Partitioning tables in SQL Analytics
Recommendations and examples for using table partitions in SQL Analytics.
What are table partitions?
Table partitions enable you to divide your data into smaller groups of data. In most cases, table partitions are created on a date column. Partitioning is supported on all SQL Analytics table types; including clustered columnstore, clustered index, and heap. Partitioning is also supported on all distribution types, including both hash or round robin distributed.
Partitioning can benefit data maintenance and query performance. Whether it benefits both or just one is dependent on how data is loaded and whether the same column can be used for both purposes, since partitioning can only be done on one column.
Benefits to loads
The primary benefit of partitioning in SQL Analytics is to improve the efficiency and performance of loading data by use of partition deletion, switching and merging. In most cases data is partitioned on a date column that is closely tied to the order in which the data is loaded into the database. One of the greatest benefits of using partitions to maintain data it the avoidance of transaction logging. While simply inserting, updating, or deleting data can be the most straightforward approach, with a little thought and effort, using partitioning during your load process can substantially improve performance.
Partition switching can be used to quickly remove or replace a section of a table. For example, a sales fact table might contain just data for the past 36 months. At the end of every month, the oldest month of sales data is deleted from the table. This data could be deleted by using a delete statement to delete the data for the oldest month. However, deleting a large amount of data row-by-row with a delete statement can take too much time, as well as create the risk of large transactions that take a long time to rollback if something goes wrong. A more optimal approach is to drop the oldest partition of data. Where deleting the individual rows could take hours, deleting an entire partition could take seconds.
Benefits to queries
Partitioning can also be used to improve query performance. A query that applies a filter to partitioned data can limit the scan to only the qualifying partitions. This method of filtering can avoid a full table scan and only scan a smaller subset of data. With the introduction of clustered columnstore indexes, the predicate elimination performance benefits are less beneficial, but in some cases there can be a benefit to queries. For example, if the sales fact table is partitioned into 36 months using the sales date field, then queries that filter on the sale date can skip searching in partitions that don't match the filter.
While partitioning can be used to improve performance some scenarios, creating a table with too many partitions can hurt performance under some circumstances. These concerns are especially true for clustered columnstore tables. For partitioning to be helpful, it is important to understand when to use partitioning and the number of partitions to create. There is no hard fast rule as to how many partitions are too many, it depends on your data and how many partitions you loading simultaneously. A successful partitioning scheme usually has tens to hundreds of partitions, not thousands.
When creating partitions on clustered columnstore tables, it is important to consider how many rows belong to each partition. For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed. Before partitions are created, SQL Analytics already divides each table into 60 distributed databases. Any partitioning added to a table is in addition to the distributions created behind the scenes. Using this example, if the sales fact table contained 36 monthly partitions, and given that a SQL Analytics database has 60 distributions, then the sales fact table should contain 60 million rows per month, or 2.1 billion rows when all months are populated. If a table contains fewer than the recommended minimum number of rows per partition, consider using fewer partitions in order to increase the number of rows per partition. For more information, see the Indexing article, which includes queries that can assess the quality of cluster columnstore indexes.
Syntax differences from SQL Server
SQL Analytics introduces a way to define partitions that is simpler than SQL Server. Partitioning functions and schemes are not used in SQL Analytics as they are in SQL Server. Instead, all you need to do is identify partitioned column and the boundary points. While the syntax of partitioning may be slightly different from SQL Server, the basic concepts are the same. SQL Server and SQL Analytics support one partition column per table, which can be ranged partition. To learn more about partitioning, see Partitioned Tables and Indexes.
The following example uses the CREATE TABLE statement to partition the FactInternetSales table on the OrderDateKey column:
CREATE TABLE [dbo].[FactInternetSales] ( [ProductKey] int NOT NULL , [OrderDateKey] int NOT NULL , [CustomerKey] int NOT NULL , [PromotionKey] int NOT NULL , [SalesOrderNumber] nvarchar(20) NOT NULL , [OrderQuantity] smallint NOT NULL , [UnitPrice] money NOT NULL , [SalesAmount] money NOT NULL ) WITH ( CLUSTERED COLUMNSTORE INDEX , DISTRIBUTION = HASH([ProductKey]) , PARTITION ( [OrderDateKey] RANGE RIGHT FOR VALUES (20000101,20010101,20020101 ,20030101,20040101,20050101 ) ) ) ;
Migrating partitioning from SQL Server
To migrate SQL Server partition definitions to SQL Analytics simply:
- Eliminate the SQL Server partition scheme.
- Add the partition function definition to your CREATE TABLE.
If you are migrating a partitioned table from a SQL Server instance, the following SQL can help you to figure out the number of rows that in each partition. Keep in mind that if the same partitioning granularity is used on SQL Analytics, the number of rows per partition decreases by a factor of 60.
-- Partition information for a SQL Server Database SELECT s.[name] AS [schema_name] , t.[name] AS [table_name] , i.[name] AS [index_name] , p.[partition_number] AS [partition_number] , SUM(a.[used_pages]*8.0) AS [partition_size_kb] , SUM(a.[used_pages]*8.0)/1024 AS [partition_size_mb] , SUM(a.[used_pages]*8.0)/1048576 AS [partition_size_gb] , p.[rows] AS [partition_row_count] , rv.[value] AS [partition_boundary_value] , p.[data_compression_desc] AS [partition_compression_desc] FROM sys.schemas s JOIN sys.tables t ON t.[schema_id] = s.[schema_id] JOIN sys.partitions p ON p.[object_id] = t.[object_id] JOIN sys.allocation_units a ON a.[container_id] = p.[partition_id] JOIN sys.indexes i ON i.[object_id] = p.[object_id] AND i.[index_id] = p.[index_id] JOIN sys.data_spaces ds ON ds.[data_space_id] = i.[data_space_id] LEFT JOIN sys.partition_schemes ps ON ps.[data_space_id] = ds.[data_space_id] LEFT JOIN sys.partition_functions pf ON pf.[function_id] = ps.[function_id] LEFT JOIN sys.partition_range_values rv ON rv.[function_id] = pf.[function_id] AND rv.[boundary_id] = p.[partition_number] WHERE p.[index_id] <=1 GROUP BY s.[name] , t.[name] , i.[name] , p.[partition_number] , p.[rows] , rv.[value] , p.[data_compression_desc] ;
SQL Analytics supports partition splitting, merging, and switching. Each of these functions is executed using the ALTER TABLE statement.
To switch partitions between two tables, you must ensure that the partitions align on their respective boundaries and that the table definitions match. As check constraints are not available to enforce the range of values in a table, the source table must contain the same partition boundaries as the target table. If the partition boundaries are not then same, then the partition switch will fail as the partition metadata will not be synchronized.
How to split a partition that contains data
The most efficient method to split a partition that already contains data is to use a
CTAS statement. If the partitioned table is a clustered columnstore, then the table partition must be empty before it can be split.
The following example creates a partitioned columnstore table. It inserts one row into each partition:
CREATE TABLE [dbo].[FactInternetSales] ( [ProductKey] int NOT NULL , [OrderDateKey] int NOT NULL , [CustomerKey] int NOT NULL , [PromotionKey] int NOT NULL , [SalesOrderNumber] nvarchar(20) NOT NULL , [OrderQuantity] smallint NOT NULL , [UnitPrice] money NOT NULL , [SalesAmount] money NOT NULL ) WITH ( CLUSTERED COLUMNSTORE INDEX , DISTRIBUTION = HASH([ProductKey]) , PARTITION ( [OrderDateKey] RANGE RIGHT FOR VALUES (20000101 ) ) ) ; INSERT INTO dbo.FactInternetSales VALUES (1,19990101,1,1,1,1,1,1); INSERT INTO dbo.FactInternetSales VALUES (1,20000101,1,1,1,1,1,1);
The following query finds the row count by using the
sys.partitions catalog view:
SELECT QUOTENAME(s.[name])+'.'+QUOTENAME(t.[name]) as Table_name , i.[name] as Index_name , p.partition_number as Partition_nmbr , p.[rows] as Row_count , p.[data_compression_desc] as Data_Compression_desc FROM sys.partitions p JOIN sys.tables t ON p.[object_id] = t.[object_id] JOIN sys.schemas s ON t.[schema_id] = s.[schema_id] JOIN sys.indexes i ON p.[object_id] = i.[object_Id] AND p.[index_Id] = i.[index_Id] WHERE t.[name] = 'FactInternetSales' ;
The following split command receives an error message:
ALTER TABLE FactInternetSales SPLIT RANGE (20010101);
Msg 35346, Level 15, State 1, Line 44 SPLIT clause of ALTER PARTITION statement failed because the partition is not empty. Only empty partitions can be split in when a columnstore index exists on the table. Consider disabling the columnstore index before issuing the ALTER PARTITION statement, then rebuilding the columnstore index after ALTER PARTITION is complete.
However, you can use
CTAS to create a new table to hold the data.
CREATE TABLE dbo.FactInternetSales_20000101 WITH ( DISTRIBUTION = HASH(ProductKey) , CLUSTERED COLUMNSTORE INDEX , PARTITION ( [OrderDateKey] RANGE RIGHT FOR VALUES (20000101 ) ) ) AS SELECT * FROM FactInternetSales WHERE 1=2 ;
As the partition boundaries are aligned, a switch is permitted. This will leave the source table with an empty partition that you can subsequently split.
ALTER TABLE FactInternetSales SWITCH PARTITION 2 TO FactInternetSales_20000101 PARTITION 2; ALTER TABLE FactInternetSales SPLIT RANGE (20010101);
All that is left is to align the data to the new partition boundaries using
CTAS, and then switch the data back into the main table.
CREATE TABLE [dbo].[FactInternetSales_20000101_20010101] WITH ( DISTRIBUTION = HASH([ProductKey]) , CLUSTERED COLUMNSTORE INDEX , PARTITION ( [OrderDateKey] RANGE RIGHT FOR VALUES (20000101,20010101 ) ) ) AS SELECT * FROM [dbo].[FactInternetSales_20000101] WHERE [OrderDateKey] >= 20000101 AND [OrderDateKey] < 20010101 ; ALTER TABLE dbo.FactInternetSales_20000101_20010101 SWITCH PARTITION 2 TO dbo.FactInternetSales PARTITION 2;
Once you have completed the movement of the data, it is a good idea to refresh the statistics on the target table. Updating statistics ensures the statistics accurately reflect the new distribution of the data in their respective partitions.
UPDATE STATISTICS [dbo].[FactInternetSales];
Load new data into partitions that contain data in one step
Loading data into partitions with partition switching is a convenient way stage new data in a table that is not visible to users the switch in the new data. It can be challenging on busy systems to deal with the locking contention associated with partition switching. To clear out the existing data in a partition, an
ALTER TABLE used to be required to switch out the data. Then another
ALTER TABLE was required to switch in the new data. In SQL Analytics, the
TRUNCATE_TARGET option is supported in the
ALTER TABLE command. With
ALTER TABLE command overwrites existing data in the partition with new data. Below is an example which uses
CTAS to create a new table with the existing data, inserts new data, then switches all the data back into the target table, overwriting the existing data.
CREATE TABLE [dbo].[FactInternetSales_NewSales] WITH ( DISTRIBUTION = HASH([ProductKey]) , CLUSTERED COLUMNSTORE INDEX , PARTITION ( [OrderDateKey] RANGE RIGHT FOR VALUES (20000101,20010101 ) ) ) AS SELECT * FROM [dbo].[FactInternetSales] WHERE [OrderDateKey] >= 20000101 AND [OrderDateKey] < 20010101 ; INSERT INTO dbo.FactInternetSales_NewSales VALUES (1,20000101,2,2,2,2,2,2); ALTER TABLE dbo.FactInternetSales_NewSales SWITCH PARTITION 2 TO dbo.FactInternetSales PARTITION 2 WITH (TRUNCATE_TARGET = ON);
Table partitioning source control
To avoid your table definition from rusting in your source control system, you may want to consider the following approach:
Create the table as a partitioned table but with no partition values
CREATE TABLE [dbo].[FactInternetSales] ( [ProductKey] int NOT NULL , [OrderDateKey] int NOT NULL , [CustomerKey] int NOT NULL , [PromotionKey] int NOT NULL , [SalesOrderNumber] nvarchar(20) NOT NULL , [OrderQuantity] smallint NOT NULL , [UnitPrice] money NOT NULL , [SalesAmount] money NOT NULL ) WITH ( CLUSTERED COLUMNSTORE INDEX , DISTRIBUTION = HASH([ProductKey]) , PARTITION ( [OrderDateKey] RANGE RIGHT FOR VALUES () ) ) ;
SPLITthe table as part of the deployment process:
-- Create a table containing the partition boundaries CREATE TABLE #partitions WITH ( LOCATION = USER_DB , DISTRIBUTION = HASH(ptn_no) ) AS SELECT ptn_no , ROW_NUMBER() OVER (ORDER BY (ptn_no)) as seq_no FROM ( SELECT CAST(20000101 AS INT) ptn_no UNION ALL SELECT CAST(20010101 AS INT) UNION ALL SELECT CAST(20020101 AS INT) UNION ALL SELECT CAST(20030101 AS INT) UNION ALL SELECT CAST(20040101 AS INT) ) a ; -- Iterate over the partition boundaries and split the table DECLARE @c INT = (SELECT COUNT(*) FROM #partitions) , @i INT = 1 --iterator for while loop , @q NVARCHAR(4000) --query , @p NVARCHAR(20) = N'' --partition_number , @s NVARCHAR(128) = N'dbo' --schema , @t NVARCHAR(128) = N'FactInternetSales' --table ; WHILE @i <= @c BEGIN SET @p = (SELECT ptn_no FROM #partitions WHERE seq_no = @i); SET @q = (SELECT N'ALTER TABLE '+@s+N'.'+@t+N' SPLIT RANGE ('+@p+N');'); -- PRINT @q; EXECUTE sp_executesql @q; SET @i+=1; END -- Code clean-up DROP TABLE #partitions;
With this approach the code in source control remains static and the partitioning boundary values are allowed to be dynamic; evolving with the database over time.
For more information about developing tables, see the articles on Table Overview.