接上文：SQL Server 列存储索引性能总结（7）——导入数据到列存储索引的Delta Store，前面提到了几次Dictionary，本文快速介绍一下它到底是什么，以便更好地理解列存储。
不过这部分不会讲太深入，因为这个功能只能用于SQL Server而不适用于SQL DB和SQL DW（现在称为Azure Synapse Analytics
）
环境
本文继续使用ContosoRetailDW作为演示。但是首先我们用下面的脚本查一下哪些表适合使用聚集列存储索引（超过100万行）：
-- 返回建议用于数据仓库环境的聚集列存储的表
SELECT object_schema_name(t.object_id) AS 'Schema'
,object_name(t.object_id) AS 'Table'
,sum(p.rows) AS 'Row Count'
,(
SELECT count(*)
FROM sys.columns AS col
WHERE t.object_id = col.object_id
) AS 'Cols Count'
,(
SELECT sum(col.max_length)
FROM sys.columns AS col
JOIN sys.types AS tp ON col.system_type_id = tp.system_type_id
WHERE t.object_id = col.object_id
) AS 'Cols Max Length'
,(
SELECT count(*)
FROM sys.columns AS col
JOIN sys.types AS tp ON col.system_type_id = tp.system_type_id
WHERE t.object_id = col.object_id
AND (
UPPER(tp.name) IN (
'TEXT'
,'NTEXT'
,'TIMESTAMP'
,'HIERARCHYID'
,'SQL_VARIANT'
,'XML'
,'GEOGRAPHY'
,'GEOMETRY'
)
OR (
UPPER(tp.name) IN (
'VARCHAR'
,'NVARCHAR'
)
AND (
col.max_length = 8000
OR col.max_length = - 1
)
)
)
) AS 'Unsupported Columns'
,(
SELECT count(*)
FROM sys.objects
WHERE type = 'PK'
AND parent_object_id = t.object_id
) AS 'Primary Key'
,(
SELECT count(*)
FROM sys.objects
WHERE type = 'F'
AND parent_object_id = t.object_id
) AS 'Foreign Keys'
,(
SELECT count(*)
FROM sys.objects
WHERE type IN (
'UQ'
,'D'
,'C'
)
AND parent_object_id = t.object_id
) AS 'Constraints'
,(
SELECT count(*)
FROM sys.objects
WHERE type IN (
'TA'
,'TR'
)
AND parent_object_id = t.object_id
) AS 'Triggers'
,t.is_tracked_by_cdc AS 'CDC'
,t.is_memory_optimized AS 'Hekaton'
,t.is_replicated AS 'Replication'
,coalesce(t.filestream_data_space_id, 0, 1) AS 'FileStream'
,t.is_filetable AS 'FileTable'
FROM sys.tables t
INNER JOIN sys.partitions AS p ON t.object_id = p.object_id
WHERE p.data_compression IN (
0
,1
,2
) -- None, Row, Page
AND p.index_id IN (
0
,1
)
AND (
SELECT count(*)
FROM sys.indexes ind
WHERE t.object_id = ind.object_id
AND ind.type IN (
5
,6
)
) = 0
GROUP BY t.object_id
,t.is_tracked_by_cdc
,t.is_memory_optimized
,t.is_filetable
,t.is_replicated
,t.filestream_data_space_id
HAVING sum(p.rows) > 1000000
ORDER BY sum(p.rows) DESC
不过需要提醒一下，这个脚本并不是万能的，也不是绝对的标准，所以仅供参考。这里主要关注在大表环境。结果如下，有5个表满足条件。FactOnlineSales, FactInventory, FactSalesQuota, FactSales, FactStrategyPlan。
因为这些表存在不少主键和外键，为了减少影响，这里先清理一下，如果你担心影响后续操作的话，把数据库备份一次再操作：
-- 删除主键:
ALTER TABLE dbo.[FactOnlineSales] DROP CONSTRAINT [PK_FactOnlineSales_SalesKey]
ALTER TABLE dbo.[FactStrategyPlan] DROP CONSTRAINT [PK_FactStrategyPlan_StrategyPlanKey]
ALTER TABLE dbo.[FactSales] DROP CONSTRAINT [PK_FactSales_SalesKey]
ALTER TABLE dbo.[FactInventory] DROP CONSTRAINT [PK_FactInventory_InventoryKey]
ALTER TABLE dbo.[FactSalesQuota] DROP CONSTRAINT [PK_FactSalesQuota_SalesQuotaKey]
-- 删除外键:
ALTER TABLE dbo.[FactOnlineSales] DROP CONSTRAINT [FK_FactOnlineSales_DimCurrency]
ALTER TABLE dbo.[FactOnlineSales] DROP CONSTRAINT [FK_FactOnlineSales_DimCustomer]
ALTER TABLE dbo.[FactOnlineSales] DROP CONSTRAINT [FK_FactOnlineSales_DimDate]
ALTER TABLE dbo.[FactOnlineSales] DROP CONSTRAINT [FK_FactOnlineSales_DimProduct]
ALTER TABLE dbo.[FactOnlineSales] DROP CONSTRAINT [FK_FactOnlineSales_DimPromotion]
ALTER TABLE dbo.[FactOnlineSales] DROP CONSTRAINT [FK_FactOnlineSales_DimStore]
ALTER TABLE dbo.[FactStrategyPlan] DROP CONSTRAINT [FK_FactStrategyPlan_DimAccount]
ALTER TABLE dbo.[FactStrategyPlan] DROP CONSTRAINT [FK_FactStrategyPlan_DimCurrency]
ALTER TABLE dbo.[FactStrategyPlan] DROP CONSTRAINT [FK_FactStrategyPlan_DimDate]
ALTER TABLE dbo.[FactStrategyPlan] DROP CONSTRAINT [FK_FactStrategyPlan_DimEntity]
ALTER TABLE dbo.[FactStrategyPlan] DROP CONSTRAINT [FK_FactStrategyPlan_DimProductCategory]
ALTER TABLE dbo.[FactStrategyPlan] DROP CONSTRAINT [FK_FactStrategyPlan_DimScenario]
ALTER TABLE dbo.[FactSales] DROP CONSTRAINT [FK_FactSales_DimChannel]
ALTER TABLE dbo.[FactSales] DROP CONSTRAINT [FK_FactSales_DimCurrency]
ALTER TABLE dbo.[FactSales] DROP CONSTRAINT [FK_FactSales_DimDate]
ALTER TABLE dbo.[FactSales] DROP CONSTRAINT [FK_FactSales_DimProduct]
ALTER TABLE dbo.[FactSales] DROP CONSTRAINT [FK_FactSales_DimPromotion]
ALTER TABLE dbo.[FactSales] DROP CONSTRAINT [FK_FactSales_DimStore]
ALTER TABLE dbo.[FactInventory] DROP CONSTRAINT [FK_FactInventory_DimCurrency]
ALTER TABLE dbo.[FactInventory] DROP CONSTRAINT [FK_FactInventory_DimDate]
ALTER TABLE dbo.[FactInventory] DROP CONSTRAINT [FK_FactInventory_DimProduct]
ALTER TABLE dbo.[FactInventory] DROP CONSTRAINT [FK_FactInventory_DimStore]
ALTER TABLE dbo.[FactSalesQuota] DROP CONSTRAINT [FK_FactSalesQuota_DimChannel]
ALTER TABLE dbo.[FactSalesQuota] DROP CONSTRAINT [FK_FactSalesQuota_DimCurrency]
ALTER TABLE dbo.[FactSalesQuota] DROP CONSTRAINT [FK_FactSalesQuota_DimDate]
ALTER TABLE dbo.[FactSalesQuota] DROP CONSTRAINT [FK_FactSalesQuota_DimProduct]
ALTER TABLE dbo.[FactSalesQuota] DROP CONSTRAINT [FK_FactSalesQuota_DimScenario]
ALTER TABLE dbo.[FactSalesQuota] DROP CONSTRAINT [FK_FactSalesQuota_DimStore]
然后我们创建聚集列存储索引（后称CCI，clustered columnstore index）到每个表上:
Create Clustered Columnstore Index CCI on dbo.FactOnlineSales WITH (DATA_COMPRESSION = COLUMNSTORE);
Create Clustered Columnstore Index CCI on dbo.FactStrategyPlan WITH (DATA_COMPRESSION = COLUMNSTORE);
Create Clustered Columnstore Index CCI on dbo.FactSales WITH (DATA_COMPRESSION = COLUMNSTORE);
Create Clustered Columnstore Index CCI on dbo.FactInventory WITH (DATA_COMPRESSION = COLUMNSTORE);
Create Clustered Columnstore Index CCI on dbo.FactSalesQuota WITH (DATA_COMPRESSION = COLUMNSTORE);
我们会用下面的脚本来查询dictionary的信息：
select object_name(object_id), dictionary_id
, count(*) as 'Number of Dictionaries'
, sum(entry_count) as 'Entry Count'
, min(on_disk_size) as 'Min Size'
, max(on_disk_size) as 'Max Size'
, avg(on_disk_size) as 'Avg Size'
from sys.column_store_dictionaries dict
join sys.partitions part
on dict.hobt_id = part.hobt_id
group by object_id, dictionary_id
order by object_name(object_id), dictionary_id
从上面的结果大概可以看出，随着dictionary_id的增加，可用的dictionaries数量减少。
介绍和总结
在列存储中，有些列要求额外使用字典（dictionary），比如字符类型，用于编码转换成可用类型。字典包含全局（global）和本地（local），与片段关联，其中全局字典可以横跨全部列。
字典用于对某些而不是全部数据类型进行编码，所以不是所有的列存储中的列都有字典。
另外条目数也就是（entry count）那里，它并非直接上升或者下降，而实先升后降。另外可以看到不同的空间，字典数的差异非常大。
其实字典主要用于字符类型，创建一个字典存储列中的唯一值列表，列表中有数据值和字典值对应，然后在列存储中存储字典值而不是本来的数据值，比如颜色列，红黄蓝绿分别以1/2/3/4存储在字典和列存储中，这样存储空间可以进一步减少（注意这个例子只是形象化，并不是真的按照这个规则存储颜色）。
大部分情况下会使用全局字典存储所有相关列，但是也有部分使用本地字典来存储。
到了这里，我们很容易想到虽然存储空间少了，但是你引入了一个“关联或者配置表”，在操作的时候就多了一步。但是不可否则这种做法确实大大地降低了存储空间，至于能否得到比关联开销更大的查询提升，那需要具体问题具体分析。一般情况下我们能做的通常只是重建或重组索引。
