SQL Server 2016 SP1 CU2 がリリースされていました。
https://support.microsoft.com/en-us/help/4013106/cumulative-update-2-for-sql-server-2016-sp1
sys.dm_db_stats_histogram が DMV に追加されたようです。
ryuchan.hatenablog.com
SQL Server 2016 SP1 CU2 の環境で下記のクエリを実行してみましょう。
SELECT
s.*,
sh.*
FROM
sys.stats s
CROSS APPLY sys.dm_db_stats_histogram(s.object_id,s.stats_id) sh
何かいいサンプル作りたいなぁと思いましたが、面倒なので ( 子供の面倒と仕事がある... ) MSSQL Tiger Team のブログに非常に良いサンプルがありました。早速見てみましょう。
blogs.msdn.microsoft.com
はい、本当にそう思います。まわりにそんな人いたらいいんですが....
Statistics being the building blocks on which the Query Optimizer reasons to compile a good enough plan to resolve queries, it’s very common that anyone doing query performance troubleshooting needs to use DBCC SHOW_STATISTICS to understand how statistics are being used, and how accurately they represent data distribution.
それを見ようとする人もなかなかいませんが...
Let’s say you wanted to understand where the estimation came from, then naturally you will want to look for whatever stats object references that single column (using a single column predicate for simplicity sake). If you need to programmatically access this data, then usually you would dump DBCC SHOW_STATISTICS … WITH HISTOGRAM to a table, and then use it from there. That is not ideal.
そして、今回 CU2 で dm_db_stats_histogram が追加されました。
With the latest release of SQL Server 2016 SP1 CU2, we added a new Dynamic Management Function (DMF) sys.dm_db_stats_histogram, which is similar to running the above DBCC statement. Note: this DMF is also available in SQL Server vNext CTP 1.3.
DBCC SHOW_STATISTICS … WITH STATS_HEADER を sys.dm_db_stats_properties で参照することができます。
This further completes the story we started with sys.dm_db_stats_properties, which has a similar output to running DBCC SHOW_STATISTICS … WITH STATS_HEADER.
ここが肝です。指定された述語から統計情報を用いて算出された推定行数を求めることができます。
But to make it more interesting, here’s one example on how you can leverage these DMFs inline, to get information on which stat and respective histogram steps cover my predicate, in the scope of my table and column:
SELECT
ss.name,
ss.stats_id,
shr.steps,
shr.rows,
shr.rows_sampled,
shr.modification_counter,
shr.last_updated,
SUM(sh.range_rows + sh.equal_rows) AS predicate_step_rows
FROM
sys.stats ss
INNER JOIN
sys.stats_columns sc
ON ss.stats_id = sc.stats_id
AND ss.object_id = sc.object_id
INNER JOIN
sys.all_columns ac
ON ac.column_id = sc.column_id
AND ac.object_id = sc.object_id CROSS APPLY sys.dm_db_stats_properties(ss.object_id, ss.stats_id) shr CROSS APPLY sys.dm_db_stats_histogram(ss.object_id, ss.stats_id) sh
WHERE
ss.[object_id] = OBJECT_ID('FactResellerSales')
AND ac.name = 'OrderDate'
AND sh.range_high_key BETWEEN CAST('20110101' AS DATE) AND CAST('20110606' AS DATE)
GROUP BY
ss.name,
ss.stats_id,
shr.steps,
shr.rows,
shr.rows_sampled,
shr.modification_counter,
shr.last_updated
このあたりを拡張して、統計情報に関するクエリを作成できそうです。さて、子供と少しだけお出かけして、仕事でもするか....
肉屋 格之進Ｆ
