Stop the Chaos! Get Real Oracle Performance by Query Tuning Part 2

Stop the Chaos! Get Real Oracle
Performance by Query Tuning – Part 2
Janis Griffin
Senior DBA / Performance Evangelist

© 2017 SolarWinds Worldwide, LLC. All rights reserved.
Who am I?
• Senior DBA / Performance Evangelist for SolarWinds
• Janis.Griffin@solarwinds.com
• Twitter® - @DoBoutAnything
• Current – 25+ Years in Oracle®, DB2®, ASE, SQL Server®, MySQL®
• DBA and Developer
• Specialize in performance Tuning
• Review database performance for customers and prospects
• Common question: “How do I tune it?”

Agenda
• A quick review of part 1
• Using wait time analysis (WTA)
• Reviewing the execution plan
• Understanding the optimizer
• Finding the expensive steps
• Discuss several tuning techniques
• SQL diagramming
• Partitioning and other useful features
• How to identify coding mistakes
• Engineer out the stupid
• What to do if you can’t change the code
• Third-party applications
• Avoiding hints

• Who registered yesterday for SQL Tuning?
SELECT s.fname, s.lname, r.signup_date
FROM student s
INNER JOIN registration r ON s.student_id = r.student_id
INNER JOIN class c ON r.class_id = c.class_id
WHERE c.name = 'SQL TUNING'
AND r.signup_date BETWEEN :beg_date AND :end_date
AND r.cancelled = 'N‘
• Execution Stats – 21,829 Buffer Gets
• Execution Time – 22 seconds to execute
• Wait Events – Waits 90% direct path read
Case Study for Review
User
complained.
Query spent
over 3 hours in
the database.

Execution Plan

Relationship Diagram

• Recommends – three new indexes
Tuning Advisor
DECLARE
l_sql_tune_task_id VARCHAR2(100);
BEGIN
l_sql_tune_task_id := DBMS_SQLTUNE.create_tuning_task ( sql_id => '&sql_id',
scope => DBMS_SQLTUNE.scope_comprehensive, time_limit => 60,
task_name => '&sql_id', description => 'Tuning task for class registration query');
DBMS_OUTPUT.put_line('l_sql_tune_task_id: ' || l_sql_tune_task_id);
END;
/
EXEC DBMS_SQLTUNE.execute_tuning_task(task_name => '&sql_id');

• Need to know the size of the actual data sets for each step in execution plan
• In Joins (Right, Left, Outer)
• What are the filtering predicates?
• When is each filtering predicate applied?
• Try to filter earlier rather than later
• Compare size of final result set with data read
• Find the driving table
• To reduce buffer gets
Tune the Query
SELECT s.fname, s.lname, r.signup_date
FROM student s
INNER JOIN registration r ON s.student_id = r.student_id
INNER JOIN class c ON r.class_id = c.class_id
WHERE c.name = 'SQL TUNING'
AND r.signup_date BETWEEN :beg_date AND :end_date
AND r.cancelled = 'N'
Joins
Filtering
Predicates

• “SQL Tuning” by Dan Tow
• Great book that teaches SQL Diagramming
• http://www.singingsql.com
SQL Diagramming
registration
student class
5
1
30
1
4%
.1%
select count(1) from registration where cancelled = 'N'
and signup_date between '2016-12-10 00:00' and '2016-12-11 00:00'
64112 / 1783066 * 100 = 3.59 or 4%
select count(1) from class where name = 'SQL TUNING'
2 / 1,267 * 100 = .1%

• CREATE INDEX cl_name ON class(name);
• Execution Stats – 20,348 buffer gets
• Why is a full table scan still occurring on REGISTRATION?
New Execution Plan

• CLASS_ID not left leading in index
• Execution Stats – 20,348 buffer gets
• Twice the work to use Primary Key Index on REGISTRATION
Review Index Order

• CREATE INDEX reg_alt ON registration(class_id);
• Execution Stats – 3000 Buffer Gets / Average Execs - .008 Secs
New Execution Plan

• CREATE INDEX reg_cancel_signup ON registration(cancelled,signup_date);
Tuning Advisor Suggested Index
Execution Stats:
1107 Buffer Gets
Avg Executions:
0.140 Secs

• CREATE INDEX reg_alt ON registration(class_id,signup_date, cancelled);
• Execution Stats – 445 Buffer Gets / Average Execs - .002 Secs
Better Execution Plan

Performance Improved
reg_canceled_signup index
Average Wait Time per Execution for SQL Statement Class_Registration | CECE_JGRIFFIN-2
January 27, 2015
Daily Time Range: 1:00 PM-10:00 PM

Case Study – Current Pay Check For Specific Employees
SELECT e.first_name, e.last_name, l.region_name
FROM emp e
INNER JOIN dept d ON e.department_id = d.department_id
INNER JOIN loc l ON l.location_id = d.location_id
WHERE (e.last_name LIKE :b1)
AND e.employee_id IN (
SELECT employee_id
FROM wage_pmt w
WHERE w.employee_id = e.employee_id
AND w.pay_date>= trunc(sysdate)-31);

Wait Time Analysis
Almost 100% on PGA
memory allocation wait. New
wait event in 12.2 – not
documented
No statistics,
Unique indexes
Added PKs and Fks

Execution Plan

Understanding the Underlying Objects

ACCEPT SQL_ID CHAR PROMPT 'Enter SQL_ID> '
DECLARE
l_sql_tune_task_id VARCHAR2(100);
BEGIN
l_sql_tune_task_id := DBMS_SQLTUNE.create_tuning_task ( sql_id => '&sql_id',
scope => DBMS_SQLTUNE.scope_comprehensive, time_limit => 60,
task_name => '&sql_id', description => 'Tuning task for Current Paycheck');
DBMS_OUTPUT.put_line('l_sql_tune_task_id: ' || l_sql_tune_task_id);
END;
/
EXEC DBMS_SQLTUNE.execute_tuning_task(task_name => '&sql_id');
SELECT DBMS_SQLTUNE.report_tuning_task('&sql_id') AS recommendations FROM dual;
EXEC DBMS_SQLTUNE.drop_tuning_task('&sql_id');
Ask the Tuning Advisor
No recommendations

SQL Diagramming
select count(1) from wage_pmt
where pay_date >= sysdate – 31
2,184 / 142,708 * 100 = 1.5%
select avg(cnt) from (select substr(last_name,1,3), count(*) cnt
from emp group by substr(last_name,1,3))
278.5 / 3,899 * 100 = .7.14% With full last nume = .1%
emp
dept
wage_pmt
975
1
37
1
.7%
2%
loc
1
1
.1%

No change?
Add Index on WAGE_PMT(employee_id)

Adjust Index on WAGE_PMT
CREATE INDEX idx_emp_date
ON wage_pmt
(employee_id, pay_date);

Improved Performance
Created index
IDX_EMP_DATE

• FETCH FIRST n ROWS ONLY
• Retrieves first rows without scanning everything
• Faster than using rownum
• OFFSET n ROWS FETCH FIRST n ROWS ONLY
• Skip some number of rows
• 12.2 Approximate Query Processing
• Used for approximate ‘count distinct’ values
• And adds percentile aggregation
• Allows for faster processing of large data sets
• Not exact but usually within 95%+ range
• Three new parameters – alter system/session
• approx_for_aggregation Default=FALSE
• approx_for_count_distinct Default=FALSE
• approx_for_percentile Default=NONE
Other Tuning Tips

Approximate SQL Example Without Changing Code
Why is it exact?
Need to
set both

• Convert non-partitioned table
• To a partitioned table ONLINE
• Many new partition options
• Automatic Lists
• Multi-column Lists
• Partitioned external tables
• Other maintenance options
New 12.2 Partitioning Features
UPDATE INDEXES clause is
optional. Indexes with SYS
names will be generated if not
used.

• Look for performance inhibitors
• Cursor or row by row processing
• Parallel processing
• Don’t use in an OLTP environment
• Use only when accessing large data sets and additional resources can be allocated
• Nested views that use db_links
• Abuse of Wild Cards (*) or No Where Clause
• Select ONLY those columns in a query which are required.
• Extra columns cause more I/O on the database and increase network traffic
• Code-based SQL Generators (e.g. Hibernate)
• Using functions on indexed columns (SUBSTR, TO_CHAR, UPPER, TRUNC)
• Optimizer can’t use the index
• Instead move the function to the constant or variable side of equation
• Consider creating a function based index
• Hard-coded hints
Engineer Out the Stupid
select… where upper(last_name) = ‘GRIFFIN’
Better way: select … where last_name = upper(:b1);

• Reduce SORT operations as they slow down your queries
• Don’t use the UNION operator if you can use UNION ALL
• Avoid the DISTINCT keyword if you don’t need it
• Ensure the left-leading column of a multi-column index is reference
• Otherwise an INDEX SKIP SCAN may occur
• Often no better than a FULL TABLE SCAN
• Try to avoid Cartesian product queries
• Use bind variables instead of literal values
• Reduces repeated parsing of the same statement
• If using sub-queries, make use of the EXISTS operator when possible
• Optimizer will stop with a match and avoid a FULL TABLE SCAN
• Try to use an index if less than 5% of the data needs to be accessed
• Exception: small table are best accessed through a FULL TABLE SCAN
• Consider keeping in memory
More Do’s and Don’ts

• Use equi-joins whenever possible
• Try not to use ‘not in’, !=, <>, not null, etc.
• Optimizer has more choices to choose from
• Avoid complex expressions such as NVL(col1,0), TO_DATE(), TO_NUMBER()
• They prevent the optimizer from assigning valid cardinality or selectivity estimates
• Can affect the overall plan and the join methods
• Avoid joining complex views
• May instantiate all views to run query against (reading too much data)
• Querying views requires all tables from the view to be accessed
• If they aren’t required, then don’t use the view
• Use the partition key in the ‘WHERE’ clause if querying a partitioned table
• Partition pruning will be used to reduce the amount of data read
Avoid Common Pitfalls

• If you can hint it, baseline it (per Tom Kyte)
• Alternative to using hints
• Hints difficult to manage over time
• Once added, usually forgotten about
• Third-party software – can’t modify code
• Example:
Merge Join Cartesian > Nested Loop
select /* jg */ p.product_name
from order_items o, product p
where o.unit_price = :b1
and o.quantity > :b2
and o.product_id = p.product_id
and p.product_id = :b3;
What to Do If You Can't Change the Query
select /*+ USE_NL(o p) */ /* jg */ p.product_name
from order_items o, product p
where o.unit_price = :b1
and o.quantity > :b2
and o.product_id = p.product_id
and p.product_id = :b3;

• Create baseline of original plan
Alter session set optimizer_capture_sql_plan_baselines = TRUE;
• Or dbms_spm.load_plans_from_cursor_cache
• Example next slide
How to Change the Baseline
From cache Baseline

• Manually run hinted query
• Hint = /*+ USE_NL(p) +/
Change the Baseline – Cont.
Get SQL_ID, Plan hash value from cache Load from cache into baseline

SELECT c_last, c_first, c_street_1, c_city, c_state, c_zip,
c_phone, o_entry_d, d_name, ol_delivery_d, ol_quantity, ol_amount
FROM order_line, orders, district, customer, stock
WHERE o_id = ol_o_id
AND o_c_id=c_id
AND s_i_id = ol_i_id
AND d_id = ol_d_id
AND ol_w_id = :B2
AND ol_d_id = :B4
AND (ol_o_id < :B3 )
AND ol_o_id >= (:B3 - 20)
AND s_w_id = :B2
AND s_quantity < :B1
AND d_id = :B4
AND c_last like :B5 ;
Another Case Study – Orders by Customer Last Name

Review the Execution Plan
select * from table (dbms_xplan.display_cursor(null,null, format=> '+report'));

• Stock:
Get Object Information
create index stock_idx
on stock
(s_i_id, s_w_id, s_quantity);

• Orders:
Get Object Information
Actual Rows = 60,000

Find the Driving Table
orders
stock
district
100
1
30
1
.7%
.03
select count(*) from order_line
where ol_o_id < 200 and ol_o_id >= 200-20;
3941 / 600916 * 100 = .6558%
select avg(cnt) from (select c_last, count(*) cnt
from customer group by c_last);
20 / 60000 * 100 = .03333%
Filter on Stock: 3109 / 283000 * 100 = 1%
order_line
customer
warehouse
WHERE o_id = ol_o_id
AND o_c_id=c_id
AND s_i_id = ol_i_id
AND d_id = ol_d_id
AND ol_w_id = :B2
AND ol_d_id = :B4
AND (ol_o_id < :B3 )
AND ol_o_id >= (:B3 - 20)
AND s_w_id = :B2
AND s_quantity < :B1
AND d_id = :B4
AND c_last like :B5 ;
1
20
1
1
6
60092
10
1%

• create index stock_idx on stock (s_i_id, s_w_id, s_quantity);
Engineer Out the Stupid

• create index orders_i2 on orders(o_id,o_c_id, o_entry_d);
Add Index on Orders to INCLUDE Customer

Did Performance Improve?
Added Index on
Stock
Added Index on
Orders

• Make sure you are tuning the correct query
• Use wait time analysis
• Understand the execution plan
• Focus on the costly steps
• Know what the optimizer knows
• Try these tuning techniques
• SQL diagraming to find the best execution plan
• Consider new fetch, approximate and partitioning features
• Engineer out the stupid
• Be on the lookout for coding mistakes
• If you can’t change the code
• Try using baselines or patches
Summary

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Stop the Chaos! Get Real Oracle Performance by Query Tuning Part 2

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Stop the Chaos! Get Real Oracle Performance by Query Tuning Part 2