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Selecting Rows Randomly from a Large Table

Marcelo De Barros, Kenton Gidewall

Microsoft Corporation

April 2008

Applies to:

   Microsoft SQL Server 2000

   Microsoft SQL Server 2005

Summary: The authors offer new ideas on how to select random rows from a large table. (4 printed pages)

If you use Microsoft SQL Server 2000, you likely have run into the following problem: You want to select a random sampling of rows from a large table with lots of rows, but you are unsure of how to do so. Having a random sampling of rows can be useful when you want to make a smaller version of the table or if you want to troubleshoot a problem by seeing what kinds of rows are in the table.

To get a random sampling, you might be tempted to select the top n rows from the table. However, this sample is not random, and the first n rows are not necessarily representative of the whole table. Other solutions exist that involve adding columns to the tables; however, adding columns is not always possible or practical.

The standard way to grab random rows from a small table is to use a query such as the following:

  SELECT TOP 10 PERCENT *
  FROM Table1
  ORDER BY NEWID()

The key here is the NEWID function, which generates a globally unique identifier (GUID) in memory for each row. By definition, the GUID is unique and fairly random; so, when you sort by that GUID with the ORDER BY clause, you get a random ordering of the rows in the table. Taking the top 10 percent (or whatever percentage you want) will give you a random sampling of the rows in the table.

Often, when questions about how to select random rows are asked in discussion groups, the NEWID query is proposed; it is simple and works very well for small tables. However, the NEWID query has a big drawback when you use it for large tables. The ORDER BY clause causes all of the rows in the table to be copied into the tempdb database, where they are sorted. This causes two problems:

  1. The sorting operation usually has a high cost associated with it. Sorting can use a lot of disk I/O and can run for a long time.
  2. In the worst-case scenario, tempdb can run out of space. In the best-case scenario, tempdb can take up a large amount of disk space that never will be reclaimed without a manual shrink command.

What you need is a way to select rows randomly that will not use tempdb and will not get much slower as the table gets larger. Here is a new idea on how to do that:

  SELECT * FROM Table1
  WHERE (ABS(CAST(
  (BINARY_CHECKSUM(*) *
  RAND()) as int)) % 100) < 10

The basic idea behind this query is that we want to generate a random number between 0 and 99 for each row in the table, and then choose all of those rows whose random number is less than the value of the specified percent. In this example, we want approximately 10 percent of the rows selected randomly; therefore, we choose all of the rows whose random number is less than 10.

Taking a closer look at how the (ABS(CAST((BINARY_CHECKSUM(*) * RAND()) as int)) portion of this query works: The BINARY_CHECKSUM function generates a checksum value that is based on the values of the columns that you specify. If two rows are different, they typically will generate different checksum numbers. The BINARY_CHECKSUM function generally is used to verify whether any of the columns in a row in a table have changed. However, for our purposes, it generates a number that looks like a random number for each row.

The shortcoming of using the BINARY_CHECKSUM function for our purpose is that, every time that it is used on a row that has not been modified, it returns the same checksum number. Thus, when it is used by itself, subsequent runs of the query return the same "random" set of rows, which obviously is not desirable.

To fix this shortcoming, we added the RAND function to the BINARY_CHECKSUM query. The RAND function scrambles the numbers that are returned by the BINARY_CHECKSUM function. Thus, we get a different set of rows each time the query is run—making it truly random. The ABS and CAST functions are used, because BINARY_CHECKSUM (*) * RAND returns a float that can be a negative number.

The asterisk (*) in BINARY_CHECKSUM (*) tells the function to use in its calculations all of the columns in the row. Alternatively, you can specify a subset of the columns in place of the asterisk. Because this function is CPU-intensive, specifying the minimum number of columns or minimum number of bytes will give you the best performance. The best candidates would be the columns in a unique index. If you decide to use specific columns instead of all of the columns, you can add NEWID as a column in the BINARY_CHECKSUM function, so that the BINARY_CHECKSUM query will return a random number each time. Thus, you do not need to use RAND in the query, which simplifies it slightly, as shown here:

  SELECT * FROM Table1
  WHERE (ABS(CAST(
  (BINARY_CHECKSUM
  (keycol1, NEWID())) as int))
  % 100) < 10

Because no sorting is involved in the BINARY_CHECKSUM query, only a single pass through the table is required to choose n % of the rows. The time and the I/O both stay linear, in proportion to the size of the table.

To test the BINARY_CHECKSUM query against the NEWID query, we set up three large tables that contain 1 million rows (435MB), 7 million rows (3GB), and 14 million rows (5.4GB), respectively, on an HP ProLiant DL580 G2 server with 1GB memory, four 2.2MHz Intel processors, and eight 36GB disks in RAID 1+0 configuration. Table 1 shows the results. Figure 1 compares graphically how long the queries took. As Figure 1 and Table 1 both show, the BINARY_CHECKSUM query saves a lot of time and I/O, compared with the NEWID query.

The SQL Server team at Microsoft realized that not being able to take random samples of rows easily was a common problem in SQL Server 2000; so, the team addressed the problem in SQL Server 2005 by introducing the TABLESAMPLE clause. This clause selects a subset of rows by choosing random data pages and returning all of the rows on those pages. However, for those of us who still have products that run on SQL Server 2000 and need backward-compatibility, or who need truly row-level randomness, the BINARY_CHECKSUM query is a very effective workaround.

Table 1. Test results

  Time (sec) Table1 logical I/O count Worktable logical I/O count Total I/O count CPU time (msec)

1 million rows

 

 

 

 

 

NEWID query

14.3

27,076

1,046,172

1,073,248

32,142

BINARY_CHECKSUM query

0.7

27,076

0

19,807

2,781

7 million rows

 

 

 

 

 

NEWID query

134

193,790

7,332,291

7,526,081

227,250

BINARY_CHECKSUM query

10

193,790

0

193,790

28,812

13 million rows

 

 

 

 

 

NEWID query

253

347,420

13,810,132

14,157,552

422,891

BINARY_CHECKSUM query

21

347,420

0

347,420

49,203

Cc441928.49d347cb-c75d-4d94-9cad-3a0d7d7a2d5b(en-us,MSDN.10).gif

Figure 1. NEWID vs. BINARY_CHECKSUM queries

About the authors

Marcelo De Barros (marcelod@microsoft.com) is a Senior Test Manager at Microsoft. He received an undergraduate degree in Computer Science in Brazil (1999), and a M.S. in Computer Science from the University of Washington (2006). Marcelo's areas of expertise include performance and scalability tests, and capacity planning.

Kenton Gidewall (kentong@microsoft.com) is a Software Design Engineer/Test at Microsoft. He has a B.S. in Computer Science from Brigham Young University (1989) and a M.S. in Computer Science from Michigan State (1992). Kenton's areas of expertise include high TPS, large-scale system development, and performance testing.

This article was published originally in SQL Server Magazine . Copyright 2007 by Penton Media. Reproduced with permission.

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