Hash indexes are based on hash functions, which doesn't sort the data at all like conventional (binary tree indexes). Hash Indexes just categorize the data into hash buckets which help in fetch the required row faster.
"HK_tbl" is a in memory table, with column "data" having a hash index.
Order by:
Consider the following query using Order by:
SELECT TOP 1000 [ID]
,[Data]
,[dt]
FROM [HKDB].[dbo].[HK_tbl] WITH (SNAPSHOT)
ORDER BY data desc;
Query plan shown below:
Observe the "Sort" operator. Sort operator indicates that the data was sorted and index didn't avoid sort operation.
Pattern match using like and '%'
SELECT TOP 1000 [ID]
,[Data]
,[dt]
FROM [HKDB].[dbo].[HK_tbl] WITH (SNAPSHOT)
WHERE data like 'C%'
Observe the filter operator. Filter operator indicates that the predicate ( or the filtering condition ) was applied after the index was scanned. The number of rows which came out of index scan operator would also confirm that index scan operator read the entire table.
Similarly, non equality wouldn't be using hash indexes. For handling range scans, sort operations, the other index type called range index ( also called as in memory non clustered index ) in "in memory" tables would help.
On next post, we will see the scenarios, which explore scenarios where hash indexes are most useful.
