# DBCC SHOW_STATISTICS (Transact-SQL)

**SQL Server 2008**

DBCC SHOW_STATISTICS displays current query optimization statistics for a table or indexed view. The query optimizer uses statistics to estimate the cardinality or number of rows in the query result, which enables the query optimizer to create a high quality query plan. For example, the query optimizer could use cardinality estimates to choose the index seek operator instead of the index scan operator in the query plan, improving query performance by avoiding a resource-intensive index scan.

The query optimizer stores statistics for a table or indexed view in a statistics object. For a table, the statistics object is created on either an index or a list of table columns. The statistics object includes a header with metadata about the statistics, a histogram with the distribution of values in the first key column of the statistics object, and a density vector to measure cross-column correlation. The Database Engine can compute cardinality estimates with any of the data in the statistics object.

DBCC SHOW_STATISTICS displays the header, histogram, and density vector based on data stored in the statistics object. The syntax lets you specify a table or indexed view along with a target index name, statistics name, or column name. This topic describes how to display the statistics and how to understand the displayed results.

For more information, see Using Statistics to Improve Query Performance.

The following table describes the columns returned in the result set when STAT_HEADER is specified.

Column name | Description |
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Name | Name of the statistics object. |

Updated | Date and time the statistics were last updated. The STATS_DATE function is an alternate way to retrieve this information. |

Rows | Total number of rows in the table or indexed view when the statistics were last updated. If the statistics are filtered or correspond to a filtered index, the number of rows might be less than the number of rows in the table. For more information, seeUsing Statistics to Improve Query Performance. |

Rows Sampled | Total number of rows sampled for statistics calculations. If Rows Sampled < Rows, the displayed histogram and density results are estimates based on the sampled rows. |

Steps | Number of steps in the histogram. Each step spans a range of column values followed by an upper bound column value. The histogram steps are defined on the first key column in the statistics. The maximum number of steps is 200. |

Density | Calculated as 1 / distinct values for all values in the first key column of the statistics object, excluding the histogram boundary values. This Density value is not used by the query optimizer and is displayed for backward compatibility with versions before SQL Server 2008. |

Average Key Length | Average number of bytes per value for all of the key columns in the statistics object. |

String Index | Yes indicates the statistics object contains string summary statistics to improve the cardinality estimates for query predicates that use the LIKE operator; for example, WHERE ProductName LIKE '%Bike'. String summary statistics are stored separately from the histogram and are created on the first key column of the statistics object when it is of type char, varchar, nchar, nvarchar, varchar(max), nvarchar(max), text, or ntext.. |

Filter Expression | Predicate for the subset of table rows included in the statistics object. NULL = non-filtered statistics. For more information about filtered predicates, see Filtered Index Design Guidelines. For more information about filtered statistics, see Using Statistics to Improve Query Performance. |

Unfiltered Rows | Total number of rows in the table before applying the filter expression. If Filter Expression is NULL, Unfiltered Rows is equal to Rows. |

The following table describes the columns returned in the result set when DENSITY_VECTOR is specified.

Column name | Description |
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All Density | Density is 1 / distinct values. Results display density for each prefix of columns in the statistics object, one row per density. A distinct value is a distinct list of the column values per row and per columns prefix. For example, if the statistics object contains key columns (A, B, C), the results report the density of the distinct lists of values in each of these column prefixes: (A), (A,B), and (A, B, C). Using the prefix (A, B, C), each of these lists is a distinct value list: (3, 5, 6), (4, 4, 6), (4, 5, 6), (4, 5, 7). Using the prefix (A, B) the same column values have these distinct value lists: (3, 5), (4, 4), and (4, 5) |

Average Length | Average length, in bytes, to store a list of the column values for the column prefix. For example, if the values in the list (3, 5, 6) each require 4 bytes the length is 12 bytes. |

Columns | Names of columns in the prefix for which All density and Average length are displayed. |

The following table describes the columns returned in the result set when the HISTOGRAM option is specified.

Column name | Description |
---|---|

RANGE_HI_KEY | Upper bound column value for a histogram step. The column value is also called a key value. |

RANGE_ROWS | Estimated number of rows whose column value falls within a histogram step, excluding the upper bound. |

EQ_ROWS | Estimated number of rows whose column value equals the upper bound of the histogram step. |

DISTINCT_RANGE_ROWS | Estimated number of rows with a distinct column value within a histogram step, excluding the upper bound. |

AVG_RANGE_ROWS | Average number of rows with duplicate column values within a histogram step, excluding the upper bound (RANGE_ROWS / DISTINCT_RANGE_ROWS for DISTINCT_RANGE_ROWS > 0). |

### Histogram

A histogram measures the frequency of occurrence for each distinct value in a data set. The query optimizer computes a histogram on the column values in the first key column of the statistics object, selecting the column values by statistically sampling the rows or by performing a full scan of all rows in the table or view. If the histogram is created from a sampled set of rows, the stored totals for number of rows and number of distinct values are estimates and do not need to be whole integers.

To create the histogram, the query optimizer sorts the column values, computes the number of values that match each distinct column value and then aggregates the column values into a maximum of 200 contiguous histogram steps. Each step includes a range of column values followed by an upper bound column value. The range includes all possible column values between boundary values, excluding the boundary values themselves. The lowest of the sorted column values is the upper boundary value for the first histogram step.

The following diagram shows a histogram with six steps. The area to the left of the first upper boundary value is the first step.

For each histogram step:

Bold line represents the upper boundary value (RANGE_HI_KEY) and the number of times it occurs (EQ_ROWS)

Solid area left of RANGE_HI_KEY represents the range of column values and the average number of times each column value occurs (AVG_RANGE_ROWS). The AVG_RANGE_ROWS for the first histogram step is always 0.

Dotted lines represent the sampled values used to estimate total number of distinct values in the range (DISTINCT_RANGE_ROWS) and total number of values in the range (RANGE_ROWS). The query optimizer uses RANGE_ROWS and DISTINCT_RANGE_ROWS to compute AVG_RANGE_ROWS and does not store the sampled values.

The query optimizer defines the histogram steps according to their statistical significance. It uses a maximum difference algorithm to minimize the number of steps in the histogram while maximizing the difference between the boundary values. The maximum number of steps is 200. The number of histogram steps can be fewer than the number of distinct values, even for columns with fewer than 200 boundary points. For example, a column with 100 distinct values can have a histogram with fewer than 100 boundary points.

### Density Vector

The query optimizer uses densities to enhance cardinality estimates for queries that return multiple columns from the same table or indexed view. The density vector contains one density for each prefix of columns in the statistics object. For example, if a statistics object has the key columns CustomerId, ItemId, Price, density is calculated on each of the following column prefixes.

Column prefix | Density calculated on |
---|---|

(CustomerId) | Rows with matching values for CustomerId |

(CustomerId, ItemId) | Rows with matching values for CustomerId and ItemId |

(CustomerId, ItemId, Price) | Rows with matching values for CustomerId, ItemId, and Price |

### Restrictions

DBCC SHOW_STATISTICS does not provide statistics for spatial indexes.

### A. Returning all statistics information

The following example displays all statistics information for the AK_Product_Name index of the Person.Address table.

USE AdventureWorks; GO DBCC SHOW_STATISTICS ("Person.Address", AK_Address_rowguid); GO

### B. Specifying the HISTOGRAM option

The following example limits the statistics information displayed for the AK_Product_Name index to the HISTOGRAM data.

USE AdventureWorks; GO DBCC SHOW_STATISTICS ("Person.Address", AK_Address_rowguid) WITH HISTOGRAM; GO

Updated content |
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Revisions throughout the document to improve accuracy. |

Topic refers to new statistics content in the topic Using Statistics to Improve Query Performance. |