariaColgan42
If you have more questions
later, feel free to ask
Thank you

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Five Things you might not know or may have forgotten about the Oracle Database

Five Things you might not know or may have forgotten about the Oracle Database

Maria Colgan

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