As the inherent structure of data and data storage is
different for In-Memory data, the previous access methods no longer apply. New the SQL Server 2014 are Hash indexes for
Memory Optimized Tables.
indexes have a substantially different structure than typical B-Tree
Indexes. The hash index consists of an
array of ‘buckets’. The array is built
by applying a deterministic hash function to an index key. The same index key will always be mapped to
the same hash bucket. As we learned in
the Row Composition blog, an in memory data row is composed of two sections,
the header and the payload. Within the header section, there are Index pointers
that link rows together. The first entry
in a particular bucket will have a pointer to the next entry and so on until
there are no more entries. All index
keys that map to the same bucket will be linked together in this manner.
Let’s take a look at how it all comes
together. The simplest way to understand
how Hash indexes work is to simplify the hash function. I’m borrowing from Kalen Delaney’s white
paper here, but for simplicity’s sake, consider a hash index on a last name
field and that the hash function simply counts the characters in a last name as
a bucket. The following image assumes an
In-memory table with a hash index on a last name column.
As Records are entered into the table, the hash is
calculated and a memory pointer maps the index to the index pointer of the first
row in each bucket, as represented by the second column in the rows on the
right. As more records are inserted,
they are given pointers to the previous entry.
When retrieving rows using a hash index, the query applies the hash
function to the search predicate to find the correct bucket, which maps to
first row’s location in memory. Every
subsequent row is retrieved by traversing the row chaining created by the index
pointer field in the row’s header.
In-Memory tables also support nonclustered indexes. These
are similar in structure to traditional non clustered indexes but have an
updated structure and access method. We’ll
explore these changes in my next blog.