Heaps, commonly known as tables without clustered indexes, play a role in the realm of database management systems such as SQL Server, Azure SQL Database, and Azure SQL Managed Instance. While clustered indexes are generally preferred for most tables, there are situations where heaps can offer advantages. In this article, we will delve into the use cases for SQL Server heaps, scenarios where caution should be exercised, and effective management techniques.
When to Consider a Heap
- Staging Tables: Heaps excel in scenarios involving large, unordered insert operations, such as staging tables. Since data can be inserted without adhering to a strict order, the insertion process tends to be faster compared to inserting into a clustered index. However, if the data within the heap will be frequently read and processed for a final destination, creating a narrow nonclustered index can enhance query performance.
- Nonclustered Index Efficiency: Heaps are sometimes used when data is consistently accessed through nonclustered indexes, and the row identifier (RID) is smaller in size compared to a clustered index key. This can help reduce the storage overhead associated with maintaining a clustered index.
Instances When a Heap May Not Be Ideal
- Sorted Data Retrieval: If data needs to be retrieved in a sorted order on a regular basis, employing a clustered index on the sorting column can eliminate the need for sorting operations, thereby improving query performance.
- Grouped Data: When data needs to be frequently grouped together, a clustered index on the grouping column can eliminate the need for costly sorting operations.
- Range Queries: If querying ranges of data from the table is a common occurrence, implementing a clustered index on the range column can eliminate the need to sort the entire heap, resulting in improved query performance.
- Large Tables with No Indexes: When dealing with large tables lacking any nonclustered indexes and the intention is to retrieve specific rows, using heaps should be approached with caution. In a heap, a table scan is required to locate any row, leading to poor query performance.
- Frequently Updated Data: If the data undergoes frequent updates, opting for a heap can result in fragmentation. Each update that increases the record size may require moving the record to a new location, introducing forwarded records and causing performance degradation during heap scans.
Effective Heap Management
To reclaim wasted space in a heap, you can rebuild it by creating a clustered index and subsequently dropping that index, or by utilizing the ALTER TABLE… REBUILD command. However, it’s important to note that creating or dropping clustered indexes involves rewriting the entire table, and any nonclustered indexes must be recreated. These operations can be time-consuming and resource-intensive.
To list all the heaps in your SQL Server, you can execute the following query:
SELECT SCHEMA_NAME(t.schema_id) AS [Schema], t.name AS [Table] FROM sys.indexes AS i INNER JOIN sys.tables AS t ON i.object_id = t.object_id WHERE i.index_id = 0 AND i.type = 0 AND t.is_ms_shipped = 0 ORDER BY [Schema], [Table];
To remove all forwarded records in SQL Server, you can use the following script:
DECLARE @TableName NVARCHAR(128) DECLARE @SchemaName NVARCHAR(128) DECLARE @SQL NVARCHAR(MAX) DECLARE ForwardedRecordsCursor CURSOR FOR SELECT OBJECT_NAME(object_id) AS [Table], OBJECT_SCHEMA_NAME(object_id) AS [Schema] FROM sys.dm_db_index_physical_stats (DB_ID(), NULL, NULL, NULL, 'DETAILED') WHERE forwarded_record_count > 0 OPEN ForwardedRecordsCursor FETCH NEXT FROM ForwardedRecordsCursor INTO @TableName, @SchemaName WHILE @@FETCH_STATUS = 0 BEGIN SET @SQL = 'ALTER TABLE ' + QUOTENAME(@SchemaName) + '.' + QUOTENAME(@TableName) + ' REBUILD;' EXEC sp_executesql @SQL FETCH NEXT FROM ForwardedRecordsCursor INTO @TableName, @SchemaName END CLOSE ForwardedRecordsCursor DEALLOCATE ForwardedRecordsCursor
Having a thorough understanding of when to utilize or avoid heaps, as well as mastering effective heap management techniques, is essential for optimizing database performance. While heaps offer advantages in certain scenarios, such as facilitating large-scale unordered insert operations or enabling efficient data access through nonclustered indexes, they have limitations regarding sorted data retrieval, grouping, range queries, and frequent updates.
When deciding whether to employ heaps, it is crucial to carefully evaluate the trade-offs and determine whether the benefits outweigh the potential drawbacks. In most cases, tables should have a deliberately chosen clustered index unless there exists a compelling reason to retain them as heaps. Do follow me on my youtube channel.
Reference: Pinal Dave (http://blog.SQLAuthority.com)