1 / 34

Unlocking the Mysteries Behind Update Statistics

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

phillip-whitfield
Download Presentation

Unlocking the Mysteries Behind Update Statistics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Unlocking the Mysteries Behind Update Statistics John F. Miller III

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

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

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

  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. Update Statistics LowBasic Algorithm • 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

  7. How to Read Distributions To get the range of values look at the highest value in the previous bin. # 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 --- 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) • 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

  9. Example - Dealing with Data Skew --- 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) • 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

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

  11. Scan • The table is scanned in its entirety for update stats high, while it is only sampled for update stats medium (see Sample Size) • The reading of rows is done in dirty read isolation, regardless of what the user has set for their transaction level.

  12. Scan • This scan of the table may occur several times depending on the amount of sort memory available and the number of columns to collect statistics about. • The approximate number of table scans is defined by the (size of the data to sort) / (amount of sort memory)

  13. Sort • The rows processed by the scan phase are passed directly to the sort package. • Each column in the row for which statistics are being generated is passed to a unique invocation of a sort.

  14. Build • After the sort is completed we read the sorted column data finding out the number of duplicates and unique values creating approximately 200 range bins by default. • Any count of a duplicates value that exceeds 25% the size of a bin will be placed in an overflow bin.

  15. Insert • Now we have to delete the old distributions and insert the new distributions. As long as the user was not in a transaction this will be done as its own transaction. This transaction will last for less than 1 second and will hold NO locks on the tables, but locks on the system catalogs while the update occurs.

  16. Sample Size • HIGH • The entire tables is scanned and all rows are used. • 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. See the following chart.

  17. Update Statistics Medium Sample Size

  18. Update Statistics Medium Memory Requirements

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

  20. 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

  21. 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

  22. ExamplesMemory for Incore Sort

  23. ExamplesNumber of Table Scans

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

  25. Resolution • 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

  26. Improvements in update statistics in 7.31.UD2 • UPDATE STATISTICS CAN NOT ALLOCATE SORT MEMORY BETWEEN 4 AND 100 MB • The default has been raised from 4MB to 15MB • User can now configure the amount of memory • UPDATE STATISTICS USES SLOW SCANNING TECHNOLOGY WHEN SCANNING A TABLE -- ENABLE LIGHT SCANS • Implemented light scans • Set oriented reads

  27. Improvements in update statistics in 7.31.UD2 • THE PLAN UPDATE STATISTICS MAKE WHEN SCANNING IS NOT VIEWABLE BY THE DBA • Set explain will now print the scan path and resource usage • UPDATE STATISTICS LOW ON FRAGMENT INDEXES RUNS SERIALLY AND VERY SLOW • 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

  28. Improvements in update statistics in 7.31.UD2 • ERROR 126 WHEN EXECUTING UPDATE STATISTICS AND STORED PROCECURE (ALSO ERRORS 312/100) • Errors when trying to insert the distributions because set lock mode to wait was not handled properly inside update statistics • SCANNING AN INDEX WHICH IS FRAGMENT IS SLOW DUE TO THE INEFFICIENT MERGE IN THE FRAGMENT MANAGER • Binary search used instead of the previous nest loop merge when ordering index fragments • Most noticeable when the number of fragments in an index is large

  29. Tuning with the New Statistics • 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)

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

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

  32. 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

  33. 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 in 7.31.UD2 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

  34. 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

More Related