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Informix Chat with the Labs

Unlocking the Mysteries Behind Update Statistics. John F. Miller III. STSM. Informix Chat with the Labs. Throw dice, how many will be 1?. The Dice Problem. How many dice are you throwing? How many sides does each dice have? Are all the dice the same?. Questions about the Dice.

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Informix Chat with the Labs

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  1. Unlocking the Mysteries Behind Update Statistics John F. Miller III STSM Informix Chat with the Labs

  2. Throw dice, how many will be 1? The Dice Problem

  3. How many dice are you throwing? How many sides does each dice have? Are all the dice the same? Questions about the Dice The better the information, the more accurate the estimate.

  4. Collects information for the optimizer Statistics LOW Distributions MEDIUM & HIGH Drop Distributions Compile stored procedures What does Update Statistics do?

  5. systables systables syscolumns syscolumns sysindexes sysindexes Number of Rows Number of pages to store the data Second largest value for a column Second smallest value for a column # of unique values for the lead key How highly clustered the values for the lead key Statistics Collected

  6. Walk the leaf pages in each index Submit btree cleaner requests when deleted items are found causing re-balancing of indexes Collects the following information Number of unique items Number of leave pages How clustered the data is Second highest and lowest value Update Statistics LowBasic Algorithm

  7. To get the range of values look at the highest value in the previous bin. How to Read Distributions # of rows represented in this bin --- DISTRIBUTION --- ( -1 1: ( 868317, 70, 75) 2: ( 868317, 24, 100) 3: ( 868317, 12, 116) 4: ( 868317, 30, 147) 5: ( 868317, 39, 194) 6: ( 868317, 28, 222) --- OVERFLOW --- 1: ( 779848, 43) 2: ( 462364, 45) # of unique values Highest Value in this bin The value # of rows for this value

  8. Example - Approximating a Value • There are 868317 rows containing a value between -1 and 75 • There are 70 unique values in this range • The optimizer will deduce 868317 / 70 = 12,404 records for each value between -1 and 75 --- DISTRIBUTION --- ( -1 1: ( 868317, 70, 75) 2: ( 868317, 24, 100) 3: ( 868317, 12, 116) 4: ( 868317, 30, 147) 5: ( 868317, 39, 194) 6: ( 868317, 28, 222) --- OVERFLOW --- 1: ( 779848, 43) 2: ( 462364, 45)

  9. Example - Dealing with Data Skew • Data skew • For the value 43 how many records will the optimizer estimate will exist? • Answer 779848 values • Any value that exceeds 25% of the bin size will be placed in an overflow bin --- DISTRIBUTION --- ( -1 1: ( 868317, 70, 75) 2: ( 868317, 24, 100) 3: ( 868317, 12, 116) 4: ( 868317, 30, 147) 5: ( 868317, 39, 194) 6: ( 868317, 28, 222) --- OVERFLOW --- 1: ( 779848, 43) 2: ( 462364, 45)

  10. Basic Algorithm for Distributions • Build distributions • Begin transaction • Delete old columns distributions • Insert new columns distributions • Commit transaction • Develop scan plan based on available resources • Scan table • High = All rows • Medium = Sample of rows • Sort each column

  11. Sample Size • HIGH • All rows in the table • Medium • Misconception about the number of rows sampled is based on the number of rows in the table, this is incorrect. • The number of samples depends on the Confidence and Resolution. • If the sample size is greater than the number of row in the table Medium turns into High mode

  12. Update Statistics Medium Sample Size

  13. How Much Information is Enough?? The better the information, the more accurate the estimate.

  14. Examining the Running QueryNo Statistics VS Medium Statistics No Statistics QUERY: ------ select * from t1 where c1 > 20200 Estimated Cost: 20888 Estimated # of Rows Returned: 6760 1) miller3.t1: SEQUENTIAL SCAN Filters: miller3.t1.c1 > 20200 Medium Statistics QUERY: ------ select * from t1 where c1 > 20200 Estimated Cost: 21 Estimated # of Rows Returned: 19 1) miller3.t1: INDEX PATH (1) Index Keys: c1 (Serial, fragments: ALL) Lower Index Filter: t1.c1 > 20250 Overall performance improved The estimates were more accurate The query plan changed

  15. Examining the Running QueryMedium Statistics VS High Statistics Medium Statistics QUERY: ------ select * from t1 where c1 > 20200 Estimated Cost: 21 Estimated # of Rows Returned: 19 1) miller3.t1: INDEX PATH (1) Index Keys: c1 Lower Index Filter: t1.c1 > 20250 High Statistics QUERY: ------ select * from t1 where c1 > 20200 Estimated Cost: 33 Estimated # of Rows Returned: 30 1) miller3.t1: INDEX PATH (1) Index Keys: c1 Lower Index Filter: t1.c1 > 20250 Overall performance did not change The estimates were slightly more accurate The query plan did not change

  16. All version of 9.40 and 10.00 9.30.UC3 9.21 Not fixed 7.31.UD2 Version of Update Statistics Improvements

  17. Update statistics can not allocated memory between 4MB and 100MB of sort memory The default has been raised from 4MB to 15MB User can now configure the amount of memory Use DBUPSPACE has been augmented to include memory Format of DBUPSPACE {max disk space}:{default memory} To increase the memory to 35 MB, set DBUPSPACE=0:35. Allow update statistics to use light scans when scanning a a table Implemented light scans Set oriented reads Improvements in Update Statistics

  18. Information about building data distributions is not viewable by the DBA Set explain will now print the scan path and resource usage when building data distributions Update statistics low on fragmented tables does not run in parallel With PDQ turned on each index fragment will be scanned in parallel PDQ at 1 means 10% of the index fragments scanned in parallel, while PDQ at 10 means all the index fragments will be scanned in parallel Improvements in update statistics

  19. Various errors (126, 312, 100,…) when executing update statistics Errors when trying to insert the distributions because set lock mode to wait was not handled properly inside update statistics Range scanning a fragmented index is slow Replace the next loop merge with a binary search merge when ordering items from index fragments Most noticeable when the number of fragments in an index is large Improvements in Update Statistics

  20. Update Statistics Medium Memory Requirements

  21. In memory sort Approximate Memory = number of rows * sum(column widths + 2 * sizeof(pointer) ) Update Statistics High Memory Requirements

  22. Estimated Update Stats memory is below 100MB Hard coded limit of 4MB Attempts to minimize the scans by fitting as many columns into 4MB Estimated Update Stats memory is above 100MB Memory is requested from MGM Attempt to minimize the scans by fitting as many columns in the MGM memory Memory Rules

  23. Examples • Customer Table Cust_id integer Fname char(50) Lname char(50) Address1 char(200) Address2 char(200) State char(2) zipcode integer • Number of Rows 500,000

  24. ExamplesMemory for Incore Sort

  25. ExamplesNumber of Table Scans

  26. A factor in the number of samples used by update statistics medium Confidence

  27. Percentage of data that is represented in a distribution bin Example 100,000 rows in the table Resolution of 2% Each bin will represent 2,000 rows Resolution

  28. Example • Following Example • Table size 215,000 rows • Row size 445 bytes • Uniprocessor

  29. Table: jmiller.t9 Mode: HIGH Number of Bins: 267 Bin size 1082 Sort data 101.4 MB Sort memory granted 4.0 MB Estimated number of table scans 10 PASS #1 c9 PASS #2 c5 PASS #3 c7 PASS #4 c6 ….. PASS #10 c4 Completed pass 1 in 0 minutes 24 seconds Completed pass 2 in 0 minutes 20 seconds Completed pass 3 in 0 minutes 17 seconds Completed pass 4 in 0 minutes 17 seconds Completed pass 5 in 0 minutes 17 seconds Completed pass 6 in 0 minutes 15 seconds Completed pass 7 in 0 minutes 14 seconds Completed pass 8 in 0 minutes 15 seconds Completed pass 9 in 0 minutes 16 seconds Completed pass 10 in 0 minutes 14 seconds Example of the current update statistics Total Time 146 seconds

  30. Completed pass 1 in 0 minutes 34 seconds Completed pass 2 in 0 minutes 19 seconds Completed pass 3 in 0 minutes 16 seconds Completed pass 4 in 0 minutes 14 seconds Completed pass 5 in 0 minutes 15 seconds The New Defaults Table: jmiller.t9 Mode: HIGH Number of Bins: 267 Bin size 1082 Sort data 101.4 MB Sort memory granted 15.0 MB Estimated number of table scans 7 PASS #1 c9,c8,c10,c5,c7 PASS #2 c6,c1 PASS #3 c3 PASS #4 c2 PASS #5 c4 Total Time 98 seconds New Memory Default

  31. Table: jmiller.t9 Mode: HIGH Number of Bins: 267 Bin size 1082 Sort data 101.4 MB PDQ memory granted 106.5 MB Estimated number of table scans 1 PASS #1 c1,c2,c3,c4,c5,c6,c7,c8,c9,c10 Index scans disabled Light scans enabled Completed pass 1 in 0 minutes 29 seconds Enabling PDQ with Update Statistics PDQ Memory Features Enabled Total Time 29 seconds

  32. Turn on PDQ when running update statistics, but only for tables Avoid PDQ when updating statistics for procedures When running high or medium increase the memory update statistics has to work with Enable parallel sorting (i.e. PSORT_NPROCS) Tuning with the New Statistics

  33. Change the RESOLUTION to 1.5 Increasing the number of bins for the distributions Increasing the sample size for update statistics medium Considerations

  34. Old Recommendations • Start one update statistics for each column of a table Fname Lname Address Three sequential scans of the table

  35. New Recommendations • Start one update statistics for ALL columns giving it more resources (memory) • Requires only one scan of the table to produce distributions on several columns. Fname Lname Address One scans of the table

  36. An Overview of the IBM Informix Dynamic Server Optimizer www.ibm.com/developerworks/db2/zones/informix/library/techarticle/0211desai/0211desai.html Understanding and Tuning Update Statistics www.ibm.com/developerworks/db2/zones/informix/library/techarticle/miller/0203miller.html Predicate Inference in Informix Dynamic Server www.ibm.com/developerworks/db2/zones/informix/library/techarticle/0206goswami/0206goswami.html IBM Informix Performance Manual IBM Informix SQL Reference Manual Other Information

  37. Questions

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