Oracle DB Statistics What do they actually do and why do I care about the details

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Oracle DB StatisticsWhat do they actually do and
why do I care about the details

• Why the optimizer does what it does

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Scenario

• The CEO of your organization is waiting for the
  management reports to present to the board.
  Hes getting nervous... He really needs those
  reports

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Scenario

• The CEOs admin assistant says the reports are
  running slow Something about the database
• How does the CEO respond

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Scenario

• Did they run database statistics
• Why did he say this Because statistics are
  MAGICAL

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Goals of the Presentation

• Discuss what statistics actually collects
• Where are they stored
• How are they used
• And through this acquire understanding of
• How the optimizer works
• It truly isnt always bad SQL
• provide insight to why the optimizer occasionally
  picks a bad plan over a good plan
• It isnt magical. Just as in most disciplines
  without a basic understanding of the
  fundamentals your skilsl can only reach so high

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Example xample

• drop table PATIENT_LIST
• -- Create table w/ 200 Rows
• create table PATIENT_LIST(last_name char(1000)
  first_name char(1000) gender char(1)
  first_preg_date date)
• insert into patient_list select
  Fsysdate - rownum from dba_tables where
  rownum lt 101
• insert into patient_list select
  MNULL from dba_tables where rownum lt 101
• -- create index
• create index patient_list_ndx on
  patient_list(gender first_preg_date)
• -- collect stats
• exec dbms_stats.gather_table_stats(user
  PATIENT_LIST estimate_percent gt 100
  method_optgt FOR ALL COLUMNS SIZE 1 cascade
  gttrue)
• set autotrace on
• select from patient_list where gender M and
  first_preg_date is not null

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Here is the explain plan

• The explain Plan
• 0 SELECT STATEMENT OptimizerCHOOSE (Cost2
  Card50 Bytes750)
• 1 0 TABLE ACCESS (FULL) OF PATIENT_LIST
  (TABLE) (Cost2 Card50 Bytes750)
• The CARD (CARDINALITY) is 50. Cardinality is
  the number of rows the optimizer expects to come
  back
• TEST TIME! What is the explanation (dont
  worry multiple choice)

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What does this Mean

• (a) MEN REVOLT!!!! We no longer need women to
  reproduce!
• (b) The optimizer is flawed file an SR!
• (c) statistics simply have their limitations

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How did Oracle calculate 50

• 200 Rows in the Table
• 50 have first_preg_date NULL
• 2 Distinct values for Gender 50
• 200 Rows 50 50 50 Rows
• Not Rocket science but it is obviously way off
• If the table happened to 10TB the calculation
  would have occurred the same way
• Sounds like a mathematical statistical
  limitation

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Limitation Oracle Should fix that

• Statistics collects only very rudimentary
  metadata about tables indexes and table columns
• To blindly widen the net could cause your
  statistical metadata to grow exponentially
  causing the optimizer to slow significantly (not
  to mention the stats job) for the sake of only a
  relatively small number of queries

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In a nutshell how does the optimizer work

• Upon a hard parse oracle comes up with a large
  number of possible execution plans
• The optimizer simply uses basic mathematical
  statistics (and limited Oracle proprietary
  algorithms) to assign cardinalities to each step
  of these execution plans (stats are used here)
• Then the optimizer assigns a cost to each step
  (how much work to collect that cardinality)
  (stats are used here)
• Then for each plan the optimizer sums the total
  cost and picks the plan with the lowest number

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What are Table Level Statistics

• ALL_TABLES
• The Biggies
• NUM_ROWS - Number of rows in the table
• BLOCKS - Number of used data blocks in the table
• Considerations
• DEGREE - Number of threads per instance for
  scanning the table (parallelism)
• PARTITIONED - Indicates whether this table is
  partitioned. Set to YES if it is partitioned.
• AVG_ROW_LEN - Average length of a row in the
  table (in bytes)

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What are Column Statistics

• This is where method_opt comes in
• FOR ALL COLUMNS
• FOR ALL INDEXED COLUMS
• ALL_TAB_COL_STATISTICS
• The Biggies
• NUM_DISTINCT - Number of distinct values in the
  column
• LOW_VALUE RAW(32) - Low value in the column
• HIGH_VALUE RAW(32) - High value in the column
• NUM_NULLS - Number of nulls in the column
• Considerations
• AVG_COL_LEN NUMBER Average length of the column
  (in bytes)
• HISTOGRAM - Indicates existence/type of histogram
  (we shall discuss this later)
• Calculated based on other stats
• DENSITY NUMBER Density of the column

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What is out of Range

• This can occur when the a where clause predicate
  on a column is less then LOW_VALUE or greater
  then HIGH_VALUE
• Can be caused by
• big data shifts dumped into the table after the
  most recent analyze
• Sample rate too low
• Problem can appear random when using bind
  variables
• Nice to know but the values in ALL_TAB_COL_STATIST
  ICS
• LOW_VALUE
• HIGH _VALUE
• Are Raw data type how do I translate

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Why do I care about Out of Range

• The further you get out of range the lower the
  expected cardinality
• The optimizer can greatly underestimate the
  cardinality of the rows coming back causing a
  suboptimal plan (plan starts in the wrong place
  wrong index is used)

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How do you detect Out of Range Issues

• This is tough if you keep your stats from
  getting stale look at the slow sql plan in
  vsql_plan. Look at the cardinalities if one
  appears way off this might be it (but just
  because you dont see the symptoms doesnt mean
  this isnt the prob). (Much worse w/ binds).
• When using binds try to extract first parse
  bind variables from the data dictionary or AWR.
  You may have to trust your gut.

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LOW_VALUE - HIGH_VALUEconversion

• create or replace function display_raw (rawval
  raw type varchar2)
• return varchar2
• is
• cn number
• cv varchar2(32)
• cd date
• cnv nvarchar2(32)
• cr rowid
• cc char(32)
• begin
• if (type NUMBER) then
• dbms_stats.convert_raw_value(rawval cn)
• return to_char(cn)
• elsif (type VARCHAR2) then
• dbms_stats.convert_raw_value(rawval cv)
• return to_char(cv)
• elsif (type DATE) or (type LIKE
  TIMESTAMP) then
• dbms_stats.convert_raw_value(rawval cd)
• return to_char(cd)

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Here is the SQL

• select
• a.column_name
• display_raw(a.low_valueb.data_type) as
  low_val
• display_raw(a.high_valueb.data_type) as
  high_val
• b.data_type a.NUM_DISTINCT a.DENSITY
  a.NUM_NULLS
• from
• all_tab_col_statistics a all_tab_cols b
• where
• a.table_nameACAT_CTRL_FACT and
• a.table_nameb.table_name and
• a.column_nameb.column_name and
• a.owner b.owner

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What are Index Stats

• ALL_IND_STATISTICS
• BLEVEL- B-Tree level
• LEAF_BLOCKS - Number of leaf blocks in the index
• DISTINCT_KEYS - Number of distinct keys in the
  index key
• CLUSTERING_FACTOR - Indicates the amount of order
  of the rows in the table based on the values of
  the index.

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Why do I have to sometime run stats in the middle
of the day to fix a query

• Likely candidates
• Cursor Invalidation (for reused sql)
• Simply a bad peaked bind variable
• Out of Range issue due to new data or stats
  sampling issue
• Significant data change at the column level
  (update) since last analyze
• Significant change at the number of rows (or
  table size) typically because stats havent
  been run for a long time since last analyze

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Statistics Types on Partitioned tables

• Statistics are collected two ways on a
  partitioned table (or 3 on subpartioned)
• Global (Same as a normal table)
• ALL_TABLES
• ALL_TAB_COL_STATISTICS
• ALL_TAB_HISTOGRAMS
• ALL_INDEXES
• Partitioned
• ALL_TAB_PARTITIONS
• ALL_PART_COL_STATISTICS
• ALL_PART_HISTOGRAMS
• ALL_IND_PARTITIONS
• Subpartitions
• ALL_SUBPART_COL_STATISTICS
• ALL_SUBPART_HISTOGRAMS
• ALL_TAB_SUBPARTITIONS

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Why so many statistics on Partitioned Tables

• When oracle received a SQL statement that
  references a partitioned table it will use the
  values in the where clause to use the lowest
  degree of stats possible
• Select from part_table (global stats)
• Select from part_table where partitioned_key
  X (partition stats)
• Select from part_table where sub_partitioned_key
  X (sub_partition stats)
• Select from part_table where partitioned_key in
  ( X Y) (global stats)

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Gotchas with stats on partitioned tables

• If you use bind variables (i.e. partitioned_key
  (X) and gender Y) the database will find
  the best plan for that value for the data in
  Xs partition. Maybe that is a bad plan in
  another partition.
• Imagine if the first hard parsed query in the
  morning hits an empty partition (cheap FTS).
  Then the next query hits a big partition. Same
  plan (supposedly some work is done on this issue
  in 11G)

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Gotchas with stats on partitioned tables

• Image a Credit Card transaction table partitioned
  by load date (1000 Days)
• Each load date will contain 4 partial transaction
  dates
• No histograms
• Local Stats on any given partition say 4 distinct
  values for load date
• Global Stats say 1004 distinct values
• Index on Load Data
• All input values are in range
• 
  
• Select from CC_TRANS where load_date X and
  trans_date Y
• Optimizer says FTS (full partion scan) best plan
  GOOD
• Select from CC_TRANS where load_date in (XZ)
• and trans_date Y
• Optimizer says Index Lookup best plan BAD
• Select from CC_TRANS where load_date gt X
• and trans_date Y

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Out of Range issues

• Partitioned tables tend to be big tables so
  adding 1M rows may not be a big change (stale).
  
• If you are adding a new partition every day
  (partitioned by transaction date) every day the
  max(transaction date) for the global table grows.
  Global stats doesnt know. Everyday will grow
  to be further out of range. Execution plans
  against global stats may perform terribly when
  sql tries to hit recent out-of-range values.
• In 10.2.0.4 and after new copy table statistics
  procedure is available to counteract some of
  these issues

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Histograms what are they

• Dan Hotka Histograms are a cost-based
  optimizer (CBO) feature that allows for Oracle to
  see the possible number of values of a particular
  column. This is known as data skewing and
  histograms can track the number of occurrences of
  a particular data value when deciding on what
  type of index to use or even whether to use an
  index at all. Histograms give the CBO the ability
  to see the uniqueness of a particular data value.

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Histogram metadata

• ALL_TAB_HISTOGRAMS
• ENDPOINT_NUMBER - Histogram bucket number
• ENDPOINT_VALUE - Normalized endpoint value for
  this bucket
• ENDPOINT_ACTUAL_VALUE - Actual (not normalized)
  string value of the endpoint for this bucket

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Lets give Histograms a look

• We will create a frequency based histogram
• create table gender as select M gender from
  dba_objects
• insert into gender select F from dba_objects
  where rownum lt 10
• commit
• EXEC DBMS_STATS.GATHER_TABLE_STATS(ownname gt
  user tabname gt GENDER ESTIMATE_PERCENT
  gt100 method_opt gt FOR ALL COLUMNS SIZE 10)
• select num_rows from all_tables where owner
  user and table_name GENDER
• Result 36769

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Histograms still looking

• SELECT column_name
• ENDPOINT_NUMBER
• endpoint_value
• ENDPOINT_ACTUAL_VALUE
• SUBSTR (SYSTEM.hexstr (endpoint_value)
  1 30)
• FROM all_tab_histograms a
• WHERE table_name GENDER AND owner USER
• hexstr function comes from asktom.oracle.com
  

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Histogram Explain Plans

• set echo on
• set autotrace traceonly explain
• SQLgt select from gender where gender F
• Execution Plan
• --------------------------------------------------
  --------
• 0 SELECT STATEMENT OptimizerCHOOSE
  (Cost19 Card9 Bytes18)
• 1 0 TABLE ACCESS (FULL) OF GENDER
  (TABLE) (Cost19 Card9 Bytes18)
• SQLgt select from gender where gender M
• Execution Plan
• --------------------------------------------------
  --------
• 0 SELECT STATEMENT OptimizerCHOOSE
  (Cost19 Card36760 Bytes
• 73520)
• 1 0 TABLE ACCESS (FULL) OF GENDER
  (TABLE) (Cost19 Card36760 Bytes73520)

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Histograms two types

• Frequency Based (less the 255 sampled values)
• Army personnel table
• Female 5
• Male 95
• Height based (not bound by of distinct values)
• Bell shape curve distribution i.e. IQ scale
• Say (making this up)
• 3 of adults have an IQ between 0 70
• 47 of adults have an IQ between 70 100
• 47 of adults have an IQ between 100 130
• 3 of adults have an IQ between 130 200
• Oracle implementation will break up the numbers
  evenly by the range (for instance 10 buckets 10
  of the range will be in each bucket)

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Histograms in 10G

• 10G defaults to size auto in method_opt
• This means there is an algorithm in oracle to
  view recent behavior in the database to determine
  if histograms are needed
• I believe 10G size auto is too aggressive in
  creating histograms. Creates histograms on too
  many columns
• And histogram collection time is significant and
  resource intensive

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10G Histogram Oddities

• I have seen 10G put height based histograms on
  Last Name columns
• If histograms tell use there are 1M rows with
  last names between Pallack and Phillips how can
  histograms help the optimizer guess how many
  Penellopeys (uncommon) there are..
• Clearly there are Common last names but range
  based histograms cant hold them (too many) and
  height based dont make sense.

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Histogram Bind Variables

• The combination of histograms and bind variable
  usage can get you in trouble (11G has some
  advances in this)
• The first hard parse determines your plan. What
  if the bind variable value references infrequent
  skewed data which is not used again. Subsequent
  queries go after data on the other side of the
  skew
• Im not saying lack of histograms doesnt have
  its downfalls but the existence of histograms
  can cause large volatility which brings the
  phone calls.
• If a small discrete set of values consider using
  literals in where predicate for histogram columns
  for the best plan every time.

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But 10G takes care of Stale Stats

• Oracle Documentation - If a monitored table has
  been modified more than 10 then these
  statistics are considered stale and gathered
  again
• A good solid rule for 80 of all tables.
• But staleness truly cannot be measured by number
  of modified rows alone. Internal data swings
  (most likely on bigger tables) can cause HUGE
  issues. Consider a transaction table with a new
  distinct ORDR CODE for every day.
• If there is a frequency histogram on this column
  a new ORDR CODE will not be in it. The
  optimizer using the histogram data in
  combination of the columns distinct values
  (all_tab_cols) and the number of rows in the
  all_tables the optimizer may guess as loww as 1
  row.

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Statistics Guidelines

• Many tables fall into batch processes If
  possible the app teams should run stats after
  each batch.
• Partitioned tables should collect global stats
  each week
• Tables partitioned by month should additionally
  collect global stats immediately after the first
  load of monthly data.
• If you periodically find bad plans based on
  wrong statistics even after an analyze up the
  sample size.
• Sure 10G has a default daily stats job dont
  hesitate to use your own