在前一篇博文数据压缩简要的基础上，我希望把数据压缩评估自动化。于是有了这篇博文。
白皮书推荐对符合如下条件的大型表和索引使用页压缩：
表或索引的扫描操作占到所有操作的75%及以上时
表或索引的更新操作占到所有操作的20%及以下时
注意，这是白皮书中的结论和建议，但是也只能做参考，最为最佳实践的考虑点之一。
此脚本的原始作者是Louis Li。但是它的脚本有一些限制，我在这此基础上做了修改：
辅助表由用户表改成临时表
只分析页数大于1000的分区
判断范围扩大到所有的表和索引，而不只是堆和聚集索引
判断粒度改成分区级别。
增加各分区使用空间的统计
修改生成语句，增加提高性能的选项: MAXDOP=8,SORT_IN_TEMPDB=ON
修改过滤条件。原来只分析Scan大于75%的分区，这样流水日志类型的表(S~=0%,U~=0%)会被过滤掉。改成75%或者Update小于20%的。
下面脚本会找出符合以下条件的对象并生成相应的压缩数据脚本。
1. 扫描当前数据库的所有索引，找出同时符合下面条件的索引：
索引的页数超过1000
索引的SELECT操作在所有操作中的占比高于75%或者索引的UPDATE操作在所有操作中的占比小于20%
注意此处的粒度是基于分区的。所以如果表和索行，做了分区会在分区级别上做出判断。
2. 对于被上一步找出的索引，分别评估页和行压缩能节省的空间(用百分比表示)。
3. 对比行和页压缩的数据，进行推荐。对于没有UPDATE操作或者页压缩节省的空间比行压缩多10%，则推荐页压缩。其余索引都推荐行压缩。
4. 脚本的结果分为两部分，第一部分是推荐的压缩的索引，第二部分是推荐压缩的方式和相应脚本。
--Collect all index stats if object_id(‘tempdb..#index_estimates‘) is not null drop table #index_estimates go create table #index_estimates ( database_name sysname not null, [schema_name] sysname not null, table_name sysname not null, index_id int not null, partition_number int not null, update_pct decimal(5,2) not null, select_pct decimal(5,2) not null, used_size_kb int not null, constraint pk_index_estimates primary key (database_name,[schema_name],table_name,index_id,partition_number) ) ; go insert into #index_estimates select db_name() as database_name, schema_name(t.schema_id) as [schema_name], t.name, i.index_id, p.partition_number, i.leaf_update_count * 100.0 / (i.leaf_delete_count + i.leaf_insert_count + i.leaf_update_count + i.range_scan_count + i.singleton_lookup_count + i.leaf_page_merge_count) as UpdatePct, i.range_scan_count * 100.0 / (i.leaf_delete_count + i.leaf_insert_count + i.leaf_update_count + i.range_scan_count + i.singleton_lookup_count + i.leaf_page_merge_count) as SelectPct ,p.used_page_count*8 as used_size_kb from sys.dm_db_index_operational_stats(db_id(),null,null,null) i inner join sys.tables t on i.object_id = t.object_id inner join sys.dm_db_partition_stats p on i.object_id = p.object_id and i.index_id=p.index_id and i.partition_number=p.partition_number where i.leaf_delete_count + i.leaf_insert_count + i.leaf_update_count + i.range_scan_count + i.singleton_lookup_count + i.leaf_page_merge_count > 0 and p.used_page_count >= 1000 -- only consider tables contain more than 1000 pages --and i.index_id<2 --only consider heap and clustered index and ( (i.range_scan_count / (i.leaf_delete_count + i.leaf_insert_count + i.leaf_update_count + i.range_scan_count + i.singleton_lookup_count + i.leaf_page_merge_count) > .75 or (i.range_scan_count/ (i.leaf_delete_count + i.leaf_insert_count + i.leaf_update_count + i.range_scan_count + i.singleton_lookup_count + i.leaf_page_merge_count))< .2 )) order by t.name, i.index_id go --show data compression candidates select * from #index_estimates; --Prepare 2 intermediate tables for row compression and page compression estimates if OBJECT_ID(‘tempdb..#page_compression_estimates‘) is not null drop table #page_compression_estimates; go create table #page_compression_estimates ([object_name] sysname not null, [schema_name] sysname not null, index_id int not null, partition_number int not null, [size_with_current_compression_setting(KB)] bigint not null, [size_with_requested_compression_setting(KB)] bigint not null, [sample_size_with_current_compression_setting(KB)] bigint not null, [sample_size_with_requested_compression_setting(KB)] bigint not null, constraint pk_page_compression_estimates primary key ([object_name],[schema_name],index_id,partition_number) ); go if OBJECT_ID(‘tempdb..#row_compression_estimates‘) is not null drop table #row_compression_estimates; go create table #row_compression_estimates ([object_name] sysname not null, [schema_name] sysname not null, index_id int not null, partition_number int not null, [size_with_current_compression_setting(KB)] bigint not null, [size_with_requested_compression_setting(KB)] bigint not null, [sample_size_with_current_compression_setting(KB)] bigint not null, [sample_size_with_requested_compression_setting(KB)] bigint not null, constraint pk_row_compression_estimates primary key ([object_name],[schema_name],index_id,partition_number) ); go --Use cursor and dynamic sql to get estimates 9:18 on my laptop declare @script_template nvarchar(max) = ‘insert ###compression_mode##_compression_estimates exec sp_estimate_data_compression_savings ‘‘##schema_name##‘‘,‘‘##table_name##‘‘,##index_id##,##partition_number##,‘‘##compression_mode##‘‘‘; declare @executable_script nvarchar(max); declare @schema sysname, @table sysname, @index_id smallint ,@partition_number smallint,@compression_mode nvarchar(20); declare cur cursor fast_forward for select i.[schema_name], i.[table_name], i.index_id, i.partition_number, em.estimate_mode from #index_estimates i cross join (values(‘row‘),(‘page‘)) AS em(estimate_mode) group by i.[schema_name], i.[table_name], em.estimate_mode, i.index_id, i.partition_number; open cur; fetch next from cur into @schema, @table,@index_id,@partition_number, @compression_mode; while (@@FETCH_STATUS=0) begin set @executable_script = REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(@script_template,‘##schema_name##‘,@schema),‘##table_name##‘,@table),‘##compression_mode##‘,@compression_mode),‘##index_id##‘,@index_id),‘##partition_number##‘,@partition_number); print @executable_script; exec(@executable_script); fetch next from cur into @schema,@table,@index_id,@partition_number, @compression_mode; end close cur; deallocate cur; --Show estimates and proposed data compression with all_estimates as ( select ‘[‘ + i.schema_name + ‘].[‘ + i.table_name + ‘]‘ as table_name, case when i.index_id > 0 then ‘[‘ + idx.name + ‘]‘ else null end as index_name, i.partition_number, i.select_pct, i.update_pct, case when r.[size_with_current_compression_setting(KB)] > 0 then 100 - r.[size_with_requested_compression_setting(KB)] * 100.0 / r.[size_with_current_compression_setting(KB)] else 0.0 end as row_compression_saving_pct, case when p.[size_with_current_compression_setting(KB)] > 0 then 100 - p.[size_with_requested_compression_setting(KB)] * 100.0 / p.[size_with_current_compression_setting(KB)] else 0.0 end as page_compression_saving_pct, (case when ps.name is null then 0 else 1 end) as is_partitioned from #index_estimates i inner join #row_compression_estimates r on i.schema_name = r.schema_name and i.table_name = r.object_name and i.index_id = r.index_id inner join #page_compression_estimates p on i.schema_name = p.schema_name and i.table_name = p.object_name and i.index_id = p.index_id inner join sys.indexes idx on i.index_id = idx.index_id and object_name(idx.object_id) = i.table_name left join sys.partition_schemes ps on idx.data_space_id=ps.data_space_id ), recommend_compression as ( select table_name, index_name, select_pct, update_pct, row_compression_saving_pct, page_compression_saving_pct, partition_number, is_partitioned, case when update_pct = 0 then ‘Page‘ when update_pct >= 20 then ‘Row‘ when update_pct > 0 and update_pct < 20 and page_compression_saving_pct - row_compression_saving_pct < 10 then ‘Row‘ else ‘Page‘ end as recommended_data_compression from all_estimates where row_compression_saving_pct > 0 and page_compression_saving_pct > 0 ) select table_name, index_name, select_pct, update_pct, cast(row_compression_saving_pct as decimal(5,2)) as row_compression_saving_pct, cast(page_compression_saving_pct as decimal(5,2)) as page_compression_saving_pct, recommended_data_compression, case when index_name is null and is_partitioned =0 then ‘ALTER TABLE ‘ + table_name + ‘ REBUILD WITH ( data_compression = ‘ + recommended_data_compression + ‘,MAXDOP=8)‘ when index_name is null and is_partitioned =2 then ‘ALTER TABLE ‘ + table_name + ‘ REBUILD PARTITION=‘+CAST(partition_number AS VARCHAR(2))+‘ WITH ( data_compression = ‘ + recommended_data_compression + ‘,MAXDOP=8)‘ when index_name is not null and is_partitioned =0 then ‘ALTER INDEX ‘ + index_name + ‘ ON ‘ + table_name + ‘ REBUILD WITH (data_compression = ‘ + recommended_data_compression + ‘,MAXDOP=8,SORT_IN_TEMPDB=ON)‘ when index_name is not null and is_partitioned =1 then ‘ALTER INDEX ‘ + index_name + ‘ ON ‘ + table_name + ‘ REBUILD PARTITION=‘+CAST(partition_number AS VARCHAR(2))+‘ WITH ( data_compression = ‘ + recommended_data_compression + ‘,MAXDOP=8,SORT_IN_TEMPDB=ON)‘ end collate database_default as [statement] from recommend_compression order by table_name --Clean up drop table #index_estimates; drop table #page_compression_estimates; drop table #row_compression_estimates;
注意:
这个脚本的分析时长由要分析对象的数量和数据量决定。可能你会发现，这个跟在SSMS中的Storage-Compression中评估值有一些差别。两种方式都使用的是sp_estimate_data_compression_savings,但是SSMS中不会指定@index_id参数，所以它评估的表中或者分区中所有对象的总合，这对于多个索引的表是非常不准确的。
1. 此脚本，我在很多生产环境中已经使用，均表现正常。但是如果你使用此脚本，请认真评估再使用。
2. 数据压缩还会跟复制，AlwaysOn，列存储等相互影响，这又是另一个故事了。
3. 数据压缩不会压缩行外的LOB数据。如果要压缩只能在程序端压缩，或者使用FileStream+压缩卷。SQL Server 2016提供了新的函数COMPRESS/DECOMPRESS来压缩单个数据，但是也不是用来解决行外LOB压缩问题的。
本文出自 “Joe TJ” 博客，请务必保留此出处http://joetang.blog.51cto.com/2296191/1826394
原文地址：http://joetang.blog.51cto.com/2296191/1826394
