Optimizing transactions in dedicated SQL pool in Azure Synapse Analytics
Learn how to optimize the performance of your transactional code in dedicated SQL pool while minimizing risk for long rollbacks.
Transactions and logging
Transactions are an important component of a relational SQL pool engine. Transactions are used during data modification. These transactions can be explicit or implicit. Single INSERT, UPDATE, and DELETE statements are all examples of implicit transactions. Explicit transactions use BEGIN TRAN, COMMIT TRAN, or ROLLBACK TRAN. Explicit transactions are typically used when multiple modification statements need to be tied together in a single atomic unit.
Changes to the SQL pool are tracked using transaction logs. Each distribution has its own transaction log. Transaction log writes are automatic. There is no configuration required. However, whilst this process guarantees the write it does introduce an overhead in the system. You can minimize this impact by writing transactionally efficient code. Transactionally efficient code broadly falls into two categories.
- Use minimal logging constructs whenever possible
- Process data using scoped batches to avoid singular long running transactions
- Adopt a partition switching pattern for large modifications to a given partition
Minimal vs. full logging
Unlike fully logged operations, which use the transaction log to keep track of every row change, minimally logged operations keep track of extent allocations and meta-data changes only. Therefore, minimal logging involves logging only the information that is required to roll back the transaction after a failure, or for an explicit request (ROLLBACK TRAN). As much less information is tracked in the transaction log, a minimally logged operation performs better than a similarly sized fully logged operation. Furthermore, because fewer writes go the transaction log, a much smaller amount of log data is generated and so is more I/O efficient.
The transaction safety limits only apply to fully logged operations.
Note
Minimally logged operations can participate in explicit transactions. As all changes in allocation structures are tracked, it is possible to roll back minimally logged operations.
Minimally logged operations
The following operations are capable of being minimally logged:
- CREATE TABLE AS SELECT (CTAS)
- INSERT..SELECT
- CREATE INDEX
- ALTER INDEX REBUILD
- DROP INDEX
- TRUNCATE TABLE
- DROP TABLE
- ALTER TABLE SWITCH PARTITION
Note
Internal data movement operations (such as BROADCAST and SHUFFLE) are not affected by the transaction safety limit.
Minimal logging with bulk load
CTAS and INSERT...SELECT are both bulk load operations. However, both are influenced by the target table definition and depend on the load scenario. The following table explains when bulk operations are fully or minimally logged:
| Primary Index | Load Scenario | Logging Mode |
|---|---|---|
| Heap | Any | Minimal |
| Clustered Index | Empty target table | Minimal |
| Clustered Index | Loaded rows do not overlap with existing pages in target | Minimal |
| Clustered Index | Loaded rows overlap with existing pages in target | Full |
| Clustered Columnstore Index | Batch size >= 102,400 per partition aligned distribution | Minimal |
| Clustered Columnstore Index | Batch size < 102,400 per partition aligned distribution | Full |
It is worth noting that any writes to update secondary or non-clustered indexes will always be fully logged operations.
Important
A dedicated SQL pool has 60 distributions. Therefore, assuming all rows are evenly distributed and landing in a single partition, your batch will need to contain 6,144,000 rows or larger to be minimally logged when writing to a Clustered Columnstore Index. If the table is partitioned and the rows being inserted span partition boundaries, then you will need 6,144,000 rows per partition boundary assuming even data distribution. Each partition in each distribution must independently exceed the 102,400 row threshold for the insert to be minimally logged into the distribution.
Loading data into a non-empty table with a clustered index can often contain a mixture of fully logged and minimally logged rows. A clustered index is a balanced tree (b-tree) of pages. If the page being written to already contains rows from another transaction, then these writes will be fully logged. However, if the page is empty then the write to that page will be minimally logged.
Optimizing deletes
DELETE is a fully logged operation. If you need to delete a large amount of data in a table or a partition, it often makes more sense to SELECT the data you wish to keep, which can be run as a minimally logged operation. To select the data, create a new table with CTAS. Once created, use RENAME to swap out your old table with the newly created table.
-- Delete all sales transactions for Promotions except PromotionKey 2.
--Step 01. Create a new table select only the records we want to kep (PromotionKey 2)
CREATE TABLE [dbo].[FactInternetSales_d]
WITH
( CLUSTERED COLUMNSTORE INDEX
, DISTRIBUTION = HASH([ProductKey])
, PARTITION ( [OrderDateKey] RANGE RIGHT
FOR VALUES ( 20000101, 20010101, 20020101, 20030101, 20040101, 20050101
, 20060101, 20070101, 20080101, 20090101, 20100101, 20110101
, 20120101, 20130101, 20140101, 20150101, 20160101, 20170101
, 20180101, 20190101, 20200101, 20210101, 20220101, 20230101
, 20240101, 20250101, 20260101, 20270101, 20280101, 20290101
)
)
AS
SELECT *
FROM [dbo].[FactInternetSales]
WHERE [PromotionKey] = 2
OPTION (LABEL = 'CTAS : Delete')
;
--Step 02. Rename the Tables to replace the
RENAME OBJECT [dbo].[FactInternetSales] TO [FactInternetSales_old];
RENAME OBJECT [dbo].[FactInternetSales_d] TO [FactInternetSales];
Optimizing updates
UPDATE is a fully logged operation. If you need to update a large number of rows in a table or a partition, it can often be far more efficient to use a minimally logged operation such as CTAS to do so.
In the example below a full table update has been converted to a CTAS so that minimal logging is possible.
In this case, we are retrospectively adding a discount amount to the sales in the table:
--Step 01. Create a new table containing the "Update".
CREATE TABLE [dbo].[FactInternetSales_u]
WITH
( CLUSTERED INDEX
, DISTRIBUTION = HASH([ProductKey])
, PARTITION ( [OrderDateKey] RANGE RIGHT
FOR VALUES ( 20000101, 20010101, 20020101, 20030101, 20040101, 20050101
, 20060101, 20070101, 20080101, 20090101, 20100101, 20110101
, 20120101, 20130101, 20140101, 20150101, 20160101, 20170101
, 20180101, 20190101, 20200101, 20210101, 20220101, 20230101
, 20240101, 20250101, 20260101, 20270101, 20280101, 20290101
)
)
)
AS
SELECT
[ProductKey]
, [OrderDateKey]
, [DueDateKey]
, [ShipDateKey]
, [CustomerKey]
, [PromotionKey]
, [CurrencyKey]
, [SalesTerritoryKey]
, [SalesOrderNumber]
, [SalesOrderLineNumber]
, [RevisionNumber]
, [OrderQuantity]
, [UnitPrice]
, [ExtendedAmount]
, [UnitPriceDiscountPct]
, ISNULL(CAST(5 as float),0) AS [DiscountAmount]
, [ProductStandardCost]
, [TotalProductCost]
, ISNULL(CAST(CASE WHEN [SalesAmount] <=5 THEN 0
ELSE [SalesAmount] - 5
END AS MONEY),0) AS [SalesAmount]
, [TaxAmt]
, [Freight]
, [CarrierTrackingNumber]
, [CustomerPONumber]
FROM [dbo].[FactInternetSales]
OPTION (LABEL = 'CTAS : Update')
;
--Step 02. Rename the tables
RENAME OBJECT [dbo].[FactInternetSales] TO [FactInternetSales_old];
RENAME OBJECT [dbo].[FactInternetSales_u] TO [FactInternetSales];
--Step 03. Drop the old table
DROP TABLE [dbo].[FactInternetSales_old]
Note
Re-creating large tables can benefit from using dedicated SQL pool workload management features. For more information, see Resource classes for workload management.
Optimizing with partition switching
If faced with large-scale modifications inside a table partition, then a partition switching pattern makes sense. If the data modification is significant and spans multiple partitions, then iterating over the partitions achieves the same result.
The steps to perform a partition switch are as follows:
- Create an empty out partition
- Perform the 'update' as a CTAS
- Switch out the existing data to the out table
- Switch in the new data
- Clean up the data
However, to help identify the partitions to switch, create the following helper procedure.
CREATE PROCEDURE dbo.partition_data_get
@schema_name NVARCHAR(128)
, @table_name NVARCHAR(128)
, @boundary_value INT
AS
IF OBJECT_ID('tempdb..#ptn_data') IS NOT NULL
BEGIN
DROP TABLE #ptn_data
END
CREATE TABLE #ptn_data
WITH ( DISTRIBUTION = ROUND_ROBIN
, HEAP
)
AS
WITH CTE
AS
(
SELECT s.name AS [schema_name]
, t.name AS [table_name]
, p.partition_number AS [ptn_nmbr]
, p.[rows] AS [ptn_rows]
, CAST(r.[value] AS INT) AS [boundary_value]
FROM sys.schemas AS s
JOIN sys.tables AS t ON s.[schema_id] = t.[schema_id]
JOIN sys.indexes AS i ON t.[object_id] = i.[object_id]
JOIN sys.partitions AS p ON i.[object_id] = p.[object_id]
AND i.[index_id] = p.[index_id]
JOIN sys.partition_schemes AS h ON i.[data_space_id] = h.[data_space_id]
JOIN sys.partition_functions AS f ON h.[function_id] = f.[function_id]
LEFT JOIN sys.partition_range_values AS r ON f.[function_id] = r.[function_id]
AND r.[boundary_id] = p.[partition_number]
WHERE i.[index_id] <= 1
)
SELECT *
FROM CTE
WHERE [schema_name] = @schema_name
AND [table_name] = @table_name
AND [boundary_value] = @boundary_value
OPTION (LABEL = 'dbo.partition_data_get : CTAS : #ptn_data')
;
GO
This procedure maximizes code reuse and keeps the partition switching example more compact.
The following code demonstrates the steps mentioned previously to achieve a full partition switching routine.
--Create a partitioned aligned empty table to switch out the data
IF OBJECT_ID('[dbo].[FactInternetSales_out]') IS NOT NULL
BEGIN
DROP TABLE [dbo].[FactInternetSales_out]
END
CREATE TABLE [dbo].[FactInternetSales_out]
WITH
( DISTRIBUTION = HASH([ProductKey])
, CLUSTERED COLUMNSTORE INDEX
, PARTITION ( [OrderDateKey] RANGE RIGHT
FOR VALUES ( 20020101, 20030101
)
)
)
AS
SELECT *
FROM [dbo].[FactInternetSales]
WHERE 1=2
OPTION (LABEL = 'CTAS : Partition Switch IN : UPDATE')
;
--Create a partitioned aligned table and update the data in the select portion of the CTAS
IF OBJECT_ID('[dbo].[FactInternetSales_in]') IS NOT NULL
BEGIN
DROP TABLE [dbo].[FactInternetSales_in]
END
CREATE TABLE [dbo].[FactInternetSales_in]
WITH
( DISTRIBUTION = HASH([ProductKey])
, CLUSTERED COLUMNSTORE INDEX
, PARTITION ( [OrderDateKey] RANGE RIGHT
FOR VALUES ( 20020101, 20030101
)
)
)
AS
SELECT
[ProductKey]
, [OrderDateKey]
, [DueDateKey]
, [ShipDateKey]
, [CustomerKey]
, [PromotionKey]
, [CurrencyKey]
, [SalesTerritoryKey]
, [SalesOrderNumber]
, [SalesOrderLineNumber]
, [RevisionNumber]
, [OrderQuantity]
, [UnitPrice]
, [ExtendedAmount]
, [UnitPriceDiscountPct]
, ISNULL(CAST(5 as float),0) AS [DiscountAmount]
, [ProductStandardCost]
, [TotalProductCost]
, ISNULL(CAST(CASE WHEN [SalesAmount] <=5 THEN 0
ELSE [SalesAmount] - 5
END AS MONEY),0) AS [SalesAmount]
, [TaxAmt]
, [Freight]
, [CarrierTrackingNumber]
, [CustomerPONumber]
FROM [dbo].[FactInternetSales]
WHERE OrderDateKey BETWEEN 20020101 AND 20021231
OPTION (LABEL = 'CTAS : Partition Switch IN : UPDATE')
;
--Use the helper procedure to identify the partitions
--The source table
EXEC dbo.partition_data_get 'dbo','FactInternetSales',20030101
DECLARE @ptn_nmbr_src INT = (SELECT ptn_nmbr FROM #ptn_data)
SELECT @ptn_nmbr_src
--The "in" table
EXEC dbo.partition_data_get 'dbo','FactInternetSales_in',20030101
DECLARE @ptn_nmbr_in INT = (SELECT ptn_nmbr FROM #ptn_data)
SELECT @ptn_nmbr_in
--The "out" table
EXEC dbo.partition_data_get 'dbo','FactInternetSales_out',20030101
DECLARE @ptn_nmbr_out INT = (SELECT ptn_nmbr FROM #ptn_data)
SELECT @ptn_nmbr_out
--Switch the partitions over
DECLARE @SQL NVARCHAR(4000) = '
ALTER TABLE [dbo].[FactInternetSales] SWITCH PARTITION '+CAST(@ptn_nmbr_src AS VARCHAR(20)) +' TO [dbo].[FactInternetSales_out] PARTITION ' +CAST(@ptn_nmbr_out AS VARCHAR(20))+';
ALTER TABLE [dbo].[FactInternetSales_in] SWITCH PARTITION '+CAST(@ptn_nmbr_in AS VARCHAR(20)) +' TO [dbo].[FactInternetSales] PARTITION ' +CAST(@ptn_nmbr_src AS VARCHAR(20))+';'
EXEC sp_executesql @SQL
--Perform the clean-up
TRUNCATE TABLE dbo.FactInternetSales_out;
TRUNCATE TABLE dbo.FactInternetSales_in;
DROP TABLE dbo.FactInternetSales_out
DROP TABLE dbo.FactInternetSales_in
DROP TABLE #ptn_data
Minimize logging with small batches
For large data modification operations, it may make sense to divide the operation into chunks or batches to scope the unit of work.
A following code is a working example. The batch size has been set to a trivial number to highlight the technique. In reality, the batch size would be significantly larger.
SET NO_COUNT ON;
IF OBJECT_ID('tempdb..#t') IS NOT NULL
BEGIN
DROP TABLE #t;
PRINT '#t dropped';
END
CREATE TABLE #t
WITH ( DISTRIBUTION = ROUND_ROBIN
, HEAP
)
AS
SELECT ROW_NUMBER() OVER(ORDER BY (SELECT NULL)) AS seq_nmbr
, SalesOrderNumber
, SalesOrderLineNumber
FROM dbo.FactInternetSales
WHERE [OrderDateKey] BETWEEN 20010101 and 20011231
;
DECLARE @seq_start INT = 1
, @batch_iterator INT = 1
, @batch_size INT = 50
, @max_seq_nmbr INT = (SELECT MAX(seq_nmbr) FROM dbo.#t)
;
DECLARE @batch_count INT = (SELECT CEILING((@max_seq_nmbr*1.0)/@batch_size))
, @seq_end INT = @batch_size
;
SELECT COUNT(*)
FROM dbo.FactInternetSales f
PRINT 'MAX_seq_nmbr '+CAST(@max_seq_nmbr AS VARCHAR(20))
PRINT 'MAX_Batch_count '+CAST(@batch_count AS VARCHAR(20))
WHILE @batch_iterator <= @batch_count
BEGIN
DELETE
FROM dbo.FactInternetSales
WHERE EXISTS
(
SELECT 1
FROM #t t
WHERE seq_nmbr BETWEEN @seq_start AND @seq_end
AND FactInternetSales.SalesOrderNumber = t.SalesOrderNumber
AND FactInternetSales.SalesOrderLineNumber = t.SalesOrderLineNumber
)
;
SET @seq_start = @seq_end
SET @seq_end = (@seq_start+@batch_size);
SET @batch_iterator +=1;
END
Pause and scaling guidance
Dedicated SQL pool lets you pause, resume, and scale your dedicated SQL pool on demand. When you pause or scale your dedicated SQL pool, it is important to understand that any in-flight transactions are terminated immediately; causing any open transactions to be rolled back. If your workload had issued a long running and incomplete data modification prior to the pause or scale operation, then this work will need to be undone. This undoing might impact the time it takes to pause or scale your dedicated SQL pool.
Important
Both UPDATE and DELETE are fully logged operations and so these undo/redo operations can take significantly longer than equivalent minimally logged operations.
The best scenario is to let in flight data modification transactions complete prior to pausing or scaling a dedicated SQL pool. However, this scenario might not always be practical. To mitigate the risk of a long rollback, consider one of the following options:
- Rewrite long running operations using CTAS
- Break the operation into chunks; operating on a subset of the rows
Next steps
See Transactions in dedicated SQL pool to learn more about isolation levels and transactional limits. For an overview of other Best Practices, see Dedicated SQL pool best practices.
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