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Tuning the Optimizer Statistics >Eric Miner Senior Engineer Data Server Technology

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There are Two Kinds of Optimizer Statistics >Table/Index level- describes a table and its index(es) >Page/row counts, cluster ratios, deleted and forwarded rows >Some are updated dynamically as DML occurs page/ row counts, deleted rows, forwarded rows, cluster ratios >Stored in systabstats >Column level - describes the data to the optimizer >Histogram (distribution), density values, default selectivity values >Static, need to be updated or written directly >Stored in sysstatistics >This presentation deals with the column level statistics

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What Are These Column Level Statistics Used For? >The Histogram Values >Describes the distribution of values in the column >Belongs to a column, not an index >Used in costing SARGs >A step is the point in the column where a value is read to obtain a boundary value >A cell represents the rows that fall between two steps >Each cell has a weight which is the fraction of rows in the column it represents - read as a percentage of all rows >There are approximately the same number of rows in each cell - except Frequency count cells

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Some Quick Definitions Range cell density: Total density: Range selectivity: default used (0.33) In between selectivity: default used (0.25) Histogram for column: A" Column datatype: integer Requested step count: 20 Actual step count: 10 Step Weight Value <= <= <= < = <= <

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What Are These Column Level Statistics Used For? cont. >A cell can represent either a single value or multiple values of the column >Range cell - more than one value <= <= All values between and are in cell 3 >Frequency count cell - only one value (very accurate) < = <= = <= Cells 6 and 8 represent only one value >Cell (step) 1 represents the NULL values in the column

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Statistics On Inner Columns of Composite Indexes Stats on inner columns of composite indexes Think of a composite index as a 3D object, columns with statistics are transparent, those without statistics are opaque >Columns with statistics give the optimizer a clearer picture of an index – sometimes good, sometimes not >This is a fairly common practice >Does add maintenance >update index statistics most commonly used to do this update index statistics tab_name [ind_name]

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Statistics On Inner Columns of Composite Indexes cont. Index on columns E and B – No statistics on column b select * from TW4 where E = "yes" and b >= and id >= and F > "May 14, 2002 and A_A = Beginning selection of qualifying indexes for table TW4', varno = 0, objectid The table (Allpages) has rows, pages, Estimated selectivity for E, selectivity = , upper limit = No statistics available for B, using the default range selectivity to estimate selectivity. Estimated selectivity for B, selectivity =

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Statistics On Inner Columns of Composite Indexes cont. The best qualifying index is E_B' (indid 7) costing pages, with an estimate of 191 rows to be returned per scan of the table FINAL PLAN (total cost = ): varno=0 (TW4) indexid=0 () path=0xfbccc120 pathtype=sclause method=NESTED ITERATION Table: TW4 scan count 1, logical reads:(regular=24098 apf=0 total=24098) physical reads: (regular=16468 apf=0 total=16468), apf IOs used=0

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Statistics On Inner Columns of Composite Indexes cont. Statistics are now on column B Estimated selectivity for E, selectivity = , upper limit = Estimated selectivity for B, selectivity = , upper limit = The best qualifying index is E_B' (indid 7) costing 3317 pages,with an estimate of 13 rows to be returned per scan of the table FINAL PLAN (total cost = 55108): varno=0 (TW4) indexid=7 (E_B) path=0xfbd1da08 pathtype=sclause method=NESTED ITERATION Table: TW4 scan count 1, logical reads:(regular=4070 apf=0 total=4070), physical reads: (regular=820 apf=0 total=820),

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Statistics On Non-Indexed Columns and Joins Stats on non-indexed columns Cant help with index selection but can affect join ordering >Columns with statistics give the optimizer a clearer picture of the column – no hard coded assumptions have to be used >When costing joins of non-indexed columns having statistics may result in better plans than using the default values >Without statistics there will be no Total density or histogram that the optimizer can use to cost the column in the join >Yes, in some circumstances histograms can be used in costing joins – if there is a SARG on the joining column and that column is also in the join table then the SARG from the joining table can be used to filter the join table >If there is no SARG on the join column or on the joining column the Total density value (with stats) or the default value (w/o stats) will be used

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Statistics On Non-Indexed Columns and Joins cont. SARG example select....from TW1, TW4 where TW1.A = TW4.A and TW1.A = 10 Selecting best index for the JOIN CLAUSE: TW4.A = TW1.A TW4.A = 10 Estimated selectivity for a, selectivity = ,upper limit = Histogram values used select....from TW1, TW4 where TW1.A = TW4.A and TW1.B = 10 Selecting best index for the JOIN CLAUSE: TW4.A = TW1.A Estimated selectivity for a, selectivity = Total density value used

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Statistics On Non-Indexed Columns and Joins - Example select * from TW1,TW2 where TW1.A=TW2.A and TW1.A = A simple join with a SARG on the join column of one table Table TW2 column A has no statistics, TW1 column A does Selecting best index for the JOIN CLAUSE: (for TW2.A) TW2.A = TW1.A TW2.A = Inherited from SARG on TW1 But, cant help…no stats Estimated selectivity for A, selectivity = The best qualifying access is a table scan, costing pages, with an estimate of rows to be returned per scan of the table, using no data prefetch (size 2K I/O), in data cache 'default data cache' (cacheid 0) with MRU replacement Join selectivity is Inherited SARG from other table doesnt help in this case

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Statistics On Non-Indexed Columns and Joins – Example cont. Without statistics on TW2.A the plan includes a reformat with TW1 as the outer table FINAL PLAN (total cost = ): varno=0 (TW1) indexid=2 (A_E_F) path=0xfbd46800 pathtype=sclause method=NESTED ITERATION varno=1 (TW2) indexid=0 () path=0xfbd0bb10 pathtype=join method=REFORMATTING >Not the best plan – but the optimizer had little to go on

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Statistics On Non-Indexed Columns and Joins – Example cont. Table TW2 column A now has statistics. The inherited SARG on TW1.A can now be used to help filter the join on TW2.A Selecting best index for the JOIN CLAUSE: TW2.A = TW1.A TW2.A = Estimated selectivity for A, selectivity = , upper limit = The best qualifying access is a table scan, costing pages, with an estimate of 724 rows to be returned per scan of the table, using no data prefetch (size 2K I/O), in data cache 'default data cache' (cacheid 0) with MRU replacement Join selectivity is

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Statistics On Non-Indexed Columns and Joins – Example cont. With statistics on TW2.A reformatting is not used and the join order has changed FINAL PLAN (total cost = ): varno=1 (TW2) indexid=0 () path=0xfbd0b800 pathtype=sclause method=NESTED ITERATION varno=0 (TW1) indexid=2 (A_E_F) path=0xfbd46800 pathtype=sclause method=NESTED ITERATION

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The Effects of Changing the Number of Steps (Cells) The Number of Cells (steps) Affects SARG Costing – As the Number Of Steps Changes Costing Does Too Cell weights and Range cell density are used in costing SARGs >Cell weight is used as columns upper limit Range cell density is used as selectivity for Equi-SARGs – as seen in 302 output >Result(s) of interpolation is used as column selectivity for Range SARGs >Increasing the number of steps narrows the average cell width, thus the weight of Range cells decreases >Can also result in more Frequency count cells and thus change the Range cell density value >More cells means more granular cells

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The Effects of Changing the Number of Steps (Cells) cont. Average cell width = # of rows/(# of requested steps –1) >Table has 1 million rows, requested 20 steps - >1,000,000/19 = 52,632 rows per cell >1,000,000/199 = 5,025 rows per cell >What does this mean? >As you increase the number of steps (cells) they become narrower – representing fewer values >Well see that this has an effect on how the optimizer estimates the cost of a SARG >update statistics ……. using X values create index ….. using X values

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The Effects of Changing the Number of Steps (Cells) cont. Changing the number of steps – effects on Equi-SARGs select A from TW2 where B = With 20 cells (steps) in the histogram Range cell density: <= <= SARG value falls into cell 10 Estimated selectivity for B, selectivity = , upper limit = Range cell weight of density qualifying cell

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The Effects of Changing the Number of Steps (Cells) cont. With 200 cells (steps) in the histogram Range cell density: <= <= >SARG value falls into cell 78 Estimated selectivity for B, selectivity = , upper limit = In this case more cells result in a lower estimated selectivity >Increasing the number of steps has decreased the average width and lowered the Range cell density and the average cell weight. >Range cell density decreased because Frequency count cells appeared in the histogram

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The Effects of Changing the Number of Steps (Cells) cont. Changing the number of steps – effects on Range SARGs - select * from TW2 where B between and With 20 cells (steps) in the histogram Range cell density: <= <= >SARG values fall into cell 10 Estimated selectivity for B, selectivity = , upper limit = >Here selectivity is the product of interpolation, upper limit is the weight of the qualifying cell. >Interpolation estimates how much of cell will qualify for the range SARG

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The Effects of Changing the Number of Steps (Cells) cont. select * from TW2 where B between and With 200 cells (steps) in the histogram Range cell density: <= <= <= <= <= <= <= <= <= Estimated selectivity for B, selectivity = , upper limit = >The SARG values now span multiple cells >Interpolation estimates amount of cells 68 and 75 to use since not all of those two cells qualify

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Some Statistics Related FAQs cont. How many steps should I request? >It will depend on your data and your queries >Increase requested steps to get Frequency count cells when there are highly duplicated values >FC only represents one value - very accurate weight >Range SARGs will estimate what portion of a cell qualifies for the SARG >More cells means narrower cells (represent fewer values) >Narrower cells mean more accurate estimates >Can have an affect on equi-SARGs - lower selectivity

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Removing Statistics Can Effect Query Plans Sometimes no statistics are better then having them This will usually be an issue when very dense columns are involved Histogram for column: E" Step Weight Value < "no" = "no" < "yes" = "yes This can also show up when you have spikes (Frequency count cells) in the distribution

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Removing Statistics Can Effect Query Plans cont. select count(*) from TW4 where E = yes and C = The table…has rows, pages, Estimated selectivity for E, selectivity = , upper limit = Estimating selectivity of index E_AA_B', indid 6 scan selectivity ,filter selectivity rows, pages The best qualifying index is E_AA_B' (indid 6) costing pages, with an estimate of 526 rows FROM TABLE TW4 Nested iteration. Table Scan.

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Removing Statistics Can Effect Query Plans cont. delete statistics TW4(E) Estimated selectivity for E, selectivity = Estimating selectivity of index E_AA_B', indid 6 scan selectivity ,filter selectivity rows, pages The best qualifying index is E_AA_B (indid 6) costing pages, with an estimate of 92 rows FROM TABLE TW4 Nested iteration. Index : E_AA_B Forward scan. Positioning by key.

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Maintaining Tuned Statistics Tuned statistics will add to your maintenance Any statistical value you write to sysstatistics either via optdiag or sp_modifystats will be overwritten by update statistics >Keep optdiag input files for reuse >If needed get an optdiag output file, edit it and read it in >Keep scripts that run sp_modifystats >Rewrite tuned statistics after running update statistics that affects the column with the modified statistics

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Monitoring Table/Index Level Fragmentation Using The Statistics Can Be Both An Optimizer and Space Management Concern The more fragmentation the less efficient page reads are >Deleted rows – fewer rows per page, affects costing >Forwarded rows – 2 I/O each, optimizer adds to costing >Empty data/leaf pages – more reads may be necessary >Clustering can get worse >Watch the DPCR of the table or APL clustered index >In general the Cluster Ratios are not a good indicator of fragmentation since they are often normally low >Use optdiag outputs to monitor these values

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Monitoring Table/Index Level Fragmentation Using The Statistics cont. >ASE 12.0 and above check the Space utilization value >Large I/O efficiency is another value to watch Empty data page count: 0 Forwarded row count: Deleted row count: Derived statistics: Data page cluster ratio: Space utilization: Large I/O efficiency: >Space utilization and Large I/O efficiency are not used by the optimizer >The further from 1 the more fragmentation there is

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Maintaining the Statistics When data changes the statistics become out of date In general up to date statistics are needed to get the best query plans >Statistics are usually updated using update statistics commands >The more statistics you have the more maintenance >Its a trade off between the gain in query performance and the increased statistics maintenance >Theres no point in updating statistics if the table is static

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Update Statistics >Update statistics has been extended to allow for placement of statistics on columns >update statistics table_name (col_name) >update index statistics table_name [ind_name] >update all statistics table_name >Specify the requested number of steps (cells) to use when building the columns histogram >update statistics table_name (col_name) using 200 values

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How Update Statistics Works Column and table/index values have to be read in order to gather the statistics >What does it do? >Reads the column to gather information for density and histogram, writes the column level statistics >While reading the column it gathers index/table level statistics – row & page count, forwarded rows, deleted rows, the cluster ratios, etc. >Takes a sample value every X rows for a histogram boundary value - (based on the number of rows and requested steps) If same value for multiple steps save it to make an FC

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How Update Statistics Works cont. >Values have to be in sorted order for statistics gathering >If its the leading column of an index no sort is necessary >Just scan index leaf for statistics >If not the leading column of an index - create a worktable, read values in, sort and scan for statistics update statistics tab_name (col_name)- a table scan will be done to read the column update index statistics (ind_name)- then only an index scan (with a sort of the inner columns) >The sort is done in a worktable in tempdb. update index and update all statistics will use a lot of tempdb space unless sampling is used

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Some Statistics Related Myths & Legends Update statistics will result in improved performance >Only guarantees up to date statistics >Due to distribution statistics may not give a pretty picture of the column Always use update all statistics >Rarely need statistics on all columns of a table >Can take a VERY long time to run, makes maintenance difficult at best >Should consider adding stats to composite index columns

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Statistics Tools >Some useful tools for working with the statistics >Some are by Sybase some are by users >Optdiag - read, write and simulate the statistics >Well known and documented >sp_modifystats - make modifications to density values (more functionality coming soon , , 12.5) >sp__optdiag (thats a double underscore) - >by Kevin Sherlock >Displays the statistics ala optdiag output - very handy >http://www.sypron.nl/download.html

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Sampling for Update Statistics A new feature in Designed to dramatically reduce the time it takes to update statistics – can dramatically speed up the running of update statistics >Opens your maintenance window >Decreases the cost of using ASE Randomly selected pages are read instead of reading all pages to gather the column level statistics – less I/O >The percentage of pages to be sampled can be specified update statistics tab_name with sampling = X percent >X is the percentage of pages you want to sample Can be between 1 and 100

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Definitions >Column Level Statistics – those statistics that describe the values in the column to the optimizer – an attribute of a colum (i.e.; the histogram and density values) >Sampling – randomly reading rows from a specified percentage (subset) of pages rather than all pages of the table in order to gather column level statistics >Sampling Rate – the specified percentage of pages to read >Full Scan – to gather statistics by reading all pages of the object (table or index) >Major Attribute of an Index – the leading column of an index as listed in the create index command

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Sampling for Update Statistics cont. >Unofficial tests show that a sampling rate of 10% on a 1 million row numeric column reduces the time for update statistics to run from 9 minutes to 30 seconds

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Sampling for Update Statistics cont. >The Resulting histogram will be based on the values that are sampled It will differ from a histogram obtained from a full scan update statistics The lower the specified percentage of sampling the more the histogram will differ from a full scan histogram Test your queries against sampled statistics. In most cases you wont see any major changes Density values not updated by sampling >In most cases this wont be an issue.

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Why Sampling for Update Statistics? >As datasets have grown the time it takes to run update statistics has also grown – Dramatically!! >This became more of an issue with update index statistics introduced in 11.9.x due to extra sort in worktable >TCO and auto-tuning/admin require a faster way to run update statistics >Without a faster update statistics neither efforts would succeed >Speeding up update statistics is a long standing Customer feature request >Random page sampling is the most I/O efficient method >Dramatically decreased the time to run update statistics

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Why Sampling for Update Statistics? cont. >Some time test results – Not official, not for general release >your mileage may vary >Timings are from tests run by Sybase QA >1 million row int colum – timings based on elapsed time 20% sampling rate – Full scan time : Sampling time : Percentage of savings time(elapsed time):83% 10% sampling rate – Full scan time : Sampling time : Percentage of savings time(elapsed time):92% >Variations in full scan time are taken into account

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How Does It Work? >Specify the percentage of pages to read via update statistics >with sampling = X percent Percent value can be between 1 and 100 >with extensions must follow using – with sampling = x percent and/or with consumers = x must follow using X values update statistics authors(auth_id) using 40 values with percent = 10 >Sampling reads all rows from each page read >Row values are moved to the worktable to be sorted and the statistics gathered >This saves tempdb space since the sampled sets of values are smaller than if the whole column was read into the worktable

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How Does It Work? cont. Specific update statistics syntax and their affects update statistics table_name [index_name] with sampling = X percent >Will full scan index pages to update/create statistics on the major attribute(s) of the specified index or all indexes on the table ignoring the specified sampling rate – sampling will not be done

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How Does It Work? cont. update index statistics tab_name [ind_name] with sampling = X percent >Will full scan index pages to update/create statistics for the major attribute(s) of the indexes or specified index on the table, ignoring sampling. >For minor index attributes will use sampling to scan the requested percentage of pages, read those values into a worktable, sort and gather statistics from there. >The space used in tempdb will decrease as the sampling rate decreases update statistics tab_name (col_name) with sampling = X percent >Will use sampling to update/create statistics for the specified column using the specified sampling rate. This applies to all columns whether major attributes of an index or not >Will not affect multi-column density values

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How Does It Work? cont. update all statistics table_name with sampling = X percent >Will full scan index pages to gather statistics for the major attribute of all indexes – will not use sampling on these columns >Will use sampling to gather statistics for all columns that are not the major attribute of an index >The space used in tempdb will decrease as the sampling rate decreases

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How Does It Work? cont. >Sampling is not used for create index >Since a full scan is required to build an index there is no additional cost for building the statistics

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Trade Offs >A sampled set of anything is not as accurate as examining the most effective sampling rate for a given dataset >A histogram created with sampling is not likely to match a histogram created via a full scan >Histogram boundary values will vary >Cell weights will vary >Minimum and maximum histogram boundary values will vary >Since cell weight(s) and Range cell density are used to cost all SARGs a histogram from a sampled set will have an affect on SARG costing >Variations in the upper and lower histogram values may result in out of bounds costing by the optimizer >The smaller the sampling rate the greater the variance is likely to be

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Trade Offs cont. >If there are existing density values they will not be overwritten. If there are no density values a default value of will be used for both Range cell and Total density values >There is currently no information saved about the use of sampling (whether or not it was used and the sampling rate) >Different cell types may appear >As the sampling rate decreases it is possible that Frequency count and/or Range cells may appear where they didnt exist prior to sampling >The same pages will be resampled if the dataset is static and the same sampling rate used

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Examples of Variations in the Histogram Full scan histogram - Step Weight Value <= <= <= <= <= <= <= <= <= >Note boundary values, cell weights and the upper and lower boundary values >Variations within the histogram are the main issue that needs to be tested

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Examples of Variations in the Histogram cont. 10% sampled histogram - Step Weight Value <= <= <= <= <= <= <= <= <= >Note variations in the boundary values, cell weights and the upper and lower boundary values

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Tuning and Troubleshooting >Trial-and-error testing/tuning will need to be done to determine the most optimal sampling rate for a given dataset >In most cases variations in the statistics will have no affect In other cases small variations may change query plans >There is no rule of thumb on what sampling rate to use >In some cases the same sampling rate may be fine across all or most tables/columns. >In some cases sampling may not result in efficient plans >Use showplan and traceon 302/310 outputs to track changes to the query plan as the sampling rate changes >Using sample queries get above outputs from statistics gathered by a full scan. Update statistics with the sampling rate, rerun query and compare outputs

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Tuning and Troubleshooting cont. >Use optdiag to monitor changes to the histogram >Check optdiag of full scan histogram for upper and lower boundary values these can be inserted into the histogram if needed >Keep a copy of optdiag output file as a backup of statistics in case old values need to be reloaded

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Future Enhancements >This first implementation of sampling will require some enhancements >Scale density values gathered by sampling so that they are more accurate >Track the min/max values in the column in order to maintain the upper and lower boundary values of the histogram >Sampling index pages >Will help decrease the time of running update statistics even further >Add a mechanism to record if sampling was used and what sampling rate was last used >Add this information to optdiag and traceon 302 (and future optimizer diagnostics)

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Where To Get More Information >The Sybase Customer newsgroups >http://support.sybase.com/newsgroups >The Sybase list server >The external Sybase FAQ >http://www.isug.com/Sybase_FAQ/ >Join the ISUG, ISUG Technical Journal, feature requests >http://www.isug.com

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Where To Get More Information >The latest Performance and Tuning Guide >Dont be put off by the ASE 12.0 in the title, it covers the features/functionality too >http://sybooks.sybase.com/onlinebooks/group-as/asg1200e >Any Whats New docs for a new ASE release >Tech Docs at Sybase Support >http://techinfo.sybase.com/css/techinfo.nsf/Home >Upgrade/Migration help page >http://www.sybase.com/support/techdocs/migration

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Sybase Developer Network (SDN) Additional Resources for Developers/DBAs >Single point of access to developer software, services, and up-to-date technical information: >White papers and documentation >Collaboration with other developers and Sybase engineers >Code samples and beta programs >Technical recordings >Free software

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