Indexes on Memory-Optimized Tables
Applies to: SQL Server (all supported versions) Azure SQL Database Azure SQL Managed Instance
All memory-optimized tables must have at least one index, because it is the indexes that connect the rows together. On a memory-optimized table, every index is also memory-optimized. There are several ways in which an index on a memory-optimized table differs from a traditional index on a disk-base table:
- Data rows are not stored on pages, so there is no collection of pages or extents, no partitions or allocation units that can be referenced to get all the pages for a table. There is the concept of index pages for one of the available types of indexes, but they are stored differently than indexes for disk-based tables. They do not accrue the traditional type of fragmentation within a page, so they have no fillfactor.
- Changes made to indexes on memory-optimized tables during data manipulation are never written to disk. Only the data rows, and changes to the data, are written to the transaction log.
- Memory-optimized indexes are rebuilt when the database is brought back online.
All indexes on memory-optimized tables are created based on the index definitions during database recovery.
The index must be one of the following:
- Hash index
- Memory-optimized Nonclustered index (meaning the default internal structure of a B-tree)
Hash indexes are discussed in more detail in Hash Indexes for Memory-Optimized Tables.
Nonclustered indexes are discussed in more detail in Nonclustered Index for Memory-Optimized Tables.
Columnstore indexes are discussed in another article.
Syntax for memory-optimized indexes
Each CREATE TABLE statement for a memory-optimized table must include an index, either explicitly through an INDEX or implicitly through a PRIMAY KEY or UNIQUE constraint.
To be declared with the default DURABILITY = SCHEMA_AND_DATA, the memory-optimized table must have a primary key. The PRIMARY KEY NONCLUSTERED clause in the following CREATE TABLE statement satisfies two requirements:
Provides an index to meet the minimum requirement of one index in the CREATE TABLE statement.
Provides the primary key that is required for the SCHEMA_AND_DATA clause.
CREATE TABLE SupportEvent ( SupportEventId int NOT NULL PRIMARY KEY NONCLUSTERED, ... ) WITH ( MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA);
SQL Server 2014 (12.x) and SQL Server 2016 (13.x) have a limit of 8 indexes per memory-optimized table or table type. Starting with SQL Server 2017 (14.x) and in Azure SQL Database, there is no longer a limit on the number of indexes specific to memory-optimized tables and table types.
Code sample for syntax
This subsection contains a Transact-SQL code block that demonstrates the syntax to create various indexes on a memory-optimized table. The code demonstrates the following:
Create a memory-optimized table.
Use ALTER TABLE statements to add two indexes.
INSERT a few rows of data.
DROP TABLE IF EXISTS SupportEvent; go CREATE TABLE SupportEvent ( SupportEventId int not null identity(1,1) PRIMARY KEY NONCLUSTERED, StartDateTime datetime2 not null, CustomerName nvarchar(16) not null, SupportEngineerName nvarchar(16) null, Priority int null, Description nvarchar(64) null ) WITH ( MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA); go -------------------- ALTER TABLE SupportEvent ADD CONSTRAINT constraintUnique_SDT_CN UNIQUE NONCLUSTERED (StartDateTime DESC, CustomerName); go ALTER TABLE SupportEvent ADD INDEX idx_hash_SupportEngineerName HASH (SupportEngineerName) WITH (BUCKET_COUNT = 64); -- Nonunique. go -------------------- INSERT INTO SupportEvent (StartDateTime, CustomerName, SupportEngineerName, Priority, Description) VALUES ('2016-02-23 13:40:41:123', 'Abby', 'Zeke', 2, 'Display problem.' ), ('2016-02-24 13:40:41:323', 'Ben' , null , 1, 'Cannot find help.' ), ('2016-02-25 13:40:41:523', 'Carl', 'Liz' , 2, 'Button is gray.' ), ('2016-02-26 13:40:41:723', 'Dave', 'Zeke', 2, 'Cannot unhide column.'); go
Duplicate index key values
Duplicate values for an index key might reduce the performance of memory-optimized tables. Duplicates for the system to traverse entry chains for most index read and write operations. When a chain of duplicate entries exceeds 100 entries, the performance degradation can become measurable.
Duplicate hash values
This problem is more visible in the case of hash indexes. Hash indexes suffer more due to the following considerations:
- The lower cost per operation for hash indexes.
- The interference of large duplicate chains with the hash collision chain.
To reduce duplication in an index, try the following adjustments:
- Use a nonclustered index.
- Add additional columns to the end of the index key, to reduce the number of duplicates.
- For example, you could add columns that are also in the primary key.
For more information about hash collisions, see Hash Indexes for Memory-Optimized Tables.
Here is an example of how to avoid any performance inefficiency in your index.
Customers table that has a primary key on
CustomerId, and has an index on column
CustomerCategoryID. Typically there will be many customers in a given category. Thus there will be many duplicate values for CustomerCategoryID inside a given key of the index.
In this scenario, the best practice is to use a nonclustered index on
(CustomerCategoryID, CustomerId). This index can be used for queries that use a predicate involving
CustomerCategoryID, yet the index key does not contain duplication. Therefore, no inefficiencies in index maintenance are cause by either the duplicate CustomerCategoryID values, or by the extra column in the index.
The following query shows the average number of duplicate index key values for the index on
CustomerCategoryID in table
Sales.Customers, in the sample database WideWorldImporters.
SELECT AVG(row_count) FROM (SELECT COUNT(*) AS row_count FROM Sales.Customers GROUP BY CustomerCategoryID) a
To evaluate the average number of index key duplicates for your own table and index, replace
Sales.Customers with your table name, and replace
CustomerCategoryID with the list of index key columns.
Comparing when to use each index type
The nature of your particular queries determines which type of index is the best choice.
When implementing memory-optimized tables in an existing application, the general recommendation is to start with nonclustered indexes, as their capabilities more closely resemble the capabilities of traditional clustered and nonclustered indexes on disk-based tables.
Recommendations for nonclustered index use
A nonclustered index is preferable over a hash index when:
- Queries have an
ORDER BYclause on the indexed column.
- Queries where only the leading column(s) of a multi-column index is tested.
- Queries test the indexed column by use of a
- An inequality:
WHERE StatusCode != 'Done'
- A value range scan:
WHERE Quantity >= 100
- An inequality:
In all the following SELECTs, a nonclustered index is preferable over a hash index:
SELECT CustomerName, Priority, Description FROM SupportEvent WHERE StartDateTime > DateAdd(day, -7, GetUtcDate()); SELECT StartDateTime, CustomerName FROM SupportEvent ORDER BY StartDateTime DESC; -- ASC would cause a scan. SELECT CustomerName FROM SupportEvent WHERE StartDateTime = '2016-02-26';
Recommendations for hash index use
Hash indexes are primarily used for point lookups and not for range scans.
A hash index is preferable over a nonclustered index when queries use equality predicates, and the
WHERE clause maps to all index key columns, as in the following example:
SELECT CustomerName FROM SupportEvent WHERE SupportEngineerName = 'Liz';
A multi-column index could be a nonclustered index or a hash index. Suppose the index columns are col1 and col2. Given the following
SELECT statement, only the nonclustered index would be useful to the query optimizer:
SELECT col1, col3 FROM MyTable_memop WHERE col1 = 'dn';
The hash index needs the
WHERE clause to specify an equality test for each of the columns in its key. Else the hash index is not useful to the query optimizer.
Neither index type is useful if the
WHERE clause specifies only the second column in the index key.
Summary table to compare index use scenarios
The following table lists all operations that are supported by the different index types. Yes means that the index can efficiently service the request, and No means that the index cannot efficiently satisfy the request.
|Index Scan, retrieve all table rows.||Yes||Yes||Yes|
|Index seek on equality predicates (=).||Yes
(Full key is required.)
|Index seek on inequality and range predicates
(>, <, <=, >=,
(Results in an index scan.)
|Retrieve rows in a sort order that matches the index definition.||No||Yes||Yes|
|Retrieve rows in a sort-order that matches the reverse of the index definition.||No||No||Yes|
1 For a memory-optimized Nonclustered index, the full key is not required to perform an index seek.
Automatic index and statistics management
Leverage solutions such as Adaptive Index Defrag to automatically manage index defragmentation and statistics updates for one or more databases. This procedure automatically chooses whether to rebuild or reorganize an index according to its fragmentation level, amongst other parameters, and update statistics with a linear threshold.
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