Download Top 10 Database tuning tips

Top 10 Oracle
database tuning tips
and techniques
Guy Harrison
The top 10
Be methodical and
empirical
Optimize
your database
design
Index
Wisely
Write
efficient
code the
Optimize
optimizer
Tune
SQL and
MonitorPL/SQL
and Manage
contention
Optimize
memory to
IO TUNE it
Tune IOreduce
last, but
well
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Be methodical and
empirical
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Hint 1: Methodical and Empirical tuning
Methodical:
• Have a plan
• Focus on root causes, not symptoms
• Hypothesis and experiment, not trial
error
Empirical
• Measure performance
• Compare before and after
• What is your control group?
• Beware of “old DBA tales” and “silver
bullets”
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Hint 1: Methodical and Empirical tuning
Performance
Troubleshootin
Tuning by
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• Identify the element of your workload
contributing most to service time
• Workload could be SQL or logical
• Element is typically CPU time + Oracle
time
• Find a way to reduce that element
• As above, but prioritize higher layers of the
(which have flow on effects into lower
• Distinguish between symptoms and causes
Basics of a database request
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The 4 layers in database performance
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What’s wrong with
the donkey?
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Measurement
• Standard SQL views
• AWR reports
• DB control/Cloud control
• Toad/Spotlight other 3rd
party tools
• A single query can tell you a
lot
http://guyharrison.net/OPS
GSamples/Ch03/timeModel
Simple.sql
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Database design
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Database design
Third Normal
form (3NF)
3NF discretion
Denormalizati
on
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• The key, the whole key and nothing but
the key
• OR – Don’t repeat yourself (DRY)
• Datatypes (VARCHARs vs LOBS, etc)
• Subtypes
• NULLS
• Replicate column values to avoid joins
• Summary tables and materialized views
• Vertical partitioning
3NF is mostly Don’t Repeat Yourself (DRY)
• We don’t want to repeat the
student name every time they take
a new test
• We don’t want to repeat the
test_name for every student that
takes it
• We don’t want the “repeating
group” of answers
– Also, we might not know how many
questions there will be in future tests
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3NF version
• Students take tests
• Tests have questions
• Students taking tests provide answers
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Don’t go to far!
• In this example the designer pulled
address into a separate table and city,
country into another table.
• It’s correct in theory, but it means that
every time we want an employees
address we have to join three tables.
• It would have been better to have just
one table
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Logical to Physical: Subtypes
“Customers are people too”
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Subtypes
• It’s usually better to put all the data in one table, or have two
tables for each subtype
• Having a “supertype” table and two “subtype” tables means you
always have to join tables
Probably don’t
want to do
this
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Denormalization
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Index Wisely
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Indexes
Indexes exist mainly to improve performance
• Choosing the best set of indexes is crucial
• Create the best set of Concatenated (multi-column)
support your queries.
• Indexes slow down transactions, so don’t create too many
Think of indexes on a table like indexes in a
• You use the index when you want to look up one thing or a
things
• You don’t use the index to read the whole book or read a
chapter
• You do use an index when you want to go to a specific
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Concatenated index effectiveness
last,first,birthyear,id
• The more columns in the
index the less IO it takes to
resolve a query using all the
columns
3
last,first,BirthYear
4
last+first name
• The order of the columns
affects how the index can be
used
6
last name
63
None
1459
0
500
Logical IO
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1000
• If the index has all the
columns, don’t need to touch
the table at all
1500
SELECT cust_id
FROM sh.customers c
WHERE cust_first_name = 'Connor'
AND cust_last_name = 'Bishop'
AND cust_year_of_birth = 1976;
Index overhead
7
16,316
Number of indexes
6
14,285
5
• This chart shows how inserts
slow down as more and more
indexes are added
12,727
4
10,719
3
• Deleting a row with lot’s of
indexes is particularly
expensive
8,691
2
6,671
1,191
1 (PK only)
0
2,000
4,000
6,000
8,000 10,000 12,000 14,000 16,000 18,000
Logical reads required
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• Indexes speed up queries,
but make DML (insert,
update, Delete) slower
Index or FTS?
1000
Elasped Time (s)
100
10
Break even points
Full Scan no caching
Index sorted data, no caching
Index unsorted, cached data
Full Table scan, cached data
1
0
10
20
30
40
50
Pct of table accessed
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60
70
80
90
100
Application code
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Database coding guidelines
SQL
Execution
Transaction
design
Network
overhead
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• Don’t call the database unless you
have to – cache data instead.
• Reduce “hard” parsing by using bind
variables
• Minimize lock duration using optimistic
and Pessimistic locking strategies
• Array fetch and Insert
• Stored procedures
Parse overhead
• It’s easy enough in most programming languages to create a new SQL
every time you execute the query:
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Better to use the same SQL with different
arguments:
• Prepare the statement once, then execute many
times with different arguments
• Using bind variables
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Reduction in parse (SQL Compile) time
Bind Variables
No Bind variables
0
200
400
600
Parse Time
800
Other
1,000 executions of the code on preceding two
slides in Oracle
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1000
1200
1400
Designing transactions
• There are two ways to design transaction locking
• Pessimistic works best if you think someone else is going to “grab” your row before
you’re finished
• Optimistic works best if you thing no one else will “grab” the row
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Duration of lock
Duration of lock
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Reduce Network traffic
• “Round trips” to the database can add a lot of overhead
• Two ways to reduce round trips:
– Use the “Array” interface in your program code
– Use stored procedures for complex interactions with the database
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Array fetch performance
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0
0
20
40
60
80
Array fetch size
Logical Reads
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100
Network round trips
120
140
Network – stored procedures
• A stored procedure is code stored in the
database
• If you have a transaction that goes “back and
forth” to the database, consider a stored
procedure
• ESPECIALLY if you are working across a slow
network
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Optimize the optimizer
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Table and index
Structure
Cardinality
Estimates
Object Statistics
DB parameters
And config
Optimizer inputs
IO and CPU
Estimates
• Remember: Garbage In : Garbage
Out
• The optimizer can only do a good
job if statistics are up to date
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System Statistics
Cost estimate
Histograms
• Indexes are only good for getting
small amounts of the table
20,000
18,000
Number of rows
16,000
• So it might be a good idea to use
an index to get “New Zealand”
customers, but not “United
States”
14,000
12,000
10,000
8,000
6,000
4,000
2,000
Saudi Arabia
South Africa
Turkey
New Zealand
Denmark
Argentina
Singapore
Japan
Poland
China
Australia
Brazil
Canada
Spain
France
United Kingdom
Italy
Germany
United States of America
0
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• A histogram allows the optimizer
to understand how the data is
distributed and make the best
decision
• Create histograms to correct bad
plans on skewed tables
Without a histogram
Number of rows the
estimated
Real number of
rows
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Optimizer chooses
not to use an index
With a histogram
Optimizer estimate
is correct
Optimizer chooses
to use an index
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DBMS_STATS
GATHER_INDEX_STATS , GATHER_SCHEMA_STATS,
GATHER_TABLE_STATS
Basic statistics
method_opt
=> 'FOR ALL INDEXED COLUMNS SIZE AUTO'
Histograms
method_opt
=>
Multi-column extended statistics
'FOR ALL COLUMNS FOR COLUMNS (col1, col2)'
method_opt
=> 'FOR ALL COLUMNS FOR COLUMNS
(function(column)))'
Expression extended statistics
DBMS_STATS.gather_system_stats (
gathering_mode => 'NOWORKLOAD');
Non-workload system statistics
DBMS_STATS.gather_system_stats
(gathering_mode
=> 'INTERVAL',
interval
=> 60);
Workload system statistics
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Tune SQL and
PL/SQL
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Find them and tune them
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Find SQL
•
•
•
•
Mine V$SQL
ASH, AWR tables
Database control, grid/cloud control
Toad, Spotlight
Tune SQL
•
•
•
•
•
SQL Profiles
SQL Baselines
Rewrites
Hints (be careful)
SQL Optimizer
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V$SQL is your friend
• Also, V$SQL_PLAN,
V$SQL_PLAN_STATISTICS
• Tkprof, session trace
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Now let’s look at the
donkey
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Monitor and manage
contention
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Contention – the proverbial bottleneck
Application
Demand for DB
services
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Contention for limited or
serialized resources causes
waits and or queuing
Apparent
demand at lower
layers is reduced
Types of contention
Locks
Latches and
Mutex
Buffer
contention
Latches are very light
weight locks that protect
memory instead of tables
When sessions have to
wait for a memory
“buffer” to become
available
Usually locking problems
are due to application
locks (remember
optimistic locking)?
Sometimes internal locks
can cause problems.
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Latches and mutex contention
• Latches are like locks, but
instead of protecting table rows,
they protect memory (buffers)
user
user
user
• If two sessions try to access the
same area of memory, then one
will wait
Buffers
• Instead of “sleeping” (like a lock)
they waiting session will “spin”
on the CPU for a very short time
• Latch problems may indicate
“hot” blocks
• They might cause CPU drain
(because of spinning)
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Database
files
Free buffer waits
Write to
buffers
User
Buffers
Buffer
Waits
Read
from
• When buffers are modified they are
called “dirty” – DBWR writes to disk
buffers
Read
from
disk
Database
Writer
Write
dirty
blocks
to disk
Database
files
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• Database buffers improve performance
by caching data in memory
• When all the blocks are “dirty” then
sessions have to wait for the buffers to
be written before new data can be read
• This might mean that your DBWR can’t
write to disk fast enough
Specific contention scenarios
Lock/Latch
Possible cause
Library cache mutex
Hard parsing no bind variables. Try
CURSOR_SHARING=SIMILAR
Library cache pin
PL/SQL package parsing and invalidations
Shared pool latch
Hard parsing and possibly excessive SGA resizing
Cache buffers chains
Hot blocks, very high logical read rates
Free Buffer waits
DBWR failing to “keep up” with block changes
Flashback buf free
Insufficient IO bandwidth for flashback area
Buffer busy
Hot blocks – partitioning might help
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Optimize memory to
reduce IO
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Memory is primarily used to avoid
IO
• Buffer cache avoids IO to table/index
tablespaces
Oracle Session
50
Buffer pools
Program Global
Area (PGA)
Sort area
Hash Area
Data (USER)
tablespace
Temporary
tablespace
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• PGA avoids IO to temporary tablespace
Temp tablespace IO can easily overwhelm
table/index IO
Time
Multi-pass
Disk Sort
Single Pass
Disk Sort
Memory Sort
Available Memory
More Memory
Table/Index IO
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CPU Time
Less Memory
Temp Segment IO
Automatic Memory Management
• Introduced in 11g, AMM manages allocations between and within
PGA and SGA
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AMM can (rarely?) lead to thrashing or starvation
• ALWAYS set minimum values for key components of the SGA
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Tune IO last, but tune
it well
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IO Tuning
• Disk IO is the slowest part of the database system, so it’s critical to
performance
• FIRST:
– Tune SQL and application
– Remove contention
– Allocate memory
• Only when you’ve done that will your IO demand be realistic. Then
you can tune your IO
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Basics of IO tuning (magnetic disks)
• The amount of IO you can support depends on the number of disks you have
• Provide enough disks to support the amount of IO you need
• even if that means the disks are not filled with data
• Magnetics disks can do between 75-150 IO per second (IOPS) – do the math!
SAS 15,000 rpm
SAS 10,000 rpm
SATA 10,000 rpm
SATA 7,200 rpm
0
20
40
60
80
100
IO/ps
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120
140
160
180
200
Disk Drive latency
100
90
80
Response Time (ms)
• Latency is the time taken to perform a single
70
IO
• Most disk drives can return an IO in 5-10 ms60
– Faster if the disk has a memory cache
• Disk latency increases with:
– Throughput (best at 50-75% of maximum)
– Disk fill (best at 50% capacity)
• To get best performance disks should be
– Sparsely populated
– Under only moderate load
• RAID 0+1 is the preferred configuration
50
40
30
20
10
0
0
100
200
300
IO/second
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400
500
The more that things change....
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Disks are not getting faster
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SSD - Cheaper by the IO
SSD DDR-RAM
15
SSD PCI flash
25
SSD SATA Flash
80
Magnetic Disk
4,000
0
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1,000
2,000 3,000
Seek time (us)
4,000
5,000
But not by the
GB
12
10
10
10
2.9
2.3
2.2
7.4
8
1.7
$$/GB
$$/GB
1.3
6
1
2011
1
5.3
2013
2012
2014
4
3.2
0.35
2.9
0.28
2.2
2.3
1.7
0.21
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1.3
0.17
1
0.35
0.28
0.21
0.17
0.13
0.13
0
0.12011
2012
2013
2014
2015
HDD
61
2015
MLC SDD
SLC SSD
Tiered storage management
Main Memory
DDR SSD
$/GB
Fast Disk (SAS, RAID 0+1)
Slow Disk (SATA, RAID 5)
Tape, Flat Files, Hadoop
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$/IOP
Flash SSD
Random reads – FusionIO
CPU
Table on SSD
Other
121
DB File IO
Flash cache IO
SAS disk, flash
cache
583
SAS disk, no
flash cache
2,211
0
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500
1000
1500
Elapsed time (s)
2000
2500
Full table scans
Table on SSD
72
Flash cache doesn’t accelerate
Full table scans b/c scans use
direct path reads and flash
cache only accelerates
buffered reads
SAS disk, flash cache
CPU
398
Other
DB File IO
Flash Cache IO
SAS disk, no flash cache
418
0
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100
200
300
Elasped time (s)
400
500
Disk Sorts – temp tablespace SSD vs HDD
4000
3500
Multi-pass
Disk Sort
2500
2000
1500
Single Pass
Disk Sort
1000
500
0
300
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250
200
150
Sort Area Size
SAS based TTS
100
SSD based TTS
50
0
Elapsed time (s)
3000
Concurrent redo workload (x10)
Flash based redo log
1,637
331
1,681
CPU
Other
Log File IO
SAS based redo log
1,605
0
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500
1,000
397
1,500
1,944
2,000 2,500 3,000
Elapsed time (s)
3,500
4,000
4,500
Tune RAC
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RAC will scale well,
providing that….
a) The time taken to
transfer a block
across
Load is reasonably
well balanced
the
interconnect
across the
instances
in theiscluster
much less than the
time taken to read
The overhead of maintaining cluster
from disk
consistency does not dominate
overall response time
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Toad: your
companion
in the Big
Data
revolution
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Thank you
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