SQL/MX UPDATE STATISTICS Enhancements
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- Ruth Amberly Quinn
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1 SQL/MX UPDATE STATISTICS Enhancements Introduction... 2 UPDATE STATISTICS Background... 2 Tests Performed... 2 Test Results... 3 For more information... 7
2 Introduction HP NonStop SQL/MX Release includes a new internal algorithm that improves the accuracy of histograms created by the UPDATE STATISTICS statement. This white paper describes several tests that were performed to measure the improvements in histogram accuracy. The test results and accompanying discussion provide clues to understanding how statistics are updated and how to use the UPDATE STATISTICS statement more effectively. UPDATE STATISTICS Background The SQL/MX UPDATE STATISTICS statement gathers information about the distribution of specified columns in a table and stores this information in the form of a histogram in user metadata tables. Histograms that are built by UPDATE STATISTICS consist of some number of intervals, each representing a range of column values. Within each histogram interval, two counts are stored: Number of rows Number of distinct values in the interval For example, if statistics are updated on the Salary field of an Employee table, the resulting histogram contains intervals that divide up the range of salaries in the table. Each interval, in turn, contains a count of the number of Employee rows and the number of distinct salaries in the associated range. The SQL/MX compiler uses histograms that are built by the UPDATE STATISTICS statement to translate the text of an SQL statement into an efficient execution plan. To ensure that the compiler selects the most efficient plan, the histograms used during compilation must be as accurate as possible. It is especially important for the distinct value counts, also known as unique entry counts (UECs), to be accurate. The compiler uses these counts to make important decisions, such as determining the order in which tables in a query are joined and deciding which join methods to use. The UPDATE STATISTICS statement supports options that allow histograms to be built from all the rows in a table or from a sample of rows of a specified size. Building histograms from all rows in a table results in perfectly accurate histograms, but also prohibitively long run times for large tables. When histograms are built from a sample, the row count and UEC in each interval must be estimated from the sample, and the estimates are rarely perfectly accurate. Fortunately, SQL/MX users can use relatively small sample sizes to achieve acceptable levels of histogram accuracy and UPDATE STATISTICS performance. However, selecting a sample size that provides a good balance between accuracy and performance can be a difficult task. In general, for a given sample size, the histogram accuracy improvements introduced in Release result in more accurate histogram UECs than the histograms built by UPDATE STATISTICS before Release Alternatively, the improvements allow the same level of accuracy as in previous releases, but with a smaller sample size. The use of a smaller sample size is desirable, because it reduces the time it takes UPDATE STATISTICS to build histograms. The improved accuracy of UPDATE STATISTICS in Release results from the use of a new internal algorithm for estimating the UEC of a column in a full table from a sample of rows in the table. You can use the test results described in this paper to help determine an appropriate sample size when updating statistics. Tests Performed UPDATE STATISTICS tests were performed on three different data sets: Synthetic data drawn from Normal, Uniform, and Zipf distributions Synthetic data from a large SQL/MX retail demonstration system 2
3 Real customer data The tables ranged in size from approximately 10 million to 1 billion rows, and the UPDATE STATISTICS sample sizes ranged from 0.1 to 20 percent. For each data set and sample size, UPDATE STATISTICS was run both with and without the accuracy enhancement. For each column tested, the estimated UEC was retrieved from the histogram tables and recorded. The UEC estimates were then compared to the actual or true UEC for each column, and the error was determined. For each test table, the results presented in this paper show the average UEC estimation error for the columns on which statistics were updated. The UEC estimation error for a column was calculated according to this formula: Error = estimateduec actualuec / actualuec * 100 For example, suppose this UPDATE STATISTICS statement is executed: UPDATE STATISTICS FOR TABLE Employee ON (Salary) SAMPLE RANDOM 1 PERCENT; As part of executing this statement, UPDATE STATISTICS stores the estimated UEC for the Salary column in the histogram metadata tables. Assume for this example that the UEC estimated by UPDATE STATISTICS from the 1 percent random sample is 45,000, while the actual UEC is 50,000. The estimation error, in this case, is 10 percent ( 45,000 50,000 / 50,000 * 100). Test Results The first test data set consisted of SQL/MX tables containing synthetic data drawn from Uniform, Normal, and Zipf distributions. In the Uniform distribution, distinct values occur an equal number of times. This distribution was included in the testing because it is well understood, and generating data from it is straightforward. The Normal distribution is the well-known bell curve that has a frequentlyoccurring mean, or average value, and then values that occur less frequently as the distance from the mean increases. Many phenomena are normally distributed, such as the height of humans and measurements like reading ability and job satisfaction. The Zipf distribution is characterized by high skew, where there are a few very large values and many smaller values. Examples of this distribution are city populations, frequency of access to Web pages, and corporate revenue. Each of the test tables contained 10 million rows and three columns on which histograms were built. Sample sizes from 1 to 20 percent were tested. Row-level sampling was used in all cases. UPDATE STATISTICS also supports cluster sampling, which is an alternative to row-level sampling. In row-level sampling, a table is scanned and a test performed on each row to see if it should be included in the sample. The outcome of this test depends only on the specified sample size, and not on the values in a row. In cluster sampling, a similar scan is done, but the test is performed on each cluster (a contiguous set of disk blocks), rather than on individual rows. If a cluster is selected, all the rows in the cluster are added to the sample. Cluster sampling is much faster than row sampling. Clusters that are not selected for a sample are skipped and not actually read from disk, thus there are fewer I/O operations compared to row sampling. However, cluster sampling tends to produce samples that are less random, which generally results in less-accurate statistics. The test results for the first data set are shown in this table: 3
4 Table Row Count Sample* Error Error Uniform 10,000,000 1% row % 4.8% Uniform 10,000,000 5% row 558.5% 4.3% Uniform 10,000,000 10% row 191.4% 4.2% Uniform 10,000,000 20% row 108.6% 4.1% Normal 10,000,000 1% row 806.3% 18.8% Normal 10,000,000 5% row 15.6% 10.2% Normal 10,000,000 10% row 12.6% 6.7% Normal 10,000,000 20% row 9.7% 5.2% Zipf 10,000,000 1% row 786.3% 6.2% Zipf 10,000,000 5% row 833.1% 4.3% Zipf 10,000,000 10% row 626.0% 4.2% Zipf 10,000,000 20% row 208.8% 4.1% *The UPDATE STATISTICS sample size can be specified as a percentage of the rows in a table (for example, 1%) or as an absolute number of rows (for example, , denoted as 1M in this paper). The sample methods used for testing were row-level sampling and cluster sampling. The error values that are reported in the table are the average UEC errors of the three columns in each test table for each sample size. The average Release estimation error for each table and sample size is always less than the error for previous releases and, in most cases, significantly less. While the average estimation error is less in all cases, in a couple of instances the Release estimation error for individual test table columns was greater than in prior releases. Therefore, when upgrading to Release 2.1.1, the UECs produced by UPDATE STATISTICS, and the execution plans produced by the compiler, should be examined for performance-sensitive tables and queries. In the rare cases where a pre release produced more efficient plans, UPDATE STATISTICS options, such as sample size, should be tuned (for example, increased) so that the same or more efficient plans are produced with Release The second test data set consisted of three SQL/MX tables containing synthetic data generated for a large retail demonstration system. The tables contained between 4 and 66 columns and ranged from about 40 million rows to just over 1 billion rows. In this set of tests, the UPDATE STATISTICS clustersampling option was used. As discussed earlier, the use of cluster sampling speeds up the sampling operation but generally results in a less-random sample. The results are shown in this table: Table Row Count Sample Error Error Table 1 39,975,000 1% Cluster % 73.5% Table 2 1,007,520, % Cluster 12.0% 0.0% Table 3 1,086,420, % Cluster 256.9% 10.9% As before, the Release estimation error is significantly less than the error for previous releases. 4
5 The third and final test data set consisted of six SQL/MP tables containing actual customer data. The tables contained between 7 and 24 columns and ranged from about 15 million rows to just under 1 billion rows. Row-level sampling was used with various sample sizes. The results are shown in this table: Table Row Count Sample Error Error Table 1 23,074,465 1% row 64.2% 31.7% Table 1 23,074,465 2% row 77.3% 24.7% Table 2 22,495,302 1% row 242.8% 51.8% Table 2 22,495,302 2% row 134.9% 35.3% Table 3 15,017,615 1% row 138.9% 39.6% Table 3 15,017,615 2% row 51.8% 30.1% Table 4 146,370,017 1M row 463.0% 51.9% Table 4 146,370,017 2M row 252.3% 40.4% Table 5 413,374,858 1M row 958.1% 97.6% Table 5 413,374,858 2M row 541.6% 80.3% Table 6 900,619,606 1M row % 95.6% Table 6 900,619,606 2M row % 58.4% Again, the accuracy is significantly better in Release than in previous releases. For the third data set, UPDATE STATISTICS run times were recorded. The tests were run on a threenode HP NonStop S86000 system, with four CPUs per node. The results are shown in this table: Table Row Count Sample Columns Time (m) Time (m) Performance Increase Table 1 23,074,465 1% row % Table 1 23,074,465 2% row Table 2 22,495,302 1% row % Table 2 22,495,302 2% row Table 3 15,017,615 1% row % Table 3 15,017,615 2% row Table 4 146,370,017 1M row % Table 4 146,370,017 2M row Table 5 413,374,858 1M row % Table 5 413,374,858 2M row Table 6 900,619,606 1M row % Table 6 900,619,606 2M row
6 For each test table, the results show the number of columns on which statistics are gathered and the actual run times, in minutes, for the pre and Release versions of UPDATE STATISTICS. For the same sample size, the Release version of UPDATE STATISTICS is approximately 1 to 2 percent slower than its predecessors.. However, with Release 2.1.1, you can use smaller sample sizes and achieve better performance and accuracy. The last column in the table compares the performance of UPDATE STATISTICS in Release using the smaller sample size for each table to the performance of pre UPDATE STATISTICS using the larger sample size. The smaller sample size yields a performance increase in the 15 to 30 percent range. In all six of the test tables, using the smaller sample size with Release provides greater accuracy than using the larger sample size with previous versions of UPDATE STATISTICS. 6
7 For more information Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. The only warranties for HP products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. HP shall not be liable for technical or editorial errors or omissions contained herein , 09/2005
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